Observation List
Get a list of Observation objects.
GET /api/v3/observations/?format=api&offset=8300
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This will be used to evaluate and develop improvements to the representation of mixed-phase clouds in the Unified Model that is used by the Met Office for operational Numerical Weather Prediction and climate simulations." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 25829, 25833, 51049, 60307, 60308, 60309 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). 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The ACAO campaign took place in March 2022 based out of Kiruna in Sweden and included flights C271 to C282 inclusive.\r\n\r\nThe campaign by scientists from the Met Office and Universities of Manchester and Leeds was to make novel in-situ airborne measurements of the evolution of cold-air outbreaks (CAOs), in terms of the meteorology, atmospheric composition and clouds, from the Marginal Ice Zone (MIZ) to Scandinavia." } ], "responsiblepartyinfo_set": [ 178859, 178860, 178861, 178862, 178863, 178864, 178865, 178866, 178867 ], "onlineresource_set": [ 52219, 52220 ] }, { "ob_id": 37454, "uuid": "9d8303ca10fd4c9b8fdb5e718daf6b26", "title": "FAAM C283 ACSIS flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS project.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2023-02-13T17:15:36", "updateFrequency": "asNeeded", "dataLineage": "Data were collected by instrument scientists during the flight before preparation and delivery for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "ACSIS, FAAM, airborne, atmospheric measurments", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-06-10T14:34:03", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3487, "bboxName": "", "eastBoundLongitude": 1.903689980506897, "westBoundLongitude": -2.001546621322632, "southBoundLatitude": 50.02960205078125, "northBoundLatitude": 52.81119155883789 }, "verticalExtent": null, "result_field": { "ob_id": 37453, "dataPath": "/badc/faam/data/2022/c283-apr-27", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4793003193, "numberOfFiles": 54, "fileFormat": "Data are netCDF and NASA-Ames formatted. 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Many observed changes are unprecedented in instrumental records. Changes in the NA directly affect the UK’s climate, weather and air quality, with major economic impacts on agriculture, fisheries, water, energy, transport and health. The NA also has global importance, since changes here drive changes in climate, hazardous weather and air quality further afield, such as in North America, Africa and Asia.\r\n\r\nACSIS (the North Atlantic Climate System Integrated Study) was an integrated programme of sustained observations, synthesis, and numerical modelling designed to address the overarching objective of enhancing the UK's capability to detect, attribute and predict changes in the North Atlantic (NA) Climate System, comprising: the North Atlantic Ocean, the atmosphere above it including its composition, and interactions with Arctic Sea Ice and the Greenland Ice Sheet. Specific objectives are:\r\n1. To provide the UK science community with sustained observations, data syntheses, leading-edge numerical simulations, and analysis tools, to facilitate world-class research on changes in the NA climate system and their impacts.\r\n2. To provide a quantitative, multivariate, description of how the NA climate system is changing.\r\n3. To determine the primary drivers and processes that are shaping change in the NA climate system now and will shape change in the near future.\r\n4. To determine the extent to which future changes in the NA climate system are predictable.\r\nACSIS enabled and delivered research to address the following research questions:\r\nRQ1. How have changes in natural and anthropogenic emissions and atmospheric circulation combined to shape multiyear trends in NA atmospheric composition and radiative forcing?\r\nRQ2. How have natural variability and radiative forcing combined to shape multi- year trends in the NA physical climate system?\r\nRQ3. To what extent are changes in the NA climate system predictable on multi-year timescales?\r\nACSIS was a partnership between six NERC centres (NCAS, NOC, BAS, NCEO, CPOM, PML) and the UK Met Office, exploiting the partners' unique capabilities in observing and simulating the atmosphere including its composition, the ocean, the cryosphere, and the fully coupled climate system.\r\nThe observational component brought together records from Earth-based (e.g. Cape Verde observatory, FAAM missions, RAPID, Argo, OSNAP) and spacebased (e.g. Cryosat, MetOP) platforms with a focus on the sustained observations that are necessary to measure changes on multi-year timescales.\r\nACSIS worked closely with the NERC-Met Office UKESM programme on Earth System Modelling, and contributed to and benefited from UK participation in international observing programmes such as UK-US RAPID, EU ATLANTOS and Global Atmospheric Watch, and modelling programmes such as CMIP6 and EU PRIMAVERA.\r\nThe legacy of ACSIS includes: new long-term multivariate observational datasets and syntheses; new modelling capabilities and simulations with unprecedented fidelity. ACSIS provided advances in understanding and predicting changes in the NA climate system that can be exploited in further research and related activities, for example to assess the impact of these changes on the UK and other countries - e.g. in terms of the consequences for hazardous weather risk, the environment and businesses. ACSIS outputs will also inform policy on climate change adaptation and air quality.\r\nACSIS was fully funded for five years (2016-2021)by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) (grant NE/N018028/1) which aimed to encourage its research centres to work closely together to tackle major scientific and societal challenges. ACSIS is one of the projects funded through this new way of allocating national capability funding, designed to enable more ambitious science than any single research organisation could provide." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 14845, 14846, 14848, 14850, 14851, 14852, 14853, 14854, 14855, 14857, 14924, 14927, 14928, 14935, 14946, 15040, 15041, 15325, 15338, 15343, 15813, 15818, 15819, 15820, 15822, 15823, 15836, 15845, 16569, 16658, 20689, 20695, 20696, 20697, 21049, 22462, 22465, 22472, 22847, 24825, 47310, 47311, 47312, 47313, 47314, 47315, 47316, 47317, 47318, 47319, 47320, 47321, 47322, 47323, 47324, 47325, 47326, 47406, 47407, 47408, 47409, 47410, 47411, 47412, 47413, 47414, 47415, 47416, 47417, 47418, 47419, 47420, 47421, 47422, 47423, 47424, 47425, 47426, 47427, 47428, 47429, 47430, 47431, 47432, 47433, 47434, 47435, 47436, 47463, 47464, 47465, 47466, 47467, 47468, 47469, 47470, 47471, 47472, 47473, 47474, 47475, 47476, 47477, 47478, 47479, 47480, 47481, 47482, 47483, 47484, 47485, 47486, 47487, 47488, 47489, 47490, 47491, 47492, 47493, 47494, 47495, 47497, 47498, 47499, 47500, 47501, 47502, 47503, 47504, 47505, 47506, 47507, 47508, 47509, 47510, 47511, 47512, 47513, 47514, 47515, 47516, 47517, 47518, 47519, 47520, 47521, 47522, 47523, 47524, 47525, 47526, 47527, 47528, 47529, 47530, 47531, 47532, 47533, 47534, 47535, 47536, 47537, 47538, 47539, 47540, 47541, 47542, 47543, 47544, 47547 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). 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Results of this project will improve our understanding of the impact of ship emissions on air quality and climate.\r\nShips generally burn low quality fuel and emit large quantities of sulfur dioxide and particulates, or aerosols (harmful at high concentrations), into the atmosphere above the ocean. In the presence of clouds the sulfur dioxide is rapidly converted into more particle mass growing them to sizes where they act as sites for cloud droplet formation. Given that about 70% of shipping activities occur within 400 km of the coast, ships are a large source of air pollution in coastal regions, causing 400k premature mortalities per year globally. In the UK, air pollution (including ship emissions) is responsible for 40,000 premature mortalities each year. In an effort to reduce air pollution from shipping activity, the United Nation's International Maritime Organization (IMO) is introducing new regulations from January 2020 that will require ships in international waters to reduce their maximum sulfur emissions from 3.5% by mass of fuel to 0.5%. \r\n\t\r\nParticulates emitted by ships may enhance the number of cloud droplets and potentially form regions of brighter clouds known as ship tracks. Largely because of this effect, some global models predict that ship emissions of particulates currently have a significant cooling influence on the global climate, masking a fraction of the warming caused by greenhouse gas emissions. So whilst a reduction in ship sulfur emission is predicted to almost halve the number of premature deaths globally via a reduction in sulfate aerosols, a lack of similar reductions in greenhouse gases from shipping (e.g. CO2) could lead to an overall climate warming. However, the magnitude of the cooling caused by particulates is very uncertain, with large discrepancies between global model and satellite-based estimates. This may be due to imprecise representations of the effects of aerosols on clouds in global models or biases in satellite detections of ship tracks. Furthermore, how shipping companies respond to the 2020 regulation (i.e. degree and method of compliance), in international waters where surveillance is challenging, is largely unknown and requires observational verification. \r\n\r\nThis project will take advantage of this unique and drastic \"inverse geoengineering\" event in 2020.\r\n\r\nGrant Ref: NE/S004467/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 14845, 14846, 14848, 14850, 14851, 14852, 14853, 14854, 14855, 14857, 14924, 14927, 14928, 14935, 14946, 15040, 15041, 15325, 15338, 15343, 15813, 15818, 15819, 15820, 15822, 15823, 15836, 15845, 16569, 16658, 20689, 20695, 20696, 20697, 21049, 22462, 22465, 22472, 22847, 24825, 47310, 47311, 47312, 47313, 47314, 47315, 47316, 47317, 47318, 47319, 47320, 47321, 47322, 47323, 47324, 47325, 47326, 47406, 47407, 47408, 47409, 47410, 47411, 47412, 47413, 47414, 47415, 47416, 47417, 47418, 47419, 47420, 47421, 47422, 47423, 47424, 47425, 47426, 47427, 47428, 47429, 47430, 47431, 47432, 47433, 47434, 47435, 47436, 47463, 47464, 47465, 47466, 47467, 47468, 47469, 47470, 47471, 47472, 47473, 47474, 47475, 47476, 47477, 47478, 47479, 47480, 47481, 47482, 47483, 47484, 47485, 47486, 47487, 47488, 47489, 47490, 47491, 47492, 47493, 47494, 47495, 47497, 47498, 47499, 47500, 47501, 47502, 47503, 47504, 47505, 47506, 47507, 47508, 47509, 47510, 47511, 47512, 47513, 47514, 47515, 47516, 47517, 47518, 47519, 47520, 47521, 47522, 47523, 47524, 47525, 47526, 47527, 47528, 47529, 47530, 47531, 47532, 47533, 47534, 47535, 47536, 47537, 47538, 47539, 47540, 47541, 47542, 47543, 47544, 47547 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). 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Furthermore, how shipping companies respond to the 2020 regulation (i.e. degree and method of compliance), in international waters where surveillance is challenging, is largely unknown and requires observational verification. \r\n\r\nThis project will take advantage of this unique and drastic \"inverse geoengineering\" event in 2020.\r\n\r\nGrant Ref: NE/S004467/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50851, 50853, 50856, 50857, 50934, 50936, 50937, 50942, 50949, 50950, 50952, 50953, 50965, 50967, 50969, 50970, 50971, 50973, 50978, 50979, 50982, 50983, 50984, 50985, 50986, 50987, 50988, 50989, 50990, 50991, 50992, 50993, 50994, 50995, 50996, 50997, 50998, 50999, 51000, 51001, 51002, 51003, 51004, 51005, 51006, 51007, 51008, 51009, 51010, 51011, 51012, 51013, 51014, 51015, 51016, 51017, 51018, 51019, 51020, 51021, 51022, 51023, 51024, 51025, 51026, 51027, 51028, 51029, 51030, 51031, 51032, 51033, 51034, 51035, 51036, 51037, 51038, 51039, 51040, 51041, 51042, 51044, 51045, 51054, 51055, 51056, 51057, 51058, 51059, 51060, 51061, 51062, 51063, 51064, 51065, 51066, 51067, 51068, 51069, 51070, 51071, 51072, 51073, 51074, 51075, 51076, 51077, 51078, 51079, 51080, 51081, 51082, 51083, 51084, 51087, 51222, 51223, 51225, 51227, 51228, 51229, 51230, 51271, 51272, 51279, 51280, 53949, 53951, 53954, 54967, 54971, 54975, 54976, 59964, 59965, 59966, 59967, 59968, 59969, 59970, 59971, 59972, 59973, 59974, 59975, 59976, 60050, 60051, 60052, 60053, 60054, 60055, 60056, 60057, 60058, 60059, 60060, 60061, 60062, 60063, 60064, 60065, 60066, 60067, 60068, 60069, 60070, 60071, 60072, 60073, 60074, 60075, 60077, 60078, 60079, 60080, 60081, 60087, 60089, 60090, 60091, 60092, 60093, 60094, 60095, 60096, 60097, 60098, 60099, 60100, 60101, 60102, 60103, 60104, 60105, 60106, 60107, 60108, 60109, 60110, 60111, 60112, 60113, 60114, 60115, 60116, 60117, 60118, 60119, 60202, 60203, 60204, 60205, 60206, 60207, 60208, 60209, 60210, 60211, 60212, 60213, 60214, 60215, 60216, 60217, 60218, 60219, 60220, 60221, 60222, 60223, 60224, 60225, 60226, 60227, 60228, 60229, 60230, 60231, 60232, 60233, 60234, 60235, 60236, 60237, 60238, 60239, 60240, 60241, 60242, 60243, 60244, 60245, 60246, 60247, 60248, 60249, 60250, 60251, 60252, 60253, 60254, 60255, 60256, 60257, 60258, 60259, 60260, 60261, 60262, 60263, 60264, 60265, 60266, 60267, 60268, 60269, 60270, 60271, 60272, 60273, 60274, 60275, 60276, 60277, 60278, 60279, 60280, 60281, 60282, 60283, 60284, 60287, 60308, 60309, 62646, 62647, 62648, 62649, 62650, 62651, 62652, 62653, 62654, 62655, 62656, 62657, 62664, 62665, 62666, 62669, 62671, 62678, 62679, 63157, 63158, 63159, 63160, 72012, 73930, 73931, 73932, 73933, 73934, 73935, 73936, 73937, 73938, 73939, 73940, 73941, 73942, 73943, 73944, 73945, 73946, 73947, 73948, 73949, 73950, 73951, 73952, 73953, 73954, 73955, 73956, 73957, 73958, 73959, 73960, 73961, 73962, 73963, 73964, 73965, 73966, 73967, 74055, 74056, 74057, 74058, 74059, 74060, 74061, 74063, 74064, 74065, 74066, 74067, 74068, 74069, 74070, 74071, 74072, 74073, 74074, 74075, 74076, 74077, 74078, 74079, 74080, 74081, 74082, 74083, 74084, 74085, 74086, 74087, 74088, 74089, 74090, 74092, 74093, 74095, 74096, 74097, 74099, 74100, 74101, 74102, 74103, 74104, 74105, 74155, 74157, 74504, 74505, 74506, 74507, 74508, 74509, 74510, 74511, 74512, 74513, 74514, 74515, 74516, 82404 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). 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Many observed changes are unprecedented in instrumental records. Changes in the NA directly affect the UK’s climate, weather and air quality, with major economic impacts on agriculture, fisheries, water, energy, transport and health. The NA also has global importance, since changes here drive changes in climate, hazardous weather and air quality further afield, such as in North America, Africa and Asia.\r\n\r\nACSIS (the North Atlantic Climate System Integrated Study) was an integrated programme of sustained observations, synthesis, and numerical modelling designed to address the overarching objective of enhancing the UK's capability to detect, attribute and predict changes in the North Atlantic (NA) Climate System, comprising: the North Atlantic Ocean, the atmosphere above it including its composition, and interactions with Arctic Sea Ice and the Greenland Ice Sheet. Specific objectives are:\r\n1. To provide the UK science community with sustained observations, data syntheses, leading-edge numerical simulations, and analysis tools, to facilitate world-class research on changes in the NA climate system and their impacts.\r\n2. To provide a quantitative, multivariate, description of how the NA climate system is changing.\r\n3. To determine the primary drivers and processes that are shaping change in the NA climate system now and will shape change in the near future.\r\n4. To determine the extent to which future changes in the NA climate system are predictable.\r\nACSIS enabled and delivered research to address the following research questions:\r\nRQ1. How have changes in natural and anthropogenic emissions and atmospheric circulation combined to shape multiyear trends in NA atmospheric composition and radiative forcing?\r\nRQ2. How have natural variability and radiative forcing combined to shape multi- year trends in the NA physical climate system?\r\nRQ3. 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Many observed changes are unprecedented in instrumental records. Changes in the NA directly affect the UK’s climate, weather and air quality, with major economic impacts on agriculture, fisheries, water, energy, transport and health. The NA also has global importance, since changes here drive changes in climate, hazardous weather and air quality further afield, such as in North America, Africa and Asia.\r\n\r\nACSIS (the North Atlantic Climate System Integrated Study) was an integrated programme of sustained observations, synthesis, and numerical modelling designed to address the overarching objective of enhancing the UK's capability to detect, attribute and predict changes in the North Atlantic (NA) Climate System, comprising: the North Atlantic Ocean, the atmosphere above it including its composition, and interactions with Arctic Sea Ice and the Greenland Ice Sheet. Specific objectives are:\r\n1. To provide the UK science community with sustained observations, data syntheses, leading-edge numerical simulations, and analysis tools, to facilitate world-class research on changes in the NA climate system and their impacts.\r\n2. To provide a quantitative, multivariate, description of how the NA climate system is changing.\r\n3. To determine the primary drivers and processes that are shaping change in the NA climate system now and will shape change in the near future.\r\n4. To determine the extent to which future changes in the NA climate system are predictable.\r\nACSIS enabled and delivered research to address the following research questions:\r\nRQ1. How have changes in natural and anthropogenic emissions and atmospheric circulation combined to shape multiyear trends in NA atmospheric composition and radiative forcing?\r\nRQ2. How have natural variability and radiative forcing combined to shape multi- year trends in the NA physical climate system?\r\nRQ3. To what extent are changes in the NA climate system predictable on multi-year timescales?\r\nACSIS was a partnership between six NERC centres (NCAS, NOC, BAS, NCEO, CPOM, PML) and the UK Met Office, exploiting the partners' unique capabilities in observing and simulating the atmosphere including its composition, the ocean, the cryosphere, and the fully coupled climate system.\r\nThe observational component brought together records from Earth-based (e.g. Cape Verde observatory, FAAM missions, RAPID, Argo, OSNAP) and spacebased (e.g. Cryosat, MetOP) platforms with a focus on the sustained observations that are necessary to measure changes on multi-year timescales.\r\nACSIS worked closely with the NERC-Met Office UKESM programme on Earth System Modelling, and contributed to and benefited from UK participation in international observing programmes such as UK-US RAPID, EU ATLANTOS and Global Atmospheric Watch, and modelling programmes such as CMIP6 and EU PRIMAVERA.\r\nThe legacy of ACSIS includes: new long-term multivariate observational datasets and syntheses; new modelling capabilities and simulations with unprecedented fidelity. 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Many observed changes are unprecedented in instrumental records. Changes in the NA directly affect the UK’s climate, weather and air quality, with major economic impacts on agriculture, fisheries, water, energy, transport and health. The NA also has global importance, since changes here drive changes in climate, hazardous weather and air quality further afield, such as in North America, Africa and Asia.\r\n\r\nACSIS (the North Atlantic Climate System Integrated Study) was an integrated programme of sustained observations, synthesis, and numerical modelling designed to address the overarching objective of enhancing the UK's capability to detect, attribute and predict changes in the North Atlantic (NA) Climate System, comprising: the North Atlantic Ocean, the atmosphere above it including its composition, and interactions with Arctic Sea Ice and the Greenland Ice Sheet. Specific objectives are:\r\n1. To provide the UK science community with sustained observations, data syntheses, leading-edge numerical simulations, and analysis tools, to facilitate world-class research on changes in the NA climate system and their impacts.\r\n2. To provide a quantitative, multivariate, description of how the NA climate system is changing.\r\n3. To determine the primary drivers and processes that are shaping change in the NA climate system now and will shape change in the near future.\r\n4. To determine the extent to which future changes in the NA climate system are predictable.\r\nACSIS enabled and delivered research to address the following research questions:\r\nRQ1. How have changes in natural and anthropogenic emissions and atmospheric circulation combined to shape multiyear trends in NA atmospheric composition and radiative forcing?\r\nRQ2. How have natural variability and radiative forcing combined to shape multi- year trends in the NA physical climate system?\r\nRQ3. To what extent are changes in the NA climate system predictable on multi-year timescales?\r\nACSIS was a partnership between six NERC centres (NCAS, NOC, BAS, NCEO, CPOM, PML) and the UK Met Office, exploiting the partners' unique capabilities in observing and simulating the atmosphere including its composition, the ocean, the cryosphere, and the fully coupled climate system.\r\nThe observational component brought together records from Earth-based (e.g. Cape Verde observatory, FAAM missions, RAPID, Argo, OSNAP) and spacebased (e.g. Cryosat, MetOP) platforms with a focus on the sustained observations that are necessary to measure changes on multi-year timescales.\r\nACSIS worked closely with the NERC-Met Office UKESM programme on Earth System Modelling, and contributed to and benefited from UK participation in international observing programmes such as UK-US RAPID, EU ATLANTOS and Global Atmospheric Watch, and modelling programmes such as CMIP6 and EU PRIMAVERA.\r\nThe legacy of ACSIS includes: new long-term multivariate observational datasets and syntheses; new modelling capabilities and simulations with unprecedented fidelity. ACSIS provided advances in understanding and predicting changes in the NA climate system that can be exploited in further research and related activities, for example to assess the impact of these changes on the UK and other countries - e.g. in terms of the consequences for hazardous weather risk, the environment and businesses. ACSIS outputs will also inform policy on climate change adaptation and air quality.\r\nACSIS was fully funded for five years (2016-2021)by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) (grant NE/N018028/1) which aimed to encourage its research centres to work closely together to tackle major scientific and societal challenges. 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The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. 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Many observed changes are unprecedented in instrumental records. Changes in the NA directly affect the UK’s climate, weather and air quality, with major economic impacts on agriculture, fisheries, water, energy, transport and health. The NA also has global importance, since changes here drive changes in climate, hazardous weather and air quality further afield, such as in North America, Africa and Asia.\r\n\r\nACSIS (the North Atlantic Climate System Integrated Study) was an integrated programme of sustained observations, synthesis, and numerical modelling designed to address the overarching objective of enhancing the UK's capability to detect, attribute and predict changes in the North Atlantic (NA) Climate System, comprising: the North Atlantic Ocean, the atmosphere above it including its composition, and interactions with Arctic Sea Ice and the Greenland Ice Sheet. Specific objectives are:\r\n1. To provide the UK science community with sustained observations, data syntheses, leading-edge numerical simulations, and analysis tools, to facilitate world-class research on changes in the NA climate system and their impacts.\r\n2. To provide a quantitative, multivariate, description of how the NA climate system is changing.\r\n3. To determine the primary drivers and processes that are shaping change in the NA climate system now and will shape change in the near future.\r\n4. To determine the extent to which future changes in the NA climate system are predictable.\r\nACSIS enabled and delivered research to address the following research questions:\r\nRQ1. How have changes in natural and anthropogenic emissions and atmospheric circulation combined to shape multiyear trends in NA atmospheric composition and radiative forcing?\r\nRQ2. How have natural variability and radiative forcing combined to shape multi- year trends in the NA physical climate system?\r\nRQ3. To what extent are changes in the NA climate system predictable on multi-year timescales?\r\nACSIS was a partnership between six NERC centres (NCAS, NOC, BAS, NCEO, CPOM, PML) and the UK Met Office, exploiting the partners' unique capabilities in observing and simulating the atmosphere including its composition, the ocean, the cryosphere, and the fully coupled climate system.\r\nThe observational component brought together records from Earth-based (e.g. Cape Verde observatory, FAAM missions, RAPID, Argo, OSNAP) and spacebased (e.g. Cryosat, MetOP) platforms with a focus on the sustained observations that are necessary to measure changes on multi-year timescales.\r\nACSIS worked closely with the NERC-Met Office UKESM programme on Earth System Modelling, and contributed to and benefited from UK participation in international observing programmes such as UK-US RAPID, EU ATLANTOS and Global Atmospheric Watch, and modelling programmes such as CMIP6 and EU PRIMAVERA.\r\nThe legacy of ACSIS includes: new long-term multivariate observational datasets and syntheses; new modelling capabilities and simulations with unprecedented fidelity. 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Many observed changes are unprecedented in instrumental records. Changes in the NA directly affect the UK’s climate, weather and air quality, with major economic impacts on agriculture, fisheries, water, energy, transport and health. The NA also has global importance, since changes here drive changes in climate, hazardous weather and air quality further afield, such as in North America, Africa and Asia.\r\n\r\nACSIS (the North Atlantic Climate System Integrated Study) was an integrated programme of sustained observations, synthesis, and numerical modelling designed to address the overarching objective of enhancing the UK's capability to detect, attribute and predict changes in the North Atlantic (NA) Climate System, comprising: the North Atlantic Ocean, the atmosphere above it including its composition, and interactions with Arctic Sea Ice and the Greenland Ice Sheet. Specific objectives are:\r\n1. To provide the UK science community with sustained observations, data syntheses, leading-edge numerical simulations, and analysis tools, to facilitate world-class research on changes in the NA climate system and their impacts.\r\n2. To provide a quantitative, multivariate, description of how the NA climate system is changing.\r\n3. To determine the primary drivers and processes that are shaping change in the NA climate system now and will shape change in the near future.\r\n4. To determine the extent to which future changes in the NA climate system are predictable.\r\nACSIS enabled and delivered research to address the following research questions:\r\nRQ1. How have changes in natural and anthropogenic emissions and atmospheric circulation combined to shape multiyear trends in NA atmospheric composition and radiative forcing?\r\nRQ2. How have natural variability and radiative forcing combined to shape multi- year trends in the NA physical climate system?\r\nRQ3. To what extent are changes in the NA climate system predictable on multi-year timescales?\r\nACSIS was a partnership between six NERC centres (NCAS, NOC, BAS, NCEO, CPOM, PML) and the UK Met Office, exploiting the partners' unique capabilities in observing and simulating the atmosphere including its composition, the ocean, the cryosphere, and the fully coupled climate system.\r\nThe observational component brought together records from Earth-based (e.g. Cape Verde observatory, FAAM missions, RAPID, Argo, OSNAP) and spacebased (e.g. Cryosat, MetOP) platforms with a focus on the sustained observations that are necessary to measure changes on multi-year timescales.\r\nACSIS worked closely with the NERC-Met Office UKESM programme on Earth System Modelling, and contributed to and benefited from UK participation in international observing programmes such as UK-US RAPID, EU ATLANTOS and Global Atmospheric Watch, and modelling programmes such as CMIP6 and EU PRIMAVERA.\r\nThe legacy of ACSIS includes: new long-term multivariate observational datasets and syntheses; new modelling capabilities and simulations with unprecedented fidelity. ACSIS provided advances in understanding and predicting changes in the NA climate system that can be exploited in further research and related activities, for example to assess the impact of these changes on the UK and other countries - e.g. in terms of the consequences for hazardous weather risk, the environment and businesses. ACSIS outputs will also inform policy on climate change adaptation and air quality.\r\nACSIS was fully funded for five years (2016-2021)by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) (grant NE/N018028/1) which aimed to encourage its research centres to work closely together to tackle major scientific and societal challenges. ACSIS is one of the projects funded through this new way of allocating national capability funding, designed to enable more ambitious science than any single research organisation could provide." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50851, 50853, 50856, 50857, 50936, 50937, 50942, 50949, 50950, 50952, 50953, 50965, 50967, 50969, 50970, 50971, 50973, 50978, 50979, 50982, 50983, 50984, 50985, 50986, 50987, 50988, 50989, 50990, 50991, 50992, 50993, 50994, 50995, 50996, 50997, 50998, 50999, 51000, 51001, 51002, 51003, 51004, 51005, 51006, 51007, 51008, 51009, 51010, 51011, 51012, 51013, 51014, 51015, 51016, 51017, 51018, 51019, 51020, 51021, 51022, 51023, 51024, 51025, 51026, 51027, 51028, 51029, 51030, 51031, 51032, 51033, 51034, 51035, 51036, 51037, 51038, 51039, 51040, 51041, 51042, 51044, 51045, 51054, 51055, 51056, 51057, 51058, 51059, 51060, 51061, 51062, 51063, 51064, 51065, 51066, 51067, 51068, 51069, 51070, 51071, 51072, 51073, 51074, 51075, 51076, 51077, 51078, 51079, 51080, 51081, 51082, 51083, 51084, 51087, 51222, 51223, 51225, 51227, 51228, 51229, 51230, 51271, 51272, 51279, 51280, 53949, 53951, 53954, 53996, 54967, 54971, 54975, 54976, 59964, 59965, 59966, 59967, 59968, 59969, 59970, 59971, 59972, 59973, 59974, 59975, 60050, 60051, 60052, 60053, 60054, 60055, 60056, 60057, 60058, 60059, 60060, 60061, 60062, 60063, 60064, 60065, 60066, 60067, 60068, 60069, 60070, 60071, 60072, 60073, 60074, 60075, 60077, 60078, 60079, 60089, 60090, 60091, 60092, 60093, 60094, 60095, 60096, 60097, 60098, 60099, 60100, 60101, 60102, 60103, 60104, 60105, 60106, 60107, 60108, 60109, 60110, 60111, 60112, 60113, 60114, 60115, 60116, 60117, 60118, 60119, 60202, 60203, 60204, 60205, 60206, 60207, 60208, 60209, 60210, 60211, 60212, 60213, 60214, 60215, 60216, 60217, 60218, 60219, 60220, 60221, 60222, 60223, 60224, 60225, 60226, 60227, 60228, 60229, 60230, 60231, 60232, 60233, 60234, 60235, 60236, 60237, 60238, 60239, 60240, 60241, 60242, 60243, 60244, 60245, 60246, 60247, 60248, 60249, 60250, 60251, 60252, 60253, 60254, 60255, 60256, 60257, 60258, 60259, 60260, 60261, 60262, 60263, 60267, 60268, 60269, 60270, 60271, 60272, 60273, 60274, 60275, 60276, 60279, 60280, 60281, 60282, 60283, 60284, 60287, 60308, 60309, 62646, 62647, 62648, 62650, 62651, 62652, 62653, 62654, 62655, 62656, 62657, 62664, 62669, 62671, 62678, 62679, 63157, 63158, 63159, 63160, 72012, 73930, 73931, 73932, 73933, 73934, 73935, 73936, 73937, 73938, 73939, 73940, 73941, 73942, 73943, 73944, 73945, 73946, 73947, 73948, 73949, 73950, 73951, 73952, 73953, 73954, 73955, 73956, 73957, 73958, 73959, 73960, 73961, 73962, 73963, 73964, 73965, 73966, 73967, 74056, 74060, 74061, 74063, 74065, 74067, 74068, 74071, 74073, 74074, 74075, 74076, 74078, 74079, 74081, 74083, 74084, 74085, 74086, 74087, 74088, 74089, 74090, 74092, 74095, 74096, 74097, 74098, 74099, 74102, 74103, 74104, 74105, 74155, 74157, 74504, 74505, 74506, 74507, 74510, 74511, 74512, 74513, 74514, 74515, 74516, 82223, 82404 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. 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Ancillary files may be plain ASCII or PDF formatted. 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It focused on understanding dynamical aspects of convection to provide observational data to develop next generation km scale and urban-scale models to improve prediction of convective storms in km scale Numerical Weather Prediction (NWP) and climate models.\r\n\r\nThe emphasis of WesCon was understanding of dynamical processes (particularly updrafts and turbulence) and their interaction with other processes of importance. \r\n\r\nThe project involved the use of the FAAM Bae-146 aircraft, the Jade-Dimona aircraft and groundbased instruments at Cardington, Netheravon, and nearby ground sites. This project ran in conjunction with WOEST: WesCon - Observing the Evolving Structures of Turbulence project involving groundbased, sonde and radar observations. Both WesCon and WOEST were part of the wider ParaChute programme.\r\n\r\nThe Wessex Convection experiment (WesCon) took place from during summer 2023" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50851, 50853, 50856, 50857, 50934, 50936, 50937, 50942, 50949, 50950, 50952, 50953, 50965, 50967, 50969, 50970, 50971, 50973, 50978, 50979, 50982, 50983, 50984, 50985, 50986, 50987, 50988, 50989, 50990, 50991, 50992, 50993, 50994, 50995, 50996, 50997, 50998, 50999, 51000, 51001, 51002, 51003, 51004, 51005, 51006, 51007, 51008, 51009, 51010, 51011, 51012, 51013, 51014, 51015, 51016, 51017, 51018, 51019, 51020, 51021, 51022, 51023, 51024, 51025, 51026, 51027, 51028, 51029, 51030, 51031, 51032, 51033, 51034, 51035, 51036, 51037, 51038, 51039, 51040, 51041, 51042, 51044, 51045, 51054, 51055, 51056, 51057, 51058, 51059, 51060, 51061, 51062, 51063, 51064, 51065, 51066, 51067, 51068, 51069, 51070, 51071, 51072, 51073, 51074, 51075, 51076, 51077, 51078, 51079, 51080, 51081, 51082, 51083, 51084, 51087, 51222, 51223, 51225, 51227, 51228, 51229, 51230, 51271, 51272, 51279, 51280, 53949, 53951, 53954, 54967, 54971, 54975, 54976, 59964, 59965, 59966, 59967, 59968, 59969, 59970, 59971, 59972, 59973, 59974, 59975, 59976, 60050, 60051, 60052, 60053, 60054, 60055, 60056, 60057, 60058, 60059, 60060, 60061, 60062, 60063, 60064, 60065, 60066, 60067, 60068, 60069, 60070, 60071, 60072, 60073, 60074, 60075, 60077, 60078, 60079, 60080, 60081, 60087, 60089, 60090, 60091, 60092, 60093, 60094, 60095, 60096, 60097, 60098, 60099, 60100, 60101, 60102, 60103, 60104, 60105, 60106, 60107, 60108, 60109, 60110, 60111, 60112, 60113, 60114, 60115, 60116, 60117, 60118, 60119, 60202, 60203, 60204, 60205, 60206, 60207, 60208, 60209, 60210, 60211, 60212, 60213, 60214, 60215, 60216, 60217, 60218, 60219, 60220, 60221, 60222, 60223, 60224, 60225, 60226, 60227, 60228, 60229, 60230, 60231, 60232, 60233, 60234, 60235, 60236, 60237, 60238, 60239, 60240, 60241, 60242, 60243, 60244, 60245, 60246, 60247, 60248, 60249, 60250, 60251, 60252, 60253, 60254, 60255, 60256, 60257, 60258, 60259, 60260, 60261, 60262, 60263, 60264, 60265, 60266, 60267, 60268, 60269, 60270, 60271, 60272, 60273, 60274, 60275, 60276, 60277, 60278, 60279, 60280, 60281, 60282, 60283, 60284, 60287, 60308, 60309, 62646, 62647, 62648, 62649, 62650, 62651, 62652, 62653, 62654, 62655, 62656, 62657, 62664, 62665, 62666, 62669, 62671, 62678, 62679, 72012, 74056, 74060, 74061, 74063, 74065, 74067, 74068, 74071, 74073, 74074, 74075, 74076, 74078, 74079, 74081, 74083, 74084, 74085, 74086, 74087, 74088, 74089, 74090, 74092, 74095, 74096, 74097, 74098, 74099, 74102, 74103, 74104, 74105, 74504, 74505, 74506, 74507, 74510, 74511, 74512, 74513, 74514, 74515, 74516, 76041, 76042, 76043, 76044, 76045, 76046, 76047, 82223 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. Raw data are retained for longterm preservation but are not intended for general use." }, { "ob_id": 37504, "uuid": "69bbdb1aedcb40fc9ff3e324b693f8b5", "short_code": "coll", "title": "WesCon: in-situ airborne observations by the FAAM BAE-146 aircraft and Jade-one Dimona aircraft", "abstract": "In-situ airborne observations by the FAAM BAE-146 aircraft for WesCon: Wessex summertime convection experiment. \r\nMeteorological in-situ data were collected by a range of instruments on board the FAAM BAe-146 aircraft and the Jade-one Dimona aircraft during a series of flights from May to August 2023 over the southern UK.\r\nThese airborne measurements were made to complement groundbased, sonde and radar observations made for the concurrent WesCon - Observing the Evolving Structures of Turbulence (WOEST) project." } ], "responsiblepartyinfo_set": [ 179069, 179070, 179071, 179072, 179073, 179074, 179075, 179076, 179077, 179078 ], "onlineresource_set": [] }, { "ob_id": 37505, "uuid": "6f800cbda88d424cbcc59181b8b85aaa", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.21 (v20220613)", "abstract": "Data for Figure 3.21 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.21 shows the seasonal evolution of observed and simulated Arctic and Antarctic sea ice area (SIA) over 1979-2017.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has several subplots, but they are unidentified, so the data is stored in the parent directory.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains Sea Ice Area anomalies over 1979-2017 relative to the 1979-2000 means from:\r\n \r\n - Observations (OSISAF, NASA Team, and Bootstrap)\r\n - Historical simulations from CMIP5 and CMIP6 multi-model means\r\n - Natural only simulations from CMIP5 and CMIP6 multi-model means\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - *_arctic_* files are used for the plots on the left side of the figure\r\n - *_antarctic_* files are used for the plots on the right side of the figure\r\n - *_OBS_NASATeam* files are used for the first row of the plot\r\n - *_OBS_Bootstrap* are used for the second row of the plot\r\n - *_OBS_OSISAF* are used for the third row of the plot\r\n - *_ALL_CMIP5* are used in the fourth row of the plot\r\n - *_ALL_CMIP6* are used in the fifth row of the plot\r\n - *_NAT_CMIP5* are used in the sixth row of the plot\r\n - *_NAT_CMIP6* are used in the seventh row of the plot\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The significance are for the grey dots, it's nan or 1 values. The data has to be overplotted to colored squares. Grey dots indicate multi-model mean anomalies stronger than inter-model spread (beyond ± 1 standard deviation).\r\n\r\n\r\nThe coordinates of the data are indices, but in global attributes 'comments' of each file there are relations of indices to months, since months are the y coordinate.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:03", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Sea Ice Area Anomalies, CMIP5, CMIP6", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-24T15:49:23", "doiPublishedTime": "2023-02-08T18:05:29.103101", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 118, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37506, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig21/v20220613", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 298735, "numberOfFiles": 25, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10354, "startTime": "1979-02-01T12:00:00", "endTime": "2017-12-31T12:00:00" }, "resultQuality": { "ob_id": 3966, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37507, "uuid": "ed6fc23ca6f745a398bcacd3da747175", "short_code": "comp", "title": "Caption for Figure 3.21 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Seasonal evolution of observed and simulated Arctic (left) and Antarctic (right) sea ice area (SIA) over 1979–2017. SIA anomalies relative to the 1979–2000 means from observations (OBS from OSISAF, NASA Team, and Bootstrap, top) and historical (ALL, middle) and natural only (NAT, bottom) simulations from CMIP5 and CMIP6 multi-models. These anomalies were obtained by computing non-overlapping three-year mean SIA anomalies for March (February for Antarctic SIA), June, September, and December separately. CMIP5 historical simulations are extended by using RCP4.5 scenario simulations after 2005 while CMIP6 historical simulations are extended by using SSP2-4.5 scenario simulations after 2014. CMIP5 NAT simulations end in 2012. Numbers in brackets represent the number of models used. The multi-model mean is obtained by taking the ensemble mean for each model first and then averaging over models. Grey dots indicate multi-model mean anomalies stronger than inter-model spread (beyond ± 1 standard deviation). Results remain very similar when based on sea ice extent (SIE – not shown). Units: 106 km2. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 60438, 63666, 63667, 63668, 63669 ], "vocabularyKeywords": [], "identifier_set": [ 12367 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 179085, 179086, 179087, 179088, 179089, 179090, 179091, 179092, 179093, 179094 ], "onlineresource_set": [ 52259, 52378, 52260, 82873, 88571, 89938, 89939, 89940, 89941, 89942, 89943, 89944, 89945, 89946, 89947, 89948, 89949, 89950, 89951, 89952, 89953, 89954, 89955, 89956, 89957, 89958, 89959, 89960, 89961, 89962, 89963, 89964 ] }, { "ob_id": 37508, "uuid": "0915a82fa8a84e21bcb5467be84d49fc", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.22 (v20220613)", "abstract": "Data for Figure 3.22 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.22 shows time series of Northern Hemisphere March-April mean snow cover extent (SCE) from observations, CMIP5 and CMIP6 simulations.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n There are technically two panels top and bottom (CMIP5 and CMIP6), however, the data is stored in the parent directory.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The data is for the Northern Hemisphere snow cover extent anomalies (SCEA) from models and observations:\r\n \r\n - The SCEA observational data from GLDAS-NOAH (1948-2012), Brown-NOAA (1923-2017), Mudryk et al 2020 (1968-2017)\r\n - The SCEA modelled by CMIP5 historical-rcp45 experiment (1923-2017)\r\n - The SCEA modelled by CMIP5 historicalNat experiment (1923-2012)\r\n - The SCEA modelled by CMIP6 historical-ssp245 experiment (1923-2017)\r\n - The SCEA modelled by CMIP6 hist-nat experiment (1923-2017)\r\n - The SCEA modelled by CMIP5 and CMIP6 piControl experiments\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n snow_cover_extent_cmip5_obs.csv is the data for the green and brown lines and shadings in the upper panel and grey lines (1923-2017)\r\n snow_cover_extent_cmip6_obs.csv is the data for the green and brown lines and shadings in the lower panel and grey lines (1923-2017)\r\n snow_cover_extent_piControl.csv for the blue error bars in the both panels\r\n Additional details of data provided in relation to figure in the file header (BADC-CSV file)\r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\n CMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:07", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Snow Cover Extent Anomalies, CMIP6, attribution", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-24T15:49:31", "doiPublishedTime": "2023-02-08T18:06:14.054237", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 119, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37509, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig22/v20220613", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 14968, "numberOfFiles": 6, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10355, "startTime": "1923-03-01T12:00:00", "endTime": "2017-04-30T12:00:00" }, "resultQuality": { "ob_id": 3967, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37510, "uuid": "388488ad642c4071bd76357010fd0995", "short_code": "comp", "title": "Caption for Figure 3.22 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Time series of Northern Hemisphere March–April mean snow cover extent (SCE) from observations, CMIP5 and CMIP6 simulations. The observations (grey lines) are updated Brown-NOAA (Brown and Robinson, 2011), Mudryk et al. (2020), and GLDAS2. CMIP5 (top) and CMIP6 (bottom) simulations of the response to natural plus anthropogenic forcing are shown in orange, natural forcing only in green, and the pre-industrial control simulation range is presented in blue. Five-year mean anomalies are shown for the 1923–2017 period with the x-axis representing the centre years of each five-year mean. CMIP5 all forcing simulations are extended by using RCP4.5 scenario simulations after 2005 while CMIP6 all forcing simulations are extended by using SSP2-4.5 scenario simulations after 2014. Shading indicates 5th–95th percentile ranges for CMIP5 and CMIP6 all and natural forcings simulations, and solid lines are ensemble means, based on all available ensemble members with equal weight given to each model (Section 3.2). The blue vertical bar indicates the mean 5th–95th percentile range of pre-industrial control simulation anomalies, based on non-overlapping segments. The numbers in brackets indicate the number of models used. Anomalies are relative to the average over 1971–2000. For models, SCE is restricted to gridcells with land fraction ≥50%. Greenland is excluded from the total area summation. Figure is modified from Paik et al. (2020a), their Figure 1. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 64247, 65321, 65322, 65323, 65324, 65325, 65326, 65327, 65328, 65329, 65330, 65331, 65332, 65333, 65334, 65335 ], "vocabularyKeywords": [], "identifier_set": [ 12368 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 179097, 179098, 179099, 179100, 179101, 179102, 179103, 179104, 179105, 179106, 179107 ], "onlineresource_set": [ 52379, 52261, 52262, 82874, 88572 ] }, { "ob_id": 37511, "uuid": "becdaa43cf884c299435dc319e758f4e", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.20 (v20220613)", "abstract": "Data for Figure 3.20 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.20 shows means and trends in Arctic sea ice area (SIA) in September and Antarctic SIA in February for 1979-2017 from CMIP5 and CMIP6 models.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n Technically figure has four panels, but they are not named so the data is stored in the parent directory. \r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n Data is for September Arctic and February Antarctic Sea Ice Areas (SIAs) and their trends from models and observations:\r\n \r\n - SIAs from Bootstrap, NASA-Team and OSISAF (1979-2017)\r\n - SIAs from CMIP5 historical-rcp45 experiment (1979-2017)\r\n - SIAs from CMIP6 historical-ssp245 experiment (1979-2017)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - sia_point_nh_cmip5.csv has Arctic sea ice area means and decadal trends for September calculated from CMIP5 and observations from 1979-2017\r\n - sia_point_nh_cmip6.csv has Arctic sea ice area means and decadal trends for September calculated from CMIP6 and observations from 1979-2017\r\n - sia_point_sh_cmip5.csv has Antarctic sea ice area means and decadal trends for February calculated from CMIP5 and observations from 1979-2017\r\n - sia_point_sh_cmip6.csv has Antarctic sea ice area means and decadal trends for February calculated from CMIP6 and observations from 1979-2017\r\n\r\n Additional details of data provided in relation to figure in the files header (BADC-CSV files)\r\n\r\n CMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\n CMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The black line which is shown in each panel is written in the comments.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:04", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Sea Ice Area, Sea Ice Area Trends, CMIP5, CMIP6", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-24T15:49:17", "doiPublishedTime": "2023-02-08T18:04:29.703171", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 120, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37512, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig20/v20220613", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 13025, "numberOfFiles": 7, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10356, "startTime": "1979-02-01T12:00:00", "endTime": "2017-09-30T12:00:00" }, "resultQuality": { "ob_id": 3968, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37513, "uuid": "ddfcacd0ee474e8182ff88c546c32d92", "short_code": "comp", "title": "Caption for Figure 3.20 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Mean (x-axis) and trend (y-axis) in Arctic sea ice area (SIA) in September (left) and Antarctic SIA in February (right) for 1979–2017 from CMIP5 (upper) and CMIP6 (lower) models. All individual models (ensemble means) and the multi-model mean values are compared with the observations (OSISAF, NASA Team, and Bootstrap). Solid line indicates a linear regression slope with corresponding correlation coefficient (r) and p-value provided. Note the different scales used on the y-axis for Arctic and Antarctic SIA. Results remain essentially the same when using sea ice extent (SIE; not shown). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 64391, 64392, 64393 ], "vocabularyKeywords": [], "identifier_set": [ 12366 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 179110, 179111, 179112, 179113, 179114, 179115, 179116, 179117, 179118, 179119 ], "onlineresource_set": [ 52264, 52377, 52263, 82872, 88570, 89965, 89966, 89967, 89968, 89969, 89970, 89971, 89972, 89973, 89974, 89975, 89976, 89977, 89978, 89979, 89980, 89981, 89982, 89983, 89984, 89985, 89986, 89987, 89988 ] }, { "ob_id": 37514, "uuid": "a71383af93af4f58ae27d66ba15b3543", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.24 (v20220614)", "abstract": "Data for Figure 3.24 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.24 shows biases in zonal mean and equatorial sea surface temperature (SST) in CMIP5 and CMIP6 models.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has three panels (a), (b), (c), with data provided for all panels in subdirectories named panel_a, panel_b and panel_c.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The dataset contains sea surface temperature (SST) data (1979-1999): \r\n \r\n - Modelled zonal mean SST biases from CMIP5\r\n - Modelled zonal mean SST biases from CMIP6\r\n - Modelled zonal mean SST biases from HighResMIP\r\n - Modelled equatorial SST biases from CMIP5\r\n - Modelled equatorial SST biases from CMIP6\r\n - Modelled equatorial SST biases from HighResMIP\r\n - Modelled mean equatorial SST from CMIP5\r\n - Modelled mean equatorial SST from CMIP6\r\n - Modelled mean equatorial SST from HighResMIP\r\n - Observed mean equatorial SST from HadISST v1\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - panel_a/zonal_sst_bias.csv has zonal mean sea surface temperature bias over the period 1979-1999, there are data for blue (CMIP5), red (CMIP6) and green (HighResMIP) shadings representing 5th and 95th percentile over ensemble\r\n - panel_b/equatorial_sst_bias.csv has equatorial mean sea surface temperature bias over the period 1979-1999, there are data for blue (CMIP5), red (CMIP6) and green (HighResMIP) shadings representing 5th and 95th percentile over ensemble\r\n - panel_c/equatorial_sst_means.csv has equatorial mean sea surface temperature over the period 1979-1999, there are data for black (HadISSTv1), blue (CMIP5), red (CMIP6) and green (HighResMIP) shadings representing 5th and 95th percentile over ensemble\r\n Details about the data provided in relation to the figure in the header of every file.\r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\n CMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n For equatorial SSTs and equatorial SST biases, the data has longitude coordinate which goes 20 to 380 degrees. It was done with python package iris not to break the lines through Atlantic.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:05", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Equatorial Sea Surface Temperature, Zonal Sea Surface Temperature Biases, Equatorial Sea Surface Temperature Biases, CMIP5, CMIP6, HighResMIP, HadISST", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-24T15:51:34", "doiPublishedTime": "2023-02-08T18:13:25.927568", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 121, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37515, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig24/v20220614", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 104720, "numberOfFiles": 6, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10357, "startTime": "1979-01-01T12:00:00", "endTime": "1999-12-31T12:00:00" }, "resultQuality": { "ob_id": 3969, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-14" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37516, "uuid": "3464cd9f09f2465eac27fbb68000aedf", "short_code": "comp", "title": "Caption for Figure 3.24 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Biases in zonal mean and equatorial sea surface temperature (SST) in CMIP5 and CMIP6 models. CMIP6 (red), CMIP5 (blue) and HighResMIP (green) multi-model mean (a) zonally averaged SST bias; (b) equatorial SST bias; and (c) equatorial SST compared to observed mean SST (black line) for 1979–1999. The inter-model 5th and 95th percentiles are depicted by the respective shaded range. Model climatologies are derived from the 1979–1999 mean of the historical simulations, using one simulation per model. The Hadley Centre Sea Ice and Sea Surface Temperature version 1 (HadISST) (Rayner et al., 2003) observational climatology for 1979–1999 is used as the reference for the error calculation in (a) and (b); and for observations in (c). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 65300, 65301, 65302, 65303, 65304, 65305, 65306, 65307, 65308, 65309, 65310, 65311, 65312, 65313, 65314, 65315, 65316, 65317, 65318, 65319, 65320 ], "vocabularyKeywords": [], "identifier_set": [ 12370 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 179122, 179123, 179124, 179125, 179126, 179127, 179128, 179129, 179130 ], "onlineresource_set": [ 52266, 52382, 52381, 82876, 88574, 89891, 89892, 89893, 89894, 89895, 89896, 89897, 89898, 89899, 89900, 89901, 89902, 89903, 89904, 89905, 89906, 89907, 89908, 89909, 89910, 89911, 94618 ] }, { "ob_id": 37517, "uuid": "dce3253d984c4342899b01548f52ba5f", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.25 (v20220614)", "abstract": "Data for Figure 3.25 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.25 shows CMIP6 potential temperature and salinity biases for the global ocean, Atlantic, Pacific and Indian Oceans.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n There are panels (a), (b), (c), (d), (e), (f), (g), (h). The data is in respective subdirectories.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The dataset contains modelled and observational ocean data (1981-2010) for different ocean basins (global, Atlantic, Pacific, Indian): \r\n \r\n - Potential temperature from WOA18 observations\r\n - Salinity from WOA18 observations\r\n - Potential temperature bias (CMIP6 - WOA18)\r\n - Salinity bias (CMIP6 - WOA18)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a\r\n - panel_a/potential_temperature_bias_global_panel_a.nc: data for colored filled contours showing temperature bias from 1981 to 2010\r\n - panel_a/WOA_potential_temperature_global_panel_a.nc: data for black contours showing WOA18 temperature from 1981 to 2010\r\n \r\n Panel b\r\n - panel_b/salinity_bias_global_panel_b.nc: data for colored filled contours showing salinity bias from 1981 to 2010\r\n - panel_b/WOA_salinity_global_panel_b.nc: data for black contours showing WOA18 salinity from 1981 to 2010\r\n \r\n Panel c\r\n - panel_c/potential_temperature_bias_atlantic_panel_c.nc: data for colored filled contours showing temperature bias from 1981 to 2010\r\n - panel_c/WOA_potential_temperature_atlantic_panel_c.nc: data for black contours showing WOA18 temperature from 1981 to 2010\r\n \r\n Panel d\r\n - panel_d/salinity_bias_atlantic_panel_d.nc: data for colored filled contours showing salinity bias from 1981 to 2010\r\n - panel_d/WOA_salinity_atlantic_panel_d.nc: data for black contours showing WOA18 salinity from 1981 to 2010\r\n \r\n Panel e\r\n - panel_e/potential_temperature_bias_pacific_panel_e.nc: data for colored filled contours showing temperature bias from 1981 to 2010\r\n - panel_e/WOA_potential_temperature_pacific_panel_e.nc: data for black contours showing WOA18 temperature from 1981 to 2010\r\n \r\n Panel f\r\n - panel_f/salinity_bias_pacific_panel_f.nc: data for colored filled contours showing salinity bias from 1981 to 2010\r\n - panel_f/WOA_salinity_pacific_panel_f.nc: data for black contours showing WOA18 salinity from 1981 to 2010\r\n \r\n Panel g\r\n - panel_g/potential_temperature_bias_indian_panel_g.nc: data for colored filled contours showing temperature bias from 1981 to 2010\r\n - panel_g/WOA_potential_temperature_indian_panel_g.nc: data for black contours showing WOA18 temperature from 1981 to 2010\r\n \r\n Panel h\r\n - panel_h/salinity_bias_indian_panel_h.nc: data for colored filled contours showing salinity bias from 1981 to 2010\r\n - panel_h/WOA_salinity_indian_panel_h.nc: data for black contours showing WOA18 salinity from 1981 to 2010\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:03", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Potential Temperature, Salinity, Atlantic ocean, Pacific ocean, Indian ocean", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-24T15:51:40", "doiPublishedTime": "2023-02-08T18:19:55.758576", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 122, "highestLevelBound": 0.0, "lowestLevelBound": 5000.0, "units": "" }, "result_field": { "ob_id": 37518, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig25/v20220614", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 791937, "numberOfFiles": 21, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10358, "startTime": "1981-01-01T12:00:00", "endTime": "2010-12-31T12:00:00" }, "resultQuality": { "ob_id": 3970, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-14" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37519, "uuid": "18c19d847b114b3fababeb3f73778d59", "short_code": "comp", "title": "Caption for Figure 3.25 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "CMIP6 potential temperature and salinity biases for the global ocean, Atlantic, Pacific and Indian Oceans. Shown in colour are the time-mean differences between the CMIP6 historical multi-model climatological mean and observations, zonally averaged for each basin (excluding marginal and regional seas). The observed climatological values are obtained from the World Ocean Atlas 2018 (WOA18, 1981-–2010; Prepared by the Ocean Climate Laboratory, National Oceanographic Data Center, Silver Spring, MD, USA), and are shown as labelled black contours for each of the basins. The simulated annual mean climatologies for 1981 to 2010 are calculated from available CMIP6 historical simulations, and the WOA18 climatology utilized synthesized observed data from 1981 to 2010. A total of 30 available CMIP6 models have contributed to the temperature panels (left column) and 28 models to the salinity panels (right column). Potential temperature units are °C and salinity units are the Practical Salinity Scale 1978 [PSS-78]. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 49490, 52664, 52665, 60438, 63660, 63662, 63663, 63664, 63665, 89613 ], "vocabularyKeywords": [], "identifier_set": [ 12371 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 179133, 179134, 179135, 179136, 179137, 179138, 179139, 179140, 179141 ], "onlineresource_set": [ 52267, 52383, 52265, 82877, 88575 ] }, { "ob_id": 37520, "uuid": "38512cd8209b4669a0743e9672f70a6e", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.28 (v20220614)", "abstract": "Data for Figure 3.28 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.28 shows long-term trends in halosteric and thermosteric sea level in CMIP6 models and observations.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has panels (a), (b), (c), (d), (e), (f), with data provided for all panels in subdirectories named panel_a, panel_b, panel_c, panel_d, panel_e and panel_f.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The datasets contains: \r\n \r\n - Atlantic and Pacific halosteric sea level trend from CMIP6 models (1950-2014)\r\n - Atlantic and Pacific halosteric sea level trend from Durack&Wijffels observations (1950-2019)\r\n - Atlantic and Pacific halosteric sea level trend from EN4 observations (1950-2019)\r\n - Atlantic and Pacific halosteric sea level trend from Ishii observations (1955-2019)\r\n - Atlantic and Pacific thermosteric sea level trend from CMIP6 models (1950-2014)\r\n - Atlantic and Pacific thermosteric sea level trend from Durack&Wijffels observations (1950-2019)\r\n - Atlantic and Pacific thermosteric sea level trend from EN4 observations (1950-2019)\r\n - Atlantic and Pacific thermosteric sea level trend from Ishii observations (1955-2019)\r\n - Global halosteric sea level trends from Durack&Wijffels observations (1950-2019)\r\n - Global halosteric sea level trends from EN4 observations (1950-2019)\r\n - Global halosteric sea level trends from Ishii observations (1955-2019)\r\n - Global halosteric sea level trends from CMIP6 multi-model mean (1950-2014)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - panel_a/halosteric_trends_hist-nat.csv has data for green and black markers.\r\n - panel_a/halosteric_trends_historical.csv has data for orange and black markers.\r\n - panel_b/thermosteric_trends_hist-nat.csv has data for green and black markers.\r\n - panel_b/thermosteric_trends_historical.csv has data for orange and black markers.\r\n - panel_c/halosteric_trends_map_DW.nc has data for filled colored contours.\r\n - panel_d/halosteric_trends_map_EN4.nc has data for filled colored contours.\r\n - panel_e/halosteric_trends_map_Ishii.nc has data for filled colored contours.\r\n - panel_f/halosteric_trends_map_cmip6.nc has data for filled colored contours.\r\n For panels a and b details about the data provided in relation to the figure in the header of every file.\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The observational data from here (top right panel) is taken from the file:\r\n\r\nDurackandWijffels_GlobalOceanChanges_19500101-20191231__210122-205355_beta.nc. The field of interest are salinity_mean (shown as black contours) and salinity_change (shown in colourscale). The file was archived as input data for Figure 2.27. The link to this dataset is provided in the Related Documents section of this catalogue record.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the input dataset for figure 3.28\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to the figure on the IPCC AR6 website", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:24:25", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Halosteric Sea Level Trends, Thermosperic Sea Level Trends, observations, CMIP6, historical experiment, hist-nat experiment", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-24T15:51:53", "doiPublishedTime": "2023-02-08T19:09:35.045515", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 123, "highestLevelBound": 0.0, "lowestLevelBound": 2000.0, "units": "" }, "result_field": { "ob_id": 37521, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig28/v20220614", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1063413, "numberOfFiles": 11, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10359, "startTime": "1950-01-01T12:00:00", "endTime": "2019-12-31T12:00:00" }, "resultQuality": { "ob_id": 3971, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-14" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37522, "uuid": "063505b8b9c54ef49d9600ab96d0e544", "short_code": "comp", "title": "Caption for Figure 3.28 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Long-term trends in halosteric and thermosteric sea level in CMIP6 models and observations. Units are mm yr–1. In the right-hand column, three observed maps of 0 to 2000 m halosteric sea level trends: top (D&W) from Durack and Wijffels (2010), 1950–2019, updated; upper-middle (EN4) from Good et al. (2013), 1950–2019, updated; and lower-middle (Ishii) from Ishii et al. (2017), 1955–2019, updated. Bottom-right: the CMIP6 historical multi-model mean (1950–2014). Red and orange colours show a halosteric contraction (enhanced salinity) and blue and green a halosteric expansion (reduced salinity). In the left-hand column, basin-integrated halosteric (top) and thermosteric (bottom) trends for the Atlantic and Pacific, the two largest ocean basins, where Pacific anomalies are presented on the x-axis and Atlantic on the y-axis. Observational estimates are presented in black, CMIP6 historical (all forcings) simulations are shown in orange squares, with the multi-model mean shown as a dark orange diamond with a black bounding box. CMIP6 hist-nat (historical natural forcings only) simulations are shown in green squares with the multi-model mean as a dark green diamond with a black bounding box. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 9042, 9043, 52664, 52665, 63172, 63173, 63659 ], "vocabularyKeywords": [], "identifier_set": [ 12374 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 179144, 179145, 179146, 179147, 179148, 179149, 179150, 179151, 179152 ], "onlineresource_set": [ 52268, 52386, 52269, 82637, 88578, 89856, 89857, 89858, 89859, 89860, 89861, 89862, 89863, 89864, 89865, 89866, 94614 ] }, { "ob_id": 37523, "uuid": "a3902bb4d1b543b39cc85380df8d1586", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.30 (v20220614)", "abstract": "Data for Figure 3.30 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.30 shows observed and CMIP6 simulated AMOC mean state, variability and long-term trends.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 6 subpanels with data provided for all panels in subdirectories named panel_a, panel_b, panel_c, panel_d, panel_e and panel_f.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains: \r\n \r\n - AMOC streamfunction profiles from CMIP5 (1860-2004) and CMIP6 (1860-2014) historical simulations\r\n - AMOC mean maximum overturning depth from CMIP5 (1860-2004) and CMIP6 (1860-2014) historical simulations\r\n - AMOC mean maximum overturning depth from RAPID observational dataset (2004-2018)\r\n - AMOC mean maximum overturning streamfunction from CMIP5 (1860-2004) and CMIP6 (1860-2014) historical simulations\r\n - AMOC mean maximum overturning streamfunction from RAPID observational dataset (2004-2018)\r\n - AMOC 8-year trends from CMIP5 and CMIP6 simulations and RAPID observations (2004-2012)\r\n - Interannual AMOC changes from CMIP5 and CMIP6 simulations and RAPID observations (2008-2010)\r\n - Longterm AMOC trends (1850-2014) from CMIP6 simulations\r\n - Longterm AMOC trends (1940-1985) from CMIP6 simulations\r\n - Longterm AMOC trends (1985-2014) from CMIP6 simulations\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - panel_a/amoc_mean_state_boxes.csv has the data for the grey observations lines and blue and red boxes with whiskers\r\n - panel_a/amoc_profiles_shadings.csv has data for the blue and red profile shadings.\r\n - panel_a/amoc_profile_cmip5.csv has data for the blue profile\r\n - panel_a/amoc_profile_cmip6.csv has data for the red profile\r\n - panel_b/amoc_trends_2004_2012.csv has data for boxes and whiskers and outlier dots\r\n - panel_b/amoc_trends_cmip5_cmip6_additional_outliers.csv has data for additional outlier dots for CMIP5 and CMIP6\r\n - panel_c/interannual_variability_AMOC.csv has data for boxes and whiskers and outlier dots\r\n - panel_c/interannual_variability_AMOC_cmip5_cmip6_additional_outliers.csv has data for additional outlier dots for CMIP5 and CMIP6\r\n - panel_d/amoc_longtern_trend_1850_2014.csv has data for grey, green, blue and orange boxes and whiskers\r\n - panel_e/amoc_longtern_trend_1940_1985.csv has data for grey, green, blue and orange boxes and whiskers\r\n - panel_f/amoc_longtern_trend_1985_2014.csv has data for grey, green, blue and orange boxes and whiskers\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nAMOC is the Atlantic Meridional Overturning Circulation.\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to the figure on the IPCC AR6 website", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:24:30", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, AMOC trend, AMOC, CMIP6, CMIP5, RAPID", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-24T15:53:59", "doiPublishedTime": "2023-02-08T19:17:44.666707", "removedDataTime": null, "geographicExtent": { "ob_id": 3506, "bboxName": "", "eastBoundLongitude": 50.0, "westBoundLongitude": -90.0, "southBoundLatitude": 26.5, "northBoundLatitude": 26.5 }, "verticalExtent": { "ob_id": 124, "highestLevelBound": 0.0, "lowestLevelBound": 5000.0, "units": "" }, "result_field": { "ob_id": 37524, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig30/v20220614", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 201303, "numberOfFiles": 14, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10360, "startTime": "1860-01-01T12:00:00", "endTime": "2014-12-31T12:00:00" }, "resultQuality": { "ob_id": 3972, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-14" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37525, "uuid": "2b6efdfc0f8844f2beecaa3402236315", "short_code": "comp", "title": "Caption for Figure 3.30 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Observed and CMIP6 simulated AMOC mean state, variability and long-term trends. (a) AMOC meridional stream function profiles at 26.5°N from the historical CMIP5 (1860–2004) and CMIP6 (1860–2014) simulations compared with the mean maximum overturning depth (horizontal grey line) and magnitude (vertical grey line) from the RAPID observations (2004–2018). The distributions of model ranges of AMOC maximum magnitude and depth are respectively displayed on the x- and y-axis. (b) Distributions of overlapping eight-year AMOC trends from individual CMIP6 historical simulations (pink box plots) are plotted along with the combined distributions of all available CMIP5 (blue boxplot) and CMIP6 (red boxplot) models. For reference, the observed eight-year trend calculated between 2004–2012 is also shown as a horizontal grey line (following Roberts et al., 2014). (c) Distributions of interannual AMOC variability from individual CMIP6 model historical simulations, along with the combined distributions of all available CMIP5 and CMIP6 models. Interannual variability in models and observations are estimated as annual mean (April–March) differences, and the horizontal grey line is the observed value for 2009/2010 minus 2008/2009 (following Roberts et al., 2014). (d–f) Distributions of linear AMOC trends calculated over various time periods (see panel titles) in CMIP6 simulations forced with: greenhouse gas forcing only (GHG), natural forcing only (NAT), anthropogenic aerosol forcing only (AER) and all forcing combined (Historical; HIST). (a–f) Boxes indicate the 25th to 75th percentile range, whiskers indicate 1st and 99th percentiles, and dots indicate outliers, while the horizontal black line is the multi-model mean trend. In (d–f) the multi-model mean trend is also written above each distribution. The multi-model distributions in (a–c) were produced with one historical ensemble member per model for which the AMOC variable was available (listed), while those in (d–f) were produced with the AMOC detection and attribution simulation datasets utilized by Menary et al. (2020). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 64365, 64366, 64367, 64368, 64369, 64370, 64371, 64372, 64373, 64374, 64375, 64376, 64377, 64378, 64379, 64380, 64381, 64382, 64383, 64384, 64385, 64386, 64387, 64388, 64389, 64390 ], "vocabularyKeywords": [], "identifier_set": [ 12376 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 179155, 179156, 179157, 179158, 179159, 179160, 179161, 179162, 179163 ], "onlineresource_set": [ 52271, 52270, 52387, 82635, 88581 ] }, { "ob_id": 37526, "uuid": "ef5ca18bcaf441d9993f181a058016ba", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.35 (v20220614)", "abstract": "Data for Figure 3.35 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.35 shows Southern Annular Mode indices in the last millennium. \r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels, and all the data are provided in sam_millennium.nc. \r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n \r\n - Annual SAM reconstructions.\r\n - Annual-mean SAM index by CMIP5 and CMIP6 Last Millennium simulations extended by historical simulations.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a:\r\n - sam_abram_runmean, sam_datwyler_runmean: thin blue and brown lines\r\n - sam_abram_lowpass, sam_datwyler_lowpass: thick blue and brown lines\r\n\r\nPanel b:\r\n - sam_cmip_runmean: thin lines\r\n . MIROC-ES2L: ensemble = 10 (violet)\r\n . MRI-ESM2-0: ensemble = 11 (green)\r\n . CMIP5: ensemble = 1, 2, 3, 4, 5, 6, 7, 8, 9 (grey)\r\n - sam_cmip_lowpass: thick lines\r\n . MIROC-ES2L: ensemble = 10 (violet)\r\n . MRI-ESM2-0: ensemble = 11 (green)\r\n . CMIP5: ensemble = 1, 2, 3, 4, 5, 6, 7, 8, 9 (grey)\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to the figure on the IPCC AR6 website", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:02", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, SAM, modes of variability, CMIP5, CMIP6, PMIP, last millennium", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-27T11:25:20", "doiPublishedTime": "2023-02-08T19:23:39.404089", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 125, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37527, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig35/v20220614", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 124650, "numberOfFiles": 4, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10361, "startTime": "1950-01-01T12:00:00", "endTime": "2050-12-31T12:00:00" }, "resultQuality": { "ob_id": 3973, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-14" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37528, "uuid": "43abaed7d0994ecab6ab4d97eb291925", "short_code": "comp", "title": "Caption for Figure 3.35 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Southern Annular Mode (SAM) indices in the last millennium. (a) Annual SAM reconstructions by Abram et al. (2014) and Dätwyler et al. (2018). (b) The annual-mean SAM index defined by Gong and Wang (1999) in CMIP5 and CMIP6 last millennium simulations extended by historical simulations. All indices are normalized with respect to 1961–1990 means and standard deviations. Thin lines and thick lines show seven-year and 70-year moving averages, respectively. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 21859, 60501, 63653, 63654, 63655, 63656, 63657, 63658 ], "vocabularyKeywords": [], "identifier_set": [ 12381 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 179166, 179167, 179168, 179169, 179170, 179171, 179172, 179173, 179174, 179568, 179175 ], "onlineresource_set": [ 52391, 52272, 52273, 82630, 88586, 89804, 89805, 89806, 89807, 89808, 89809, 89810, 89811, 89812, 89813, 89814, 89815, 89816, 89817, 89818, 89819, 89820, 89821, 89822, 89823, 94626 ] }, { "ob_id": 37529, "uuid": "02006a22c33b42039d96be53d332930a", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.39 (v20220614)", "abstract": "Data for Figure 3.39 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.39 shows the observed and simulated Pacific Decadal Variability (PDV).\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has six panels. Files are not separated according to the panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n pdv.obs.nc contains\r\n - Observed SST anomalies associated with the PDV pattern\r\n - Observed PDV index time series (unfiltered)\r\n - Observed PDV index time series (low-pass filtered)\r\n - Taylor statistics of the observed PDV patterns\r\n - Statistical significance of the observed SST anomalies associated with the PDV pattern\r\n \r\n pdv.hist.cmip6.nc contains\r\n - Simulated SST anomalies associated with the PDV pattern\r\n - Simulated PDV index time series (unfiltered)\r\n - Simulated PDV index time series (low-pass filtered)\r\n - Taylor statistics of the simulated PDV patterns\r\n based on CMIP6 historical simulations.\r\n \r\n pdv.hist.cmip5.nc contains\r\n - Simulated SST anomalies associated with the PDV pattern\r\n - Simulated PDV index time series (unfiltered)\r\n - Simulated PDV index time series (low-pass filtered)\r\n - Taylor statistics of the simulated PDV patterns\r\n based on CMIP5 historical simulations.\r\n \r\n pdv.piControl.cmip6.nc contains\r\n - Simulated SST anomalies associated with the PDV pattern\r\n - Simulated PDV index time series (unfiltered)\r\n - Simulated PDV index time series (low-pass filtered)\r\n - Taylor statistics of the simulated PDV patterns\r\n based on CMIP6 piControl simulations.\r\n \r\n pdv.piControl.cmip5.nc contains\r\n - Simulated SST anomalies associated with the PDV pattern\r\n - Simulated PDV index time series (unfiltered)\r\n - Simulated PDV index time series (low-pass filtered)\r\n - Taylor statistics of the simulated PDV patterns\r\n based on CMIP5 piControl simulations.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a:\r\n - ipo_pattern_obs_ref in pdv.obs.nc: shading\r\n - ipo_pattern_obs_signif (dataset = 1) in pdv.obs.nc: cross markers\r\n \r\n Panel b:\r\n - Multimodel ensemble mean of ipo_model_pattern in pdv.hist.cmip6.nc: shading, with their sign agreement for hatching\r\n \r\n Panel c:\r\n - tay_stats (stat = 0, 1) in pdv.obs.nc: black dots\r\n - tay_stats (stat = 0, 1) in pdv.hist.cmip6.nc: red crosses, and their multimodel ensemble mean for the red dot\r\n - tay_stats (stat = 0, 1) in pdv.hist.cmip5.nc: blue crosses, and their multimodel ensemble mean for the blue dot\r\n \r\n Panel d:\r\n - Lag-1 autocorrelation of tpi in pdv.obs.nc: black horizontal lines in left\r\n . ERSSTv5: dataset = 1\r\n . HadISST: dataset = 2\r\n . COBE-SST2: dataset = 3\r\n - Multimodel ensemble mean and percentiles of lag-1 autocorrelation of tpi in pdv.piControl.cmip5.nc: blue open box-whisker in the left\r\n - Multimodel ensemble mean and percentiles of lag-1 autocorrelation of tpi in pdv.piControl.cmip6.nc: red open box-whisker in the left\r\n - Multimodel ensemble mean and percentiles of lag-1 autocorrelation of tpi in pdv.hist.cmip5.nc: blue filled box-whisker in the left\r\n - Multimodel ensemble mean and percentiles of lag-1 autocorrelation of tpi in pdv.hist.cmip6.nc: red filled box-whisker in the left\r\n - Lag-10 autocorrelation of tpi_lp in pdv.obs.nc: black horizontal lines in right\r\n . ERSSTv5: dataset = 1\r\n . HadISST: dataset = 2\r\n . COBE-SST2: dataset = 3\r\n - Multimodel ensemble mean and percentiles of lag-10 autocorrelation of tpi_lp in pdv.piControl.cmip5.nc: blue open box-whisker in the right\r\n - Multimodel ensemble mean and percentiles of lag-10 autocorrelation of tpi_lp in pdv.piControl.cmip6.nc: red open box-whisker in the right\r\n - Multimodel ensemble mean and percentiles of lag-10 autocorrelation of tpi_lp in pdv.hist.cmip5.nc: blue filled box-whisker in the right\r\n - Multimodel ensemble mean and percentiles of lag-10 autocorrelation of tpi_lp in pdv.hist.cmip6.nc: red filled box-whisker in the right\r\n \r\n Panel e:\r\n - Standard deviation of tpi in pdv.obs.nc: black horizontal lines in left\r\n . ERSSTv5: dataset = 1\r\n . HadISST: dataset = 2\r\n . COBE-SST2: dataset = 3\r\n - Multimodel ensemble mean and percentiles of standard deviation of tpi in pdv.piControl.cmip5.nc: blue open box-whisker in the left\r\n - Multimodel ensemble mean and percentiles of standard deviation of tpi in pdv.piControl.cmip6.nc: red open box-whisker in the left\r\n - Multimodel ensemble mean and percentiles of standard deviation of tpi in pdv.hist.cmip5.nc: blue filled box-whisker in the left\r\n - Multimodel ensemble mean and percentiles of standard deviation of tpi in pdv.hist.cmip6.nc: red filled box-whisker in the left\r\n - Standard deviation of tpi_lp in pdv.obs.nc: black horizontal lines in right\r\n . ERSSTv5: dataset = 1\r\n . HadISST: dataset = 2\r\n . COBE-SST2: dataset = 3\r\n - Multimodel ensemble mean and percentiles of standard deviation of tpi_lp in pdv.piControl.cmip5.nc: blue open box-whisker in the right\r\n - Multimodel ensemble mean and percentiles of standard deviation of tpi_lp in pdv.piControl.cmip6.nc: red open box-whisker in the right\r\n - Multimodel ensemble mean and percentiles of standard deviation of tpi_lp in pdv.hist.cmip5.nc: blue filled box-whisker in the right\r\n - Multimodel ensemble mean and percentiles of standard deviation of tpi_lp in pdv.hist.cmip6.nc: red filled box-whisker in the right\r\n \r\n Panel f:\r\n - tpi_lp in pdv.obs.nc: black curves\r\n . ERSSTv5: dataset = 1\r\n . HadISST: dataset = 2\r\n . COBE-SST2: dataset = 3\r\n - tpi_lp in pdv.hist.cmip6.nc: 5th-95th percentiles in red shading, multimodel ensemble mean and its 5-95% confidence interval for red curves\r\n - tpi_lp in pdv.hist.cmip5.nc: 5th-95th percentiles in blue shading, multimodel ensemble mean for blue curve\r\n\r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nSST stands for Sea Surface Temperature. \r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Multimodel ensemble means and percentiles of historical simulations of CMIP5 and CMIP6 are calculated after weighting individual members with the inverse of the ensemble size of the same model. ensemble_assign in each file provides the model number to which each ensemble member belongs. This weighting does not apply to the sign agreement calculation.\r\n\r\n\r\npiControl simulations from CMIP5 and CMIP6 consist of a single member from each model, so the weighting is not applied.\r\n\r\n\r\nMultimodel ensemble means of the pattern correlation in Taylor statistics in (c) and the autocorrelation of the index in (d) are calculated via Fisher z-transformation and back transformation.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to the figure on the IPCC AR6 website", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:02", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, ENSO teleconnections", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-27T10:42:19", "doiPublishedTime": "2023-02-08T19:31:29.026442", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 126, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37530, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig39/v20220614", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 43817223, "numberOfFiles": 8, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10362, "startTime": "1900-01-01T12:00:00", "endTime": "2014-12-31T12:00:00" }, "resultQuality": { "ob_id": 3974, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-14" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37531, "uuid": "d0efa8b19231476ea3f974a39a15f294", "short_code": "comp", "title": "Caption for Figure 3.39 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Model evaluation of the Pacific Decadal Variability (PDV). (a, b) Sea surface temperature (SST) anomalies (ºC) regressed onto the Tripole Index (TPI; Henley et al., 2015) for 1900–2014 in (a) ERSST version 5 and (b) CMIP6 multi-model ensemble (MME) mean composite obtained by weighting ensemble members by the inverse of the model ensemble size. A 10-year low-pass filter was applied beforehand. Cross marks in (a) represent regions where the anomalies are not significant at the 10% level based on t-test. Diagonal lines in (b) indicate regions where less than 80% of the runs agree in sign. (c) A Taylor diagram summarizing the representation of the PDV pattern in CMIP5 (each a cross in light blue, and the weighted multi-mode mean as a dot in dark blue), CMIP6 (each ensemble member is shown as a cross in red, weighted multi-model mean as a dot in orange) and observations over 40ºS–60ºN and 110ºE–70ºW. The reference pattern is taken from ERSST version 5 and black dots indicate other observational products, Hadley Centre Sea Ice and Sea Surface Temperature data set version 1 (HadISST version 1) and Centennial in situ Observation-Based Estimates of Sea Surface Temperature version 2 (COBE-SST2). (d) Autocorrelation of unfiltered annual TPI at lag one year and 10-year low-pass filtered TPI at lag 10 years for observations over 1900–2014 (horizontal lines) and 115-year chunks of pre-industrial control simulations (open boxes) and individual historical simulations over 1900–2014 (filled boxes) from CMIP5 (blue) and CMIP6 (red). (e) As in (d), but standard deviation of the unfiltered and filtered TPI (ºC). Boxes and whiskers show weighted multi-model mean, interquartile ranges and 5th and 95th percentiles. (f) Time series of the 10-year low-pass filtered TPI (ºC) in ERSST version 5, HadISST version 1 and COBE-SST2 observational estimates (black) and CMIP5 and CMIP6 historical simulations. The thick red and light blue lines are the weighted multi-model mean for the historical simulations in CMIP5 and CMIP6, respectively, and the envelopes represent the 5th–95th percentile range across ensemble members. The 5–95% confidence interval for the CMIP6 multi-model mean is given in thin dashed lines. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 46697, 46959, 46960, 46966, 52664, 52665, 60438, 63645, 63649, 63650, 63651, 63652 ], "vocabularyKeywords": [], "identifier_set": [ 12385 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 179178, 179179, 179180, 179181, 179182, 179183, 179184, 179185, 179186, 179187, 179188 ], "onlineresource_set": [ 52394, 52274, 52275, 82618, 88589, 94623 ] }, { "ob_id": 37532, "uuid": "12f0d7db5ed747d2940210e52211ed6a", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.40 (v20220614)", "abstract": "Data for Figure 3.40 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.40 shows the observed and simulated Atlantic Multidecadal Variability (AMV).\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has six panels. Files are not separated according to the panels.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n amv.obs.nc contains\r\n - Observed SST anomalies associated with the AMV pattern\r\n - Observed AMV index time series (unfiltered)\r\n - Observed AMV index time series (low-pass filtered)\r\n - Taylor statistics of the observed AMV patterns\r\n \r\n amv.hist.cmip6.nc contains\r\n - Statistical significance of the observed SST anomalies associated with the AMV pattern\r\n - Simulated SST anomalies associated with the AMV pattern\r\n - Simulated AMV index time series (unfiltered)\r\n - Simulated AMV index time series (low-pass filtered)\r\n - Taylor statistics of the simulated AMV patterns\r\n \r\n based on CMIP6 historical simulations.\r\n \r\n amv.hist.cmip5.nc contains\r\n - Simulated SST anomalies associated with the AMV pattern\r\n - Simulated AMV index time series (unfiltered)\r\n - Simulated AMV index time series (low-pass filtered)\r\n - Taylor statistics of the simulated AMV patterns\r\n based on CMIP5 historical simulations.\r\n \r\n amv.piControl.cmip6.nc contains\r\n - Simulated SST anomalies associated with the AMV pattern\r\n - Simulated AMV index time series (unfiltered)\r\n - Simulated AMV index time series (low-pass filtered)\r\n - Taylor statistics of the simulated AMV patterns\r\n based on CMIP6 piControl simulations.\r\n \r\n amv.piControl.cmip5.nc contains\r\n - Simulated SST anomalies associated with the AMV pattern\r\n - Simulated AMV index time series (unfiltered)\r\n - Simulated AMV index time series (low-pass filtered)\r\n - Taylor statistics of the simulated AMV patterns\r\n based on CMIP5 piControl simulations.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a:\r\n - amv_pattern_obs_ref in amv.obs.nc: shading\r\n - amv_pattern_obs_signif (dataset = 1) in amv.obs.nc: cross markers\r\n \r\n Panel b:\r\n - Multimodel ensemble mean of amv_pattern in amv.hist.cmip6.nc: shading, with their sign agreement for hatching\r\n \r\n Panel c:\r\n - tay_stats (stat = 0, 1) in amv.obs.nc: black dots\r\n - tay_stats (stat = 0, 1) in amv.hist.cmip6.nc: red crosses, and their multimodel ensemble mean for the red dot\r\n - tay_stats (stat = 0, 1) in amv.hist.cmip5.nc: blue crosses, and their multimodel ensemble mean for the blue dot\r\n \r\n Panel d:\r\n - Lag-1 autocorrelation of amv_timeseries_raw in amv.obs.nc: black horizontal lines in left\r\n . ERSSTv5: dataset = 1\r\n . HadISST: dataset = 2\r\n . COBE-SST2: dataset = 3\r\n - Multimodel ensemble mean and percentiles of lag-1 autocorrelation of amv_timeseries_raw in amv.piControl.cmip5.nc: blue open box-whisker in the left\r\n - Multimodel ensemble mean and percentiles of lag-1 autocorrelation of amv_timeseries_raw in amv.piControl.cmip6.nc: red open box-whisker in the left\r\n - Multimodel ensemble mean and percentiles of lag-1 autocorrelation of amv_timeseries_raw in amv.hist.cmip5.nc: blue filled box-whisker in the left\r\n - Multimodel ensemble mean and percentiles of lag-1 autocorrelation of amv_timeseries_raw in amv.hist.cmip6.nc: red filled box-whisker in the left\r\n - Lag-10 autocorrelation of amv_timeseries in amv.obs.nc: black horizontal lines in right\r\n . ERSSTv5: dataset = 1\r\n . HadISST: dataset = 2\r\n . COBE-SST2: dataset = 3\r\n - Multimodel ensemble mean and percentiles of lag-10 autocorrelation of amv_timeseries in amv.piControl.cmip5.nc: blue open box-whisker in the right\r\n - Multimodel ensemble mean and percentiles of lag-10 autocorrelation of amv_timeseries in amv.piControl.cmip6.nc: red open box-whisker in the right\r\n - Multimodel ensemble mean and percentiles of lag-10 autocorrelation of amv_timeseries in amv.hist.cmip5.nc: blue filled box-whisker in the right\r\n - Multimodel ensemble mean and percentiles of lag-10 autocorrelation of amv_timeseries in amv.hist.cmip6.nc: red filled box-whisker in the right\r\n \r\n Panel e:\r\n - Standard deviation of amv_timeseries_raw in amv.obs.nc: black horizontal lines in left\r\n . ERSSTv5: dataset = 1\r\n . HadISST: dataset = 2\r\n . COBE-SST2: dataset = 3\r\n - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries_raw in amv.piControl.cmip5.nc: blue open box-whisker in the left\r\n - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries_raw in amv.piControl.cmip6.nc: red open box-whisker in the left\r\n - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries_raw in amv.hist.cmip5.nc: blue filled box-whisker in the left\r\n - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries_raw in amv.hist.cmip6.nc: red filled box-whisker in the left\r\n - Standard deviation of amv_timeseries in amv.obs.nc: black horizontal lines in right\r\n . ERSSTv5: dataset = 1\r\n . HadISST: dataset = 2\r\n . COBE-SST2: dataset = 3\r\n - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries in amv.piControl.cmip5.nc: blue open box-whisker in the right\r\n - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries in amv.piControl.cmip6.nc: red open box-whisker in the right\r\n - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries in amv.hist.cmip5.nc: blue filled box-whisker in the right\r\n - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries in amv.hist.cmip6.nc: red filled box-whisker in the right\r\n \r\n Panel f:\r\n - amv_timeseries in amv.obs.nc: black curves\r\n . ERSSTv5: dataset = 1\r\n . HadISST: dataset = 2\r\n . COBE-SST2: dataset = 3\r\n - amv_timeseries in amv.hist.cmip6.nc: 5th-95th percentiles in red shading, multimodel ensemble mean and its 5-95% confidence interval for red curves\r\n - amv_timeseries in amv.hist.cmip5.nc: 5th-95th percentiles in blue shading, multimodel ensemble mean for blue curve\r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nSST stands for Sea Surface Temperature.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Multimodel ensemble means and percentiles of historical simulations of CMIP5 and CMIP6 are calculated after weighting individual members with the inverse of the ensemble size of the same model. ensemble_assign in each file provides the model number to which each ensemble member belongs. This weighting does not apply to the sign agreement calculation.\r\n\r\n\r\npiControl simulations from CMIP5 and CMIP6 consist of a single member from each model, so the weighting is not applied.\r\n\r\n\r\nMultimodel ensemble means of the pattern correlation in Taylor statistics in (c) and the autocorrelation of the index in (d) are calculated via Fisher z-transformation and back transformation. \r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to the figure on the IPCC AR6 website", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:02", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Atlantic Multidecadal Variability, modes of variability, CMIP5, CMIP6", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-27T10:42:28", "doiPublishedTime": "2023-02-08T19:32:03.240091", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 127, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37533, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig40/v20220614", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 43817985, "numberOfFiles": 8, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10363, "startTime": "1900-01-01T12:00:00", "endTime": "2014-12-31T12:00:00" }, "resultQuality": { "ob_id": 3975, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-14" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37534, "uuid": "fa024534389041a0b2c82b3fb7c70f6d", "short_code": "comp", "title": "Caption for Figure 3.40 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Model evaluation of Atlantic Multi-decadal Variability (AMV). (a, b) Sea surface temperature (SST) anomalies (ºC) regressed onto the AMV index defined as the 10-year low-pass filtered North Atlantic (0º–60°N, 80°W–0°E) area-weighted SST* anomalies over 1900–2014 in (a) ERSST version 5 and (b) the CMIP6 multi-model ensemble (MME) mean composite obtained by weighting ensemble members by the inverse of each model’s ensemble size. The asterisk denotes that the global mean SST anomaly has been removed at each time step of the computation. Cross marks in (a) represent regions where the anomalies are not significant at the 10% level based on a t-test. Diagonal lines in (b) show regions where less than 80% of the runs agree in sign. (c) A Taylor diagram summarizing the representation of the AMV pattern in CMIP5 (each member is shown as a cross in light blue, and the weighted multi-model mean is shown as a dot in dark blue), CMIP6 (each member is shown as a cross in red, and the weighted multi-model mean is shown as a dot in orange) and observations over [0º–60°N, 80°W–0°E]. The reference pattern is taken from ERSST version 5 and black dots indicate other observational products (HadISST version 1 and COBE-SST2). (d) Autocorrelation of unfiltered annual AMV index at lag one year and 10-year low-pass filtered AMV index at lag 10 years for observations over 1900–2014 (horizontal lines) and 115-year chunks of pre-industrial control simulations (open boxes) and individual historical simulations over 1900–2014 (filled boxes) from CMIP5 (blue) and CMIP6 (red). (e) As in (d), but showing standard deviation of the unfiltered and filtered AMV indices (ºC). Boxes and whiskers show the weighted multi-model mean, interquartile ranges and 5th and 95th percentiles. (f) Time series of the AMV index (ºC) in ERSST version 5, HadISST version 1 and COBE-SST2 observational estimates (black) and CMIP5 and CMIP6 historical simulations. The thick red and light blue lines are the weighted multi-model mean for the historical simulations in CMIP5 and CMIP6, respectively, and the envelopes represent the 5th–95th percentile range obtained from all ensemble members. The 5–95% confidence interval for the CMIP6 multi-model mean is shown by the thin dashed line. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 46697, 46959, 46960, 46966, 52664, 52665, 60438, 63643, 63644, 63645, 63646, 63647, 63648 ], "vocabularyKeywords": [], "identifier_set": [ 12386 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 179191, 179192, 179193, 179194, 179195, 179196, 179197, 179198, 179199, 179200, 179201 ], "onlineresource_set": [ 52276, 52277, 52395, 82620, 88591, 94624 ] }, { "ob_id": 37543, "uuid": "e299379f837142bfb2aa6df64cc66fe7", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Cross-Chapter Box 3.1, Figure 1 (v20220615)", "abstract": "Data for Cross-Chapter Box 3.1, Figure 1 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nCross-Chapter Box 3.1, Figure 1 shows 15-year trends of surface global warming for 1998-2012 and 2012-2026.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with data provided for panels a and b in a subdirectory named panel_ab, and for panels c and d in subdirectories named panel_c and panel_d respectively.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains: \r\n \r\n - Observed and modelled global annual mean surface temperature and surface air temperature trends for 1998-2012\r\n - Modelled global annual mean surface air temperature trends for 2012-2026\r\n - Observed annual mean surface temperature trends for 1998-2012\r\n - Composite of modelled annual mean surface air temperature trends for 1998-2012\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a:\r\n - gmst_trend_1998-2012 in panel_ab/GMST_trend.csv; HadCRUT5 for histogram, ensemble mean of HadCRUT5 and other observations for open triangles at the top, and multimodel ensemble means of CMIP5 and CMIP6 for open diamonds at the top\r\n - gsat_trend_1998-2012 in panel_ab/GSAT_trend.csv; CMIP5 and CMIP6 ensembles for histograms, ERA5 for the top filled triangle, and multimodel ensemble means of CMIP5 and CMIP6 for filled diamonds at the top\r\n \r\n Panel b:\r\n - gmst_trend_2012-2026 in panel_ab/GMST_trend.csv; multimodel ensemble means of CMIP5 and CMIP6 for open diamonds at the top\r\n - gsat_trend_2012-2026 in panel_ab/GSAT_trend.csv; CMIP5 and CMIP6 ensembles for histograms, and multimodel ensemble means of CMIP5 and CMIP6 for filled diamonds at the top\r\n \r\n Panel c:\r\n - tas in panel_c/TrendPattern_HadCRUT5_mean.nc; shading, with the sig attribute for cross markers\r\n \r\n Panel d:\r\n - tas in panel_d/TrendPattern_composite.nc: shading\r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nHadCRUT5 is a gridded dataset of global historical near-surface air temperature anomalies since the year 1850.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Multimodel ensemble means and histograms are calculated after weighting each ensemble member with the inverse of the ensemble size of the same model.\r\n\r\nThe values for panels c and d are stored with the K/year unit but scaled to the K/decade, therefore they need to be multiplied by a factor of 10 in order to be consistent with the plotted values.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-07-18T02:02:02", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, global near-surface air temperature changes, decadal variability, hiatus, warming slowdown, CMIP5, CMIP6", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "5x5 Degrees", "status": "completed", "dataPublishedTime": "2022-06-27T11:42:27", "doiPublishedTime": "2023-02-08T16:47:35.138513", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 131, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "5x5" }, "result_field": { "ob_id": 37544, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_ccb1_fig1/v20220615", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 154594, "numberOfFiles": 8, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10364, "startTime": "1998-01-01T12:00:00", "endTime": "2026-12-31T12:00:00" }, "resultQuality": { "ob_id": 3979, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37545, "uuid": "398b4cd024b74d81ab4d31376fabe3d5", "short_code": "comp", "title": "Caption for CCB 3.1, Figure 1 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "15-year trends of surface global warming for 1998–2012 and 2012–2026. (a, b) GSAT and GMST trends for 1998–2012 (a) and 2012–2026 (b). Histograms are based on GSAT in historical simulations of CMIP6 (red shading, extended by SSP2-4.5) and CMIP5 (grey shading; extended by RCP4.5). Filled and open diamonds at the top represent multi-model ensemble means of GSAT and GMST trends, respectively. Diagonal lines show histograms of HadCRUT5.0.1.0. Triangles at the top of (a) represent GMST trends of Berkeley Earth, GISTEMP, Kadow et al. (2020) and NOAAGlobalTemp-Interim, and the GSAT trend of ERA5. Selected CMIP6 members whose 1998–2012 trends are lower than the HadCRUT5.0.1.0 mean trend are indicated by purple shading (a) and (b). In (a), model GMST and GSAT, and ERA5 GSAT are masked to match HadCRUT data coverage. (c–d) Trend maps of annual near-surface temperature for 1998–2012 based on HadCRUT5.0.1.0 mean (c) and composited surface air temperature trends of subsampled CMIP6 simulations (d) that are included in purple shading area in (a). In (c), cross marks indicate trends that are not significant at the 10% level based on t-tests with serial correlation taken into account. Ensemble size used for each of the histograms and the trend composite is indicated at the top right of each of panels (a, b, d). Model ensemble members are weighted with the inverse of the ensemble size of the same model, so that individual models are equally weighted. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50300, 52664, 52665, 63642 ], "vocabularyKeywords": [], "identifier_set": [ 12347 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 179222, 179223, 179224, 179225, 179226, 179227, 179228, 179229, 179230, 179231 ], "onlineresource_set": [ 52279, 52400, 52278, 82850, 88649, 94654 ] }, { "ob_id": 37546, "uuid": "73c576b685c049258dd578f5487885f2", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Cross-Chapter Box 3.2, Figure 1 (v20220615)", "abstract": "Data for Cross-Chapter Box 3.2, Figure 1 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nCross-Chapter Box 3.2, Figure 1 shows a comparison of observed and simulated changes in global mean temperature and precipitation extremes. \r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n Technically the figure has four panels, but since they are not marked all the data is in the parent directory.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n \r\n - Global annual maximum daily maximum daily maximum temperature (TXx) anomalies from 1953 to 2017 relative to 1961-1990 from HadEX3 observations and CMIP5 and CMIP6 models (human and natural forcings simulations)\r\n - Global annual maximum daily maximum daily maximum temperature (TXx) anomalies from 1953 to 2017 relative to 1961-1990 from HadEX3 observations and CMIP5 and CMIP6 models (natural forcings simulations)\r\n - Global annual maximum 1-day precipitation (rx1day) anomalies from 1953 to 2017 relative to 1961-1990 from HadEX3 observations and CMIP5 and CMIP6 models (natural forcing only simulations)\r\n - Global annual maximum 1-day precipitation (rx1day) anomalies from 1953 to 2017 relative to 1961-1990 from HadEX3 observations and CMIP5 and CMIP6 models (human and natural forcings simulations)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - txx_anomalies_timeseries_historical.csv has data for the blue (CMIP5), red (CMIP6) and black (HadEX3) lines as well as blue and red shadings showing TXx anomalies (top left panel)\r\n - txx_anomalies_timeseries_natural has data for the blue (CMIP5), red (CMIP6) and black (HadEX3) lines as well as blue and red shadings showing TXx anomalies (bottom left panel)\r\n - rx1day_anomalies_timeseries_historical has data for the blue (CMIP5), red (CMIP6) and black (HadEX3) lines as well as blue and red shadings showing Rx1day anomalies (top right panel)\r\n - rx1day_anomalies_timeseries_natural has data for the blue (CMIP5), red (CMIP6) and black (HadEX3) lines as well as blue and red shadings showing Rx1day anomalies (bottom right panel)\r\n\r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparion Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nHadEX3 is a land-surface dataset of climate extremes indices available on a 1.875 x 1.25 longitude-latitude grid covering 1901-2018.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:24:56", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, TXx, rx1day, anomalies, CMIP5, CMIP6, HadEX3, extreme events", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-24T15:55:23", "doiPublishedTime": "2023-02-08T16:55:14.894619", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 132, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37547, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_ccb2_fig1/v20220615", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 17543, "numberOfFiles": 7, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10365, "startTime": "1953-01-01T12:00:00", "endTime": "2017-12-31T12:00:00" }, "resultQuality": { "ob_id": 3980, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37548, "uuid": "21e430cc321841e88595d14a45264ff9", "short_code": "comp", "title": "Caption for Cross-Chapter Box 3.2, Figure 1 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Comparison of observed and simulated changes in global mean temperature and precipitation extremes. Time series of globally averaged five-year mean anomalies of the annual maximum daily maximum temperature (TXx in °C) and annual maximum 1-day precipitation (Rx1day as standardized probability index in %) during 1953–2017 from the HadEX3 observations and the CMIP5 and CMIP6 multi-model ensembles with natural and human forcing (top) and natural forcing only (bottom). For CMIP5, historical simulations for 1953–2005 are combined with corresponding RCP4.5 scenario runs for 2006–2017. For CMIP6, historical simulations for 1953–2014 are combined with SSP2-4.5 scenario simulations for 2015–2017. Numbers in brackets represents the number of models used. The time-fixed observational mask has been applied to model data throughout the whole period. Gridcells with more than 70% data availability during 1953–2017 plus data for at least three years during 2013–2017 are used. Coloured lines indicate multi-model means, while shading represents 5th–95th percentile ranges, based on all available ensemble members with equal weight given to each model (Section 3.2). Anomalies are relative to 1961–1990 means. Figure is updated from Seong et al. (2021), their Figure 3 and Paik et al. (2020b), their Figure 3. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 64247, 65286, 65287, 65288, 65289, 65290, 65291, 65292, 65293, 65294, 65295, 65296, 65297, 65298, 65299 ], "vocabularyKeywords": [], "identifier_set": [ 12348 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 179234, 179235, 179236, 179237, 179238, 179239, 179240, 179241 ], "onlineresource_set": [ 52280, 52281, 52399, 88650, 94655, 94656 ] }, { "ob_id": 37549, "uuid": "c03ba3e2a7314f848e41a3a724bd8d25", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for FAQ 3.1, Figure 1 (v20220615)", "abstract": "Data for FAQ 3.1, Figure 1 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFAQ 3.1 Figure 1 shows that observed warming (1850-2018) is only reproduced in simulations including human influence.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\nWhen citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The dataset contains global surface temperature changes timeseries relative to 1850-1900 for 1850-2019 from:\r\n \r\n - CMIP6 historical+ssp245 simulations (simulations with human and natural forcing)\r\n - CMIP6 hist-GHG simulations (simulations with anthropogenic green house gases forcing)\r\n - CMIP6 hist-aer simulations (simulation with anthropogenic aerosol forcing)\r\n - CMIP6 hist-nat simulations (simulation with natural forcing only)\r\n - Observations from Chapter 2\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n gmst_anomalies_timeseries.csv. Global surface temperature changes timeseries relative to 1850-1900 for 1850-2019 from:\r\n \r\n - CMIP6 historical+ssp245 simulations (1850-2019) [mean, grey line]\r\n - CMIP6 historical+ssp245 simulations (1850-2019) [5% range, grey shading, bottom]\r\n - CMIP6 historical+ssp245 simulations (1850-2019) [95% range, grey shading, top]\r\n - CMIP6 hist-GHG simulations (1850-2019) [mean, red line]\r\n - CMIP6 hist-GHG simulations (1850-2019) [5% range, red shading, bottom]\r\n - CMIP6 hist-GHG simulations (1850-2019) [95% range, red shading, top]\r\n - CMIP6 hist-aer simulations (1850-2019) [mean, blue line]\r\n - CMIP6 hist-aer simulations (1850-2019) [5% range, blue shading, bottom]\r\n - CMIP6 hist-aer simulations (1850-2019) [95% range, blue shading, top]\r\n - CMIP6 hist-nat simulations (1850-2019) [mean, green line]\r\n - CMIP6 hist-nat simulations (1850-2019) [5% range, green shading, bottom]\r\n - CMIP6 hist-nat simulations (1850-2019) [95% range, green shading, top]\r\n - Observations from Chapter 2 (1850-2019) [black line]\r\n\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:25:02", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, global temperature, radiative forcing, attribution of climate change", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-24T15:55:29", "doiPublishedTime": "2023-07-03T12:38:27.452698", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 133, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37550, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_faq1_fig1/v20220615", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 34308, "numberOfFiles": 4, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10366, "startTime": "1850-01-01T12:00:00", "endTime": "2019-12-31T12:00:00" }, "resultQuality": { "ob_id": 3981, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37551, "uuid": "5ef06524f5f3426880b4408ae60743d3", "short_code": "comp", "title": "Caption for FAQ 3.1, Figure 1 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Observed warming (1850–2019) is only reproduced in simulations including human influence. Global surface temperature changes in observations, compared to climate model simulations of the response to all human and natural forcings (grey band), greenhouse gases only (red band), aerosols and other human drivers only (blue band) and natural forcings only (green band). Solid coloured lines show the multi-model mean, and coloured bands show the 5–95% range of individual simulations. " }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 64247, 65273, 65274, 65275, 65276, 65277, 65278, 65279, 65280, 65281, 65282, 65283, 65284, 65285 ], "vocabularyKeywords": [], "identifier_set": [ 12565 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 179244, 179245, 179246, 179247, 179248, 179249, 179250, 179251, 179252 ], "onlineresource_set": [ 52282, 52283, 82851, 88651 ] }, { "ob_id": 37552, "uuid": "f148031d13954c85b873900cd3f47170", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for FAQ 3.2, Figure 1 (v20220615)", "abstract": "Data for FAQ 3.2, Figure 1 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFAQ 3.2, Figure 1 shows annual, decadal and multi-decadal variations in average global surface temperature. \r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\nWhen citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure technically has three panels, but they are not labelled. So the datasets are stored just in the main figure folder. \r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n Dataset contains modelled GSAT anomalies from MPI-ESM grand ensemble (1950-2019):\r\n \r\n - On annual scale\r\n - On decadal scale\r\n - On multi-decadal scale\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - annual_gsat_anomalies_mpi_esm_grand_ens.csv has data for the left panel, GSAT anomalies from 1950 to 2019 from MPI-ESM grand ensemble (black, light green, light marsh green, light dark green lines)\r\n - decadal_gsat_anomalies_mpi_esm_grand_ens.csv has data for the middle panel, GSAT anomalies from 1950 to 2019 from MPI-ESM grand ensemble (black, light green, light marsh green, light dark green lines)\r\n - multi_decadal_gsat_anomalies_mpi_esm_grand_ens.csv has data for the right panel, GSAT anomalies from 1950 to 2019 from MPI-ESM grand ensemble (black, light green, light marsh green, light dark green lines)\r\n\r\n\r\nGSAT stands for Global Surface Air Temperature.\r\nMPI-ESM is a comprehensive Earth-System Model, consisting of component models for the ocean, the atmosphere and the land surface.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the GitHub repo with code for the figure", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:24:55", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Annual variations, decadal variations, multi-decadal variations, GSAT, natural variability, global surface air temperature", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-24T15:55:35", "doiPublishedTime": "2023-07-03T12:58:07.970684", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 134, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37553, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_faq2_fig1/v20220615", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 113656, "numberOfFiles": 6, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10367, "startTime": "1950-01-01T12:00:00", "endTime": "2019-12-31T12:00:00" }, "resultQuality": { "ob_id": 3982, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37554, "uuid": "50547c2a3077454a832d5e614768841d", "short_code": "comp", "title": "Caption for FAQ 3.2, Figure 1 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Annual (left), decadal (middle) and multi-decadal (right) variations in average global surface temperature. The thick black line is an estimate of the human contribution to temperature changes, based on climate models, whereas the green lines show the combined effect of natural variations and human-induced warming, different shadings of green represent different simulations, which can be viewed as showing a range of potential pasts. The influence of natural variability is shown by the green bars, and it decreases with longer time scales. The data is sourced from the CESM1 large ensemble." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 64247, 64361, 64362, 64363, 64364 ], "vocabularyKeywords": [], "identifier_set": [ 12566 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 179255, 179256, 179257, 179258, 179259, 179260, 179261, 179262, 179263 ], "onlineresource_set": [ 52284, 52285, 82852, 83808 ] }, { "ob_id": 37555, "uuid": "afe80eb32a1c4164a3b84396c6d7a5d6", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for FAQ 3.3, Figure 1 (v20220615)", "abstract": "Data for FAQ 3.3, Figure 1 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFAQ 3.3 Figure 1 shows pattern correlations between models and observations for three different variables: surface air temperature, precipitation and sea level pressure. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\nWhen citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains all correlation pattern values displayed in the figure.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n fig_FAQ_3_3.nc:\r\n \r\n - variable: 'cor' with two dimensions:\r\n . 'vars': variables on the x-axis (same order as in the figure)\r\n . 'models': name of each models (the attribute 'project' contains mapping to 'CMIP3', 'CMIP5' or 'CMIP6')\r\n\r\nCMIP3 is the third phase of the Coupled Model Intercomparison Project.\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nVar 'cor' contains the values. Coordinate 'var' is the x-axis. Coordinate 'models' is the y-axis. The attribute 'project' of the coordinate 'models' contains as string chain the mapping to CMIP3 (cyan), CMIP5 (blue) and CMIP6 (red). The multi-model mean is not part of the dataset.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:12", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, pattern correlation", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-24T15:55:40", "doiPublishedTime": "2023-02-08T16:59:18.766607", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 135, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37556, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_faq3_fig1/v20220615", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 106970, "numberOfFiles": 4, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10368, "startTime": "1980-01-01T12:00:00", "endTime": "2000-12-31T12:00:00" }, "resultQuality": { "ob_id": 3983, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37557, "uuid": "d3e73c74a0264a6bb1b7cde31c8c06b0", "short_code": "comp", "title": "Caption for FAQ 3.3, Figure 1 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Pattern correlations between models and observations of three different variables: surface air temperature, precipitation and sea level pressure. Results are shown for the three most recent generations of models, from the Coupled Model Intercomparison Project (CMIP): CMIP3 (orange), CMIP5 (blue) and CMIP6 (purple). Individual model results are shown as short lines, along with the corresponding ensemble average (long line). For the correlations the yearly averages of the models are compared with the reference observations for the period 1980-1999, with 1 representing perfect similarity between the models and observations. CMIP3 simulations performed in 2004-2008 were assessed in the IPCC Fourth Assessment, CMIP5 simulations performed in 2011-2013 were assessed in the IPCC Fifth Assessment, and CMIP6 simulations performed in 2018-–2021 are assessed in this Report. " }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 46724, 63640, 63641 ], "vocabularyKeywords": [], "identifier_set": [ 12349 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 179266, 179267, 179268, 179269, 179270, 179271, 179272, 179273 ], "onlineresource_set": [ 52401, 52286, 52287, 82853, 88652, 90258, 90259, 90260, 90261, 90262, 90263, 90264, 90265, 90266, 90267, 90268, 90269, 90270, 90271, 90272, 90273, 90274, 90275, 90276, 90277, 90278, 90279, 90280, 90281, 90282, 94657 ] }, { "ob_id": 37558, "uuid": "758419765d0f4926aa70002ec6c856b0", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.38 (v20220614)", "abstract": "Data for Figure 3.38 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.38 shows model evaluation of ENSO teleconnection for 2m-temperature and precipitation in boreal winter (December-January-February).\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n Data provided for all panels in one single directory\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains observed global patterns for:\r\n \r\n - temperature from the Berkeley Earth dataset over land \r\n - temperature from ERSSTv5 over ocean\r\n - precipitation from GPCC over land (shading, mm day–1)\r\n - precipitation from GPCP worldwide (contours, period: 1979-2014)\r\n \r\n and distributions of regression coefficients in IPCC regions for:\r\n - temperature\r\n - precipitation\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n maps:\r\n \r\n - reg_tas_NINO34_BEST_ERSSTv5_1901_2018_DJF.nc (var = 'rc', upper map over land)\r\n - reg_sst_NINO34_ERSSTv5_ERSSTv5_1901_2018_DJF.nc (var = 'rc', upper map over ocean)\r\n - reg_precip_NINO34_GPCP_ERSST5_1979_2018_DJF.nc (var = 'rc', lower map, contours)\r\n - reg_pr_NINO34_GPCC_ERSSTv5_1901_2016_DJF.nc (var = 'rc', lower map, shading)\r\n \r\n histograms:\r\n - tas_enso_regression_pdf_v4_no_cosweight_DJF.nc\r\n . upper grey histograms: var = 'region_pdfx_hist' and 'region_pdfy_hist'\r\n . MME (black line): var = 'region_ave_hist'\r\n . Observations (blue lines): var = 'region_obs'\r\n - tas_amip_hist_enso_regression_pdf_v4_no_cosweight_DJF.nc (orange dashed line): var = 'region_ave_amip_hist'\r\n \r\n => Fields correspond to regions numbers with labels in the plot, namely for temperature: 'EAU/RFE/RAR/NWN/NCA/ENA/NSA/MED/NWS/ESAF' (see variable region_info with attributes making the association between the region index and the acronym/name). \r\n - pr_enso_regression_pdf_v4_no_cosweight_DJF.nc\r\n . lower grey histograms: var = 'region_pdfx_hist' and 'region_pdfy_hist'\r\n . MME (black line): var = 'region_ave_hist'\r\n . Observations (blue lines): var = 'region_obs'\r\n - pr_amip_hist_enso_regression_pdf_v4_no_cosweight_DJF.nc (orange dahsed line): var = 'region_ave_amip_hist'\r\n \r\n => Fields correspond to regions numbers with labels in the plot, namely for precipitation: 'EAS/SEA/EAU/WNA/NCA/SES/NSA/ESAF/SEAF/MED' (see variable info_region with attributes making the association between the region index and the acronym/name).\r\n\r\n\r\nENSO is the El Niño Southern Oscillation. \r\nGPCC is the Global Precipitation Climatology Centre. \r\nGPCP is the Global Precipitation Climatology Project.\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Data provided in reg_pr_NINO34_GPCC_ERSSTv5_1901_2016_DJF.nc are in mm/month. Values should be divided by 30 for plotting in mm/day.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the figure on the IPCC AR6 website", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:23:38", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, ENSO teleconnections", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-24T15:54:05", "doiPublishedTime": "2023-02-08T19:30:56.911542", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 136, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37559, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig38/v20220614", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4252781, "numberOfFiles": 11, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10369, "startTime": "1958-01-01T12:00:00", "endTime": "2014-12-31T12:00:00" }, "resultQuality": { "ob_id": 3984, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37560, "uuid": "fc6c3c4e3da34bb697dd37452a452716", "short_code": "comp", "title": "Caption for Figure 3.38 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Model evaluation of ENSO teleconnection for 2 metre temperature and precipitation in boreal winter (December–January–February). Teleconnections are identified by linear regression with the Niño 3.4 SST index based on Extended Reconstructed Sea Surface Temperature (ERSST) version 5 during the period 1958–2014. Maps show observed patterns for temperature from the Berkeley Earth dataset over land and from ERSST version 5 over ocean (ºC, top) and for precipitation from GPCC over land (shading, mm day–1) and GPCP worldwide (contours, period: 1979–2014). Distributions of regression coefficients (grey histograms) are provided for a subset of AR6 reference regions defined in Atlas.1.3 for temperature (top) and precipitation (bottom). All fields are linearly detrended prior to computation. Multi-model multi-member ensemble means are indicated by thick vertical black lines. Blue vertical lines show three observational estimates of temperature, based on Berkeley Earth, GISTEMP and CRUTS datasets, and two observational estimates of precipitation, based on GPCC and CRUTS datasets. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 63152, 63153, 63154, 63155, 63156 ], "vocabularyKeywords": [], "identifier_set": [ 12384 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 179276, 179277, 179278, 179279, 179280, 179281, 179282, 179283, 179284 ], "onlineresource_set": [ 52288, 52289, 82617, 89780, 89781, 89782, 89783, 89784, 89785, 89786, 89787, 89788, 89789, 89790, 89791, 89792, 89793, 89794, 89795, 89796, 89797, 89798, 89799, 89800, 89801, 89802, 89803 ] }, { "ob_id": 37561, "uuid": "85168e39bfff444ba02bf55e7682f73d", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.26 (v20220616)", "abstract": "Data for Figure 3.26 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.26 shows global ocean heat content in CMIP6 simulations and observations.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n ---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n Technically the figure has 4 panels, but they are not named, so the datasets are stored in the parent directory. \r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The dataset contains simulated and observed ocean heat content timeseries:\r\n \r\n - from CMIP6 models at full depth (1850-2014)\r\n - from observations at full depth (1971-2018)\r\n - from CMIP6 models at 0-700 m (1850-2014)\r\n - from observations at 700-200 m (1971-2018)\r\n - from CMIP6 models at 700-200 m (1850-2014)\r\n - from observations at 700-200 m (1971-2018)\r\n - from CMIP6 models at deeper than 2000 m (1850-2014)\r\n - from observations at deeper than 2000 m (1992-2018)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - ocean_heat_content_anomalies_full_depth.csv has the data for the read lines and shadings (CMIP6) from 1850 to 2014 and black lines and shadings (observations) from 1971 to 2018\r\n - ocean_heat_content_anomalies_0_700_m.csv has the data for the read lines and shadings (CMIP6) from 1850 to 2014 and black lines and shadings (observations) from 1971 to 2018\r\n - ocean_heat_content_anomalies_700_2000_m.csv has the data for the read lines and shadings (CMIP6) from 1850 to 2014 and black lines and shadings (observations) from 1971 to 2018\r\n - ocean_heat_content_anomalies_over_2000_m.csv has the data for the read lines and shadings (CMIP6) from 1850 to 2014 and black lines and shadings (observations) from 1992 to 2018\r\n\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The observational data for this figure is taken from the file 'AR6_FGD_assessment_timeseries_OHC.csv' from Cross-Chapter Box1 figure 1, Chapter 9. The link to this dataset is provided in the Related Documents section of this catalogue record.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to dataset for figure CCB1 Chapter 9\r\n - Link to the figure on the IPCC AR6 website", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:01", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Ocean Heat Content, CMIP6, Observations", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-12-14T13:24:44", "doiPublishedTime": "2023-02-08T18:33:54", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 137, "highestLevelBound": 0.0, "lowestLevelBound": 5000.0, "units": "" }, "result_field": { "ob_id": 37562, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig26/v20220616", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 61165, "numberOfFiles": 7, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10374, "startTime": "1850-01-01T00:00:00", "endTime": "2018-12-31T00:00:00" }, "resultQuality": { "ob_id": 3985, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-16" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37563, "uuid": "659a77e7056c4f40a128d14c9969bdbf", "short_code": "comp", "title": "Caption for Figure 3.26 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Global ocean heat content in CMIP6 simulations and observations. Time series of observed (black) and simulated (red) global ocean heat content anomalies with respect to 1995–2014 for the full ocean depth (left-hand panel); upper layer: 0–700 m (top right-hand panel); intermediate layer: 700–2000 m (middle right-hand panel); and the abyssal ocean: >2000 m (bottom right-hand panel). The best estimate observations (black solid line) for the period of 1971–2018, along with very likely ranges (black shading) are from Section 2.3.3.1. For the models (1860–2014), ensemble members from 15 CMIP6 models are used to calculate the multi-model mean values (red solid line) after averaging across simulations for each independent model. The very likely ranges in the simulations are shown in red shading. Simulation drift has been removed from all CMIP6 historical runs using a contemporaneous portion of the linear fit to each corresponding pre-industrial control run (Gleckler et al., 2012). Units are zettajoules (ZJ; 1021 joule). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 64247, 64355, 64356, 64357, 64358, 64359, 64360 ], "vocabularyKeywords": [], "identifier_set": [ 12372 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 179287, 179288, 179289, 179290, 179291, 179292, 179293, 179294, 179295 ], "onlineresource_set": [ 52292, 52384, 52293, 82639, 83564, 88576, 94616, 94617 ] }, { "ob_id": 37564, "uuid": "d35ac1955c264deea9699d08dbc568f2", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.44 (v20220615)", "abstract": "Data for Figure 3.44 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.44 shows multivariate synopsis of paleoclimate model results compared to observational references. \r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n Figure has three rows (a), (b) and (c). The data is on the DMS in the panel_a, panel_b, panel_c subdirectories. \r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n - GSAT anomalies in MidHolocene from CMIP6, PMIP3, non-CMIP6 PMIP4 models as well as Bertlein et al. (2011) reconstructions\r\n - GSAT anomalies in LIG, LGM and EECO from CMIP6, PMIP3, non-CMIP6 PMIP4 models as well as Tierney et al. (2020) reconstructions\r\n - Regional Mean Temperature of the Warmest month, Mean Annual Precipitation and Mean Temperature of the Coldest month from CMIP6, PMIP3, non-CMIP6 PMIP4 models as well as Bertlein et al. (2011) reconstructions\r\n - Regional Mean Temperature of the Warmest month, Mean Annual Precipitation and Mean Temperature of the Coldest month from CMIP6, PMIP3, non-CMIP6 PMIP4 models as well as Tierney et al. (2020) reconstructions\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n panel_a/gmst_anomalies_paleo_climate.csv has data for all the markers in all subpanels in panel a\r\n panel_a/gmst_anomalies_paleo_climate_reconstructions.csv: relates to the pale orange (navajowhite) shading in panel (a), column 2 contains the bottom values, column 3 are the top values.\r\n panel_b/temperature_and_precipitation_paleo_midHolocene.csv has data for all the markers in all subpanels in panel b\r\n panel_c/temperature_and_precipitation_paleo_lastGlacialMaximum.csv has data for all markers in all subpanels in panel c\r\n\r\nGSAT stands for Global Surface Air Temperature.\r\nCMIP6 is the sixth stage of the Coupled Model Intercomparison Project. \r\nPMIP3 is the Paleoclimate Modelling Intercomparison Project phase 3.\r\nPMIP4 is the Paleoclimate Modelling Intercomparison Project phase 4.\r\nLIG stands for Last Interglacial.\r\nLGM stands for the Last Glacial Maximum.\r\nEECO stands for Early Eocene Climatic Optimum.\r\n\r\n ---------------------------------------------------\r\n Temporal Range of Paleoclimate Data\r\n ---------------------------------------------------\r\n This dataset also covers a paleoclimate timespan from 55000000-5000 years BP (55000000-5000 years before present). \r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The last column in each file is the color and/or shape of the marker.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the figure on the IPCC AR6 website", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:24:32", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Paleo Climates, global mean surface temperature, Last Glacial Maximum, Last Interglacial, MidHolocene, Temperature and Precipitation paleoclimate data", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-24T15:54:28", "doiPublishedTime": "2023-02-08T19:37:13.939010", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 138, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 38006, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig44/v20220615", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 29178, "numberOfFiles": 7, "fileFormat": "CSV, txt" }, "timePeriod": null, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 64343, 64344, 64345, 64346, 64347, 64348, 64349, 64350, 64351, 64352, 64353, 64354 ], "vocabularyKeywords": [], "identifier_set": [ 12390 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 179298, 179299, 179300, 179301, 179302, 179303, 179368, 193409, 179369 ], "onlineresource_set": [ 52296, 52295, 82625, 88595, 89706, 89707, 89708, 89709, 89710, 89711, 89712, 89713, 89714, 89715, 89716, 89717, 89718, 89719, 89720, 89721, 89722, 89723, 89724, 89725, 89726, 89727, 89728, 94629 ] }, { "ob_id": 37566, "uuid": "864a46cc65054008857ee5bb772a2a2b", "title": "Cloud droplet number concentration, calculated from the MODIS (Moderate resolution imaging spectroradiometer) cloud optical properties retrieval and gridded using different sampling strategies", "abstract": "This dataset contains cloud droplet number concentrations (CDNC), gridded to 1 by 1 degree resolution using a variety of sampling methods to select valid retrievals. Data from the MODIS (Moderate resolution imaging spectroradiometer) instruments on both the Terra (morning overpass) and Aqua (Afternoon overpass) satellites are available (indicated by a T or A in the filename). This product is gridded using the MODIS collection 6 definition of a day. These sampling methods have been compared against multiple flight campaigns, see Gryspeerdt et al., The impact of sampling strategy on the cloud droplet number concentration estimated from satellite data. Atmos. Meas. Tech. 2022.\"\r\n\r\nErrata: The latitude values in these files are currently inverted, resulting in the data in the files appearing 'upside-down'. As a work-around, the data arrays can be reversed along the latitude axis. Corrected versions of the files will be uploaded shortly.'", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-06-16T19:04:41", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). This dataset was derived from collection 6.1 of the MODIS cloud product (MOD06_L2, MYD06_L2", "removedDataReason": "", "keywords": "cloud droplet number concentration, CDNC, Nd, MODIS", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-20T13:51:25", "doiPublishedTime": "2022-06-20T13:51:24", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 37576, "dataPath": "/badc/deposited2022/modis_cdnc_sampling_gridded/data/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 127148603021, "numberOfFiles": 14338, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 10371, "startTime": "2000-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3987, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-16" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 37575, "uuid": "e1c6eda586694c7facb951c569c604f6", "short_code": "cmppr", "title": "Composite process for 'Cloud droplet number concentration, calculated from the MODIS (Moderate resolution imaging spectroradiometer) cloud optical properties retrieval and gridded using different sampling strategies'", "abstract": "Cloud droplet number concentrations were gridded to 1 by 1 degree resolution using a variety of sampling methods to select valid retrievals. Data from the MODIS (Moderate resolution imaging spectroradiometer) instruments on both the Terra (morning overpass) and Aqua (Afternoon overpass) satellites are available (indicated by a T or A in the filename). These sampling methods have been compared against multiple flight campaigns, see Gryspeerdt et al., The impact of sampling strategy on the cloud droplet number concentration estimated from satellite data. Atmos. Meas. Tech. 2022.\"" }, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37577, "uuid": "8032b04a93634de883b6e0a2eba6df14", "short_code": "proj", "title": "Tracking Aviation and Shipping Impacts on Clouds", "abstract": "The Tracking Aviation and Shipping Impacts on Clouds (TASIC) project uses aircraft and ship emissions as \"natural experiments\" into aerosol impacts on cloud processes, using a range of observational data, global and regional atmospheric models. \r\n\r\nThis project was undertaken as part of a Royal Society University Research Fellowship (URF/R1/191602)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 50559, 50561, 63011, 66755, 66756, 66757, 66758, 66759, 66760, 66761, 66762, 66763, 66764, 66765, 66766, 66767, 66768, 66769, 66770, 66771, 66772, 66773, 66774, 66775, 66776, 66777, 66778, 66779, 66780, 66781, 66782, 66783, 66784, 66785, 66786, 66787, 66788 ], "vocabularyKeywords": [], "identifier_set": [ 12157 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 179304, 179305, 179306, 179307, 179308, 179309, 179310, 179319, 179311, 179312, 179313, 179314, 179315, 179316, 179317, 179318 ], "onlineresource_set": [ 52294, 87674, 87949 ] }, { "ob_id": 37567, "uuid": "ceae289f1a56414ea708f43db83fc2c6", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.27 (v20220616)", "abstract": "Data for Figure 3.27 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.27 shows maps of multi-decadal salinity trends for the near-surface ocean.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n Technically there are two panels, they are named in the datasets as top and bottom, but the data is stored in the parent directory. Data provided for bottom panel.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The dataset contains salinity data:\r\n \r\n - climatological mean from CMIP6 models (1950-2014)\r\n - simulated trend from CMIP6 models (1950-2014)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - ocean_salinity_cmip6.nc: climatological salinity (1950-2014) from CMIP6 models (black contours) in a bottom panel\r\n - ocean_salinity_trends_cmip6.nc: salinity trends (1950-2014) from CMIP6 models (colored shades) in a bottom panel\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The observational data from here (top panel) is taken from the file:\r\n\r\nDurackandWijffels_GlobalOceanChanges_19500101-20191231__210122-205355_beta.nc. The field of interest are salinity_mean (shown as black contours) and salinity_change (shown in colourscale). The file was archived as input data for Figure 2.27. The link to this dataset is provided in the Related Documents section of this catalogue record.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to input data figure 2.27\r\n - Link to the figure on the IPCC AR6 website", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:11", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Climatological Salinity, Salinity Trends, CMIP6, Observations, Near-Surface Ocean", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-24T15:51:46", "doiPublishedTime": "2023-02-08T18:36:12.007002", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 139, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37568, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig27/v20220616", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 471935, "numberOfFiles": 5, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10372, "startTime": "1950-01-01T12:00:00", "endTime": "2014-12-31T12:00:00" }, "resultQuality": { "ob_id": 3988, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-16" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37569, "uuid": "b916f422ff904d9bbbde04cef5443516", "short_code": "comp", "title": "Caption for Figure 3.27 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Maps of multi-decadal salinity trends for the near-surface ocean. Units are Practical Salinity Scale 1978 [PSS-78] per decade. (Top) The best estimate (Section 2.3.3.2) observed trend (Durack and Wijffels, 2010). (Bottom) Simulated trend from the CMIP6 historical experiment multi-model mean (1950–2014). Black contours show the climatological mean salinity in increments of 0.5 PSS-78 (thick lines 1 PSS-78). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 52664, 52665, 63638, 63639 ], "vocabularyKeywords": [], "identifier_set": [ 12373 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 179320, 179321, 179322, 179323, 179324, 179325, 179326, 179327, 179328 ], "onlineresource_set": [ 52299, 52297, 52298, 52385, 82638, 88577, 89867, 89868, 89869, 89870, 89871, 89872, 89873, 89874, 89875, 89876, 89877, 89878, 89879, 89880, 89881, 89882, 89883, 89884, 89885, 89886, 89887, 89888, 89889, 89890, 94615 ] }, { "ob_id": 37570, "uuid": "a8915aca7806434984baab86835a1b18", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.29 (v20220616)", "abstract": "Data for Figure 3.29 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.29 shows simulated and observed global mean sea level change due to thermal expansion for CMIP6 models and observations relative to the baseline period 1850-1900. \r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The dataset has contains timeseries for:\r\n \r\n - CMIP6 thermosteric sea level change anomalies from 1850-2014 simulated with anthropogenic and natural forcings (historical experiment)\r\n - CMIP6 thermosteric sea level change anomalies from 1850-2014 simulated with natural forcings only (hist-nat experiment)\r\n - CMIP6 thermosteric sea level change anomalies from 1850-2014 simulated with anthropogenic greenhouse gases forcings only (hist-GHG experiment)\r\n - CMIP6 thermosteric sea level change anomalies from 1850-2014 simulated with anthropogenic aerosol forcings only (hist-aer experiment)\r\n - observed thermosteric sea level change anomalies from 1971-2018\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - global_mean_sea_level_anomalies.csv relates to brown, green, blue grey and black lines and shadings (1850-2019)\r\n Additional information about data provided in relation to figure in the file header.\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The observational datasets are the ones used in Chapter 2 and Chapter 9 CCB1 ('AR6_FGD_assessment_timeseries_ThSL.csv'). The link to this dataset is provided in the Related Documents section of this catalogue record.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to dataset for figure CCB1 Chapter 9\r\n - Link to input data figure 2.27\r\n - Link to the figure on the IPCC AR6 website", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:04", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Thermosteric sea level change, CMIP6, observations, attribution", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-12-14T13:32:35", "doiPublishedTime": "2023-02-08T19:11:03", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 140, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37571, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig29/v20220616", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 32912, "numberOfFiles": 4, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10373, "startTime": "1850-01-01T12:00:00", "endTime": "2018-12-31T12:00:00" }, "resultQuality": { "ob_id": 3989, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-16" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37572, "uuid": "dac576df690c47ec851c681f3f22128a", "short_code": "comp", "title": "Caption for Figure 3.29 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Simulated and observed global mean sea level change due to thermal expansion for CMIP6 models and observations relative to the baseline period 1850–1900. Historical simulations are shown in brown, natural only in green, greenhouse gas only in grey, and aerosol only in blue (multi-model means shown as thick lines, and shaded ranges between the 5th and 95th percentile). The best estimate observations (black solid line) for the period of 1971–2018, along with very likely ranges (black shading) are from Section 2.3.3.1 and are shifted to match the multi-model mean of the historical simulations for the 1995–2014 period. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 64247, 65258, 65259, 65260, 65261, 65262, 65263, 65264, 65265, 65266, 65267, 65268, 65269, 65270, 65271, 65272 ], "vocabularyKeywords": [], "identifier_set": [ 12375 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 179331, 179332, 179333, 179334, 179335, 179336, 179337, 179338, 179339 ], "onlineresource_set": [ 52302, 52300, 52301, 82537, 82636, 88579, 94613 ] }, { "ob_id": 37581, "uuid": "8af00e7bba784c1cbf4c16fef984aeb6", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.36 (v20220620)", "abstract": "Data for Figure 3.36 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.36 shows observed and simulated life cycle of El Niño and La Niña events.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels. All the data are provided in enso_lifecycle.nc file.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains\r\n \r\n - Composite time series of the ENSO index for El Niño events\r\n - Composite time series of the ENSO index for La Niña events\r\n - Mean duration of El Niño events\r\n - Mean duration of La Niña events\r\n\r\nin observations, CMIP5 historical-RCP4.5 and and CMIP6 historical simulations.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a:\r\n - ts_elnino_obs; black curves\r\n . ERSSTv5, dashed lines: dataset = 1\r\n . HadISST, solid lines: dataset = 2\r\n - ts_elnino_cmip5: The ENSO index time series in each ensemble member of CMIP5 models; blue curve and shading\r\n - ts_elnino_cmip6: The ENSO index time series in each ensemble member of CMIP6 models; red curve and shading\r\n \r\n Panel b:\r\n - ts_lanina_obs; black curves\r\n . ERSSTv5, dashed lines: dataset = 1\r\n . HadISST, solid lines: dataset = 2\r\n - ts_lanina_cmip5: The ENSO index time series in each ensemble member of CMIP5 models; blue curve and shading\r\n - ts_lanina_cmip6: The ENSO index time series in each ensemble member of CMIP6 models; red curve and shading\r\n \r\n Panel c:\r\n - duration_elnino_obs; black vertical lines and numbers in the top right box\r\n . ERSSTv5, dashed lines: dataset = 1\r\n . HadISST, solid lines: dataset = 2\r\n - duration_elnino_cmip5: El Nino duration in each ensemble member of CMIP5 models; blue box-whisker and number in the top right box\r\n - duration_elnino_cmip6; El Nino duration in each ensemble member of CMIP6 models; red dots, red box-whisker and number in the top right box\r\n . ACCESS-CM2: ens_cmip6 = 1 - 3\r\n . ACCESS-ESM1-5: ens_cmip6 = 4 - 23\r\n . AWI-CM-1-1-MR: ens_cmip6 = 24 - 28\r\n . AWI-ESM-1-1-LR: ens_cmip6 = 29\r\n . BCC-CSM2-MR: ens_cmip6 = 30 - 32\r\n . BCC-ESM1: ens_cmip6 = 33 - 35\r\n . CAMS-CSM1-0: ens_cmip6 = 36-38\r\n . CanESM5-CanOE: ens_cmip6 = 39 - 41\r\n . CanESM5: ens_cmip6 = 42 - 106\r\n . CESM2-FV2: ens_cmip6 = 107 - 109\r\n . CESM2: ens_cmip6 = 110 - 120\r\n . CESM2-WACCM-FV2: ens_cmip6 = 121 - 123\r\n . CESM2-WACCM: ens_cmip6 = 124 - 126\r\n . CIESM: ens_cmip6 = 127 - 129\r\n . CMCC-CM2-HR4: ens_cmip6 = 130\r\n . CMCC-CM2-SR5: ens_cmip6 = 131\r\n . CMCC-ESM2: ens_cmip6 = 132\r\n . CNRM-CM6-1-HR: ens_cmip6 = 133\r\n . CNRM-CM6-1: ens_cmip6 = 134 - 162\r\n . CNRM-ESM2-1: ens_cmip6 = 163 - 172\r\n . E3SM-1-0: ens_cmip6 = 173 - 177\r\n . E3SM-1-1-ECA: ens_cmip6 = 178\r\n . E3SM-1-1: ens_cmip6 = 179\r\n . EC-Earth3-AerChem: ens_cmip6 = 180, 181\r\n . EC-Earth3-CC: ens_cmip6 = 182\r\n . EC-Earth3: ens_cmip6 = 183 - 204\r\n . EC-Earth3-Veg-LR: ens_cmip6 = 205 - 207\r\n . EC-Earth3-Veg: ens_cmip6 = 208 - 215\r\n . FGOALS-f3-L: ens_cmip6 = 216 - 218\r\n . FGOALS-g3: ens_cmip6 = 219 - 224\r\n . FIO-ESM-2-0: ens_cmip6 = 225 - 227\r\n . GFDL-CM4: ens_cmip6 = 228\r\n . GFDL-ESM4: ens_cmip6 = 229 - 231\r\n . GISS-E2-1-G-CC: ens_cmip6 = 232\r\n . GISS-E2-1-G: ens_cmip6 = 233 - 278\r\n . GISS-E2-1-H: ens_cmip6 = 279 - 302\r\n . HadGEM3-GC31-LL: ens_cmip6 = 303 - 306\r\n . HadGEM3-GC31-MM: ens_cmip6 = 307 - 310\r\n . IITM-ESM: ens_cmip6 = 311\r\n . INM-CM4-8: ens_cmip6 = 312\r\n . INM-CM5-0: ens_cmip6 = 313 - 322\r\n . IPSL-CM5A2-INCA: ens_cmip6 = 323\r\n . IPSL-CM6A-LR: ens_cmip6 = 324 - 355\r\n . KACE-1-0-G: ens_cmip6 = 356-358\r\n . KIOST-ESM: ens_cmip6 = 359\r\n . MCM-UA-1-0: ens_cmip6 = 360, 361\r\n . MIROC6: ens_cmip6 = 362 - 411\r\n . MIROC-ES2L: ens_cmip6 = 412 - 421\r\n . MPI-ESM-1-2-HAM: ens_cmip6 = 422 - 424\r\n . MPI-ESM1-2-HR: ens_cmip6 = 425 - 434\r\n . MPI-ESM1-2-LR: ens_cmip6 = 435 - 444\r\n . MRI-ESM2-0: ens_cmip6 = 445 - 450\r\n . NESM3: ens_cmip6 = 451 - 455\r\n . NorCPM1: ens_cmip6 = 456 - 485\r\n . NorESM2-LM: ens_cmip6 = 486 - 488\r\n . NorESM2-MM: ens_cmip6 = 489 - 490\r\n . SAM0-UNICON: ens_cmip6 = 491\r\n . TaiESM1: ens_cmip6 = 492\r\n . UKESM1-0-LL: ens_cmip6 = 493 - 510\r\n \r\n Panel d:\r\n - duration_lanina_obs; black vertical lines and numbers in the top right box\r\n . ERSSTv5, dashed lines: dataset = 1\r\n . HadISST, solid lines: dataset = 2\r\n - duration_lanina_cmip5; La Nina duration in each ensemble member of CMIP5 models; blue box-whisker and number in the top right box\r\n - duration_lanina_cmip6; La Nina duration in each ensemble member of CMIP6 models; red dots, red box-whisker and number in the top right box\r\n . ACCESS-CM2: ens_cmip6 = 1 - 3\r\n . ACCESS-ESM1-5: ens_cmip6 = 4 - 23\r\n . AWI-CM-1-1-MR: ens_cmip6 = 24 - 28\r\n . AWI-ESM-1-1-LR: ens_cmip6 = 29\r\n . BCC-CSM2-MR: ens_cmip6 = 30 - 32\r\n . BCC-ESM1: ens_cmip6 = 33 - 35\r\n . CAMS-CSM1-0: ens_cmip6 = 36-38\r\n . CanESM5-CanOE: ens_cmip6 = 39 - 41\r\n . CanESM5: ens_cmip6 = 42 - 106\r\n . CESM2-FV2: ens_cmip6 = 107 - 109\r\n . CESM2: ens_cmip6 = 110 - 120\r\n . CESM2-WACCM-FV2: ens_cmip6 = 121 - 123\r\n . CESM2-WACCM: ens_cmip6 = 124 - 126\r\n . CIESM: ens_cmip6 = 127 - 129\r\n . CMCC-CM2-HR4: ens_cmip6 = 130\r\n . CMCC-CM2-SR5: ens_cmip6 = 131\r\n . CMCC-ESM2: ens_cmip6 = 132\r\n . CNRM-CM6-1-HR: ens_cmip6 = 133\r\n . CNRM-CM6-1: ens_cmip6 = 134 - 162\r\n . CNRM-ESM2-1: ens_cmip6 = 163 - 172\r\n . E3SM-1-0: ens_cmip6 = 173 - 177\r\n . E3SM-1-1-ECA: ens_cmip6 = 178\r\n . E3SM-1-1: ens_cmip6 = 179\r\n . EC-Earth3-AerChem: ens_cmip6 = 180, 181\r\n . EC-Earth3-CC: ens_cmip6 = 182\r\n . EC-Earth3: ens_cmip6 = 183 - 204\r\n . EC-Earth3-Veg-LR: ens_cmip6 = 205 - 207\r\n . EC-Earth3-Veg: ens_cmip6 = 208 - 215\r\n . FGOALS-f3-L: ens_cmip6 = 216 - 218\r\n . FGOALS-g3: ens_cmip6 = 219 - 224\r\n . FIO-ESM-2-0: ens_cmip6 = 225 - 227\r\n . GFDL-CM4: ens_cmip6 = 228\r\n . GFDL-ESM4: ens_cmip6 = 229 - 231\r\n . GISS-E2-1-G-CC: ens_cmip6 = 232\r\n . GISS-E2-1-G: ens_cmip6 = 233 - 278\r\n . GISS-E2-1-H: ens_cmip6 = 279 - 302\r\n . HadGEM3-GC31-LL: ens_cmip6 = 303 - 306\r\n . HadGEM3-GC31-MM: ens_cmip6 = 307 - 310\r\n . IITM-ESM: ens_cmip6 = 311\r\n . INM-CM4-8: ens_cmip6 = 312\r\n . INM-CM5-0: ens_cmip6 = 313 - 322\r\n . IPSL-CM5A2-INCA: ens_cmip6 = 323\r\n . IPSL-CM6A-LR: ens_cmip6 = 324 - 355\r\n . KACE-1-0-G: ens_cmip6 = 356-358\r\n . KIOST-ESM: ens_cmip6 = 359\r\n . MCM-UA-1-0: ens_cmip6 = 360, 361\r\n . MIROC6: ens_cmip6 = 362 - 411\r\n . MIROC-ES2L: ens_cmip6 = 412 - 421\r\n . MPI-ESM-1-2-HAM: ens_cmip6 = 422 - 424\r\n . MPI-ESM1-2-HR: ens_cmip6 = 425 - 434\r\n . MPI-ESM1-2-LR: ens_cmip6 = 435 - 444\r\n . MRI-ESM2-0: ens_cmip6 = 445 - 450\r\n . NESM3: ens_cmip6 = 451 - 455\r\n . NorCPM1: ens_cmip6 = 456 - 485\r\n . NorESM2-LM: ens_cmip6 = 486 - 488\r\n . NorESM2-MM: ens_cmip6 = 489 - 490\r\n . SAM0-UNICON: ens_cmip6 = 491\r\n . TaiESM1: ens_cmip6 = 492\r\n . UKESM1-0-LL: ens_cmip6 = 493 - 510\r\n\r\n\r\nAcronyms: ENSO - El Niño–Southern Oscillation, CMIP - Coupled Model Intercomparison Project, RCP - Representative Concentration Pathway, ERSST - Extended Reconstructed Sea Surface Temperature, HadISST - Hadley Centre Sea Ice and Sea Surface Temperature, ACCESS- CM2 – Australian Community Climate and Earth System Simulator coupled climate model, ACCESS- ESM – Australian Community Climate and Earth System Simulator Earth system model, AWI - Alfred Wegener Institute, BCC-CSM - Beijing Climate Center Climate System Model, CAMS - Chinese Academy of Meteorological Sciences, CanOE - Canadian Ocean Ecosystem, CESM2 - Community Earth System Model, WACCM - Whole Atmosphere Community Climate Model, CIESM - Community Integrated Earth System Model, CNCC - Centro Euro-Mediterraneo per I Cambiamenti Climatici, CNRM - Centre National de Recherches Météorologiques, E3SM - Energy Exascale Earth System Model, FGOALS - Flexible Global Ocean-Atmosphere-Land System Model, FIO-ESM - First Institute of Oceanography Earth System Model, GFDL - Geophysical Fluid Dynamics Laboratory, GISS - Goddard Institute for Space Studies, IITM - Indian Institute of Tropical Meteorology, INM - Institute for Numerical Mathematics, IPSL - Institut Pierre-Simon Laplace, KIOST-ESM - Korea Institute of Ocean Science & Technology Earth System, MIROC - Model for Interdisciplinary Research on Climate, MPI - Max-Planck-Institut für Meteorologie, NESM - Nanjing University of Information Science and Technology Earth System Model, NorCPM - Norwegian Climate Prediction Model, SAM0-UNICON - Seoul National University Atmosphere Model version 0 with a Unified Convection Scheme (SAM0-UNICON), TaiESM1 - Taiwan Earth System Model version 1, UKESM - The UK Earth System Modelling project.\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Multimodel ensemble means and percentiles are calculated after weighting individual members with the inverse of the ensemble size of the same model. The weight is provided as the weight attribute of ens_cmip5 and ens_cmip6.\r\nIf X(i) is the array, and w(i) the corresponding weight.\r\n\r\n\r\n- Mean shoud be suｍ_i(X(i) * w(i)) / sum_i(w(i))\r\n\r\n- For percentile values, \r\n\r\n1. Sort X and w so that X is in the ascending order\r\n\r\n2. Accumulate w until i = j so that accumulated(w)/sum_i(w(i)) equals or exceeds the specified percentile level (e.g. 0.05)\r\n\r\n3. Use X(j) or (X(j) + X(j - 1))/2 as the percentile value\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to the figure on the IPCC AR6 website", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:24:33", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, ENSO, modes of variability, life cycle, CMIP5, CMIP6", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-27T11:33:16", "doiPublishedTime": "2023-02-08T19:25:36.242094", "removedDataTime": null, "geographicExtent": { "ob_id": 3519, "bboxName": "", "eastBoundLongitude": -120.0, "westBoundLongitude": -170.0, "southBoundLatitude": -5.0, "northBoundLatitude": 5.0 }, "verticalExtent": { "ob_id": 141, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37582, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig36/v20220620", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 389746, "numberOfFiles": 4, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10375, "startTime": "1951-01-01T12:00:00", "endTime": "2010-12-31T12:00:00" }, "resultQuality": { "ob_id": 3990, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-20" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37583, "uuid": "fd3c87fad3974fce92c5c2469305fbb7", "short_code": "comp", "title": "Caption for Figure 3.36 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Life cycle of (left) El Niño and (right) La Niña events in observations (black) and historical simulations from CMIP5 (blue; extended with RCP4.5) and CMIP6 (red). An event is detected when the December ENSO index value in year zero exceeds 0.75 times its standard deviation for 1951–2010. (a, b) Composites of the ENSO index (ºC). The horizontal axis represents month relative to the reference December (the grey vertical bar), with numbers in parentheses indicating relative years. Shading and lines represent 5th–95th percentiles and multi-model ensemble means, respectively. (c, d) Mean durations (months) of El Niño and La Niña events defined as number of months in individual events for which the ENSO index exceeds 0.5 times its December standard deviation. Each dot represents an ensemble member from the model indicated on the vertical axis. The boxes and whiskers represent multi-model ensemble mean, interquartile ranges and 5th and 95th percentiles of CMIP5 and CMIP6. The CMIP5 and CMIP6 multi-model ensemble means and observational values are indicated at the top right of each panel. The multi-model ensemble means and percentile values are evaluated after weighting individual members with the inverse of the ensemble size of the same model, so that individual models are equally weighted irrespective of their ensemble sizes. The ENSO index is defined as the SST anomaly averaged over the Niño 3.4 region (5ºS–5ºN, 170ºW–120ºW). All results are based on five-month running mean SST anomalies with triangular-weights after linear detrending. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 46966, 63629, 63630, 63634, 63635, 63636, 63637 ], "vocabularyKeywords": [], "identifier_set": [ 12382 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 179371, 179372, 179373, 179374, 179375, 179376, 179377, 179378, 179379, 179380, 179381 ], "onlineresource_set": [ 52392, 52304, 52305, 82615, 88587, 94627 ] }, { "ob_id": 37584, "uuid": "babcd0de678e4d10aef395f1a265da03", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.37 (v20220620)", "abstract": "Data for Figure 3.37 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.37 shows observed and simulated seasonality of ENSO.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels. All the data are provided in enso_seasonality.nc.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains\r\n \r\n - Climatological standard deviation of the ENSO index\r\n - A seasonality metric of the ENSO index\r\n \r\n in observations, CMIP5 historical-RCP4.5 and CMIP6 historical simulations.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a:\r\n - stdv_enso_obs; black curves\r\n . ERSSTv5, dashed lines: dataset = 1\r\n . HadISST, solid lines: dataset = 2\r\n - stdv_enso_cmip5: Climatological standard deviation of the ENSO index time series in each ensemble member of CMIP5 models blue curve and shading\r\n - stdv_enso_cmip6: Climatological standard deviation of the ENSO index time series in each ensemble member of CMIP6 models; red curve and shading\r\n . ACCESS-CM2: ens_cmip6 = 1 - 3\r\n . ACCESS-ESM1-5: ens_cmip6 = 4 - 23\r\n . AWI-CM-1-1-MR: ens_cmip6 = 24 - 28\r\n . AWI-ESM-1-1-LR: ens_cmip6 = 29\r\n . BCC-CSM2-MR: ens_cmip6 = 30 - 32\r\n . BCC-ESM1: ens_cmip6 = 33 - 35\r\n . CAMS-CSM1-0: ens_cmip6 = 36-38\r\n . CanESM5-CanOE: ens_cmip6 = 39 - 41\r\n . CanESM5: ens_cmip6 = 42 - 106\r\n . CESM2-FV2: ens_cmip6 = 107 - 109\r\n . CESM2: ens_cmip6 = 110 - 120\r\n . CESM2-WACCM-FV2: ens_cmip6 = 121 - 123\r\n . CESM2-WACCM: ens_cmip6 = 124 - 126\r\n . CIESM: ens_cmip6 = 127 - 129\r\n . CMCC-CM2-HR4: ens_cmip6 = 130\r\n . CMCC-CM2-SR5: ens_cmip6 = 131\r\n . CMCC-ESM2: ens_cmip6 = 132\r\n . CNRM-CM6-1-HR: ens_cmip6 = 133\r\n . CNRM-CM6-1: ens_cmip6 = 134 - 162\r\n . CNRM-ESM2-1: ens_cmip6 = 163 - 172\r\n . E3SM-1-0: ens_cmip6 = 173 - 177\r\n . E3SM-1-1-ECA: ens_cmip6 = 178\r\n . E3SM-1-1: ens_cmip6 = 179\r\n . EC-Earth3-AerChem: ens_cmip6 = 180, 181\r\n . EC-Earth3-CC: ens_cmip6 = 182\r\n . EC-Earth3: ens_cmip6 = 183 - 204\r\n . EC-Earth3-Veg-LR: ens_cmip6 = 205 - 207\r\n . EC-Earth3-Veg: ens_cmip6 = 208 - 215\r\n . FGOALS-f3-L: ens_cmip6 = 216 - 218\r\n . FGOALS-g3: ens_cmip6 = 219 - 224\r\n . FIO-ESM-2-0: ens_cmip6 = 225 - 227\r\n . GFDL-CM4: ens_cmip6 = 228\r\n . GFDL-ESM4: ens_cmip6 = 229 - 231\r\n . GISS-E2-1-G-CC: ens_cmip6 = 232\r\n . GISS-E2-1-G: ens_cmip6 = 233 - 278\r\n . GISS-E2-1-H: ens_cmip6 = 279 - 302\r\n . HadGEM3-GC31-LL: ens_cmip6 = 303 - 306\r\n . HadGEM3-GC31-MM: ens_cmip6 = 307 - 310\r\n . IITM-ESM: ens_cmip6 = 311\r\n . INM-CM4-8: ens_cmip6 = 312\r\n . INM-CM5-0: ens_cmip6 = 313 - 322\r\n . IPSL-CM5A2-INCA: ens_cmip6 = 323\r\n . IPSL-CM6A-LR: ens_cmip6 = 324 - 355\r\n . KACE-1-0-G: ens_cmip6 = 356-358\r\n . KIOST-ESM: ens_cmip6 = 359\r\n . MCM-UA-1-0: ens_cmip6 = 360, 361\r\n . MIROC6: ens_cmip6 = 362 - 411\r\n . MIROC-ES2L: ens_cmip6 = 412 - 421\r\n . MPI-ESM-1-2-HAM: ens_cmip6 = 422 - 424\r\n . MPI-ESM1-2-HR: ens_cmip6 = 425 - 434\r\n . MPI-ESM1-2-LR: ens_cmip6 = 435 - 444\r\n . MRI-ESM2-0: ens_cmip6 = 445 - 450\r\n . NESM3: ens_cmip6 = 451 - 455\r\n . NorCPM1: ens_cmip6 = 456 - 485\r\n . NorESM2-LM: ens_cmip6 = 486 - 488\r\n . NorESM2-MM: ens_cmip6 = 489 - 490\r\n . SAM0-UNICON: ens_cmip6 = 491\r\n . TaiESM1: ens_cmip6 = 492\r\n . UKESM1-0-LL: ens_cmip6 = 493 - 510\r\n \r\n Panel b:\r\n - seasonality_enso_obs; black vertical lines and numbers in the top right box\r\n . ERSSTv5, dashed lines: dataset = 1\r\n . HadISST, solid lines: dataset = 2\r\n - seasonality_enso_cmip5; Seasonality metric in each ensemble member of CMIP5 models; blue box-whisker and number in the top right box\r\n - seasonality_enso_cmip6; Seasonality metric in each ensemble member of CMIP6 models; red dots, with their multimodal ensemble mean and percentiles for the red box-whisker and number in the top right box\r\n . ACCESS-CM2: ens_cmip6 = 1 - 3\r\n . ACCESS-ESM1-5: ens_cmip6 = 4 - 23\r\n . AWI-CM-1-1-MR: ens_cmip6 = 24 - 28\r\n . AWI-ESM-1-1-LR: ens_cmip6 = 29\r\n . BCC-CSM2-MR: ens_cmip6 = 30 - 32\r\n . BCC-ESM1: ens_cmip6 = 33 - 35\r\n . CAMS-CSM1-0: ens_cmip6 = 36-38\r\n . CanESM5-CanOE: ens_cmip6 = 39 - 41\r\n . CanESM5: ens_cmip6 = 42 - 106\r\n . CESM2-FV2: ens_cmip6 = 107 - 109\r\n . CESM2: ens_cmip6 = 110 - 120\r\n . CESM2-WACCM-FV2: ens_cmip6 = 121 - 123\r\n . CESM2-WACCM: ens_cmip6 = 124 - 126\r\n . CIESM: ens_cmip6 = 127 - 129\r\n . CMCC-CM2-HR4: ens_cmip6 = 130\r\n . CMCC-CM2-SR5: ens_cmip6 = 131\r\n . CMCC-ESM2: ens_cmip6 = 132\r\n . CNRM-CM6-1-HR: ens_cmip6 = 133\r\n . CNRM-CM6-1: ens_cmip6 = 134 - 162\r\n . CNRM-ESM2-1: ens_cmip6 = 163 - 172\r\n . E3SM-1-0: ens_cmip6 = 173 - 177\r\n . E3SM-1-1-ECA: ens_cmip6 = 178\r\n . E3SM-1-1: ens_cmip6 = 179\r\n . EC-Earth3-AerChem: ens_cmip6 = 180, 181\r\n . EC-Earth3-CC: ens_cmip6 = 182\r\n . EC-Earth3: ens_cmip6 = 183 - 204\r\n . EC-Earth3-Veg-LR: ens_cmip6 = 205 - 207\r\n . EC-Earth3-Veg: ens_cmip6 = 208 - 215\r\n . FGOALS-f3-L: ens_cmip6 = 216 - 218\r\n . FGOALS-g3: ens_cmip6 = 219 - 224\r\n . FIO-ESM-2-0: ens_cmip6 = 225 - 227\r\n . GFDL-CM4: ens_cmip6 = 228\r\n . GFDL-ESM4: ens_cmip6 = 229 - 231\r\n . GISS-E2-1-G-CC: ens_cmip6 = 232\r\n . GISS-E2-1-G: ens_cmip6 = 233 - 278\r\n . GISS-E2-1-H: ens_cmip6 = 279 - 302\r\n . HadGEM3-GC31-LL: ens_cmip6 = 303 - 306\r\n . HadGEM3-GC31-MM: ens_cmip6 = 307 - 310\r\n . IITM-ESM: ens_cmip6 = 311\r\n . INM-CM4-8: ens_cmip6 = 312\r\n . INM-CM5-0: ens_cmip6 = 313 - 322\r\n . IPSL-CM5A2-INCA: ens_cmip6 = 323\r\n . IPSL-CM6A-LR: ens_cmip6 = 324 - 355\r\n . KACE-1-0-G: ens_cmip6 = 356-358\r\n . KIOST-ESM: ens_cmip6 = 359\r\n . MCM-UA-1-0: ens_cmip6 = 360, 361\r\n . MIROC6: ens_cmip6 = 362 - 411\r\n . MIROC-ES2L: ens_cmip6 = 412 - 421\r\n . MPI-ESM-1-2-HAM: ens_cmip6 = 422 - 424\r\n . MPI-ESM1-2-HR: ens_cmip6 = 425 - 434\r\n . MPI-ESM1-2-LR: ens_cmip6 = 435 - 444\r\n . MRI-ESM2-0: ens_cmip6 = 445 - 450\r\n . NESM3: ens_cmip6 = 451 - 455\r\n . NorCPM1: ens_cmip6 = 456 - 485\r\n . NorESM2-LM: ens_cmip6 = 486 - 488\r\n . NorESM2-MM: ens_cmip6 = 489 - 490\r\n . SAM0-UNICON: ens_cmip6 = 491\r\n . TaiESM1: ens_cmip6 = 492\r\n . UKESM1-0-LL: ens_cmip6 = 493 - 510\r\n\r\n\r\nAcronyms - ENSO - El Niño–Southern Oscillation, CMIP - Coupled Model Intercomparison Project, RCP - Representative Concentration Pathway, ERSST - Extended Reconstructed Sea Surface Temperature, HadISST - Hadley Centre Sea Ice and Sea Surface Temperature, ACCESS- CM2 – Australian Community Climate and Earth System Simulator coupled climate model, ACCESS- ESM – Australian Community Climate and Earth System Simulator Earth system model, AWI - Alfred Wegener Institute, BCC-CSM - Beijing Climate Center Climate System Model, CAMS - Chinese Academy of Meteorological Sciences, CanOE - Canadian Ocean Ecosystem, CESM2 - Community Earth System Model, WACCM - Whole Atmosphere Community Climate Model, CIESM - Community Integrated Earth System Model, CNCC - Centro Euro-Mediterraneo per I Cambiamenti Climatici, CNRM - Centre National de Recherches Météorologiques, E3SM - Energy Exascale Earth System Model, FGOALS - Flexible Global Ocean-Atmosphere-Land System Model, FIO-ESM - First Institute of Oceanography Earth System Model, GFDL - Geophysical Fluid Dynamics Laboratory, GISS - Goddard Institute for Space Studies, IITM - Indian Institute of Tropical Meteorology, INM - Institute for Numerical Mathematics, IPSL - Institut Pierre-Simon Laplace, KIOST-ESM - Korea Institute of Ocean Science & Technology Earth System, MIROC - Model for Interdisciplinary Research on Climate, MPI - Max-Planck-Institut für Meteorologie, NESM - Nanjing University of Information Science and Technology Earth System Model, NorCPM - Norwegian Climate Prediction Model, SAM0-UNICON - Seoul National University Atmosphere Model version 0 with a Unified Convection Scheme (SAM0-UNICON), TaiESM1 - Taiwan Earth System Model version 1, UKESM - The UK Earth System Modelling project.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Multimodel ensemble means and percentiles are calculated after weighting individual members with the inverse of the ensemble size of the same model. The weight is provided as the weight attribute of ens_cmip5 and ens_cmip6.\r\n \r\n If X(i) is the array, and w(i) the corresponding weight.\r\n\r\n- Mean shoud be suｍ_i(X(i) * w(i)) / sum_i(w(i))\r\n\r\n- For percentile values, \r\n\r\n1. Sort X and w so that X is in the ascending order\r\n\r\n2. Accumulate w until i = j so that accumulated(w)/sum_i(w(i)) equals or exceeds the specified percentile level (e.g. 0.05)\r\n\r\n3. Use X(j) or (X(j) + X(j - 1))/2 as the percentile value\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to the figure on the IPCC AR6 website", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:02", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, ENSO, modes of variability, seasonality, CMIP5, CMIP6", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-27T11:38:16", "doiPublishedTime": "2023-02-08T19:27:35.665488", "removedDataTime": null, "geographicExtent": { "ob_id": 3520, "bboxName": "", "eastBoundLongitude": -120.0, "westBoundLongitude": -170.0, "southBoundLatitude": -5.0, "northBoundLatitude": 5.0 }, "verticalExtent": { "ob_id": 142, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37585, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig37/v20220620", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 55568, "numberOfFiles": 4, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10376, "startTime": "1951-01-01T12:00:00", "endTime": "2010-12-31T12:00:00" }, "resultQuality": { "ob_id": 3991, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-20" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37586, "uuid": "c78e806f67624e9c8d4f0f8a70888cd8", "short_code": "comp", "title": "Caption for Figure 3.37 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "ENSO seasonality in observations (black) and historical simulations from CMIP5 (blue; extended with RCP4.5) and CMIP6 (red) for 1951–2010. (a) Climatological standard deviation of the monthly ENSO index (SST anomaly averaged over the Niño 3.4 region; °C). Shading and lines represent 5th–95th percentiles and multi-model ensemble means, respectively. (b) Seasonality metric, which is defined for each model and each ensemble member as the ratio of the ENSO index climatological standard deviation in November–January (NDJ) to that in March–May (MAM). Each dot represents an ensemble member from the model indicated on the vertical axis. The boxes and whiskers represent the multi-model ensemble mean, interquartile ranges and 5th and 95th percentiles of CMIP5 and CMIP6 individually. The CMIP5 and CMIP6 multi-model ensemble means and observational values are indicated at the top right of the panel. The multi-model ensemble means and percentile values are evaluated after weighting individual members with the inverse of the ensemble size of the same model, so that individual models are equally weighted irrespective of their ensemble sizes. All results are based on five-month running mean SST anomalies with triangular-weights after linear detrending. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 46966, 63629, 63630, 63631, 63632, 63633 ], "vocabularyKeywords": [], "identifier_set": [ 12383 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 179384, 179385, 179386, 179387, 179388, 179389, 179390, 179391, 179392, 179393, 179394 ], "onlineresource_set": [ 52393, 52306, 52307, 82616, 88588, 94625 ] }, { "ob_id": 37588, "uuid": "10af126c9dcf40e2aff7c3cf835d7e6b", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's Lufft CHM15k \"Nimbus\" instrument A deployed at Amsterdam Ap Schiphol, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s Lufft CHM15k \"Nimbus\" deployed at Amsterdam Ap Schiphol, Netherlands.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06240.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. Note: this WIGOS ID is shared by 4 instruments located at the site. Data from the 4 instruments use one shared value for latitude and longitude:\r\n\r\nLatitude: 52.317008972168 N\r\nLongitude: 4.80366992950439 E\r\n \r\nThe actual instrument deployments are as follows:\r\n\r\nInstrument: Amsterdam AP Schiphol A\r\nLatitude: 52.317010 N\r\nLongitude: 4.803670 E\r\nAltitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 27\r\n\r\nInstrument: Amsterdam AP Schiphol B\r\nLatitude: 52.286140 N\r\nLongitude: 4.729310 E\r\n-Altitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 06\r\n \r\nInstrument: Amsterdam AP Schiphol C\r\nLatitude: 52.368290 N\r\nLongitude: 4.712660 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18R\r\n \r\nInstrument: Amsterdam AP Schiphol D\r\nLatitude: 52.3395500183105 N\r\nLongitude: 4.7407398223877 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18C \r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2025-01-18T04:30:40", "updateFrequency": "daily", "dataLineage": "Data were collected by instrument and transmitted to the central E-PROFILE processing hub at the UK's Met Office before preparation and delivery to the Centre for Environmental Data Analysis (CEDA). CEDA then produces daily concatenated files before ingestion into the CEDA Archive.", "removedDataReason": "", "keywords": "E-PROFILE, ceilometer measurements", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-06-10T23:10:06", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3521, "bboxName": "Amsterdam-Ap-Schiphol A", "eastBoundLongitude": 4.80366992950439, "westBoundLongitude": 4.80366992950439, "southBoundLatitude": 52.317008972168, "northBoundLatitude": 52.317008972168 }, "verticalExtent": null, "result_field": { "ob_id": 37587, "dataPath": "/badc/eprofile/data/daily_files/netherlands/amsterdam-ap-schiphol/knmi-lufft-chm15k_A", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3508309762, "numberOfFiles": 1324, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 10377, "startTime": "2022-06-09T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 3890, "explanation": "The data are provided as-is with no quality control undertaken by the Centre for Environmental Data Analysis (CEDA). The data suppliers have not indicated if any quality control has been undertaken on these data.", "passesTest": true, "resultTitle": "E-PROFILE QC statement", "date": "2022-02-28" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 37589, "uuid": "7f07f47e83c94867aa507ba135082a60", "short_code": "acq", "title": "KNMI: Lufft CHM15k \"Nimbus\" instrument deployed at Amsterdam Ap Schiphol", "abstract": "Lufft CHM15k \"Nimbus\" instrument instrument deployed at Amsterdam Ap Schiphol operated by Royal Netherlands Meteorological Institute (KNMI) providing cloud base height and aerosol profile data." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 220 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2527, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 7, "licenceURL": "https://artefacts.ceda.ac.uk/licences/cuncgl", "licenceClassifications": [ { "ob_id": 6, "classification": "personal" }, { "ob_id": 4, "classification": "academic" }, { "ob_id": 5, "classification": "policy" } ] } } ], "projects": [ { "ob_id": 32779, "uuid": "16a48b5339ab48cd97bb680388c5cddf", "short_code": "proj", "title": "EUMETNET E-PROFILE", "abstract": "E-PROFILE is part of the EUMETNET Composite Observing System, EUCOS, managing the European networks of radar wind profilers (RWP) and automatic lidars and ceilometers (ALC) for the monitoring of vertical profiles of wind and aerosols including volcanic ash.\r\n \r\n\r\nE-PROFILE coordinates the measurements of vertical profiles of wind from radar wind profilers (vertically pointing Doppler radars) and weather radars from a network of locations across Europe and provides the data to the end users. The main goal is to improve the overall usability of wind profiler data for operational meteorology and to provide support and expertise to both profiler operators and end users.\r\nDue to technical advances of the last years ceilometers (automatic low cost lidars) provide nowadays not only cloud base height but also information on the vertical distribution of aerosols derived from the backscatter profile. To make available this new observation capacity E-PROFILE is developing a framework to produce and exchange profiles of attenuated backscatter profiles. Automatic lidars and ceilometers of stations across Europe are added to the operational network." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 46582, 46583, 46584, 46585, 46586, 46587, 46588, 46589, 46590, 46591, 46592, 46593, 46594, 46595, 46597, 46598, 46599 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 34905, "uuid": "345c47d378b64c75b7957aef0c09c81f", "short_code": "coll", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from a network covering most of Europe with additional sites worldwide", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from a network of instruments within EUMETNET's E-PROFILE ALC network.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nMost datasets are available to registered CEDA users. For those not available to CEDA users application for access to those datasets under restricted access can be made using the links on one of the associated records. All use is made in accordance with the Closed-Use Non-Commercial General Licence. See datasets for further licencing links and for individual dataset citations.\r\n\r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.\r\n\r\nNote - the datasets listed on this collection are daily concatenated files produced from single time-step files for each instrument. CEDA holds an older archive of single time-step files (not linked to from the datasets or this collection) which will be aggregated together over time to extend these datasets further back to the start of the E-PROFILE holdings in the CEDA archives. Access to the older single time-step files ahead of their concatenation into daily files can be made via : https://data.ceda.ac.uk/badc/eprofile/data/. As these data are processed single time-step files will be removed from the archive.\r\n\r\nIt is not possible to support any requests for data that predates the CEDA holdings nor to back-fill any data gaps." } ], "responsiblepartyinfo_set": [ 179397, 179398, 179399, 179400, 179401, 179402, 179403, 179404, 179405 ], "onlineresource_set": [ 52309, 52308 ] }, { "ob_id": 37591, "uuid": "32dcaf1485de444c8ba273afde964f46", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's Lufft CHM15k \"Nimbus\" instrument B deployed at Amsterdam Ap Schiphol, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s Lufft CHM15k \"Nimbus\" deployed at Amsterdam Ap Schiphol, Netherlands.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06240.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. Note: this WIGOS ID is shared by 4 instruments located at the site. Data from the 4 instruments use one shared value for latitude and longitude:\r\n\r\nLatitude: 52.317008972168 N\r\nLongitude: 4.80366992950439 E\r\n \r\nThe actual instrument deployments are as follows:\r\n\r\nInstrument: Amsterdam AP Schiphol A\r\nLatitude: 52.317010 N\r\nLongitude: 4.803670 E\r\nAltitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 27\r\n\r\nInstrument: Amsterdam AP Schiphol B\r\nLatitude: 52.286140 N\r\nLongitude: 4.729310 E\r\n-Altitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 06\r\n \r\nInstrument: Amsterdam AP Schiphol C\r\nLatitude: 52.368290 N\r\nLongitude: 4.712660 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18R\r\n \r\nInstrument: Amsterdam AP Schiphol D\r\nLatitude: 52.3395500183105 N\r\nLongitude: 4.7407398223877 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18C \r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2025-01-18T04:30:13", "updateFrequency": "daily", "dataLineage": "Data were collected by instrument and transmitted to the central E-PROFILE processing hub at the UK's Met Office before preparation and delivery to the Centre for Environmental Data Analysis (CEDA). CEDA then produces daily concatenated files before ingestion into the CEDA Archive.", "removedDataReason": "", "keywords": "E-PROFILE, ceilometer measurements", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-06-10T23:09:26", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3526, "bboxName": "Amsterdam-Ap-Schiphol B", "eastBoundLongitude": 4.72931, "westBoundLongitude": 4.72931, "southBoundLatitude": 52.28614, "northBoundLatitude": 52.28614 }, "verticalExtent": null, "result_field": { "ob_id": 37590, "dataPath": "/badc/eprofile/data/daily_files/netherlands/amsterdam-ap-schiphol/knmi-lufft-chm15k_B", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3515475778, "numberOfFiles": 1323, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 10378, "startTime": "2022-06-09T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 3890, "explanation": "The data are provided as-is with no quality control undertaken by the Centre for Environmental Data Analysis (CEDA). The data suppliers have not indicated if any quality control has been undertaken on these data.", "passesTest": true, "resultTitle": "E-PROFILE QC statement", "date": "2022-02-28" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 37592, "uuid": "bbaa910ebffc4c01b57e7d2a0718a8b0", "short_code": "acq", "title": "KNMI: Lufft CHM15k \"Nimbus\" instrument deployed at Amsterdam Ap Schiphol", "abstract": "Lufft CHM15k \"Nimbus\" instrument instrument deployed at Amsterdam Ap Schiphol operated by Royal Netherlands Meteorological Institute (KNMI) providing cloud base height and aerosol profile data." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 220 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2527, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 7, "licenceURL": "https://artefacts.ceda.ac.uk/licences/cuncgl", "licenceClassifications": [ { "ob_id": 6, "classification": "personal" }, { "ob_id": 4, "classification": "academic" }, { "ob_id": 5, "classification": "policy" } ] } } ], "projects": [ { "ob_id": 32779, "uuid": "16a48b5339ab48cd97bb680388c5cddf", "short_code": "proj", "title": "EUMETNET E-PROFILE", "abstract": "E-PROFILE is part of the EUMETNET Composite Observing System, EUCOS, managing the European networks of radar wind profilers (RWP) and automatic lidars and ceilometers (ALC) for the monitoring of vertical profiles of wind and aerosols including volcanic ash.\r\n \r\n\r\nE-PROFILE coordinates the measurements of vertical profiles of wind from radar wind profilers (vertically pointing Doppler radars) and weather radars from a network of locations across Europe and provides the data to the end users. The main goal is to improve the overall usability of wind profiler data for operational meteorology and to provide support and expertise to both profiler operators and end users.\r\nDue to technical advances of the last years ceilometers (automatic low cost lidars) provide nowadays not only cloud base height but also information on the vertical distribution of aerosols derived from the backscatter profile. To make available this new observation capacity E-PROFILE is developing a framework to produce and exchange profiles of attenuated backscatter profiles. Automatic lidars and ceilometers of stations across Europe are added to the operational network." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 46582, 46583, 46584, 46585, 46586, 46587, 46588, 46589, 46590, 46591, 46592, 46593, 46594, 46595, 46597, 46598, 46599 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 34905, "uuid": "345c47d378b64c75b7957aef0c09c81f", "short_code": "coll", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from a network covering most of Europe with additional sites worldwide", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from a network of instruments within EUMETNET's E-PROFILE ALC network.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nMost datasets are available to registered CEDA users. For those not available to CEDA users application for access to those datasets under restricted access can be made using the links on one of the associated records. All use is made in accordance with the Closed-Use Non-Commercial General Licence. See datasets for further licencing links and for individual dataset citations.\r\n\r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.\r\n\r\nNote - the datasets listed on this collection are daily concatenated files produced from single time-step files for each instrument. CEDA holds an older archive of single time-step files (not linked to from the datasets or this collection) which will be aggregated together over time to extend these datasets further back to the start of the E-PROFILE holdings in the CEDA archives. Access to the older single time-step files ahead of their concatenation into daily files can be made via : https://data.ceda.ac.uk/badc/eprofile/data/. As these data are processed single time-step files will be removed from the archive.\r\n\r\nIt is not possible to support any requests for data that predates the CEDA holdings nor to back-fill any data gaps." } ], "responsiblepartyinfo_set": [ 179412, 179413, 179414, 179415, 179408, 179409, 179410, 179411, 179416 ], "onlineresource_set": [ 52311, 52310 ] }, { "ob_id": 37594, "uuid": "af83232f73bf42a88a1df88d1067825b", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's Lufft CHM15k \"Nimbus\" instrument C deployed at Amsterdam Ap Schiphol, Netherlands", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s Lufft CHM15k \"Nimbus\" deployed at Amsterdam Ap Schiphol, Netherlands.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06240.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. Note: this WIGOS ID is shared by 4 instruments located at the site. Data from the 4 instruments use one shared value for latitude and longitude:\r\n\r\nLatitude: 52.317008972168 N\r\nLongitude: 4.80366992950439 E\r\n \r\nThe actual instrument deployments are as follows:\r\n\r\nInstrument: Amsterdam AP Schiphol A\r\nLatitude: 52.317010 N\r\nLongitude: 4.803670 E\r\nAltitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 27\r\n\r\nInstrument: Amsterdam AP Schiphol B\r\nLatitude: 52.286140 N\r\nLongitude: 4.729310 E\r\n-Altitude: -4\r\nLocation: Amsterdam AP Schiphol end of runway 06\r\n \r\nInstrument: Amsterdam AP Schiphol C\r\nLatitude: 52.368290 N\r\nLongitude: 4.712660 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18R\r\n \r\nInstrument: Amsterdam AP Schiphol D\r\nLatitude: 52.3395500183105 N\r\nLongitude: 4.7407398223877 E\r\nAltitude: --4\r\nLocation: Amsterdam AP Schiphol end of runway 18C \r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2025-01-18T04:30:18", "updateFrequency": "daily", "dataLineage": "Data were collected by instrument and transmitted to the central E-PROFILE processing hub at the UK's Met Office before preparation and delivery to the Centre for Environmental Data Analysis (CEDA). CEDA then produces daily concatenated files before ingestion into the CEDA Archive.", "removedDataReason": "", "keywords": "E-PROFILE, ceilometer measurements", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-06-10T23:10:15", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3527, "bboxName": "Amsterdam-Ap-Schiphol C", "eastBoundLongitude": 4.71266, "westBoundLongitude": 4.71266, "southBoundLatitude": 52.36829, "northBoundLatitude": 52.36829 }, "verticalExtent": null, "result_field": { "ob_id": 37593, "dataPath": "/badc/eprofile/data/daily_files/netherlands/amsterdam-ap-schiphol/knmi-lufft-chm15k_C", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3528637031, "numberOfFiles": 1327, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 10379, "startTime": "2022-06-09T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 3890, "explanation": "The data are provided as-is with no quality control undertaken by the Centre for Environmental Data Analysis (CEDA). 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The main goal is to improve the overall usability of wind profiler data for operational meteorology and to provide support and expertise to both profiler operators and end users.\r\nDue to technical advances of the last years ceilometers (automatic low cost lidars) provide nowadays not only cloud base height but also information on the vertical distribution of aerosols derived from the backscatter profile. To make available this new observation capacity E-PROFILE is developing a framework to produce and exchange profiles of attenuated backscatter profiles. Automatic lidars and ceilometers of stations across Europe are added to the operational network." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 46582, 46583, 46584, 46585, 46586, 46587, 46588, 46589, 46590, 46591, 46592, 46593, 46594, 46595, 46597, 46598, 46599 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 34905, "uuid": "345c47d378b64c75b7957aef0c09c81f", "short_code": "coll", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from a network covering most of Europe with additional sites worldwide", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from a network of instruments within EUMETNET's E-PROFILE ALC network.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nMost datasets are available to registered CEDA users. For those not available to CEDA users application for access to those datasets under restricted access can be made using the links on one of the associated records. All use is made in accordance with the Closed-Use Non-Commercial General Licence. See datasets for further licencing links and for individual dataset citations.\r\n\r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.\r\n\r\nNote - the datasets listed on this collection are daily concatenated files produced from single time-step files for each instrument. CEDA holds an older archive of single time-step files (not linked to from the datasets or this collection) which will be aggregated together over time to extend these datasets further back to the start of the E-PROFILE holdings in the CEDA archives. Access to the older single time-step files ahead of their concatenation into daily files can be made via : https://data.ceda.ac.uk/badc/eprofile/data/. As these data are processed single time-step files will be removed from the archive.\r\n\r\nIt is not possible to support any requests for data that predates the CEDA holdings nor to back-fill any data gaps." } ], "responsiblepartyinfo_set": [ 179419, 179420, 179421, 179422, 179423, 179424, 179425, 179426, 179427 ], "onlineresource_set": [ 52313, 52312 ] }, { "ob_id": 37597, "uuid": "f18c0d83e3f44ea29fa38c8cf380537f", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from CHMI's Vaisala CL31 instrument deployed at Karlovy Vary, Czech Republic", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Czech Hydrometeorological Institute (CHMI)'s Vaisala CL31 deployed at Karlovy Vary, Czech Republic.\n\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\n\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-11414.\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool.\n \nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2023-03-31T00:09:16", "updateFrequency": "daily", "dataLineage": "Data were collected by instrument and transmitted to the central E-PROFILE processing hub at the UK's Met Office before preparation and delivery to the Centre for Environmental Data Analysis (CEDA). CEDA then produces daily concatenated files before ingestion into the CEDA Archive.", "removedDataReason": "", "keywords": "E-PROFILE, ceilometer measurements", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-05-14T03:09:03", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3522, "bboxName": "Karlovy-Vary", "eastBoundLongitude": 12.914170265197754, "westBoundLongitude": 12.914170265197754, "southBoundLatitude": 50.2016716003418, "northBoundLatitude": 50.2016716003418 }, "verticalExtent": null, "result_field": { "ob_id": 37596, "dataPath": "/badc/eprofile/data/daily_files/czech-republic/karlovy-vary/chmi-vaisala-cl31_A", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 706829958, "numberOfFiles": 279, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 10380, "startTime": "2022-05-12T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 3890, "explanation": "The data are provided as-is with no quality control undertaken by the Centre for Environmental Data Analysis (CEDA). The data suppliers have not indicated if any quality control has been undertaken on these data.", "passesTest": true, "resultTitle": "E-PROFILE QC statement", "date": "2022-02-28" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 37598, "uuid": "0a7d06a7894b4e2c993bd6a06f3641c6", "short_code": "acq", "title": "CHMI: Vaisala CL31 instrument deployed at Karlovy Vary", "abstract": "Vaisala CL31 instrument instrument deployed at Karlovy Vary operated by Czech Hydrometeorological Institute (CHMI) providing cloud base height and aerosol profile data." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 220 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2527, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 7, "licenceURL": "https://artefacts.ceda.ac.uk/licences/cuncgl", "licenceClassifications": [ { "ob_id": 6, "classification": "personal" }, { "ob_id": 4, "classification": "academic" }, { "ob_id": 5, "classification": "policy" } ] } } ], "projects": [ { "ob_id": 32779, "uuid": "16a48b5339ab48cd97bb680388c5cddf", "short_code": "proj", "title": "EUMETNET E-PROFILE", "abstract": "E-PROFILE is part of the EUMETNET Composite Observing System, EUCOS, managing the European networks of radar wind profilers (RWP) and automatic lidars and ceilometers (ALC) for the monitoring of vertical profiles of wind and aerosols including volcanic ash.\r\n \r\n\r\nE-PROFILE coordinates the measurements of vertical profiles of wind from radar wind profilers (vertically pointing Doppler radars) and weather radars from a network of locations across Europe and provides the data to the end users. The main goal is to improve the overall usability of wind profiler data for operational meteorology and to provide support and expertise to both profiler operators and end users.\r\nDue to technical advances of the last years ceilometers (automatic low cost lidars) provide nowadays not only cloud base height but also information on the vertical distribution of aerosols derived from the backscatter profile. To make available this new observation capacity E-PROFILE is developing a framework to produce and exchange profiles of attenuated backscatter profiles. Automatic lidars and ceilometers of stations across Europe are added to the operational network." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 46582, 46583, 46584, 46585, 46586, 46587, 46588, 46589, 46590, 46591, 46592, 46593, 46594, 46595, 46596, 46597, 46598 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 34905, "uuid": "345c47d378b64c75b7957aef0c09c81f", "short_code": "coll", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from a network covering most of Europe with additional sites worldwide", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from a network of instruments within EUMETNET's E-PROFILE ALC network.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nMost datasets are available to registered CEDA users. For those not available to CEDA users application for access to those datasets under restricted access can be made using the links on one of the associated records. All use is made in accordance with the Closed-Use Non-Commercial General Licence. See datasets for further licencing links and for individual dataset citations.\r\n\r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.\r\n\r\nNote - the datasets listed on this collection are daily concatenated files produced from single time-step files for each instrument. CEDA holds an older archive of single time-step files (not linked to from the datasets or this collection) which will be aggregated together over time to extend these datasets further back to the start of the E-PROFILE holdings in the CEDA archives. Access to the older single time-step files ahead of their concatenation into daily files can be made via : https://data.ceda.ac.uk/badc/eprofile/data/. As these data are processed single time-step files will be removed from the archive.\r\n\r\nIt is not possible to support any requests for data that predates the CEDA holdings nor to back-fill any data gaps." } ], "responsiblepartyinfo_set": [ 179430, 179431, 179432, 179433, 179434, 179435, 179436, 179437, 179438 ], "onlineresource_set": [ 52316, 52315 ] }, { "ob_id": 37601, "uuid": "1a734ef4a341448e9875735449db8c05", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from SMHI's Vaisala CL31 instrument deployed at Ljungby, Sweden", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Swedish Meteorological and Hydrological Institute (SMHI)'s Vaisala CL31 deployed at Ljungby, Sweden.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-02622. See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool.\r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-07-18T00:14:11", "updateFrequency": "daily", "dataLineage": "Data were collected by instrument and transmitted to the central E-PROFILE processing hub at the UK's Met Office before preparation and delivery to the Centre for Environmental Data Analysis (CEDA). CEDA then produces daily concatenated files before ingestion into the CEDA Archive.", "removedDataReason": "", "keywords": "E-PROFILE, ceilometer measurements", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-04-22T03:10:14", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3523, "bboxName": "Ljungby", "eastBoundLongitude": 13.879432678222656, "westBoundLongitude": 13.879432678222656, "southBoundLatitude": 56.852481842041016, "northBoundLatitude": 56.852481842041016 }, "verticalExtent": null, "result_field": { "ob_id": 37600, "dataPath": "/badc/eprofile/data/daily_files/sweden/ljungby/smhi-vaisala-cl31_A", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1851341028, "numberOfFiles": 1352, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 10381, "startTime": "2022-04-20T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 3890, "explanation": "The data are provided as-is with no quality control undertaken by the Centre for Environmental Data Analysis (CEDA). The data suppliers have not indicated if any quality control has been undertaken on these data.", "passesTest": true, "resultTitle": "E-PROFILE QC statement", "date": "2022-02-28" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 37602, "uuid": "082862f2b6a444a1a06119badfc943e8", "short_code": "acq", "title": "SMHI: Vaisala CL31 instrument deployed at Ljungby", "abstract": "Vaisala CL31 instrument instrument deployed at Ljungby operated by Swedish Meteorological and Hydrological Institute (SMHI) providing cloud base height and aerosol profile data." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 220 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2527, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 7, "licenceURL": "https://artefacts.ceda.ac.uk/licences/cuncgl", "licenceClassifications": [ { "ob_id": 6, "classification": "personal" }, { "ob_id": 4, "classification": "academic" }, { "ob_id": 5, "classification": "policy" } ] } } ], "projects": [ { "ob_id": 32779, "uuid": "16a48b5339ab48cd97bb680388c5cddf", "short_code": "proj", "title": "EUMETNET E-PROFILE", "abstract": "E-PROFILE is part of the EUMETNET Composite Observing System, EUCOS, managing the European networks of radar wind profilers (RWP) and automatic lidars and ceilometers (ALC) for the monitoring of vertical profiles of wind and aerosols including volcanic ash.\r\n \r\n\r\nE-PROFILE coordinates the measurements of vertical profiles of wind from radar wind profilers (vertically pointing Doppler radars) and weather radars from a network of locations across Europe and provides the data to the end users. The main goal is to improve the overall usability of wind profiler data for operational meteorology and to provide support and expertise to both profiler operators and end users.\r\nDue to technical advances of the last years ceilometers (automatic low cost lidars) provide nowadays not only cloud base height but also information on the vertical distribution of aerosols derived from the backscatter profile. To make available this new observation capacity E-PROFILE is developing a framework to produce and exchange profiles of attenuated backscatter profiles. Automatic lidars and ceilometers of stations across Europe are added to the operational network." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 46582, 46583, 46584, 46585, 46586, 46587, 46588, 46589, 46590, 46591, 46592, 46593, 46594, 46595, 46596, 46597, 46598 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 34905, "uuid": "345c47d378b64c75b7957aef0c09c81f", "short_code": "coll", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from a network covering most of Europe with additional sites worldwide", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from a network of instruments within EUMETNET's E-PROFILE ALC network.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nMost datasets are available to registered CEDA users. For those not available to CEDA users application for access to those datasets under restricted access can be made using the links on one of the associated records. All use is made in accordance with the Closed-Use Non-Commercial General Licence. See datasets for further licencing links and for individual dataset citations.\r\n\r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.\r\n\r\nNote - the datasets listed on this collection are daily concatenated files produced from single time-step files for each instrument. CEDA holds an older archive of single time-step files (not linked to from the datasets or this collection) which will be aggregated together over time to extend these datasets further back to the start of the E-PROFILE holdings in the CEDA archives. Access to the older single time-step files ahead of their concatenation into daily files can be made via : https://data.ceda.ac.uk/badc/eprofile/data/. As these data are processed single time-step files will be removed from the archive.\r\n\r\nIt is not possible to support any requests for data that predates the CEDA holdings nor to back-fill any data gaps." } ], "responsiblepartyinfo_set": [ 179444, 179445, 179446, 179447, 179448, 179449, 179450, 179451, 179452 ], "onlineresource_set": [ 52319, 52318 ] }, { "ob_id": 37604, "uuid": "f38913d950694e3e8f0a19d0dc7f378e", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.11 (v20220620)", "abstract": "Data for Figure 3.11 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.11 shows a comparison between simulated annual precipitation changes and pollen-based reconstructions at the mid-Holocene (6,000 years ago).\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n \r\n - area-averaged precipitation changes over five regions (Northern Europe, Western and Central Europe, Mediterranean, Sahara/Sahel, West Africa) as simulated by CMIP6 models\r\n - area-averaged precipitation changes over five regions (Northern Europe, Western and Central Europe, Mediterranean, Sahara/Sahel, West Africa) as simulated by CMIP5 models.\r\n - pollen-based MAP reconstructions points within the region (Northern Europe, Western and Central Europe, Mediterranean, Sahara/Sahel, West Africa)\r\n\r\n\r\nThese data are also available from a pre-existing GitHub repository which can be found under 'Sources of additional information' and the related documents.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n map_midHolocene_reconstructions.csv shows the data for bars in each region in the figure.\r\n map_midHolocene_models.csv shows the data for the multicolored circles in each region in the figure. The colors represent different models.\r\n Additional data about data provided in relation to figure can be found in the files headers.\r\n\r\n CMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\n CMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n\r\n---------------------------------------------------\r\nTemporal Range of Paleoclimate Data\r\n---------------------------------------------------\r\nThis dataset covers a paleoclimate timespan, starting and ending at 6000 years ago. \r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n- Link to the external GitHub repository also containing the figure data.\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:04", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Precipitation changes, pollen-based reconstructions, Mid-Holocene", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-06-24T15:48:57", "doiPublishedTime": "2023-02-08T17:41:30.343549", "removedDataTime": null, "geographicExtent": { "ob_id": 3525, "bboxName": "", "eastBoundLongitude": 40.0, "westBoundLongitude": -20.0, "southBoundLatitude": 0.0, "northBoundLatitude": 72.6 }, "verticalExtent": { "ob_id": 143, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37605, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig11/v20220620", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 23029, "numberOfFiles": 5, "fileFormat": "Data are netCDF formatted" }, "timePeriod": null, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 65225, 65226, 65227, 65228, 65229, 65230, 65231, 65232, 65233, 65234, 65235, 65236, 65237, 65238, 65239, 65240, 65241, 65242, 65243, 65244, 65245, 65246, 65247, 65248, 65249, 65250, 65251, 65252, 65253, 65254, 65255, 65256, 65257 ], "vocabularyKeywords": [], "identifier_set": [ 12357 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 179458, 179459, 179460, 179461, 179462, 179463, 179464, 193408, 179465 ], "onlineresource_set": [ 52323, 52321, 52320, 82605, 82863, 88560, 94602, 94599, 94600, 94601 ] }, { "ob_id": 37608, "uuid": "85409987ce6a4976b0845b512baa2843", "title": "Chapter 5 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 5.33 (v20220623)", "abstract": "Data for Figure 5.33 from Chapter 5 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 5.33 shows carbon sink response in a scenario with net carbon dioxide (CO2) removal from the atmosphere. \r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nCanadell, J.G., P.M.S. Monteiro, M.H. Costa, L. Cotrim da Cunha, P.M. Cox, A.V. Eliseev, S. Henson, M. Ishii, S. Jaccard, C. Koven, A. Lohila, P.K. Patra, S. Piao, J. Rogelj, S. Syampungani, S. Zaehle, and K. Zickfeld, 2021: Global Carbon and other Biogeochemical Cycles and Feedbacks. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 673–816, doi:10.1017/9781009157896.007.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains data for 50-year periods during 2000-2300 for:\r\n \r\n - Atmospheric CO2 concentration\r\n - Net CO2 emissions (accumulated over 50 year periods)\r\n - Net land flux (accumulated over 50 year periods)\r\n - Net ocean flux (accumulated over 50 year periods)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data file: Data_Figure_5_33.csv:\r\n \r\n - row 1: x-axis values.\r\n - row 2: light blue bars.\r\n - row 3: orange bars.\r\n - row 4: green bars.\r\n - row 5: blue bars\r\n - row 6: relates with the values written in black over the corresponding arrows (row 2 values plus values written in black)\r\n - row 7: Standard deviation over orange bars.\r\n - row 8: Standard deviation over green bars.\r\n - row 9: Standard deviation over blue bars.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n This figure was created in Excel and the error bars (standard deviation) were added in Adobe \r\n Illustrator.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 5)\r\n - Link to the Supplementary Material for Chapter 5, which contains details on the input data used in Table 5.SM.6", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:17", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, carbon fluxes, SSP1-2.6, carbon dioxide removal", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-08-02T07:38:23", "doiPublishedTime": "2023-05-15T12:42:17.222624", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 144, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37609, "dataPath": "/badc/ar6_wg1/data/ch_05/ch5_fig33/v20220623", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5816, "numberOfFiles": 4, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10382, "startTime": "2000-01-01T12:00:00", "endTime": "2300-12-31T12:00:00" }, "resultQuality": { "ob_id": 3993, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-23" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37610, "uuid": "35e0af4b45ec43cdb28b3e0b21986d7b", "short_code": "comp", "title": "Caption for Figure 5.33 from Chapter 5 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Carbon sink response in a scenario with net carbon dioxide (CO2) removal from the atmosphere. Shown are CO2 flux components from concentration-driven Earth system model (ESM) simulations during different emissions stages of SSP1-2.6 and its long-term extension: (a) Large net positive CO2 emissions; (b) small net positive CO2 emissions; (c), (d) net negative CO2 emissions; (e) net zero CO2 emissions. Positive flux components act to raise the atmospheric CO2 concentration, whereas negative components act to lower the CO2 concentration. Net CO2 emissions, land and ocean CO2 fluxes represent the multi-model mean and standard deviation (error bar) of four ESMs (CanESM5, UKESM1, CESM2-WACCM, IPSL-CM6a-LR) and one Earth system model of intermediate complexity (UVic ESCM; Mengis et al., 2020). Net CO2 emissions are calculated from concentration-driven ESM simulations as the residual from the rate of increase in atmospheric CO2 and land and ocean CO2 fluxes. Fluxes are accumulated over each 50-year period and converted to concentration units (ppm). Further details on data sources and processing are available in the chapter data table (Table 5.SM.6)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12488 ], "observationcollection_set": [ { "ob_id": 32720, "uuid": "20cd7758c7374ae8ba509354661ae1c6", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 5: Global Carbon and other Biogeochemical Cycles and Feedbacks", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 5: Global Carbon and other Biogeochemical Cycles and Feedbacks.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 5.33" } ], "responsiblepartyinfo_set": [ 179466, 179467, 179468, 179469, 179470, 179471, 179472, 179473, 179474, 179475 ], "onlineresource_set": [ 82746, 52327, 52328, 88613, 94640 ] }, { "ob_id": 37611, "uuid": "98ca52a0bcf94fc98155b7e914aa22a0", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly multisensor Infra-Red (IR) Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) land surface temperature (LST) level 3 supercollated (L3S) global product (2009-2020), version 1.00", "abstract": "This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on satellites in Geostationary Earth Orbit (GEO) and Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nLST fields are provided at 3 hourly intervals each day (00:00 UTC, 03:00 UTC, 06:00 UTC, 09:00 UTC, 12:00 UTC, 15:00 UTC, 18:00 UTC and 21:00 UTC). Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and the solar geometry angles.\r\n\r\nThe product is based on merging of available GEO data and infilling with available LEO data outside of the GEO discs. Inter-instrument biases are accounted for by cross-calibration with the IASI instruments on METOP and LSTs are retrieved using a Generalised Split Window algorithm from all instruments. As data towards the edge of the GEO disc is known to have greater uncertainty, any datum with a satellite zenith angle of more than 60 degrees is discarded. All LSTs included have an observation time that lies within +/- 30 minutes of the file nominal Universal Time.\r\n\r\nData from the following instruments is included in the dataset: geostationary, Imagers on Geostationary Operational Environmental Satellite (GOES) 12 and GOES 13, Advanced Baseline Imager (ABI) on GOES 16, Spinning Enhanced Visible Infra-Red Imager (SEVIRI) on Meteosat Second Generation (MSG) 1, MSG 2, MSG 3, and MSG 4, Japanese Advanced Meteorological Imager (JAMI) on Multifunctional Transport Satellite MTSAT) 1, and MTSAT 2; and polar, Advanced Along-Track Scanning Radiometer (AATSR) on Environmental Satellite (Envisat), Moderate-resolution Imaging Spectroradiometer (MODIS) on Earth Observation System (EOS) - Aqua and EOS - Terra, Sea and Land Surface Temperature Radiometer SLSTR on Sentinel-3A and Sentinel-3B. However, it should be noted that which instruments contribute to a particular product file depends on depends on mission start and end dates and instrument downtimes.\r\n\r\nDataset coverage starts on 1st January 2009 and ends on 31st December 2020. \r\n\r\nLSTs are provided on a global equal angle grid at a resolution of 0.05° longitude and 0.05° latitude. The dataset coverage is nominally global over the land surface but varies depending on satellite and instrument availability and coverage. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and data were processed in the UoL processing chain. The Geostationary data were produced by the Instituto Português do Mar e da Atmosfera (IPMA) before being merged into the final dataset.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-01-07T01:52:24", "updateFrequency": "notPlanned", "dataLineage": "The data record has been produced by the University of Leicester working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci), and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). \r\nContact: data.lst-cci@acri-st.fr", "removedDataReason": "", "keywords": "land surface temperature, ESA, CCI", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-07-11T14:42:00", "doiPublishedTime": "2022-07-11T14:42:29", "removedDataTime": null, "geographicExtent": { "ob_id": 2896, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 37716, "dataPath": "/neodc/esacci/land_surface_temperature/data/MULTISENSOR_IRMGP/L3S/0.05/v1.00/monthly/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 97564821985, "numberOfFiles": 1153, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 9631, "startTime": "2009-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3798, "explanation": "For information on the data quality see the associated LST_cci documentation", "passesTest": true, "resultTitle": "LST cci", "date": "2021-11-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 34746, "uuid": "b6501a3e35d24202ad89d3d5544ee6f4", "short_code": "cmppr", "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multisensor Infra-Red (IR) Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) Land surface temperature (LST) level 3 supercollated (L3S) global product (2009-2020), version 1.00", "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" }, { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2558, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 30, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_lst_terms_and_conditions.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 33361, "uuid": "555149fdc3ef4e23a1de8ece93c29f5d", "short_code": "proj", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci)", "abstract": "The land surface temperature (LST) CCI project, which is funded by the European Space Agency (ESA) as part of the Agency’s Climate Change Initiative (CCI) Programme, aims to deliver a significant improvement on the capability of current satellite LST data records to meet the challenging Global Climate Observing System (GCOS) requirements for climate applications to realise the full potential of long-term LST data for climate science.\r\n\r\nAccurate knowledge of LST plays a key role in describing the physics of land-surface processes at regional and global scales as they combine information on both the surface-atmosphere interactions and energy fluxes within the Earth Climate System. LST provides a metric of surface state when combined with vegetation parameters and soil moisture and is one of the drivers of vegetation phenology. Furthermore, LST is an independent temperature data set for quantifying climate change complementary to the near-surface air temperature ECV based on in situ measurements and reanalyses.\r\n\r\nThe team uses data from a variety of satellites to provide an accurate view of temperatures across land surfaces globally over the past +20 years. This involves developing innovative techniques to merge data from different satellites into combined long-term satellite records for climate. These will all be evaluated by scientists working at leading climate centres." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 62779, 62780, 62781, 62790, 62791, 62792, 66303, 66305, 66307, 66308, 66310, 66311, 66312, 66751, 66752, 66753, 66754 ], "vocabularyKeywords": [], "identifier_set": [ 12174 ], "observationcollection_set": [ { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." } ], "responsiblepartyinfo_set": [ 179478, 179479, 179480, 179481, 179482, 179483, 179484, 179485, 179486, 179487, 179488, 179489 ], "onlineresource_set": [ 52329, 52331, 52332, 52333, 52330, 94699, 94700 ] }, { "ob_id": 37617, "uuid": "81f53dc4487b4260b92d4dd8000a8b09", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - input data for Figure 2.29 (v20220624)", "abstract": "Input data for Figure 2.29 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 2.29 shows the surface ocean pH evolution over time from the last 65 million years onwards up to modern times. \r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with input data provided for panel d Ocean-SODA (the remaining datasets used for this figure are publicly available and reference is provided in Supplementary Material for chapter 2, Table 2.SM.1) \r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains global mean surface ocean pH from 1981-2018 for reconstructed global ocean acidification change.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel d (time series plot)\r\n \r\n - Data file: SODA_pH.txt (yearly data, 1981-2018); relates to purple line\r\n\r\nSODA stands for Satellite Oceanographic Datasets for Acidification.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Use program Chapter2_Fig.29_code_in_R to reproduce the figure (programming in R). \r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:16", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Ocean acidification, ocean pH levels", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-15T14:54:16", "doiPublishedTime": "2023-07-04T15:19:04.931209", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 146, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37621, "dataPath": "/badc/ar6_wg1/data/ch_02/inputdata_ch2_fig29/v20220624", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4884, "numberOfFiles": 4, "fileFormat": "Data provided in .txt format" }, "timePeriod": { "ob_id": 10387, "startTime": "1981-01-01T12:00:00", "endTime": "2018-12-31T12:00:00" }, "resultQuality": { "ob_id": 3995, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-24" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37619, "uuid": "5f4ba5fffd24495c8e04d15f71379351", "short_code": "comp", "title": "Caption for Figure 2.29 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Low-latitude surface ocean pH over the last 65 million years (65 Myr). (a) Low-latitude (30°N–30°S) surface ocean pH over the last 65 Myr, reconstructed using boron isotopes in foraminifera. (b) as (a) but for the last 3.5 Myr. Double headed arrow shows the approximate magnitude of glacial-interglacial pH changes. (c) Multisite composite of surface pH. In (a, b, c) uncertainty is shown at 95% confidence as a shaded band. Relevant paleoclimate reference periods (CCB2.1) have been labelled. Period windows for succeeding panels are shown as horizontal black lines in (a) and (b). (d) Estimated low-latitude surface pH from direct observations (BATS, HOT) and global mean pH (65°S–65°N) from two indirect estimates (CMEMS, OCEAN-SODA). Further details on data sources and processing are available in the chapter data table (Table 2.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12630 ], "observationcollection_set": [ { "ob_id": 32717, "uuid": "3da412ad9912427d9bb808b57faa21a7", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 2.2\r\n- data for Figure 2.4\r\n- data for Figure 2.5\r\n- data for Figure 2.6\r\n- data for Figure 2.9\r\n- data for Figure 2.11\r\n- input data for Figure 2.11\r\n- data for Figure 2.12\r\n- input data for Figure 2.12\r\n- data for Figure 2.13\r\n- input data for Figure 2.13\r\n- data for Figure 2.14\r\n- data for Figure 2.15\r\n- input data for Figure 2.15\r\n- input data for Figure 2.16\r\n- data for Figure 2.17\r\n- data for Figure 2.22\r\n- input data for Figure 2.23\r\n- data for Figure 2.25\r\n- input data for Figure 2.25\r\n- data for Figure 2.26\r\n- input data for Figure 2.27\r\n- data for Figure 2.28\r\n- input data for Figure 2.29\r\n- data for Figure 2.36\r\n- data for Figure 2.37\r\n- data for Figure 2.38\r\n- data for Cross-Chapter Box 2.1.1\r\n- data for Cross-Chapter Box 2.3.1" } ], "responsiblepartyinfo_set": [ 179517, 179518, 179519, 179520, 179521, 179522, 179523, 179524, 179525, 179526, 179527 ], "onlineresource_set": [ 52418, 52337, 52336, 82849 ] }, { "ob_id": 37622, "uuid": "b2b9bfe408f14ea7a79d9ff7aee0d0b8", "title": "WINDS-C: A 1/50° decadal regional simulation of the Southwestern Indian Ocean with high frequency surface currents for Lagrangian applications (climatological forcing based on 1993-2018)", "abstract": "WINDS-C (Western Indian Ocean Simulation, Climatological) is a high-resolution (1/50° in the horizontal) regional ocean simulation spanning the SW Indian Ocean under climatological forcing (1993-2018), using the Coastal and Regional Ocean Community model (CROCO). WINDS-C is forced at the lateral boundaries by a monthly climatology from the 1/12° CMEMS (Copernicus Marine Environment Monitoring Service) GLORYS12V1 global ocean reanalysis and barotropic tides from TPXO9, at the surface by a monthly climatology from ERA5, and also includes climatological riverine fluxes. High frequency (0.5h) surface currents are provided for Lagrangian analyses, and other surface fields are provided at a daily frequency. Full 3D zonal velocity, meridional velocity, temperature, and salinity (U/V/T/S) fields are provided every 5 days.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:12", "updateFrequency": "notPlanned", "dataLineage": "This dataset consists of two simulations of the Southwest Indian Ocean with high frequency surface currents. One simulation was climatological (WINDS-C) and one used realistic forcing (WINDS-M) from 1993-2020. The simulations were funded through PhD project NE/S007474/1, Marine dispersal and retention in the western Indian Ocean, held by Noam Vogt-Vincent who was supervised by Helen Johnson, both at University of Oxford. The outputs are archived on BODC's space at CEDA and assigned a CEDA DOI. No QC was done by BODC.", "removedDataReason": "", "keywords": "Indian Ocean, ocean current, surface velocity, temperature, salinity, ocean model, CROCO, dispersal, coral reef", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-10-12T16:00:23", "doiPublishedTime": "2022-11-08T08:30:22", "removedDataTime": null, "geographicExtent": { "ob_id": 3531, "bboxName": "", "eastBoundLongitude": 77.3, "westBoundLongitude": 34.37, "southBoundLatitude": -23.3, "northBoundLatitude": 0.0 }, "verticalExtent": null, "result_field": { "ob_id": 37623, "dataPath": "/bodc/UOX220077/WINDS-C", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5388787900564, "numberOfFiles": 52, "fileFormat": "Data are CF-Compliant NetCDF formatted data files" }, "timePeriod": { "ob_id": 10388, "startTime": "1993-01-01T00:00:00", "endTime": "2018-12-31T23:59:59" }, "resultQuality": { "ob_id": 3996, "explanation": "Data as supplied to BODC - no additional quality checks performed", "passesTest": true, "resultTitle": "BODC Data Quality Statement", "date": "2022-06-27" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37625, "uuid": "8fd4daeead734530aa9a119d5c56d306", "short_code": "comp", "title": "Coastal and Regional Ocean Community model", "abstract": "Ocean (lateral) forcing (1993-2018 climatology): CMEMS Global Ocean Physics Reanalysis (https://doi.org/10.48670/moi-00021) Atmospheric (surface) forcing (1993-2018 climatology): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate (http://doi.org/10.1002/qj.3803) Tidal forcing: TPXO9-atlas-v2a (https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2) Climatological river fluxes: Dai and Trenberth (2002) Estimates of Freshwater Discharge from Continents: Latitudinal and Seasonal Variations (https://doi.org/10.1175/1525-7541(2002)003%3C0660:EOFDFC%3E2.0.CO;2)" }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37624, "uuid": "499d381a047140cf84798a36a8d3d65d", "short_code": "proj", "title": "Marine dispersal and retention in the western Indian Ocean", "abstract": "Ocean currents transport many particulates throughout the ocean, from larvae to pollutants. Understanding these transport pathways, and their variability, is essential for many applications, such as marine conservation and pollution attribution. However, many types of particulates are too small to be tracked directly. This project is a numerical model-based investigation of marine dispersal in the western Indian Ocean, one of the least-studied parts of the global ocean. It specifically looked at two applications of marine dispersal: marine plastics accumulating at remote islands, and coral reef connectivity. To predict the sources of debris (both terrestrial and marine in origin) accumulating at remote western Indian Ocean islands, large-scale Lagrangian analyses were carried out incorporating beaching, sinking, and the effects of ocean currents, winds, and waves. The raw data from these Lagrangian analyses are archived here, from which source analyses can be carried out for various parameters using the associated scripts. To assess the connectivity of coral reefs across the southwestern Indian Ocean, we firstly ran a high (c. 2 km) resolution ocean simulation in a 28-year realistic, and 10-year climatological, configurations. The model output is available here in full, including high (30 minute) temporal resolution surface velocities. Daily Lagrangian larval dispersal simulations from all reefs in the southwestern Indian Ocean from 1993-2020 were also carried out. The raw output is archived here, and can be converted into larval fluxes (incorporating biological parameters such as mortality and competency) with the associated scripts. This work was funded by NERC Doctoral Training Partnership in Environmental Research grant NE/S007474/1 awarded to Noam Vogt-Vincent." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 22879, 49706, 49707, 49708, 49709, 49710, 49711, 49712, 49713, 49714, 49715, 49716, 49717, 49718, 49719, 49720, 49721, 49722, 49723, 49724, 49725, 49726, 49727, 49728, 49729, 49730, 49731, 49732, 49733, 49734, 49735, 49736, 49737, 49738, 49739, 49740, 49743, 49744, 49745, 49746, 49747, 49748, 49749, 49750, 49751, 50008, 50009, 50010, 50011, 50012, 50013, 50014, 50015, 50016, 50017, 50018, 50019, 50020, 50021, 50022, 50023, 50024, 50025, 50026, 50027, 50028, 50029, 50030, 50031, 50032, 50033, 50034, 50035, 50036, 50037, 50038, 50039, 50040, 50041, 50042, 50043, 50044, 50045, 50046, 50047, 50048, 50049, 50050, 50051, 50052, 50053, 50054, 50055, 50056, 50057 ], "vocabularyKeywords": [], "identifier_set": [ 12292 ], "observationcollection_set": [ { "ob_id": 37629, "uuid": "8af5dbda10b747709543113ecb71c44c", "short_code": "coll", "title": "Decadal and multidecadal simulation of the Southwestern Indian Ocean with high frequency surface currents (climatological and realistic forcing, 1993-2020)", "abstract": "This dataset collection comprises two simulations; WINDS-M (Western Indian Ocean Simulation, Multidecadal) and WINDS-C (Western Indian Ocean Simulation, Climatological). Both simulations are high-resolution (1/50 degrees in the horizontal) from 1993-2020 (for WINDS-M) and 1993-2018 (for WINDS-C), using the Coastal and Regional Ocean Community model (CROCO). WINDS-M is forced at the lateral boundaries by daily output from the 1/12degrees CMEMS (Copernicus Marine Environment Monitoring Service) GLORYS12V1 global ocean reanalysis and barotropic tides from TPXO9, at the surface by hourly output from ERA5, and also includes climatological riverine fluxes. WINDS-C is forced at the lateral boundaries by a monthly climatology from the 1/12 degrees CMEMS GLORYS12V1 global ocean reanalysis and barotropic tides from TPXO9, at the surface by a monthly climatology from ERA5, and also includes climatological riverine fluxes. High frequency (0.5h) surface currents are provided for Lagrangian analyses, and other surface fields are provided at a daily frequency for both simulations. The simulations were run using funding through the Doctoral Training Partnership in Environmental Research grant NE/S007474/1 titled Marine dispersal and retention in the western Indian Ocean" } ], "responsiblepartyinfo_set": [ 179533, 179540, 179541, 179530, 179532, 179531, 179535, 179534, 179542 ], "onlineresource_set": [ 52340, 52338, 52339, 52341, 53032 ] }, { "ob_id": 37626, "uuid": "bf6f0cfbd09e47498572f21081376702", "title": "WINDS-M: A 1/50° multidecadal regional simulation of the Southwestern Indian Ocean with high frequency surface currents for Lagrangian applications (realistic forcing, 1993-2020)", "abstract": "WINDS-M (Western Indian Ocean Simulation, Multidecadal) is a high-resolution (1/50° in the horizontal) regional ocean simulation spanning the SW Indian Ocean from 1993-2020, using the Coastal and Regional Ocean Community model (CROCO). WINDS-M is forced at the lateral boundaries by daily output from the 1/12° CMEMS (Copernicus Marine Environment Monitoring Service) GLORYS12V1 global ocean reanalysis and barotropic tides from TPXO9, at the surface hourly output from ERA5, and also includes climatological riverine fluxes. High frequency (0.5h) surface currents are provided for Lagrangian analyses, and other surface fields are provided at a daily frequency. Full 3D zonal velocity, meridional velocity, temperature, and salinity (U/V/T/S) fields are provided every 5 days.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:12", "updateFrequency": "notPlanned", "dataLineage": "This dataset consists of two simulations of the Southwest Indian Ocean with high frequency surface currents. One simulation was climatological (WINDS-C) and one used realistic forcing (WINDS-M) from 1993-2020. The simulations were funded through PhD project NE/S007474/1, Marine dispersal and retention in the western Indian Ocean, held by Noam Vogt-Vincent who was supervised by Helen Johnson, both at University of Oxford. The outputs are archived on BODC's space at CEDA and assigned a CEDA DOI. No QC was done by BODC.", "removedDataReason": "", "keywords": "Indian Ocean, ocean current, surface velocity, temperature, salinity, ocean model, CROCO, dispersal, coral reef", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-10-12T15:53:08", "doiPublishedTime": "2022-11-08T08:26:29", "removedDataTime": null, "geographicExtent": { "ob_id": 3532, "bboxName": "", "eastBoundLongitude": 77.3, "westBoundLongitude": 34.37, "southBoundLatitude": -23.3, "northBoundLatitude": 0.0 }, "verticalExtent": null, "result_field": { "ob_id": 37627, "dataPath": "/bodc/UOX220077/WINDS-M", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 16066951759705, "numberOfFiles": 129, "fileFormat": "Data are CF-Compliant NetCDF formatted data files" }, "timePeriod": { "ob_id": 10389, "startTime": "1993-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3997, "explanation": "Data as supplied to BODC - no additional quality checks performed", "passesTest": true, "resultTitle": "BODC Data Quality Statement", "date": "2022-06-27" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37625, "uuid": "8fd4daeead734530aa9a119d5c56d306", "short_code": "comp", "title": "Coastal and Regional Ocean Community model", "abstract": "Ocean (lateral) forcing (1993-2018 climatology): CMEMS Global Ocean Physics Reanalysis (https://doi.org/10.48670/moi-00021) Atmospheric (surface) forcing (1993-2018 climatology): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate (http://doi.org/10.1002/qj.3803) Tidal forcing: TPXO9-atlas-v2a (https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2) Climatological river fluxes: Dai and Trenberth (2002) Estimates of Freshwater Discharge from Continents: Latitudinal and Seasonal Variations (https://doi.org/10.1175/1525-7541(2002)003%3C0660:EOFDFC%3E2.0.CO;2)" }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37624, "uuid": "499d381a047140cf84798a36a8d3d65d", "short_code": "proj", "title": "Marine dispersal and retention in the western Indian Ocean", "abstract": "Ocean currents transport many particulates throughout the ocean, from larvae to pollutants. Understanding these transport pathways, and their variability, is essential for many applications, such as marine conservation and pollution attribution. However, many types of particulates are too small to be tracked directly. This project is a numerical model-based investigation of marine dispersal in the western Indian Ocean, one of the least-studied parts of the global ocean. It specifically looked at two applications of marine dispersal: marine plastics accumulating at remote islands, and coral reef connectivity. To predict the sources of debris (both terrestrial and marine in origin) accumulating at remote western Indian Ocean islands, large-scale Lagrangian analyses were carried out incorporating beaching, sinking, and the effects of ocean currents, winds, and waves. The raw data from these Lagrangian analyses are archived here, from which source analyses can be carried out for various parameters using the associated scripts. To assess the connectivity of coral reefs across the southwestern Indian Ocean, we firstly ran a high (c. 2 km) resolution ocean simulation in a 28-year realistic, and 10-year climatological, configurations. The model output is available here in full, including high (30 minute) temporal resolution surface velocities. Daily Lagrangian larval dispersal simulations from all reefs in the southwestern Indian Ocean from 1993-2020 were also carried out. The raw output is archived here, and can be converted into larval fluxes (incorporating biological parameters such as mortality and competency) with the associated scripts. This work was funded by NERC Doctoral Training Partnership in Environmental Research grant NE/S007474/1 awarded to Noam Vogt-Vincent." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 22879, 49706, 49707, 49708, 49709, 49710, 49711, 49712, 49713, 49714, 49715, 49716, 49717, 49718, 49719, 49720, 49721, 49722, 49723, 49724, 49725, 49726, 49727, 49728, 49729, 49730, 49731, 49732, 49733, 49734, 49735, 49737, 49738, 49739, 49740, 49741, 49742, 49745, 49746, 49747, 49748, 49751, 50003, 50004, 50005, 50006, 50007, 50008, 50009, 50010, 50011, 50012, 50013, 50014, 50015, 50016, 50017, 50018, 50019, 50020, 50021, 50022, 50023, 50024, 50025, 50026, 50027, 50028, 50029, 50030, 50031, 50032, 50033, 50034, 50035, 50036 ], "vocabularyKeywords": [], "identifier_set": [ 12290 ], "observationcollection_set": [ { "ob_id": 37629, "uuid": "8af5dbda10b747709543113ecb71c44c", "short_code": "coll", "title": "Decadal and multidecadal simulation of the Southwestern Indian Ocean with high frequency surface currents (climatological and realistic forcing, 1993-2020)", "abstract": "This dataset collection comprises two simulations; WINDS-M (Western Indian Ocean Simulation, Multidecadal) and WINDS-C (Western Indian Ocean Simulation, Climatological). Both simulations are high-resolution (1/50 degrees in the horizontal) from 1993-2020 (for WINDS-M) and 1993-2018 (for WINDS-C), using the Coastal and Regional Ocean Community model (CROCO). WINDS-M is forced at the lateral boundaries by daily output from the 1/12degrees CMEMS (Copernicus Marine Environment Monitoring Service) GLORYS12V1 global ocean reanalysis and barotropic tides from TPXO9, at the surface by hourly output from ERA5, and also includes climatological riverine fluxes. WINDS-C is forced at the lateral boundaries by a monthly climatology from the 1/12 degrees CMEMS GLORYS12V1 global ocean reanalysis and barotropic tides from TPXO9, at the surface by a monthly climatology from ERA5, and also includes climatological riverine fluxes. High frequency (0.5h) surface currents are provided for Lagrangian analyses, and other surface fields are provided at a daily frequency for both simulations. 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future changes in surface temperature and precipitation for long-term average and extreme conditions.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Seneviratne, S.I., X. Zhang, M. Adnan, W. Badi, C. Dereczynski, A. Di Luca, S. Ghosh, I. Iskandar, J. Kossin, S. Lewis, F. Otto, I. Pinto, M. Satoh, S.M. Vicente-Serrano, M. Wehner, and B. Zhou, 2021: Weather and Climate Extreme Events in a Changing Climate. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson- Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1513–1766, doi:10.1017/9781009157896.013.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with data provided for all panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n - Summer mean temperature (days) change (relative to 1850-1900)\r\n - Annual maximum temperature (°C) change (relative to 1850-1900)\r\n - Summer mean precipitatioin (%) change (relative to 1850-1900)\r\n - Annual maximum daily precipitation (%) change (relative to 1850-1900)\r\n\r\n The data is given at a global warming levels (GWL) of +4.0°C.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a:\r\n - FAQ_11_1_Figure_1a_cmip6_summer_temperature_change_at_4_0C.nc: simulated summer mean temperature change (°C) at 4.0°C global warming relative to 1850-1900\r\n \r\n Panel b:\r\n - FAQ_11_1_Figure_1b_cmip6_TXx_change_at_4_0C.nc: simulated annual maximum temperature change (°C) at 4.0°C global warming relative to 1850-1900\r\n \r\n Panel c:\r\n - FAQ_11_1_Figure_1c_cmip6_summer_prec_change_at_4_0C.nc: simulated summer mean precipitation change (%) at 4.0°C global warming relative to 1850-1900\r\n \r\n Panel d:\r\n - FAQ_11_1_Figure_1d_cmip6_Rx1day_change_at_4_0C.nc: simulated annual maximum daily precipitation change (%) at 4.0°C global warming relative to 1850-1900\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 11)\r\n - Link to the Supplementary Material for Chapter 11, which contains details on the input data used in Table 11.SM.9\r\n - Link to the Ch11 GitHub repository containing scripts for generating figures\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:15", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, SPM, Sixth Assessment Report, Working Group I, Physical Science Basis, Summary for Policymakers, FAQ 11.1 Figuer 1, global warming level, temperature, precipitation, annual maximum temperature, annual maximum daily precipitation, TXx, Rx1day projection, cmip6, long-term average, extreme conditions, summer", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "2.5 Degrees", "status": "ongoing", "dataPublishedTime": "2023-03-20T16:40:48", "doiPublishedTime": "2023-06-26T16:00:26.804683", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 147, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "2.5" }, "result_field": { "ob_id": 37648, "dataPath": "/badc/ar6_wg1/data/ch_11/ch11_faq1_fig1/v20220629", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 410091, "numberOfFiles": 7, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10390, "startTime": "1850-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 3998, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37649, "uuid": "f26066a14fbe42728e1144ce138a40d5", "short_code": "comp", "title": "Caption for FAQ 11.1, figure 1 from Chapter 11 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Global maps of future changes in surface temperature (top panels) and precipitation (bottom panels) for long-term average (left) and extreme conditions (right). All changes were estimated using the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble median for a scenario with a global warming of 4°C relative to 1850–1900 temperatures. Average surface temperatures refers to the warmest three-month season (summer in mid- to high latitudes) and extreme temperature refer to the hottest day in a year. Precipitation changes, which can include both rainfall and snowfall changes, are normalized by 1850–1900 values and shown in percentage; extreme precipitation refers to the largest daily precipitation in a year." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 63161, 63626, 63627, 63628 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 32727, "uuid": "81fbb6ae0c4e4504bc554fa7f8dcca7d", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 11: Weather and climate extreme events in a changing climate", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 11: Weather and climate extreme events in a changing climate.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\n- data for Figure FAQ 11.1, Figure 1\r\n- data for Figure 11.3\r\n- data for Figure 11.11\r\n- data for Figure 11.16\r\n- data for Figure 11.19\r\n- data for Figure 11.A.1" } ], "responsiblepartyinfo_set": [ 179587, 179588, 179589, 179590, 179591, 179592, 179593, 179594 ], "onlineresource_set": [ 52352, 52353, 82680, 82995, 83191 ] }, { "ob_id": 37650, "uuid": "592748a417ab4efca4eb98e22c9dbec4", "title": "Chapter 11 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 11.3 (v20220629)", "abstract": "Data for Figure 11.3 from Chapter 11 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 11.3 shows regional mean changes in annual hottest daily maximum temperature (TXx) for AR6 land regions and the global land, against changes in global mean surface air temperature (GSAT) as simulated by CMIP6 models under different forcing scenarios SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Seneviratne, S.I., X. Zhang, M. Adnan, W. Badi, C. Dereczynski, A. Di Luca, S. Ghosh, I. Iskandar, J. Kossin, S. Lewis, F. Otto, I. Pinto, M. Satoh, S.M. Vicente-Serrano, M. Wehner, and B. Zhou, 2021: Weather and Climate Extreme Events in a Changing Climate. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson- Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1513–1766, doi:10.1017/9781009157896.013.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has twelve panels, with data provided for all panels in one single file.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n \r\n - Annual maximum temperature change (°C) as a function of global warming levels (GWLs) relative to 1850-1900 for the IPCC climate reference regions (Iturbide et al., 2020)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Figure_11_3_cmip6_TXx_scaling.nc: data for panels (a) through (l)\r\n\r\nSSP stands for Shared Socioeconomic Pathway and RCP stands for Representative Concentration Pathway.\r\nSSP1-1.9 and SSP1-2.6 are based on Shared Socioeconomic Pathway SSP1 with low climate change mitigation and adaptation challenges. SSP1-1.9 is based on RCP1.9, a future pathway with a radiative forcing of 1.9 W/m2 in the year 2100 and SSP1-2.6 is based on RPC2.6.\r\nSSP2-4.5 is based on Shared Socioeconomic Pathway SSP2 with medium challenges to climate change mitigation and adaptation and RCP4.5, a future pathway with a radiative forcing of 4.5 W/m2 in the year 2100.\r\nSSP3-7.0 is based on SSP3 which is characterized by high challenges to both mitigation and adaptation and RCP7.0, a future pathway with a radiative forcing of 7.0 W/m2 in the year 2100.\r\nSSP5-8.5 is based on SSP5 where climate change mitigation challenges dominate and RCP8.5, a future pathway with a radiative forcing of 8.5 W/m2 in the year 2100.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Panel (a): shows the individual ensemble members and the median of three SSPs. Other panels show the multi model median (over the 'mod_ens' dimension). The regions 'global', 'ocean', 'land', 'GIC', 'EAN', and 'WAN' are not shown in the figure.\r\n\r\n---------------------------------------------------\r\nSources of additional information\r\n---------------------------------------------------\r\nThe following weblinks are provided in the Related Documents section of this catalogue record:\r\n- Link to the figure on the IPCC AR6 website\r\n- Link to the report component containing the figure (Chapter 11)\r\n- Link to the Supplementary Material for Chapter 11, which contains details on the input data used in Table 11.SM.9\r\n- Link to the code for the figure, archived on Zenodo\r\n- Link to the Ch11 GitHub repository containing scripts for generating figures", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:16", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 11, Figure 11.11, global warming level, annual maximum temperature, TXx, scaling, projection, cmip6", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-03-20T16:59:20", "doiPublishedTime": "2023-06-26T16:03:02.750420", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 148, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37651, "dataPath": "/badc/ar6_wg1/data/ch_11/ch11_fig03/v20220629", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2420093, "numberOfFiles": 4, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10391, "startTime": "1850-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 3999, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37652, "uuid": "cf0a50cd069243d580024a0467f8d178", "short_code": "comp", "title": "Caption for Figure 11.3 from Chapter 11 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Regional mean changes in annual hottest daily maximum temperature (TXx) for AR6 land regions and the global land area (except Antarctica), against changes in global mean surface air temperature (GSAT) as simulated by Coupled Model Intercomparison Project Phase 6 (CMIP6) models under different Shared Socio-economic Pathway (SSP) forcing scenarios, SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. Changes in TXx and GSAT are relative to the 1850–1900 baseline, and changes in GSAT are expressed as global warming level. (a) Individual models from the CMIP6 ensemble (grey), the multi-model median under three selected SSPs (colours), and the multi-model median (black); (b) to (l) Multi-model median for the pooled data for individual AR6 regions. Numbers in parentheses indicate the linear scaling between regional TXx and GSAT. The black line indicates the 1:1 reference scaling between TXx and GSAT. See Atlas.1.3.2 for the definition of regions. Changes in TXx are also displayed in the Interactive Atlas. For details on the methods, see Supplementary Material 11.SM.2." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 46696, 63153, 63161, 63621, 63622, 63623, 63624, 63625 ], "vocabularyKeywords": [], "identifier_set": [ 12531 ], "observationcollection_set": [ { "ob_id": 32727, "uuid": "81fbb6ae0c4e4504bc554fa7f8dcca7d", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 11: Weather and climate extreme events in a changing climate", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 11: Weather and climate extreme events in a changing climate.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\n- data for Figure FAQ 11.1, Figure 1\r\n- data for Figure 11.3\r\n- data for Figure 11.11\r\n- data for Figure 11.16\r\n- data for Figure 11.19\r\n- data for Figure 11.A.1" } ], "responsiblepartyinfo_set": [ 179597, 179598, 179599, 179600, 179601, 179602, 179603, 179604 ], "onlineresource_set": [ 52348, 82681, 52349, 82996, 83192, 88557, 94597 ] }, { "ob_id": 37653, "uuid": "7be388b022e74926b0103125d22e6b06", "title": "Chapter 11 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 11.19 (v20230203)", "abstract": "Data for Figure 11.19 from Chapter 11 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 11.19 shows projected changes in the number of consecutive dry days (CDD), annual mean soil moisture over the total column, and the frequency and intensity of one-in-ten year soil moisture (SM) drought for the June-to-August and December-to-February seasons at 1.5°C, 2°C, and 4°C of global warming compared to the 1851-1900 baseline.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Seneviratne, S.I., X. Zhang, M. Adnan, W. Badi, C. Dereczynski, A. Di Luca, S. Ghosh, I. Iskandar, J. Kossin, S. Lewis, F. Otto, I. Pinto, M. Satoh, S.M. Vicente-Serrano, M. Wehner, and B. Zhou, 2021: Weather and Climate Extreme Events in a Changing Climate. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson- Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1513–1766, doi:10.1017/9781009157896.013.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has twelve panels, with data provided for all panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n - Annual consecutive dry days change (days) (relative to 1850-1900)\r\n - Annual total column soil moisture (std) (relative to 1850-1900)\r\n - July-to-August frequency of 1-in-10 year soil moisture drought change (-) (relative to 1850-1900)\r\n - December-to-February frequency of 1-in-10 year soil moisture drought change (-) (relative to 1850-1900)\r\n\r\n\r\nThe data is given for global warming levels (GWLs), namely +1.5°C, 2.0°C, and +4.0°C.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a:\r\n - Figure_11_19a_cmip6_CDD_change_at_1_5C.nc: simulated consecutive dry days change (days) at 1.5°C global warming relative to 1850-1900\r\n \r\n Panel b:\r\n - Figure_11_19b_cmip6_CDD_change_at_2_0C.nc: simulated consecutive dry days change (days) at 2.0°C global warming relative to 1850-1900\r\n \r\n Panel c:\r\n - Figure_11_19c_cmip6_CDD_change_at_4_0C.nc: simulated consecutive dry days change (days) at 4.0°C global warming relative to 1850-1900\r\n \r\n Panel d:\r\n - Figure 11_19d_cmip6_SM_total_change_at_1_5C.nc: simulated total column soil moisture change (std) at 1.5°C global warming relative to 1850-1900\r\n \r\n Panel e:\r\n - Figure 11_19e_cmip6_SM_total_change_at_2_0C.nc: simulated total column soil moisture change (std) at 2.0°C global warming relative to 1850-1900\r\n \r\n Panel f:\r\n - Figure 11_19f_cmip6_SM_total_change_at_4_0C.nc: simulated total column soil moisture change (std) at 4.0°C global warming relative to 1850-1900\r\n \r\n Panel g:\r\n - Figure_11_19g_JJA_cmip6_SM_drought_index_change_at_1_5C.nc: simulated July-to-August frequency of 1-in-10 year soil moisture drought change (-) at 1.5°C global warming relative to 1850-1900\r\n \r\n Panel h:\r\n - Figure_11_19h_JJA_cmip6_SM_drought_index_change_at_2_0C.nc: simulated July-to-August frequency of 1-in-10 year soil moisture drought change (-) at 2.0°C global warming relative to 1850-1900\r\n \r\n Panel i:\r\n - Figure_11_19i_JJA_cmip6_SM_drought_index_change_at_4_0C.nc: simulated July-to-August frequency of 1-in-10 year soil moisture drought (-) at 4.0°C global warming relative to 1850-1900\r\n \r\n Panel j:\r\n - Figure_11_19j_cmip6_DJF_SM_drought_index_change_at_1_5C.nc: simulated December-to-February frequency of 1-in-10 year soil moisture drought change (-) at 1.5°C global warming relative to 1850-1900\r\n \r\n Panel k:\r\n - Figure_11_19k_cmip6_DJF_SM_drought_index_change_at_2_0C.nc: simulated December-to-February frequency of 1-in-10 year soil moisture drought change (-) at 2.0°C global warming relative to 1850-1900\r\n \r\n Panel l:\r\n - Figure_11_19l_cmip6_DJF_SM_drought_index_change_at_4_0C.nc: simulated December-to-February frequency of 1-in-10 year soil moisture drought change (-) at 4.0°C global warming relative to 1850-1900\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project. \r\n\r\n---------------------------------------------------\r\nNotes on reproducing the figure\r\n---------------------------------------------------\r\nFor panels g to l the data should be plotted with a logarithmic colormap. Note that grid cells with no change (0) have been replaced by 10^-5 such that the logarithm is defined.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 11)\r\n - Link to the Supplementary Material for Chapter 11, which contains details on the input data used in Table 11.SM.9\r\n - Link to the Ch11 GitHub repository containing scripts for generating figures\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:17", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 11, Figure 11.16, global warming level, consecutive dry days, total column soil moisture, June-to-August frequency of 1-in-10 year soil moisture drought, December-to-February frequency of 1-in-10 year soil moisture drought Rx1day, CDD, SM, SM drought, projection, cmip6", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "2.5 Degrees", "status": "ongoing", "dataPublishedTime": "2023-03-20T17:04:20", "doiPublishedTime": "2023-06-26T16:19:07.751170", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 149, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "2.5" }, "result_field": { "ob_id": 37654, "dataPath": "/badc/ar6_wg1/data/ch_11/ch11_fig19/v20230203", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2377844, "numberOfFiles": 15, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10392, "startTime": "1850-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4000, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37655, "uuid": "b82482de8e144324bf34599b3f6333b4", "short_code": "comp", "title": "Caption for Figure 11.19 from Chapter 11 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected changes in (a–c) the number of consecutive dry days (CDD), (d–f) annual mean soil moisture over the total column, and (g–l) the frequency and intensity of 1-in-10-year soil moisture drought for the June-to-August and December-to-February seasons at 1.5°C, 2°C, and 4°C of global warming compared to the 1850–1900 baseline. The unit for soil moisture change is the standard deviation of interannual variability in soil moisture during 1850–1900. Standard deviation is a widely used metric in characterizing drought severity. A projected reduction in mean soil moisture by one standard deviation corresponds to soil moisture conditions typical of about 1-in-6-year droughts during 1850–1900 becoming the norm in the future. Results are based on simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model ensemble under the Shared Socio-economic Pathway (SSP), SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. The numbers in the top right indicate the number of simulations included. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, where ≥80% of models agree on the sign of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas 1. For details on the methods see Supplementary Material 11.SM.2. Changes in CDDs are also displayed in the Interactive Atlas. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 46711, 50559, 50561, 63148, 63149, 63150, 63151 ], "vocabularyKeywords": [], "identifier_set": [ 12534 ], "observationcollection_set": [ { "ob_id": 32727, "uuid": "81fbb6ae0c4e4504bc554fa7f8dcca7d", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 11: Weather and climate extreme events in a changing climate", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 11: Weather and climate extreme events in a changing climate.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\n- data for Figure FAQ 11.1, Figure 1\r\n- data for Figure 11.3\r\n- data for Figure 11.11\r\n- data for Figure 11.16\r\n- data for Figure 11.19\r\n- data for Figure 11.A.1" } ], "responsiblepartyinfo_set": [ 179607, 179608, 179609, 179610, 179611, 179612, 179613, 179614, 193165 ], "onlineresource_set": [ 52346, 82684, 52347, 82999, 83195, 88556 ] }, { "ob_id": 37656, "uuid": "2f63e632dc3a494696b1b1315cbb531e", "title": "Chapter 11 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 11.A.1 (v20220629)", "abstract": "Data for Figure 11.A.1 from Chapter 11 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 11.A.1 shows regional mean changes in annual minimum temperature (TNn) for AR6 land regions and the global land, against changes in global mean surface air temperature (GSAT) as simulated by CMIP6 models under different forcing scenarios SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Seneviratne, S.I., X. Zhang, M. Adnan, W. Badi, C. Dereczynski, A. Di Luca, S. Ghosh, I. Iskandar, J. Kossin, S. Lewis, F. Otto, I. Pinto, M. Satoh, S.M. Vicente-Serrano, M. Wehner, and B. Zhou, 2021: Weather and Climate Extreme Events in a Changing Climate. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson- Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1513–1766, doi:10.1017/9781009157896.013.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has twelve panels, with data provided for all panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n - Annual minimum temperature change (°C) as a function of global warming levels (GWLs) relative to 1850-1900 for the IPCC climate reference regions (Iturbide et al., 2020)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Figure_11_A_1_cmip6_TNn_scaling.nc: data for panels (a) through (l)\r\n\r\nSSP stands for Shared Socioeconomic Pathway.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Panel (a): shows the individual ensemble members and the median of three SSPs. Other panels show the multi model median (over the 'mod_ens' dimension). The regions 'global', 'ocean', 'land', 'GIC', 'EAN', and 'WAN' are not shown in the figure.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 11)\r\n - Link to the Supplementary Material for Chapter 11, which contains details on the input data used in Table 11.SM.9\r\n - Link to the Ch11 GitHub repository containing scripts for generating figures\r\n - Link to the code for Chapter 11 figures, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:17", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 11, Figure 11.11, global warming level, annual minimum temperature, TNn, scaling, projection, cmip6", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-03-20T17:08:31", "doiPublishedTime": "2023-06-26T16:23:14.419268", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 150, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37657, "dataPath": "/badc/ar6_wg1/data/ch_11/ch11_fig11_A_1/v20220629", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2480248, "numberOfFiles": 4, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10393, "startTime": "1850-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4001, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37658, "uuid": "6402b9fe3c6d48ddb5e4c186900ca077", "short_code": "comp", "title": "Caption for Figure 11.A.1 from Chapter 11 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Figure 11.A.1:\tAs Figure 11.3 but for the annual minimum temperature (TNn)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 46696, 63153, 63620, 63621, 63622, 63623, 63624, 63625 ], "vocabularyKeywords": [], "identifier_set": [ 12535 ], "observationcollection_set": [ { "ob_id": 32727, "uuid": "81fbb6ae0c4e4504bc554fa7f8dcca7d", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 11: Weather and climate extreme events in a changing climate", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 11: Weather and climate extreme events in a changing climate.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\n- data for Figure FAQ 11.1, Figure 1\r\n- data for Figure 11.3\r\n- data for Figure 11.11\r\n- data for Figure 11.16\r\n- data for Figure 11.19\r\n- data for Figure 11.A.1" } ], "responsiblepartyinfo_set": [ 179617, 179618, 179619, 179620, 179621, 179622, 179623, 179624 ], "onlineresource_set": [ 52350, 52351, 83000, 83196, 88558, 94606 ] }, { "ob_id": 37662, "uuid": "7668ea6b334841b0b7a06c0545664858", "title": "Regional summaries from Arctic high-resolution sea ice-ocean modelling hindcasts (2008-2021)", "abstract": "Regional summaries from the high-resolution sea ice-ocean modelling ERA5JRA55-forced hindcasts (Japanese 55-year atmospheric analysis) were generated to analyse oceanic impacts on retreat of the Arctic sea-ice pack in the high Arctic and from areas of the Transpolar drift, north of Greenland and in the Fram Strait in the present climate. The model outputs span the Arctic Ocean proper and the sub-Arctic seas, covering the near-present climate period from 2008-2021 during which the largest sea ice changes have been observed.\r\n\r\nThe model configuration is Global Ocean and Sea Ice GO8p7, developed under the Joint Marine Modelling Programme (JMMP), a collaborative project between the National Oceanography Centre (NOC), British Antarctic Survey (BAS) and the Met Office. GO8p7 is based on NEMO v4.0 and the SI3 sea ice model and includes a package of modifications intending to address errors in the Southern Ocean, including a scale-dependent Gent & McWilliams parameterisation, partial slip lateral boundary conditions south of 50°S. and 4th-order horizontal tracer advection (Madec et al. 2019).\r\n\r\nThe present simulation was integrated with Japanese Reanalysis JRA55 v1.3-do from 1958 to 2021 (Tsujino et al., 2018). The model output has been validated against AMSR-E satellite sea-ice concentrations, as well as the CryoSat-2 and SMOS sea-ice thickness datasets. The monthly and 5-day averages of the key sea-ice and ocean fields for the pan-Arctic and Greenland regions were created and combined into 4-D files for easy data handling.\r\n\r\nThe model output has been validated against AMSR-E satellite sea-ice concentrations, as well as the CryoSat-2 and SMOS sea-ice thickness datasets. The monthly and 5-day averages of the key sea-ice and ocean fields for the pan-Arctic and Greenland regions were created and combined into 4-D files for easy data handling. \r\n\r\nThe model datasets were produced by National Oceanography Centre (NOC) scientists Dr Yevgeny Aksenov and Dr Stefanie Rynders, using the global model runs carried out by Dr Alex Megann. Dr Andrew Coward also assisted with data handling. The data were produced under Natural Environment Research Council (NERC) project PRE-MELT (grant references NE/T001399/1, NE/T000260/1, NE/T000546/1). The global model integrations were completed thanks to the funding from the National Environmental Research Council (NERC) national capability grant for the North Atlantic Climate System: Integrated study (ACSIS) NCLTS-M program (grant NE/N018044/1) (Megann et al., 2022a,b). Additional funding also came from NERC projects APEAR (grant reference NE/R012865/1) and CLASS (grant reference NE/R015953/1).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-10-31T02:07:12", "updateFrequency": "notPlanned", "dataLineage": "Model hindcasts were produced by scientists at the National Oceanography Centre (NOC), Southampton, UK. Outputs suitable for re-use were uploaded by the scientists to the Centre for Environmental Data Analysis (CEDA) ingestion area, where the British Oceanographic Data Centre (BODC) carried out ingestion process, creating catalogue records and making the data available for public download.", "removedDataReason": "", "keywords": "Arctic Ocean, sea-ice, JRA55 v1.3-do, GO8p7, Fram Strait, CryoSat-2, SMOS, ARCRegiH", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-08-21T14:31:22", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3538, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": 45.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 37663, "dataPath": "/bodc/SOC220091/PRE-MELT_hindcasts", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 511780457950, "numberOfFiles": 20, "fileFormat": "Data are CF-compliant NetCDF formatted data files" }, "timePeriod": { "ob_id": 10395, "startTime": "2008-01-01T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 4003, "explanation": "Data as supplied to BODC - no additional quality checks performed", "passesTest": true, "resultTitle": "BODC Data Quality Statement", "date": "2022-06-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37664, "uuid": "d29366fbf94e48df99d9a235d11c21ee", "short_code": "proj", "title": "PRE-MELT: Preconditioning the trigger for rapid Arctic ice melt", "abstract": "The oldest, thickest sea ice in the 'last ice area' of the Arctic - a region thought to be most resilient to climate warming - unexpectedly broke up twice in the past year. Our current theories assume that the end-of-summer ice-covered area will steadily retreat into the Central Arctic Basin as global warming accelerates over coming decades. However, the dynamic break-up events witnessed in 2018 challenge this prevailing view. Here we hypothesise that a weaker, increasingly mobile Central Arctic ice pack is now susceptible to dynamic episodes of fragmentation which can precondition the ice for rapid summer melt. This mechanism of dynamic seasonal preconditioning is unaccounted for in global climate models, so our best current projections are overlooking the possibility for rapid disintegration of the Arctic's last ice area. Our team has demonstrated that seasonal preconditioning is already responsible for the neighbouring Beaufort Sea becoming ice-free twice in the past five years. Even ten years ago this region contained thick perennial sea ice, mirroring the Central Arctic Ocean, but it has now transitioned to a marginal Arctic sea. Could the processes responsible for the decline of the Beaufort Sea ice pack start to manifest themselves in the Central Arctic? Currently, a shortfall in satellite observations of the Arctic pack ice in summer prevents us from testing our hypothesis. We desperately require pan-Arctic observations of ice melting rates, but so far satellite observations of sea ice thickness are only available during winter months. Our project will therefore deliver the first measurements of Arctic sea ice thickness during summer months, from twin satellites: ESA's Cryosat-2 & NASA's ICESat-2. We have designed a new classification algorithm for separating ice and ocean radar altimeter echoes, regardless of surface melting state, providing the breakthrough required to fill the existing summer observation 'gap'. Exploiting the recent launch of multiple SAR missions for polar reconnaissance, our project will integrate information on ice-pack ablation, motion and deformation to generate a unique year-round sea ice volume budget in the High Arctic. This record will inform high-resolution ice dynamics simulations, performed with a suite of state-of-the-art sea ice models from stand alone (CICE), ocean-sea ice (NEMO/CICE), to fully coupled regional high resolution (RASM), and global coarser resolution (HadGEM) models, all now equipped with the anisotropic (EAP) sea ice rheology developed by our team. Using the regional and stand-alone models we will analyse the role of mechanics in this keystone region north of Greenland to scrutinise the coupling and preconditioning of winter breakup events - such as those witnessed in 2018 - to summer melting rates. Using the coupled models, we will quantify the likelihood of the Arctic's last ice area breaking up much sooner than expected due to oceanic and atmospheric feedbacks and how this will affect the flushing of ice and freshwater into the North Atlantic." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 27692, 27702, 27703, 36269, 36270, 49678, 49679, 50623, 50624, 52949, 52960, 52977, 53036, 53040, 59641, 59646, 59647, 59657, 59659, 59666, 59669, 59673, 59776, 59779, 60141, 62271, 62272, 62275, 62276, 62287, 62295, 62296, 63050, 63051, 63053, 63054, 63055, 63056, 63057, 63058, 63062, 63063, 63065, 63066, 63068, 63070, 63071, 63073, 63074, 63075, 63079, 63080, 63081, 63082, 63083, 63084, 63085, 63086, 63087, 63088, 63089, 63090, 63091, 63092, 63093, 63094, 63095, 63096, 63097, 63099, 63100, 63101, 63102, 63103, 63104, 63105, 63106, 63108, 63109, 63118, 63119, 63120, 63121, 63122, 63123, 63127, 63128, 63129, 63130, 63131, 63132, 63133, 63134, 63135, 63136, 63137, 63138, 63139, 63140, 63141, 63142, 63143, 63144, 63145, 63146, 63147, 75365, 75366, 75367, 75368, 75370, 75372, 75374, 75375, 75376, 75377, 75378, 75379, 75380, 75381, 75383, 75385, 75386, 75387, 75389, 75390, 75391, 75392, 91551 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 40010, "uuid": "aedab4ebf3ac425ca06d4f308c5242a6", "short_code": "coll", "title": "Regional summaries from Arctic high-resolution sea ice-ocean forced modelling hindcasts (2008-2021) and forced modelling projections (2000-2050) as part of the PRE-MELT project.", "abstract": "Two sea ice-ocean forced modelling datasets (hindcasts and projections) were produced to analyse oceanic impacts on retreat of the Arctic sea-ice pack in the high Arctic, from areas of the Transpolar drift, north of Greenland and in the Fram Strait in the present and future climates and how those are linked to the changes in the Arctic ecosystems. The model outputs span the Arctic Ocean proper and the sub-Arctic seas, covering the near-present (2008-2021: hindcasts) and future climate (2000-2050: projections). \r\n\r\nThe forced modelling hindcasts were generated using the Global Ocean and Sea Ice GO8p7, developed under the Joint Marine Modelling Programme (JMMP), a collaboration between NOC, BAS and the UK Met Office. GO8p7 is based on NEMO v4.0 and the SI3 sea ice model and includes a package of modifications, including a scale-dependent Gent & McWilliams parameterisation, partial slip lateral boundary conditions south of 50°S and 4th-order horizontal tracer advection. The present simulation done under the ACSIS NCLTS-M programme was integrated with the Japanese 55-year atmospheric analysis JRA55 (v1.3-do) from 1958 to 2021. The monthly and 5-day averages of the key sea-ice and ocean fields for the pan-Arctic and Greenland regions were created and combined into 4-D files for easy data handling. \r\n\r\nThe forced modelling projections were generated using the NEMOv4.2-SI3 common NOC-UK MetOffice configuration (G8.7) coupled to the MEDUSA ecosystem model. The forcing fields were from the UK ESM1.1 model SSP370 integrations. Monthly averages of the key sea-ice, ocean and biogeochemical fields for the pan-Arctic and Greenland regions for the end of each of the decades 2020, 2030, 2040, and 2050 were created and combined into 4-D files for easy data handling. \r\n\r\nThe sea ice model output for both datasets were validated against the AMSR-E satellite sea-ice concentrations, as well as the CryoSat-2 and SMOS sea-ice thickness datasets. These data were produced by National Oceanography Centre (NOC) scientists under Natural Environment Research Council (NERC) project PRE-MELT (grant references NE/T001399/1, NE/T000260/1, NE/T000546/1). \r\n\r\nAdditional funding also came from NERC projects APEAR (grant reference NE/R012865/1), ARISE (grant reference NE/P006000/1), and Arctic PrIZE (grant reference NE/P006078/1), funded under the NERC/BMBF Changing Arctic Ocean Programme, from NERC NCLTS-M programmes ESM (grant reference NE/N018036/1) and ACSIS (grant reference NE/N018044/ 1). and from NCLTS-S programme CLASS (grant reference NE/R015953/1), from the European Commission grant CRESCENDO (grant no. 641816) and from the European Union’s Horizon 2020 research and innovation programme under grant agreement 820989 (Project COMFORT—Our common future ocean in the Earth system - quantifying coupled cycles of carbon, oxygen, and nutrients for determining and achieving safe operating spaces with respect to tipping points)." } ], "responsiblepartyinfo_set": [ 179645, 179652, 179653, 179642, 179644, 179643, 179646, 179647, 179654, 179655, 179656 ], "onlineresource_set": [ 84138, 84135, 84136, 84137 ] }, { "ob_id": 37668, "uuid": "233fbf6df84547dda891d316da93885b", "title": "Regional summaries from Arctic high-resolution sea ice-ocean forced modelling projections (2000-2050)", "abstract": "Regional summaries from the high-resolution sea ice-ocean-biogeochemical modelling UKESM1.1-forced projections were generated to analyse oceanic impacts on retreat of the Arctic sea-ice pack in the high Arctic and from areas of the Transpolar drift, north of Greenland and in the Fram Strait in the future climate up to the 2050s and subsequent impacts on the ocean biogeochemistry. The model datasets span the Arctic Ocean proper and the sub-Arctic seas, covering the near-present and future climate from 2000-2050. The model used was a NEMOv4.2-SI3 common NOC-UK MetOffice configuration (G8.7) coupled to the MEDUSA ecosystem model. The forcing fields were from the UK ESM1.1 model SSP370 integrations.\r\n\r\nThe sea ice model output has been validated against the AMSR-E satellite sea-ice concentrations, as well as the CryoSat-2 and SMOS sea-ice thickness datasets. Monthly averages of the key sea-ice and ocean fields for the pan-Arctic and Greenland regions for the end of each of the decades 2020, 2030, 2040, and 2050 were created and combined into 4-D files for easy data handling.\r\n\r\nThe model datasets were produced by National Oceanography Centre (NOC) scientists Dr Yevgeny Aksenov and Dr Stefanie Rynders, using the global model runs carried out by Dr Andrew Coward. Dr Andrew Yool advised on the runs and assisted with data handling, and Dr Stephen Kelly also assisted with data extraction. The data were produced under Natural Environment Research Council (NERC) project PRE-MELT (grant references NE/T001399/1, NE/T000260/1, NE/T000546/1). Additional funding also came from NERC projects APEAR (grant reference NE/R012865/1), ARISE (grant reference NE/P006000/1), and Arctic PrIZE (grant reference NE/P006078/1), funded under the NERC/BMBF Changing Arctic Ocean Programme, from NERC NCLTS-M programme ESM (grant reference NE/N018036/1) and NCLTS-S programme CLASS (grant reference NE/R015953/1), from the European Commission grant CRESCENDO (grant no. 641816) and from the European Union’s Horizon 2020 research and innovation programme under grant agreement 820989 (Project COMFORT—Our common future ocean in the Earth system - quantifying coupled cycles of carbon, oxygen, and nutrients for determining and achieving safe operating spaces with respect to tipping points).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-01-13T17:28:28", "updateFrequency": "notPlanned", "dataLineage": "Model projections were produced by scientists at the National Oceanography Centre (NOC), Southampton, UK. Outputs suitable for re-use were uploaded by the scientists to the Centre for Environmental Data Analysis (CEDA) ingestion area, where the British Oceanographic Data Centre (BODC) carried out ingestion process, creating catalogue records and making the data available for public download.", "removedDataReason": "", "keywords": "Arctic Ocean, sea-ice, UK ESM1.1, Fram Strait, MEDUSA, SMOS, CryoSat-2, ARCRegiF", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-08-18T09:54:32", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3540, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": 45.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 37669, "dataPath": "/bodc/SOC220091/PRE-MELT_projections", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 19680668714, "numberOfFiles": 9, "fileFormat": "Data are CF-compliant NetCDF formatted data files" }, "timePeriod": { "ob_id": 11278, "startTime": "2000-01-01T00:00:00", "endTime": "2050-12-31T23:59:59" }, "resultQuality": { "ob_id": 4005, "explanation": "Data as supplied to BODC - no additional quality checks performed", "passesTest": true, "resultTitle": "BODC Data Quality Statement", "date": "2022-06-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37671, "uuid": "09f9e20e45f94feb80baf94928e26d1d", "short_code": "comp", "title": "ARC36 stand-alone SI3 Arctic configuration", "abstract": "This is a configuration of the NEMO community ocean model based on the ORCA2_SAS_ICE reference configuration. The NEMO code is available from https://forge.nemo-ocean.eu/nemo/nemo. This configuration has a resolution of 1/36 degree and is a cut-out of the global 1/36 configuration: https://github.com/immerse-project/ORCA36-demonstrator. The code base is a pre-4.2.0 NEMO version, the model source code can be found in the file src_tar. Model setup: Follow the instructions on https://sites.nemo-ocean.io/user-guide/index.html to download and install the NEMO model version 4.2.0. Swap the src directory for the one in the tar file src_tar. Compile the ORCA2_SAS_ICE reference configuration. Put the rest of the files in this zenodo archive in the EXP00 directory, except the namelist_cfg_for_DOMAINcfg file which goes into tools/DOMAINcfg along with the grid files to be downloaded later. The files provided include example configuration namelist files namelist_cfg and namelist_ice_cfg. The atmospheric forcing used is the Drakkar forcing set (DFS) version 5.2, year 2008. The atmospheric forcing is interpolated on-the-fly, using the weights files. The weights were calculated using the nemo WEIGHTS tool. For the ocean (bottom) boundary the World Ocean Atlas 2018 multidecadal monthly averages are used. The data is already interpolated onto the ARC36 grid. Interpolation was done using the SOSIE tool. Files provided are monthly averages of sea surface salinity and temperature. Finally, the model grid domain_cfg.nc needs to be created. Download the ORCA36 files from ftp://ftp.mercator-ocean.fr/download/users/cbricaud/BENCH-ORCA36-INPUT.tar.gz, see the ORCA36 demonstrator github page. The necessary files are the coordinates and bathymetry files. To cut out the Arctic domain use ncks -F -d y,7000,,1 in.nc out.nc. Put in tools/DOMAINcfg and use the DOMAINcfg NEMO tool to create the domain_cfg.nc file using the file namelist_cfg_for_DOMAINcfg as namelist_cfg. The resulting file is large (122GB) therefore executing in parallel mode is required. The individual processor files need to be merged into one, use the REBUILD_NEMO tool. Put the resulting domain_cfg.nc file into EXP00 and run NEMO following the instructions. The ARC36 configuration was set up and run on ARCHER2 using 594 NEMO processors and 12 XIOS processors." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37664, "uuid": "d29366fbf94e48df99d9a235d11c21ee", "short_code": "proj", "title": "PRE-MELT: Preconditioning the trigger for rapid Arctic ice melt", "abstract": "The oldest, thickest sea ice in the 'last ice area' of the Arctic - a region thought to be most resilient to climate warming - unexpectedly broke up twice in the past year. Our current theories assume that the end-of-summer ice-covered area will steadily retreat into the Central Arctic Basin as global warming accelerates over coming decades. However, the dynamic break-up events witnessed in 2018 challenge this prevailing view. Here we hypothesise that a weaker, increasingly mobile Central Arctic ice pack is now susceptible to dynamic episodes of fragmentation which can precondition the ice for rapid summer melt. This mechanism of dynamic seasonal preconditioning is unaccounted for in global climate models, so our best current projections are overlooking the possibility for rapid disintegration of the Arctic's last ice area. Our team has demonstrated that seasonal preconditioning is already responsible for the neighbouring Beaufort Sea becoming ice-free twice in the past five years. Even ten years ago this region contained thick perennial sea ice, mirroring the Central Arctic Ocean, but it has now transitioned to a marginal Arctic sea. Could the processes responsible for the decline of the Beaufort Sea ice pack start to manifest themselves in the Central Arctic? Currently, a shortfall in satellite observations of the Arctic pack ice in summer prevents us from testing our hypothesis. We desperately require pan-Arctic observations of ice melting rates, but so far satellite observations of sea ice thickness are only available during winter months. Our project will therefore deliver the first measurements of Arctic sea ice thickness during summer months, from twin satellites: ESA's Cryosat-2 & NASA's ICESat-2. We have designed a new classification algorithm for separating ice and ocean radar altimeter echoes, regardless of surface melting state, providing the breakthrough required to fill the existing summer observation 'gap'. Exploiting the recent launch of multiple SAR missions for polar reconnaissance, our project will integrate information on ice-pack ablation, motion and deformation to generate a unique year-round sea ice volume budget in the High Arctic. This record will inform high-resolution ice dynamics simulations, performed with a suite of state-of-the-art sea ice models from stand alone (CICE), ocean-sea ice (NEMO/CICE), to fully coupled regional high resolution (RASM), and global coarser resolution (HadGEM) models, all now equipped with the anisotropic (EAP) sea ice rheology developed by our team. Using the regional and stand-alone models we will analyse the role of mechanics in this keystone region north of Greenland to scrutinise the coupling and preconditioning of winter breakup events - such as those witnessed in 2018 - to summer melting rates. Using the coupled models, we will quantify the likelihood of the Arctic's last ice area breaking up much sooner than expected due to oceanic and atmospheric feedbacks and how this will affect the flushing of ice and freshwater into the North Atlantic." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 27692, 27702, 27703, 36269, 36270, 50623, 50624, 52949, 52960, 52977, 59659, 60131, 60135, 60148, 62271, 62272, 62275, 62276, 62294, 62295, 63050, 63051, 63053, 63054, 63055, 63056, 63057, 63058, 63059, 63060, 63061, 63062, 63063, 63064, 63065, 63066, 63067, 63068, 63069, 63070, 63071, 63072, 63073, 63074, 63075, 63076, 63077, 63078, 63079, 63080, 63081, 63082, 63083, 63084, 63085, 63086, 63087, 63088, 63089, 63090, 63091, 63092, 63093, 63094, 63095, 63096, 63097, 63098, 63099, 63100, 63101, 63102, 63103, 63104, 63105, 63106, 63107, 63108, 63109, 63110, 63111, 63112, 63113, 63114, 63115, 63116, 63117, 63118, 63119, 63120, 63121, 63122, 63123, 63124, 63125, 63126, 75361, 75369, 91343, 91344, 91345, 91346, 91347, 91348, 91349, 91350, 91351, 91352 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 40010, "uuid": "aedab4ebf3ac425ca06d4f308c5242a6", "short_code": "coll", "title": "Regional summaries from Arctic high-resolution sea ice-ocean forced modelling hindcasts (2008-2021) and forced modelling projections (2000-2050) as part of the PRE-MELT project.", "abstract": "Two sea ice-ocean forced modelling datasets (hindcasts and projections) were produced to analyse oceanic impacts on retreat of the Arctic sea-ice pack in the high Arctic, from areas of the Transpolar drift, north of Greenland and in the Fram Strait in the present and future climates and how those are linked to the changes in the Arctic ecosystems. The model outputs span the Arctic Ocean proper and the sub-Arctic seas, covering the near-present (2008-2021: hindcasts) and future climate (2000-2050: projections). \r\n\r\nThe forced modelling hindcasts were generated using the Global Ocean and Sea Ice GO8p7, developed under the Joint Marine Modelling Programme (JMMP), a collaboration between NOC, BAS and the UK Met Office. GO8p7 is based on NEMO v4.0 and the SI3 sea ice model and includes a package of modifications, including a scale-dependent Gent & McWilliams parameterisation, partial slip lateral boundary conditions south of 50°S and 4th-order horizontal tracer advection. The present simulation done under the ACSIS NCLTS-M programme was integrated with the Japanese 55-year atmospheric analysis JRA55 (v1.3-do) from 1958 to 2021. The monthly and 5-day averages of the key sea-ice and ocean fields for the pan-Arctic and Greenland regions were created and combined into 4-D files for easy data handling. \r\n\r\nThe forced modelling projections were generated using the NEMOv4.2-SI3 common NOC-UK MetOffice configuration (G8.7) coupled to the MEDUSA ecosystem model. The forcing fields were from the UK ESM1.1 model SSP370 integrations. Monthly averages of the key sea-ice, ocean and biogeochemical fields for the pan-Arctic and Greenland regions for the end of each of the decades 2020, 2030, 2040, and 2050 were created and combined into 4-D files for easy data handling. \r\n\r\nThe sea ice model output for both datasets were validated against the AMSR-E satellite sea-ice concentrations, as well as the CryoSat-2 and SMOS sea-ice thickness datasets. These data were produced by National Oceanography Centre (NOC) scientists under Natural Environment Research Council (NERC) project PRE-MELT (grant references NE/T001399/1, NE/T000260/1, NE/T000546/1). \r\n\r\nAdditional funding also came from NERC projects APEAR (grant reference NE/R012865/1), ARISE (grant reference NE/P006000/1), and Arctic PrIZE (grant reference NE/P006078/1), funded under the NERC/BMBF Changing Arctic Ocean Programme, from NERC NCLTS-M programmes ESM (grant reference NE/N018036/1) and ACSIS (grant reference NE/N018044/ 1). and from NCLTS-S programme CLASS (grant reference NE/R015953/1), from the European Commission grant CRESCENDO (grant no. 641816) and from the European Union’s Horizon 2020 research and innovation programme under grant agreement 820989 (Project COMFORT—Our common future ocean in the Earth system - quantifying coupled cycles of carbon, oxygen, and nutrients for determining and achieving safe operating spaces with respect to tipping points)." } ], "responsiblepartyinfo_set": [ 179677, 179684, 179685, 179674, 179678, 179676, 179675, 179679, 179686, 179687, 179688, 179689 ], "onlineresource_set": [] }, { "ob_id": 37676, "uuid": "0d88dc06fd514e8199cdd653f00a7be0", "title": "ACRUISE: deep-learning inferred shiptrack clouds from AQUA MODIS daylight satellite data for 2002-2021", "abstract": "Large dataset of emission induced \"shiptrack\" clouds, detected using deep-learning, from satellite based remote sensing data with global coverage, from 2002 to 2021 for the Atmospheric Composition and Radiative forcing changes due to UN International Ship Emissions regulations (ACRUISE) project. Shiptracks were inferred from every daylight granule captured by the MODerate Imaging Spectroradiometer (MODIS) instrument, onboard the NOAA-AQUA satellite from 2002-2021 inclusive and stored in a compressed netcdf file. In addition, polygons corresponding to contours of level 0.5 and 0.8 from the inference images are provided as a light-weight alternative. These are stored in annual geopackages in the geographic projection.\r\n\r\nThe model is a standard neural-network based segmentation model with a UNet architecture, a resnet-152 backbone and sigmoid activation on the final layer that was pre-trained on the 2012 ImageNet Large Scale Visual Recognition Challenge 2012 (ILSVRC2012) ImageNet dataset. This model was trained to segment clouds formed by ship exhausts, known as shiptracks, from MODIS level 1b, day microphysics composite granules enhanced through histogram stretching.\r\n\r\nThe purpose of these data is to measure the effect that shipping fuel regulation has on climate change and to reduce the uncertainty in the relationship between aerosols and cloud formation and properties. This allows the determination of where tracks are more likely to form and the sensitivity of clouds to such perturbations.The data indicate a sharp reduction in tracks due to the more stringent ship emission regulations since 2020.\r\n\r\nA small minority of granules (<0.5%) are missing due to a combination of missing or corrupt files and/or unexpected computational processing failures. These remained unresolved as they were judged insignificant compared to model uncertainties and and of negligible additional benefit to warrant the overheads to resolve each missing granule.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-09-11T13:07:11", "updateFrequency": "notPlanned", "dataLineage": "NASA supplied AQUA MODIS level 1a data, archived at Plymouth Marine Laboratory, was processed to level 1b using NASA SeaDAS modis_L1B.py script. Satpy was used to process level 1b data into ‘day microphysics’ composites from channels 1, 20 and 32 (corresponding to wavelengths of 645nm, 3.75µm and 12.5µm respectively). Histogram equalisation was then used to enhance the image prior to training and inference by the model.\r\n\r\nModel output was written to a variable, \"shiptracks\", within a netcdf file that inherits the coordinates and metadata from the original day micro-physics granule. Contours at 0.5 and 0.8 were computed and stored in annual .gpkg files in the geographic projection.\r\n\r\nFor archiving, lossy compression was applied to inference granules: Variable \"shiptracks\" from float32 to unint16, with appropriate scaling factor. Latitude and Longitude coordinates from float64 to float32. Contours files were not compressed.\r\n\r\nDuncan Watson-Paris (DWP), Matthew Christensen (MC) and Philip Stier (PS) designed the research, DWP carried it out. Angus Laurenson and Daniel Clewley supported analysis. MC and Ed Gryspeerdt provided label data. Data were prepared by the project team and uploaded to CEDA for archival", "removedDataReason": "", "keywords": "Ship tracks, MODIS", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-09-09T10:34:19", "doiPublishedTime": "2022-09-16T13:14:45", "removedDataTime": null, "geographicExtent": { "ob_id": 3541, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 37677, "dataPath": "/badc/acruise/data/NERC_ACRUISE_MODIS_shiptracks", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 9771844028217, "numberOfFiles": 1132857, "fileFormat": "Data are NetCDF (.nc) formatted with additional geopackages files (.gpkg)" }, "timePeriod": { "ob_id": 10398, "startTime": "2002-07-03T00:00:00", "endTime": "2021-12-07T00:00:00" }, "resultQuality": { "ob_id": 4006, "explanation": "No quality checks have been made by CEDA", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 37680, "uuid": "35164039bf2a417fa41578d29df5929a", "short_code": "cmppr", "title": "Composite Process for Shiptrack clouds inferred from MODIS (MODerate Imaging Spectroradiometer) by deep learning global dataset for 2002-2021", "abstract": "Composite process covering Acquisition for: Shiptrack clouds inferred from MODIS (MODerate Imaging Spectroradiometer) by deep learning global dataset for 2002-2021 and shiptrack_semantic_segmentation_v1." }, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2543, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 2, "licenceURL": "https://artefacts.ceda.ac.uk/licences/missing_licence.pdf", "licenceClassifications": [ { "ob_id": 2, "classification": "unstated" } ] } } ], "projects": [ { "ob_id": 27736, "uuid": "d6eb4e907c124482881d7d03c06903e4", "short_code": "proj", "title": "ACRUISE : Atmospheric Composition and Radiative forcing changes due to UN International Ship Emissions regulations (ACRUISE)", "abstract": "The ACRUISE project will combine FAAM aircraft observations, long-term surface observations, satellite remote sensing, and process-level modelling, to investigate the impact of the 2020 ship sulfur emission regulation on atmospheric composition, radiative forcing and climate in the North Atlantic. Results of this project will improve our understanding of the impact of ship emissions on air quality and climate.\r\nShips generally burn low quality fuel and emit large quantities of sulfur dioxide and particulates, or aerosols (harmful at high concentrations), into the atmosphere above the ocean. In the presence of clouds the sulfur dioxide is rapidly converted into more particle mass growing them to sizes where they act as sites for cloud droplet formation. Given that about 70% of shipping activities occur within 400 km of the coast, ships are a large source of air pollution in coastal regions, causing 400k premature mortalities per year globally. In the UK, air pollution (including ship emissions) is responsible for 40,000 premature mortalities each year. In an effort to reduce air pollution from shipping activity, the United Nation's International Maritime Organization (IMO) is introducing new regulations from January 2020 that will require ships in international waters to reduce their maximum sulfur emissions from 3.5% by mass of fuel to 0.5%. \r\n\t\r\nParticulates emitted by ships may enhance the number of cloud droplets and potentially form regions of brighter clouds known as ship tracks. Largely because of this effect, some global models predict that ship emissions of particulates currently have a significant cooling influence on the global climate, masking a fraction of the warming caused by greenhouse gas emissions. So whilst a reduction in ship sulfur emission is predicted to almost halve the number of premature deaths globally via a reduction in sulfate aerosols, a lack of similar reductions in greenhouse gases from shipping (e.g. CO2) could lead to an overall climate warming. However, the magnitude of the cooling caused by particulates is very uncertain, with large discrepancies between global model and satellite-based estimates. This may be due to imprecise representations of the effects of aerosols on clouds in global models or biases in satellite detections of ship tracks. Furthermore, how shipping companies respond to the 2020 regulation (i.e. degree and method of compliance), in international waters where surveillance is challenging, is largely unknown and requires observational verification. \r\n\r\nThis project will take advantage of this unique and drastic \"inverse geoengineering\" event in 2020.\r\n\r\nGrant Ref: NE/S004467/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 7026, 7028, 49998 ], "vocabularyKeywords": [], "identifier_set": [ 12197 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 179692, 179693, 179694, 179695, 179696, 179697, 179698, 179699, 179700, 179701, 179702, 179703, 179704 ], "onlineresource_set": [ 52631, 87760, 87761, 94687 ] }, { "ob_id": 37681, "uuid": "c9397680d08442b9a1d21e7c50df4aba", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input Data for Figure 2.12 (v20220630)", "abstract": "Input Data for Figure 2.12 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 2.12 shows changes in temperature through the troposphere and stratosphere, both on near-global scales and in the tropics.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Gulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson- Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has five subpanels, with intermediate data provided for panels b to e.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains trends in temperature at various atmospheric heights for 1980–2019 and 2002–2019\r\n \r\n - for the near-global (70°N–70°S) domain.\r\n - for the tropical (20°N–20°S) region.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panels b-e: each line shows the observed trend for the specified time period/region for a given data set as a function of height.\r\n \r\n - Data files: *ROM_SAF*.nc: Radio occultation RO (ROM SAF). Violet line\r\n - Data files: *UCAR*.nc: Radio occultation RO (UCAR/NOAA). Cyan line\r\n - Data files: *Wegener*.nc: Radio occultation RO (WEGC). Blue line\r\n - Data files: *ERA5*.nc: Modern reanalysis. Cyan dotted lines.\r\n - Data files: *RICH*.nc: Radiosonde. Orange line\r\n - Data files: *RAOBCORE*.nc: Radiosonde. Yellow line\r\n - Data files: *AIRS*.nc: Infrared satellite. Green line\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nThere are notes guiding the user to reproduce the figure in the code associated to this dataset. Link to the code that reproduces the figure in the Related Documents section of this catalogue record.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:11", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, upper-air temperature, tropospheric temperature, stratospheric temperature", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-15T13:30:08", "doiPublishedTime": "2023-07-04T14:18:16.682470", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 151, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37687, "dataPath": "/badc/ar6_wg1/data/ch_02/inputdata_ch2_fig12/v20220630", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 675827, "numberOfFiles": 23, "fileFormat": "txt, netCDF" }, "timePeriod": { "ob_id": 10399, "startTime": "1980-01-01T12:00:00", "endTime": "2019-12-31T12:00:00" }, "resultQuality": { "ob_id": 4007, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37683, "uuid": "b0c4de065765470aaecdcea2791cb434", "short_code": "comp", "title": "Caption for Figure 2.12 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Temperature trends in the upper air. (a) Zonal cross-section of temperature anomaly trends (2007–2016 baseline) for 2002–2019 in the upper troposphere and lower stratosphere region. The climatological tropopause altitude is marked as a grey line. Significance is not indicated due to the short period over which trends are shown, and because the assessment findings associated to this figure relate to difference between trends at different heights, not the absolute trends. (b, c) Trends in temperature at various atmospheric heights for 1980–2019 and 2002–2019 for the near-global (70°N–70°S) domain. (d, e) as for (b, c) but for the tropical (20°N–20°S) region. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 4385, 49467, 49468, 49469, 49470, 49471, 49472, 49473, 49474, 49475, 49476, 49477, 49478, 49479 ], "vocabularyKeywords": [], "identifier_set": [ 12624 ], "observationcollection_set": [ { "ob_id": 32717, "uuid": "3da412ad9912427d9bb808b57faa21a7", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 2.2\r\n- data for Figure 2.4\r\n- data for Figure 2.5\r\n- data for Figure 2.6\r\n- data for Figure 2.9\r\n- data for Figure 2.11\r\n- input data for Figure 2.11\r\n- data for Figure 2.12\r\n- input data for Figure 2.12\r\n- data for Figure 2.13\r\n- input data for Figure 2.13\r\n- data for Figure 2.14\r\n- data for Figure 2.15\r\n- input data for Figure 2.15\r\n- input data for Figure 2.16\r\n- data for Figure 2.17\r\n- data for Figure 2.22\r\n- input data for Figure 2.23\r\n- data for Figure 2.25\r\n- input data for Figure 2.25\r\n- data for Figure 2.26\r\n- input data for Figure 2.27\r\n- data for Figure 2.28\r\n- input data for Figure 2.29\r\n- data for Figure 2.36\r\n- data for Figure 2.37\r\n- data for Figure 2.38\r\n- data for Cross-Chapter Box 2.1.1\r\n- data for Cross-Chapter Box 2.3.1" } ], "responsiblepartyinfo_set": [ 179711, 179712, 179713, 179714, 179715, 179716, 179717, 179718, 179719 ], "onlineresource_set": [ 52359, 52360, 82842, 83479 ] }, { "ob_id": 37684, "uuid": "2a1284ec9d564f679480ee013b733ae1", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 2.16 (v20220630)", "abstract": "Input data for Figure 2.16 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 2.16 provides global precipitation minus evaporation trend maps and time series from a variety of data sources\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with input data provided for all panels in the main directory\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The datasets contains:\r\n \r\n - Global precipitation and evaporation data from ERA5 reanalysis\r\n - Time series of global, land-only and ocean-only average annual P–E (mm day–1) from the following reanalysis products: 20CRv3, ERA5, ERA20CM, MERRA, CFSR, ERA20C, JRA55 and MERRA2.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a:\r\n - Data files: IntermediateData_era5_evap_2.nc and era5_tp_2.nc\r\n \r\n Panel b:\r\n - Data file: GPME2.csv and GPME2.mat\r\n \r\n Panel c:\r\n - Data file: LPME2.csv and LPME2.mat\r\n \r\n Panel d:\r\n - Data file: OPME2.csv and OPME2.mat\r\n \r\n For panels b to d:\r\n I. Column 2: orange solid line\r\n II. Column 3: cyan solid line\r\n III. Column 4: black solid line\r\n IV. Column 5: grey solid line\r\n V. Column 6: blue solid line\r\n VI. Column 7: dark green solid line\r\n VII. Column 8: brown solid line\r\n VIII. Column 9: green solid line\r\n\r\n 20CRv3 is the NOAA-CIRES-DOE Twentieth Century Reanalysis Version 3.\r\n ERA5 is a reanalysis of the global climate from 1950 to present, developed by ECMWF.\r\n ERA20CM is a twentieth century atmospheric model ensemble developed by ECMWF.\r\n MERRA stands for Modern-Era Retrospective analysis for Research and Applications.\r\n CFSR stands for Climate Forecast System Reanalysis.\r\n ERA20C is the first atmospheric reanalysis of the 20th century, from 1900-2010, developed by ECMWF.\r\n JRA55 stands for Japanese 55-year Reanalysis.\r\n MERRA2 stands for Modern-Era Retrospective analysis for Research and Applications, version 2.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Additional information to correctly reproduce the figure in the corresponding readme files for code archived on Zenodo (see the link to code provided in the Related Documents section of this catalogue record).\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:15", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, global precipitation minus evaporation", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-09-26T16:08:04", "doiPublishedTime": "2023-09-26T16:07:40.152515", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 152, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37685, "dataPath": "/badc/ar6_wg1/data/ch_02/inputdata_ch2_fig16/v20220630", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 41536648, "numberOfFiles": 11, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10405, "startTime": "1980-01-01T00:00:00", "endTime": "2019-12-31T23:59:59" }, "resultQuality": { "ob_id": 4008, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37686, "uuid": "3c5838a8bd4847cc97a8445fef27f2a2", "short_code": "comp", "title": "Caption for Figure 2.16 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Changes in precipitation minus evaporation. (a) Trends in precipitation minus evaporation (P–E) between 1980 and 2019. Trends are calculated using OLS regression with significance assessed following AR(1) adjustment after Santer et al. (2008) (‘x’ marks denote non-significant trends). Time series of (b) global, (c) land-only and (d) ocean-only average annual P–E (mm day–1). Further details on data sources and processing are available in the chapter data table (Table 2.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 63617, 63618, 63619 ], "vocabularyKeywords": [], "identifier_set": [ 12699 ], "observationcollection_set": [ { "ob_id": 32717, "uuid": "3da412ad9912427d9bb808b57faa21a7", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 2.2\r\n- data for Figure 2.4\r\n- data for Figure 2.5\r\n- data for Figure 2.6\r\n- data for Figure 2.9\r\n- data for Figure 2.11\r\n- input data for Figure 2.11\r\n- data for Figure 2.12\r\n- input data for Figure 2.12\r\n- data for Figure 2.13\r\n- input data for Figure 2.13\r\n- data for Figure 2.14\r\n- data for Figure 2.15\r\n- input data for Figure 2.15\r\n- input data for Figure 2.16\r\n- data for Figure 2.17\r\n- data for Figure 2.22\r\n- input data for Figure 2.23\r\n- data for Figure 2.25\r\n- input data for Figure 2.25\r\n- data for Figure 2.26\r\n- input data for Figure 2.27\r\n- data for Figure 2.28\r\n- input data for Figure 2.29\r\n- data for Figure 2.36\r\n- data for Figure 2.37\r\n- data for Figure 2.38\r\n- data for Cross-Chapter Box 2.1.1\r\n- data for Cross-Chapter Box 2.3.1" } ], "responsiblepartyinfo_set": [ 179722, 179723, 179724, 179725, 179726, 179727, 179728, 179729, 179730, 198427 ], "onlineresource_set": [ 52357, 80646, 52358, 82845 ] }, { "ob_id": 37688, "uuid": "b618062ee96a4d36b6010271e099a5c4", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 2.23 (v20220630)", "abstract": "Input data for Figure 2.23 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 2.23(a) shows number of a finite selection of surveyed glaciers that advanced during the past 2000 years. Figure 2.23(b) shows the annual and decadal global glacier mass change from 1961 until 2018.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Gulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson- Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels, with input data provided for panel b (green lines and shadow).\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n Global mean glacier mass balance between 2000 and 2010 and between 2010 and 2020 from Hugonnet et al. (2021).\r\n All values are in Gt yr-1.\r\n All uncertainties are 90% CI.\r\n\r\n Note: two thirds of the data (the rows 2000-2010, 2010-2020) are directly available online at https://doi.org/10.6096/13 under a CC-BY-4.0 license. The last row of 2006-2019 can be derived from the data in that same DOI using provided scripts, but is not directly available in a table.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Figure 2.23 Panel b:\r\n \r\n - green line. table_hugonnet_regions_10yr_ar6period.xlsx. Decadal (2000-2010 and 2010-2020) global mass balance (Gt yr-1) from Hugonnet et al. (2021). The mean values are computed in the matlab script from the value of each region.\r\n - green area. table_hugonnet_regions_10yr_ar6period.xlsx. Decadal (2000-2010 and 2010-2020) global mass balance (Gt yr-1) uncertainty from Hugonnet et al. (2021). The mean values are computed in the matlab script from the value of each region.\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Input datasets are provided and matlab scripts linked in the related Documents section to reproduce the figure.\r\n\r\nInput dataset: table_hugonnet_regions_10yr_ar6period.xlsx and 2.23b_Zemp_etal_results_global.csv (a table already available as supplementary material by Zemp et al. (2019)). The rest of the files are provided in the corresponding code on GitHub (matlab script). The link to the code archive on Zenodo is provided in the Related Documents section of this catalogue record.\r\n\r\n\r\nCode please be aware that you will need: \r\n - MB_figure_FGD_chapter2_jun_28_2021.m script \r\n - shadedplot.m matlab function \r\n - colorscheme.mat created by the TSU of WGI\r\n\r\n\r\n* black line. Zemp_etal_results_global.csv. Global mass change (Gt yr-1) based on spatial interpolation from 1961 to 2016; supplementary materials from Zemp et al. (2019) it is complemented with the mass change for years 2017 and 2018 from Table 1 of Zemp et al. (2020). Decadal means are computed by the matlab script.\r\n\r\n\r\n* grey area. Zemp_etal_results_global.csv. Total uncertainty of regional mass change (Gt yr-1) from 1961 to 2016; supplementary materials from Zemp et al. (2019) it is complemented with the uncertainty of mass change for years 2017 and 2018 from Table 1 of Zemp et al. (2020). Decadal uncertainties are computed by the matlab script.\r\n\r\n\r\n* blue line. Global mean glacier mass balance (Gt yr-1) between 2002 and 2016 from Wouters et al. (2019). Values are taken from Table 1 of Wouters et al. (2019) Global total of glacier mass budget. Values are declared in the matlab script.\r\n\r\n\r\n* light blue area. Global mean glacier mass balance uncertainty (Gt yr-1) between 2002 and 2016 from Wouters et al. (2019). Values are taken from Table 1 of Wouters et al. (2019) Global total of glacier mass budget uncertainty. Values are declared in the matlab script.\r\n\r\n\r\n* desert yellow line. Global mean glacier mass balance (Gt yr-1) between 2006 and 2015 from Hock et al. (2019) Values are taken from Table 2A.1 of Chapter 2 of SROCC Global values. Values are declared in the matlab script.\r\n\r\n\r\n* desert yellow area. Global mean glacier mass balance uncertainty (Gt yr-1) between 2006 and 2015 from Hock et al. (2019) Values are taken from Table 2A.1 of Chapter 2 of SROCC Global values. Values are declared in the matlab script.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:10", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Global glacier mass change", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-15T13:38:50", "doiPublishedTime": "2023-07-04T14:54:52.627516", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 153, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37689, "dataPath": "/badc/ar6_wg1/data/ch_02/inputdata_ch2_fig23/v20220630", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 20900, "numberOfFiles": 5, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10401, "startTime": "1961-01-01T12:00:00", "endTime": "2020-01-01T12:00:00" }, "resultQuality": { "ob_id": 4009, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-06-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37690, "uuid": "17a17e2d8cae429eb89fcdf75e5cccc1", "short_code": "comp", "title": "Caption for Figure 2.23 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Mountain glacier advance and annual mass change. (a) Number of a finite selection of surveyed glaciers that advanced during the past 2000 years. (b) Annual and decadal global glacier mass change (Gt yr–1) from 1961 until 2018. In addition, mass change mean estimates are shown. Ranges show the 90% confidence interval. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12627 ], "observationcollection_set": [ { "ob_id": 32717, "uuid": "3da412ad9912427d9bb808b57faa21a7", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 2.2\r\n- data for Figure 2.4\r\n- data for Figure 2.5\r\n- data for Figure 2.6\r\n- data for Figure 2.9\r\n- data for Figure 2.11\r\n- input data for Figure 2.11\r\n- data for Figure 2.12\r\n- input data for Figure 2.12\r\n- data for Figure 2.13\r\n- input data for Figure 2.13\r\n- data for Figure 2.14\r\n- data for Figure 2.15\r\n- input data for Figure 2.15\r\n- input data for Figure 2.16\r\n- data for Figure 2.17\r\n- data for Figure 2.22\r\n- input data for Figure 2.23\r\n- data for Figure 2.25\r\n- input data for Figure 2.25\r\n- data for Figure 2.26\r\n- input data for Figure 2.27\r\n- data for Figure 2.28\r\n- input data for Figure 2.29\r\n- data for Figure 2.36\r\n- data for Figure 2.37\r\n- data for Figure 2.38\r\n- data for Cross-Chapter Box 2.1.1\r\n- data for Cross-Chapter Box 2.3.1" } ], "responsiblepartyinfo_set": [ 179733, 179734, 179735, 179736, 179737, 179738, 179739, 179740, 179741 ], "onlineresource_set": [ 52402, 52355, 52356, 82846 ] }, { "ob_id": 37707, "uuid": "e9f67cfb456845b3b406328c6ae43e2d", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.12 (v20220701)", "abstract": "Data for Figure 2.12 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 2.12 shows changes in temperature through the troposphere and stratosphere, both on near-global scales and in the tropics.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 5 subpanels, with data provided for panel a.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains observed temperature anomaly trends for 2002-2019 from the ROM SAF dataset, plotted as a trend/height/latitude contour plot.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 2.12:\r\n \r\n - Data file: Figure_2_12a_data_file.nc: tdry_trends filled contours plot\r\n - Data file: Figure_2_12a_data_file.nc: lrt_temprature_altitude grey line\r\n\r\nROM SAF stands for Radio Occultation Meteorology Satellite Application Facilities.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Panel (a) is plotted using standard matplotlib software. \r\nThere are notes guiding the user to reproduce the figure in the code associated to this dataset. Link to the code that reproduces the figure in the Related Documents section of this catalogue record.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to input data figure 2.12.\r\n- Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:10", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, upper-air temperature, tropospheric temperature, stratospheric temperature", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-11T15:14:08", "doiPublishedTime": "2023-07-03T19:46:54.188861", "removedDataTime": null, "geographicExtent": { "ob_id": 3550, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -70.0, "northBoundLatitude": 70.0 }, "verticalExtent": { "ob_id": 157, "highestLevelBound": 25.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37708, "dataPath": "/badc/ar6_wg1/data/ch_02/ch2_fig12/v20220701", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 27571, "numberOfFiles": 4, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10410, "startTime": "2002-01-01T12:00:00", "endTime": "2019-12-31T12:00:00" }, "resultQuality": { "ob_id": 4015, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-07-05" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37709, "uuid": "1092d97568364adfb4ed37293aee99ab", "short_code": "comp", "title": "Caption for Figure 2.12 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Temperature trends in the upper air. (a) Zonal cross-section of temperature anomaly trends (2007–2016 baseline) for 2002–2019 in the upper troposphere and lower stratosphere region. The climatological tropopause altitude is marked as a grey line. Significance is not indicated due to the short period over which trends are shown, and because the assessment findings associated to this figure relate to difference between trends at different heights, not the absolute trends. (b, c) Trends in temperature at various atmospheric heights for 1980–2019 and 2002–2019 for the near-global (70°N–70°S) domain. (d, e) as for (b, c) but for the tropical (20°N–20°S) region. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 63612, 63613, 63614, 63615, 63616 ], "vocabularyKeywords": [], "identifier_set": [ 12614 ], "observationcollection_set": [ { "ob_id": 32717, "uuid": "3da412ad9912427d9bb808b57faa21a7", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 2.2\r\n- data for Figure 2.4\r\n- data for Figure 2.5\r\n- data for Figure 2.6\r\n- data for Figure 2.9\r\n- data for Figure 2.11\r\n- input data for Figure 2.11\r\n- data for Figure 2.12\r\n- input data for Figure 2.12\r\n- data for Figure 2.13\r\n- input data for Figure 2.13\r\n- data for Figure 2.14\r\n- data for Figure 2.15\r\n- input data for Figure 2.15\r\n- input data for Figure 2.16\r\n- data for Figure 2.17\r\n- data for Figure 2.22\r\n- input data for Figure 2.23\r\n- data for Figure 2.25\r\n- input data for Figure 2.25\r\n- data for Figure 2.26\r\n- input data for Figure 2.27\r\n- data for Figure 2.28\r\n- input data for Figure 2.29\r\n- data for Figure 2.36\r\n- data for Figure 2.37\r\n- data for Figure 2.38\r\n- data for Cross-Chapter Box 2.1.1\r\n- data for Cross-Chapter Box 2.3.1" } ], "responsiblepartyinfo_set": [ 179809, 179810, 179811, 179812, 179813, 179814, 179815, 179816, 179817 ], "onlineresource_set": [ 52422, 52425, 82828, 52420, 52421 ] }, { "ob_id": 37714, "uuid": "ef7b615816cb432088d02c97836ca9fa", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 9.3 (v20220721)", "abstract": "Data for Figure 9.3 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 9.3 shows sea surface temperature (SST) and its changes with time. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nFox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\nThe figure has 12 panels labelled (a)-(j). Data is provided provided for panels using this lettering system.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Time series of global mean SST anomaly relative to 1950–1980 climatology. Shown are paleoclimate reconstructions and PMIP models, observational reanalyses (HadISST) and multi-model means from the Coupled Model Intercomparison Project (CMIP) historical simulations, CMIP projections, and HighResMIP experiment. \r\n\r\n- Map of observed SST (1995–2014 climatology HadISST). \r\n\r\n- Historical SST changes from observations. \r\n\r\n- CMIP 2005–2100 SST change rate. (e) Bias of CMIP. (f) CMIP change rate. \r\n\r\n- 2005–2050 change rate for SSP5-8.5 for the CMIP ensemble. \r\n\r\n- Bias of HighResMIP (bottom left) over 1995–2014. \r\n\r\n- HighResMIP change rate for 1950–2014. \r\n\r\n- 2005–2050 change rate for SSP5-8.5 for the HighResMIP ensemble. No overlay indicates regions with high model agreement, where ≥80% of models agree on sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on sign of change (see Cross-Chapter Box Atlas.1 for more information). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 9.3\r\n \r\n - Data file: Fig9-3a_data_CMIPlikelybounds.nc: \r\n - Data file: Fig9-3a_data_CMIPmean.nc: \r\n - Data file: Fig9-3a_data_HighResMIPlikelybounds.nc: \r\n - Data file: Fig9-3a_data_HighResMIPmean.nc: \r\n - Data file: Fig9-3a_data_Observedmean.nc: \r\n - Data file: Fig9-3a_data_paleo.nc: \r\n - Data file: Fig9-3a_data_ssp126likelybounds.nc: \r\n - Data file: Fig9-3a_data_ssp126likelybounds_extended.nc: \r\n - Data file: Fig9-3a_data_ssp126mean.nc: \r\n - Data file: Fig9-3a_data_ssp126mean_extended.nc: \r\n - Data file: Fig9-3a_data_ssp126verylikelybounds.nc: \r\n - Data file: Fig9-3a_data_ssp245likelybounds.nc: \r\n - Data file: Fig9-3a_data_ssp245mean.nc: \r\n - Data file: Fig9-3a_data_ssp245verylikelybounds.nc: \r\n - Data file: Fig9-3a_data_ssp370likelybounds.nc: \r\n - Data file: Fig9-3a_data_ssp370mean.nc: \r\n - Data file: Fig9-3a_data_ssp370verylikelybounds.nc: \r\n - Data file: Fig9-3a_data_ssp585likelybounds.nc: \r\n - Data file: Fig9-3a_data_ssp585likelybounds_extended.nc: \r\n - Data file: Fig9-3a_data_ssp585mean.nc: \r\n - Data file: Fig9-3a_data_ssp585mean_extended.nc: \r\n - Data file: Fig9-3a_data_ssp585verylikelybounds.nc: \r\n - Data file: Fig9-3b_data.nc: \r\n - Data file: Fig9-3c_data.nc: \r\n - Data file: Fig9-3d_data.nc: \r\n - Data file: Fig9-3e_data.nc: \r\n - Data file: Fig9-3f_data.nc: \r\n - Data file: Fig9-3g_data.nc: \r\n - Data file: Fig9-3h_data.nc: \r\n - Data file: Fig9-3i_data.nc: \r\n - Data file: Fig9-3j_data.nc: \r\n\r\nPMIP is the Paleoclimate Modelling Intercomparison Project. \r\nHadISST stands for Hadley Centre Sea Ice and Sea Surface Temperature.\r\nCMIP is the Coupled Model Intercomparison Project.\r\nHighResMIP is the High Resolution Model Intercomparison Project.\r\nSST stands for Sea Surface Temperature.\r\nSSP stands for Shared Socioeconomic Pathway.\r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP stands for Representative Concentration Pathway.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n GMSST and SST maps were plotted using standard matplotlib software - code is available via the link in the documentation.\r\n\r\nThe plotting code is designed to use pre-processed CMIP data, rather than the provided netcdf files. To reproduce these figures form the metadata please modify the example code linked in the Related Documents section of this catalogue record.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n - Link to the data and code used to produce this figure and others in Chapter 9, archived on Zenodo.\r\n - Link to example code on GitHub\r\n - Link to the output data for this figure, contained in a dedicated GitHub repository.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:12", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, SST, sea surface temperature", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-02-01T15:31:54", "doiPublishedTime": "2023-05-15T12:51:13.368398", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38017, "dataPath": "/badc/ar6_wg1/data/ch_09/ch9_fig03/v20220721", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 9475740, "numberOfFiles": 34, "fileFormat": "NetCDF, txt" }, "timePeriod": { "ob_id": 10412, "startTime": "1995-01-01T00:00:00", "endTime": "2050-12-31T23:59:59" }, "resultQuality": { "ob_id": 4195, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39610, "uuid": "f2958997cd8345759467257dcf78f62e", "short_code": "comp", "title": "Caption for Figure 9.3 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Sea surface temperature (SST) and its changes with time. (a) Time series of global mean SST anomaly relative to 1950–1980 climatology. Shown are paleoclimate reconstructions and PMIP models, observational reanalyses (HadISST) and multi-model means from the Coupled Model Intercomparison Project (CMIP) historical simulations, CMIP projections, and HighResMIP experiment. (b) Map of observed SST (1995–2014 climatology HadISST). (c) Historical SST changes from observations. (d) CMIP 2005–2100 SST change rate. (e) Bias of CMIP. (f) CMIP change rate. (g) 2005–2050 change rate for SSP5-8.5 for the CMIP ensemble. (h) Bias of HighResMIP (bottom left) over 1995–2014. (i) HighResMIP change rate for 1950–2014. (j) 2005–2050 change rate for SSP5-8.5 for the HighResMIP ensemble. No overlay indicates regions with high model agreement, where ≥80% of models agree on sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on sign of change (see Cross-Chapter Box Atlas.1 for more information). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1347, 12600, 12601, 20451, 49849, 63049, 90469, 90470, 90471, 90472, 90473, 90474, 90475, 90476, 90477, 90478, 90479, 90480, 90481, 90482, 90483, 90484, 90485, 90486, 90487 ], "vocabularyKeywords": [], "identifier_set": [ 12489 ], "observationcollection_set": [ { "ob_id": 32725, "uuid": "d75f0692e2594df8af882c04db5ba3fe", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 9.3\r\n- data for Figure 9.4\r\n- data for Figure 9.5\r\n- data for Figure 9.6\r\n- data for Figure 9.7\r\n- data for Figure 9.9\r\n- data for Figure 9.10\r\n- data for Figure 9.11\r\n- data for Figure 9.12\r\n- data for Figure 9.13\r\n- data for Figure 9.14\r\n- data for Figure 9.15\r\n- data for Figure 9.22\r\n- data for Figure 9.24\r\n- data for Figure 9.26\r\n- data for Figure 9.28\r\n- data for Figure 9.29\r\n- data for Figure 9.30\r\n- data for Figure 9.32\r\n- data for Cross-Chapter Box 9.1, Figure 1\r\n- input data for Cross-Chapter Box 9.1, Figure 1" } ], "responsiblepartyinfo_set": [ 179834, 179835, 179836, 179837, 179838, 179839, 179840, 180165, 180166 ], "onlineresource_set": [ 52523, 52524, 52522, 52521, 82557, 82891, 88634, 94651 ] }, { "ob_id": 37717, "uuid": "b35923b0641944178d0c9e17ce7dc9cb", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 9.9 (v20220721)", "abstract": "Data for Figure 9.9 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 9.9 shows long-term trends of ocean heat content (OHC) and surface temperature. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nFox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\nThe figure has 4 panels with data provided for all panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Ice-core rare gas estimates of past mean OHC (units = ZJ), scaled to global mean ocean temperature (°C), and to steric global mean sea level (GMSL) (m) per CCB-2 (red dashed line), compared to surface temperatures (black solid line, gold solid line; °C rightmost axis). \r\n- Southern Ocean sea surface temperature (SST) from multiple proxies in 11 sediment cores and from ice core deuterium excess (Uemura et al., 2018). \r\n(a) Penultimate glacial interval to last interglacial, 150,000–100,000 yr B2K (before 2000) (Shackleton et al., 2020). First panel.\r\n(b) Last glacial interval to modern interglacial, 40,000–0 yr B2K (Baggenstos et al., 2019; Shackleton et al., 2019). Second panel.\r\n(c) Long-term projected (2000 to 12000 CE) changes of OHC (dashed lines) in response to four greenhouse gas emissions scenarios (Clark et al., 2016) scale similarly to large-scale paleo changes but lag projected global mean SST (solid lines). Third panel.\r\n(d) Model simulated 1500–1999 OHC (Gregory et al., 2006) and 1955–2019 observations (Levitus et al., 2012) updated by NOAA NODC. Fourth panel.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 9.9\r\n \r\n - Data file: Fig9-9_data_Baggenstoslikely_Deglacial.nc\r\n - Data file: Fig9-9_data_Baggenstosmean_Deglacial.nc\r\n - Data file: Fig9-9_data_GlobalMeanSST_Deglacial.nc\r\n - Data file: Fig9-9_data_GlobalMeanSST_LIG.nc\r\n - Data file: Fig9-9_data_HadCM3_Modern.nc\r\n - Data file: Fig9-9_data_Levitus_Modern.nc\r\n - Data file: Fig9-9_data_SST_to_OHC_Conversion_Factor.nc\r\n - Data file: Fig9-9_data_Shackletonlikely_Deglacial.nc\r\n - Data file: Fig9-9_data_Shackletonlikely_LIG.nc\r\n - Data file: Fig9-9_data_Shackletonmean_Deglacial.nc\r\n - Data file: Fig9-9_data_Shackletonmean_LIG.nc\r\n - Data file: Fig9-9_data_SouthernOceanSST_Deglacial.nc\r\n - Data file: Fig9-9_data_SouthernOceanSST_LIG.nc\r\n - Data file: Fig9-9_data_Uemera_Modern.nc\r\n - Data file: Fig9-9_data_ohc_1280Gt.nc\r\n - Data file: Fig9-9_data_ohc_2560Gt.nc\r\n - Data file: Fig9-9_data_ohc_3840Gt.nc\r\n - Data file: Fig9-9_data_ohc_5120Gt.nc\r\n\r\nChanges in OHC (dashed lines) track changes in Southern Ocean SST (solid lines). \r\nAll data expressed as anomalies relative to pre-industrial time. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)\r\n\r\nOHC stands for Ocean Heat Content.\r\nGMSL stands for Global Mean Sea Level.\r\nNOAA NODC is the National Oceanic and Atmospheric Administration's National Oceanographic Data Center.\r\n\r\n---------------------------------------------------\r\nTemporal Range of Paleoclimate Data\r\n---------------------------------------------------\r\nThis dataset covers a paleoclimate timespan from -150kyr (150 thousand years ago) to 14000 CE (long-term future projection).\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nOHC and SST time series were plotted using standard matplotlib software - code is available via the link in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n - Link to the data and code used to produce this figure and others in Chapter 9, archived on Zenodo.\r\n - Link to the output data for this figure, contained in a dedicated GitHub repository.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-02-01T14:59:52", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, ocean heat content, OHC, sea surface temperature, SST, global temperatures, global mean sea level, GMSL", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-02-08T12:18:04", "doiPublishedTime": "2023-05-15T16:52:54.354563", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38022, "dataPath": "/badc/ar6_wg1/data/ch_09/ch9_fig09/v20220721", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1400385, "numberOfFiles": 21, "fileFormat": "NetCDF, txt" }, "timePeriod": { "ob_id": 10974, "startTime": "0001-01-01T00:00:00", "endTime": "2100-01-01T00:00:00" }, "resultQuality": { "ob_id": 18, "explanation": "Research data from the OP3 campaign.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-01-26" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39615, "uuid": "8d9cb1477e5f42159a1adddbb69a04f6", "short_code": "comp", "title": "Caption for Figure 9.9 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Long-term trends of ocean heat content (OHC) and surface temperature. (a, b) Ice-core rare gas estimates of past mean OHC (ZJ), scaled to global mean ocean temperature (°C), and to steric global mean sea level (GMSL) (m) per CCB-2 (red dashed line), compared to surface temperatures (black solid line, gold solid line; °C rightmost axis). Southern Ocean sea surface temperature (SST) from multiple proxies in 11 sediment cores and from ice core deuterium excess (Uemura et al., 2018). (a) Penultimate glacial interval to last interglacial, 150,000–100,000 yr B2K (before 2000) (Shackleton et al., 2020). (b) Last glacial interval to modern interglacial, 40,000–0 yr B2K (Baggenstos et al., 2019; Shackleton et al., 2019). Changes in OHC (dashed lines) track changes in Southern Ocean SST (solid lines). (c) Long-term projected (2000 to 12000 CE) changes of OHC (dashed lines) in response to four greenhouse gas emissions scenarios (Clark et al., 2016) scale similarly to large-scale paleo changes but lag projected global mean SST (solid lines). (d) model simulated 1500–1999 OHC (Gregory et al., 2006) and 1955–2019 observations (Levitus et al., 2012) updated by NOAA NODC. All data expressed as anomalies relative to pre-industrial time. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1347, 50298, 50300, 50301, 50303, 50305, 50307 ], "vocabularyKeywords": [], "identifier_set": [ 12494 ], "observationcollection_set": [ { "ob_id": 32725, "uuid": "d75f0692e2594df8af882c04db5ba3fe", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 9.3\r\n- data for Figure 9.4\r\n- data for Figure 9.5\r\n- data for Figure 9.6\r\n- data for Figure 9.7\r\n- data for Figure 9.9\r\n- data for Figure 9.10\r\n- data for Figure 9.11\r\n- data for Figure 9.12\r\n- data for Figure 9.13\r\n- data for Figure 9.14\r\n- data for Figure 9.15\r\n- data for Figure 9.22\r\n- data for Figure 9.24\r\n- data for Figure 9.26\r\n- data for Figure 9.28\r\n- data for Figure 9.29\r\n- data for Figure 9.30\r\n- data for Figure 9.32\r\n- data for Cross-Chapter Box 9.1, Figure 1\r\n- input data for Cross-Chapter Box 9.1, Figure 1" } ], "responsiblepartyinfo_set": [ 179887, 179888, 179889, 179890, 179891, 179892, 179928, 187088, 187089 ], "onlineresource_set": [ 52514, 52515, 52516, 52513, 82562 ] }, { "ob_id": 37719, "uuid": "e2d7ec1924b04bebbb4044982e2be0ff", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 9.7 (v20220721)", "abstract": "Data for Figure 9.7 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 9.7 shows meridional-depth profiles of zonal-mean potential temperature in the ocean and its rate of change in the upper 2000 m of the Global, Pacific, Atlantic and Indian oceans. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nFox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 8 panels labelled (a)-(h), with data provided for all panels using this lettering system in the GitHub repository linked in the documentation.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains: \r\n\r\n- Observed temperatures from Argo climatology 2005–2014 (a, e, i, m)\r\n- Bias of CMIP6 ensemble over same period 2005-2014 (b, f, j, n)\r\n- Future changes under SSP1-2.6 (c, g, k, o) and SSP5-8.5 (d, h, l, p), 1995-2100. \r\n\r\nNo overlay indicates regions with high model agreement, where ≥80% of models agree on the sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change (see Cross-Chapter Box Atlas.1 for more information). \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 9.SM.9)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 9.7\r\n \r\n - Data file: Fig9-7a_data.nc:\r\n - Data file: Fig9-7b_data.nc:\r\n - Data file: Fig9-7c_data.nc:\r\n - Data file: Fig9-7d_data.nc:\r\n - Data file: Fig9-7e_data.nc:\r\n - Data file: Fig9-7f_data.nc:\r\n - Data file: Fig9-7g_data.nc:\r\n - Data file: Fig9-7h_data.nc:\r\n - Data file: Fig9-7i_data.nc:\r\n - Data file: Fig9-7j_data.nc:\r\n - Data file: Fig9-7k_data.nc: \r\n - Data file: Fig9-7l_data.nc:\r\n - Data file: Fig9-7m_data.nc:\r\n- Data file: Fig9-7n_data.nc:\r\n- Data file: Fig9-7o_data.nc:\r\n- Data file: Fig9-7p_data.nc:\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nSSP stands for Shared Socioeconomic Pathway. \r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5.\r\nSSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Zonal transects were calculated and plotted using standard matplotlib software - code is available via the link in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n - Link to the data and code used to produce this figure and others in Chapter 9, archived on Zenodo.\r\n - Link to the output data for this figure, contained in a dedicated GitHub repository.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-02-01T14:51:32", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, ocean temperature, potential temperature, transect, zonal transects, global ocean, Indian Ocean, Pacific Ocean, Atlantic Ocean", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-02-03T09:33:24", "doiPublishedTime": "2023-05-15T16:47:47.021781", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38021, "dataPath": "/badc/ar6_wg1/data/ch_09/ch9_fig07/v20220721", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 8424797, "numberOfFiles": 19, "fileFormat": "NetCDF, txt" }, "timePeriod": { "ob_id": 10413, "startTime": "1995-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": { "ob_id": 4199, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39614, "uuid": "6622d59f1a84422084f5890fb68664b6", "short_code": "comp", "title": "Caption for Figure 9.7 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Meridional-depth profiles of zonal-mean potential temperature in the ocean and its rate of change in the upper 2000 m of the Global, Pacific, Atlantic and Indian oceans. Shown are (a, e, i, m) observed temperature (Argo climatology 2005–2014), (b, f, j, n) bias of the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble over this period, and future changes under (c, g, k, o) SSP1-2.6 and (d, h, l, p) SSP5-8.5. No overlay indicates regions with high model agreement, where ≥80% of models agree on the sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change (see Cross-Chapter Box Atlas.1 for more information). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 12600, 49849, 49858, 49859, 49860, 49861 ], "vocabularyKeywords": [], "identifier_set": [ 12493 ], "observationcollection_set": [ { "ob_id": 32725, "uuid": "d75f0692e2594df8af882c04db5ba3fe", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 9.3\r\n- data for Figure 9.4\r\n- data for Figure 9.5\r\n- data for Figure 9.6\r\n- data for Figure 9.7\r\n- data for Figure 9.9\r\n- data for Figure 9.10\r\n- data for Figure 9.11\r\n- data for Figure 9.12\r\n- data for Figure 9.13\r\n- data for Figure 9.14\r\n- data for Figure 9.15\r\n- data for Figure 9.22\r\n- data for Figure 9.24\r\n- data for Figure 9.26\r\n- data for Figure 9.28\r\n- data for Figure 9.29\r\n- data for Figure 9.30\r\n- data for Figure 9.32\r\n- data for Cross-Chapter Box 9.1, Figure 1\r\n- input data for Cross-Chapter Box 9.1, Figure 1" } ], "responsiblepartyinfo_set": [ 179899, 179900, 179901, 179902, 179903, 179904, 179926, 180175 ], "onlineresource_set": [ 52510, 52511, 52512, 52509, 82561, 88638 ] }, { "ob_id": 37720, "uuid": "439ccb0b0eb04c17b5c6897fb9cb550b", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 9.6 (v20220721)", "abstract": "Data for Figure 9.6 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 9.6 shows ocean heat content (OHC) and its changes with time. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nFox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\nThe figure has 8 panels labelled (a)-(g), with data provided for all panels using this lettering system in the GitHub repository linked in the documentation.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- (a) Time series of global OHC anomaly relative to a 2005–2014 climatology in the upper 2000 m of the ocean, combining observations (Ishii et al., 2017; Baggenstos et al., 2019; Shackleton et al., 2020), model-observation hybrids (Cheng et al., 2019; Zanna et al., 2019), and multi-model means from CMIP6, historical (29 models) and SSP scenarios. \r\n\r\n- Maps of OHC from CMIP6 ensemble bias and observed (Ishii et al., 2017) trends of OHC for 0–700 m for the period 1971–2014 (b, c)\r\n\r\n- Maps of OHC from CMIP6 ensemble bias and observed (Ishii et al., 2017) trends of OHC for 0–2000 m for the period 2005–2017 (e, f). \r\n\r\n- Projected rate of change 2015–2100 for (d) SSP5-8.5 and (g) SSP1-2.6 scenarios from CMIP6 ensemble means. \r\n\r\n- Projected change in 0–700 m OHC for (d) SSP1-2.6 and (g) SSP5-8.5 in the CMIP6 ensembles, for the period 2091–2100 versus 2005–2014.\r\n\r\nLabel subscripts in (a) indicate number of models per SSP. \r\nNo overlay indicates regions with high model agreement, where ≥80% of models agree on the sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change (see Cross-Chapter Box Atlas.1 for more information). \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 9.SM.9)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 9.6\r\n \r\n - Data file: Fig9-6a_data_CMIPlikelybounds.nc:\r\n - Data file: Fig9-6a_data_CMIPmean.nc:\r\n - Data file: Fig9-6a_data_HybridChengmean.nc:\r\n - Data file: Fig9-6a_data_HybridZannamean.nc:\r\n - Data file: Fig9-6a_data_Observedlikelybounds.nc:\r\n - Data file: Fig9-6a_data_Observedmean.nc:\r\n - Data file: Fig9-6a_data_paleo.nc:\r\n - Data file: Fig9-6a_data_ssp126likelybounds.nc:\r\n - Data file: Fig9-6a_data_ssp126mean.nc:\r\n - Data file: Fig9-6a_data_ssp126verylikelybounds.nc:\r\n - Data file: Fig9-6a_data_ssp245likelybounds.nc:\r\n - Data file: Fig9-6a_data_ssp245mean.nc:\r\n - Data file: Fig9-6a_data_ssp245verylikelybounds.nc:\r\n - Data file: Fig9-6a_data_ssp370likelybounds.nc:\r\n - Data file: Fig9-6a_data_ssp370mean.nc: \r\n - Data file: Fig9-6a_data_ssp370verylikelybounds.nc: \r\n - Data file: Fig9-6a_data_ssp585likelybounds.nc:\r\n - Data file: Fig9-6a_data_ssp585mean.nc:\r\n - Data file: Fig9-6a_data_ssp585verylikelybounds.nc:\r\n - Data file: Fig9-6b_data.nc:\r\n - Data file: Fig9-6c_data.nc:\r\n - Data file: Fig9-6d_data.nc:\r\n - Data file: Fig9-6e_data.nc:\r\n - Data file: Fig9-6f_data.nc:\r\n - Data file: Fig9-6g_data.nc:\r\n\r\nOHC stands for Ocean Heat Content.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project. \r\nSSP stands for Shared Socioeconomic Pathway.\r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5.\r\nSSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n OHC maps and OHC timeseries were plotted using standard matplotlib software - code is available via the link in the documentation.\r\n\r\nThe plotting code is designed to use pre-processed CMIP data, rather than the provided netcdf files. To reproduce these figures form the metadata please modify the example code given here: https://github.com/BrodiePearson/IPCC_AR6_Chapter9_Figures/blob/main/Plotting_code_and_data/Fig9_03_SST/Plot_Figure/Example_plotting_from_metadata.m\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n - Link to the data and code used to produce this figure and others in Chapter 9, archived on Zenodo.\r\n - Link to the output data for this figure, contained in a dedicated GitHub repository.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-02-01T14:53:51", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Ocean Heat Content, OHC", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-02-03T09:32:57", "doiPublishedTime": "2023-05-15T16:30:58.064660", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38020, "dataPath": "/badc/ar6_wg1/data/ch_09/ch9_fig06/v20220721", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5993533, "numberOfFiles": 28, "fileFormat": "NetCDF, txt" }, "timePeriod": { "ob_id": 10414, "startTime": "1971-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": { "ob_id": 4198, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39613, "uuid": "81e91e9e0bfc44f0a7003c735989e621", "short_code": "comp", "title": "Caption for Figure 9.6 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Ocean heat content (OHC) and its changes with time. (a) Time series of global OHC anomaly relative to a 2005–2014 climatology in the upper 2000 m of the ocean. Shown are observations (Ishii et al., 2017; Baggenstos et al., 2019; Shackleton et al., 2020), model-observation hybrids (Cheng et al., 2019; Zanna et al., 2019), and multi-model means from the Coupled Model Intercomparison Project Phase 6 (CMIP6) historical (29 models) and Shared Socio-economic Pathway (SSP) scenarios (label subscripts indicate number of models per SSP). (b–g) Maps of OHC across different time periods, in different layers, and from different datasets/experiments. Maps show the CMIP6 ensemble bias and observed (Ishii et al., 2017) trends of OHC for (b, c) 0–700 m for the period 1971–2014, and (e, f) 0–2000 m for the period 2005–2017. CMIP6 ensemble mean maps show projected rate of change 2015–2100 for (d) SSP5-8.5 and (g) SSP1-2.6 scenarios. Also shown are the projected change in 0–700 m OHC for (d) SSP1-2.6 and (g) SSP5-8.5 in the CMIP6 ensembles, for the period 2091–2100 versus 2005–2014. No overlay indicates regions with high model agreement, where ≥80% of models agree on the sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change (see Cross-Chapter Box Atlas.1 for more information). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1347, 12600, 12601, 49849, 50281, 50282, 50283, 50284, 50285, 50286, 50287, 50288, 50289, 50290, 50291, 50292, 50293, 50294, 50295, 50296, 50297 ], "vocabularyKeywords": [], "identifier_set": [ 12492 ], "observationcollection_set": [ { "ob_id": 32725, "uuid": "d75f0692e2594df8af882c04db5ba3fe", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 9.3\r\n- data for Figure 9.4\r\n- data for Figure 9.5\r\n- data for Figure 9.6\r\n- data for Figure 9.7\r\n- data for Figure 9.9\r\n- data for Figure 9.10\r\n- data for Figure 9.11\r\n- data for Figure 9.12\r\n- data for Figure 9.13\r\n- data for Figure 9.14\r\n- data for Figure 9.15\r\n- data for Figure 9.22\r\n- data for Figure 9.24\r\n- data for Figure 9.26\r\n- data for Figure 9.28\r\n- data for Figure 9.29\r\n- data for Figure 9.30\r\n- data for Figure 9.32\r\n- data for Cross-Chapter Box 9.1, Figure 1\r\n- input data for Cross-Chapter Box 9.1, Figure 1" } ], "responsiblepartyinfo_set": [ 179905, 179906, 179907, 179908, 179909, 179910, 179925, 180171, 180172, 180173, 180174 ], "onlineresource_set": [ 52506, 52505, 82560, 52507, 52508, 88637 ] }, { "ob_id": 37721, "uuid": "8d9719be04d148d88d5ed8edd0426cf2", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 9.5 (v20220721)", "abstract": "Data for Figure 9.5 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 9.5 shows mixed-layer depth in winter and summer. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nFox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\nThe figure has 8 panels labelled (a)-(h), with data provided for all panels using this lettering system in the GitHub repository linked in the documentation.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Mixed-layer depth in (a–d) winter and (e–h) summer (a, e).\r\n- Observed climatological mean mixed-layer depth (based on density threshold) from the Argo Mixed Layer Depth Climatology (Holte et al., 2017) using observations for 2000–2019 (b, f).\r\n- Bias between the observation-based estimate (2000–2019) and the 1995–2014 CMIP6 climatological mean mixed-layer depth (c, d, g, h) .\r\n- Projected mixed-layer depth (MLD) change from 1995–2014 to 2081–2100 under (c, g) SSP1-2.6 and (d, h) SSP5-8.5 scenarios. \r\n\r\nThe (a–d) winter row shows December–January–February (DJF) in the Northern Hemisphere and June–July–August (JJA) in the Southern Hemisphere; the (e–h) summer row shows JJA in the Northern Hemisphere and DJF in the Southern Hemisphere. The mixed-layer depth is the depth where the potential density is 0.03 kg m–3 denser than at 10 m. \r\n\r\nNo overlay indicates regions with high model agreement, where ≥80% of models agree on the sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change (see Cross-Chapter Box Atlas.1 for more information). \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 9.SM.9). \r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 9.5\r\n \r\n- Data file: Fig9-5a_data.nc: \r\n- Data file: Fig9-5b_data.nc: \r\n- Data file: Fig9-5c_data.nc: \r\n- Data file: Fig9-5d_data.nc: \r\n- Data file: Fig9-5e_data.nc: \r\n- Data file: Fig9-5f_data.nc: \r\n- Data file: Fig9-5g_data.nc: \r\n- Data file: Fig9-5h_data.nc: \r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nSSP stands for Shared Socioeconomic Pathway. \r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5.\r\nSSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nMixed layer depth maps were plotted using standard matplotlib software - code is available via the link in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n - Link to the data and code used to produce this figure and others in Chapter 9, archived on Zenodo.\r\n - Link to the output data for this figure, contained in a dedicated GitHub repository.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T01:42:26", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Mixed Layer Depth, MLD", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-02-01T16:05:02", "doiPublishedTime": "2023-05-15T12:58:13.652943", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38019, "dataPath": "/badc/ar6_wg1/data/ch_09/ch9_fig05/v20220721", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 7461292, "numberOfFiles": 11, "fileFormat": "NetCDF, txt" }, "timePeriod": { "ob_id": 10412, "startTime": "1995-01-01T00:00:00", "endTime": "2050-12-31T23:59:59" }, "resultQuality": { "ob_id": 4197, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39612, "uuid": "270817e8fee640bb94777de50bd4491a", "short_code": "comp", "title": "Caption for Figure 9.5 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Mixed-layer depth in (a–d) winter and (e–h) summer. (a, e) Observed climatological mean mixed-layer depth (based on density threshold) from the Argo Mixed Layer Depth Climatology (Holte et al., 2017) usingobservations for 2000–2019. (b, f) Bias between the observation-based estimate (2000–2019) and the 1995–2014 Coupled Model Intercomparison Project Phase 6 (CMIP6) climatological mean mixed-layer depth. (c, d, g, h) Projected mixed-layer depth (MLD) change from 1995–2014 to 2081–2100 under (c, g) SSP1-2.6 and (d, h) SSP5-8.5 scenarios. The (a–d) winter row shows December–January–February (DJF) in the Northern Hemisphere and June–July–August (JJA) in the Southern Hemisphere; The (e–h) summer row shows JJA in the Northern Hemisphere and DJF in the Southern Hemisphere. The mixed-layer depth is the depth where the potential density is 0.03 kg m–3denser than at 10 m. No overlay indicates regions with high model agreement, where ≥80% of models agree on the sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change (see Cross-Chapter Box Atlas.1 for more information). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 12600, 12601, 49849, 50279, 50280 ], "vocabularyKeywords": [], "identifier_set": [ 12491 ], "observationcollection_set": [ { "ob_id": 32725, "uuid": "d75f0692e2594df8af882c04db5ba3fe", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 9.3\r\n- data for Figure 9.4\r\n- data for Figure 9.5\r\n- data for Figure 9.6\r\n- data for Figure 9.7\r\n- data for Figure 9.9\r\n- data for Figure 9.10\r\n- data for Figure 9.11\r\n- data for Figure 9.12\r\n- data for Figure 9.13\r\n- data for Figure 9.14\r\n- data for Figure 9.15\r\n- data for Figure 9.22\r\n- data for Figure 9.24\r\n- data for Figure 9.26\r\n- data for Figure 9.28\r\n- data for Figure 9.29\r\n- data for Figure 9.30\r\n- data for Figure 9.32\r\n- data for Cross-Chapter Box 9.1, Figure 1\r\n- input data for Cross-Chapter Box 9.1, Figure 1" } ], "responsiblepartyinfo_set": [ 179911, 179912, 179913, 179914, 179915, 179916, 179924, 180170, 180169, 180168 ], "onlineresource_set": [ 52520, 52518, 52519, 52517, 82559, 88636, 94652 ] }, { "ob_id": 37722, "uuid": "fdfeb81d2ffd42c3ba2bbb00b681317c", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 9.4 (v20220721)", "abstract": "Data for Figure 9.4 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 9.4 shows global maps of observed mean fluxes, the observed trends in these fluxes, and the projected rate of change in these fluxes from SSP5-8.5, for freshwater, net heat, and wind stress magnitude (momentum).\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nFox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\nThe figure has 9 panels labelled (a)-(i), with data provided for all panels using this lettering system in the GitHub repository linked in the documentation.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Freshwater flux (a–c), net heat flux (d–f), and momentum flux or wind stress magnitude (g–i).\r\n- Means and observed trends between 1995–2014 (freshwater and wind stress) or 2001–2014 (heat). \r\n- SSP5-8.5 projected rates between 1995–2100 using 20-year averages at each end of the time period. - - Objective interpolation from CERES, EBAF v4 (Kato et al., 2018), OAFlux-HR (Yu, 2019) and GPCP (Adler et al., 2003) of fluxes and flux trends (b, e, h). \r\n\r\nObserved trends with no overlay indicate regions where the trends are significant at p = 0.34 level. Crosses indicate regions where trends are not significant. For (c, f, i) projections, no overlay indicates regions with high model agreement, where ≥80% of models agree on the sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change (see Cross-Chapter Box Atlas.1 for more information). \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 9.SM.9). \r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 9.4\r\n \r\n - Data file: Fig9-4a_data.nc: \r\n - Data file: Fig9-4b_data.nc: \r\n - Data file: Fig9-4c_data.nc: \r\n - Data file: Fig9-4d_data.nc: \r\n - Data file: Fig9-4e_data.nc: \r\n - Data file: Fig9-4f_data.nc: \r\n - Data file: Fig9-4g_data.nc: \r\n - Data file: Fig9-4g_data_tauu.nc: \r\n - Data file: Fig9-4g_data_tauv.nc: \r\n - Data file: Fig9-4h_data.nc: \r\n - Data file: Fig9-4i_data.nc: \r\n\r\nCERES stands for Clouds and the Earth’s Radiant Energy System.\r\nEBAF stands for Energy Balanced And Filled. \r\nOAFlux-HR stands for Objectively Analyzed air–sea Fluxes-High Resolution.\r\nGPCP is the Global Precipitation Climatology Project.\r\nSSP stands for Shared Socio-Economic Pathway. \r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nFlux maps were plotted using standard matplotlib software, code is available via the link in the documentation.\r\n\r\nSome of the plotting code is designed to use pre-processed CMIP data, rather than the provided netcdf files. To reproduce these figures form the metadata please modify the example code linked in the Related Documents section of this catalogue record.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n- Link to the data and code used to produce this figure and others in Chapter 9, archived on Zenodo.\r\n- Link to example code on GitHub\r\n- Link to the output data for this figure, contained in a dedicated GitHub repository.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T01:45:15", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, surface flux, freshwater flux, surface heat flux, heat flux, wind stress", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-02-01T15:41:42", "doiPublishedTime": "2023-05-15T12:56:02.790427", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38018, "dataPath": "/badc/ar6_wg1/data/ch_09/ch9_fig04/v20220721", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 7083026, "numberOfFiles": 14, "fileFormat": "NetCDF, txt" }, "timePeriod": { "ob_id": 10413, "startTime": "1995-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": { "ob_id": 4196, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39611, "uuid": "e57907dd153348b48f0e75a74498a75e", "short_code": "comp", "title": "Caption for Figure 9.4 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Global maps of observed mean fluxes (a, d, g), the observed trends in these fluxes (b, e, h) and the projected rate of change in these fluxes from SSP5-8.5 (c, f, i). Shown are the freshwater flux (a–c), net heat flux (d–f), and momentum flux or wind stress magnitude (g–i), with positive numbers indicating ocean freshening, warming, and accelerating respectively. The means and observed trends are calculated between 1995–2014 (freshwater and wind stress) or 2001–2014 (heat). The SSP5-8.5 projected rates are between 1995–2100 using 20-year averages at each end of the time period. Observations show objective interpolation from Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) v4 (Kato et al., 2018), Objectively Analyzed air–sea Fluxes-High Resolution (OAFlux-HR) (Yu, 2019), and Global Precipitation Climatology Project (GPCP) (Adler et al., 2003) of fluxes and flux trends (b, e, h). Observed trends with no overlay indicate regions where the trends are significant at p = 0.34 level. Crosses indicate regions where trends are not significant. For (c, f, i) projections, no overlay indicates regions with high model agreement, where ≥80% of models agree on the sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change (see Cross-Chapter Box Atlas.1 for more information). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 12600, 12601, 49849, 49850, 49851, 49852, 49853, 49854, 49855, 49856, 49857 ], "vocabularyKeywords": [], "identifier_set": [ 12490 ], "observationcollection_set": [ { "ob_id": 32725, "uuid": "d75f0692e2594df8af882c04db5ba3fe", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 9.3\r\n- data for Figure 9.4\r\n- data for Figure 9.5\r\n- data for Figure 9.6\r\n- data for Figure 9.7\r\n- data for Figure 9.9\r\n- data for Figure 9.10\r\n- data for Figure 9.11\r\n- data for Figure 9.12\r\n- data for Figure 9.13\r\n- data for Figure 9.14\r\n- data for Figure 9.15\r\n- data for Figure 9.22\r\n- data for Figure 9.24\r\n- data for Figure 9.26\r\n- data for Figure 9.28\r\n- data for Figure 9.29\r\n- data for Figure 9.30\r\n- data for Figure 9.32\r\n- data for Cross-Chapter Box 9.1, Figure 1\r\n- input data for Cross-Chapter Box 9.1, Figure 1" } ], "responsiblepartyinfo_set": [ 179917, 179918, 179919, 179920, 179921, 179922, 179923, 180167 ], "onlineresource_set": [ 52502, 52503, 52504, 52501, 82558, 82925, 88635 ] }, { "ob_id": 37724, "uuid": "65c832a5eeda4ed7a9b0a8af6cf5058d", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 9.15 (v20220712)", "abstract": "Data for Figure 9.15 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 9.15 shows Antarctic sea ice historical records and CMIP6 projections. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nFox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 2 subpanels, with data provided for both panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- (Left panel) Absolute anomaly of observed monthly mean Antarctic sea ice area during the period 1979–2019 relative to the average monthly mean Antarctic sea ice area during the period 1979–2008. \r\n- (Right panel) Sea ice coverage in the Antarctic as given by the average of the three most widely used satellite-based estimates for September and February, which usually are the months of maximum and minimum sea ice coverage, respectively. \r\n\r\nFirst column: Mean sea ice coverage during the decade 1979–1988. \r\nSecond column: Mean sea ice coverage during the decade 2010–2019. \r\nThird column: Absolute change in sea ice concentration between these two decades, with grid lines indicating non-significant differences. \r\nFourth column: Number of available CMIP6 models that simulate a mean sea ice concentration above 15% for the decade 2045–2054. \r\n\r\nThe average observational record of sea ice area is derived from the UHH sea ice area product (Doerr et al., 2021), based on the average sea ice concentration of OSISAF/CCI (OSI-450 for 1979–2015, OSI-430b for 2016–2019) (Lavergne et al., 2019), NASA Team (version 1, 1979–2019) (Cavalieri et al., 1996) and Bootstrap (version 3, 1979–2019) (Comiso, 2017) that is also used for the figure panels showing observed sea ice concentration. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 9.SM.9).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 9.15\r\n \r\n - Data file: NSIDC_polehole_big.nc\r\n - Data file: NSIDC_polehole_small.nc\r\n - Data file: SeaIceArea__NorthernHemisphere__monthly__UHH__v2019_fv0.01.nc\r\n - Data file: SeaIceArea__SouthernHemisphere__monthly__UHH__v2019_fv0.01.nc\r\n - Data file: cryo_div.txt\r\n - Data file: cryo_seq.txt\r\n\r\nDatafile 'mapplot_data.npz' included in the 'Plotted Data' folder of the GitHub repository is not archived here but on Zenodo at the link provided in the Related Documents section of this catalogue record.\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nNSIDC is the National Snow and Ice Data Center.\r\nUHH is the University of Hamburg (Universität Hamburg).\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nBoth panels were plotted using standard matplotlib software - code is available via the link in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n- Link to the data and code used to produce this figure and others in Chapter 9, archived on Zenodo.\r\n- Link to the code and output data for this figure, contained in a dedicated GitHub repository.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:21:35", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Antarctic, sea ice", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-02-15T15:57:09", "doiPublishedTime": "2023-05-16T15:59:18.340127", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38027, "dataPath": "/badc/ar6_wg1/data/ch_09/ch9_fig15/v20220721", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4975832, "numberOfFiles": 9, "fileFormat": "NetCDF, txt" }, "timePeriod": { "ob_id": 10417, "startTime": "1979-01-01T00:00:00", "endTime": "2054-12-31T23:59:59" }, "resultQuality": { "ob_id": 4206, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39678, "uuid": "26daf647d5ff4e87b1acb1912bd90fbc", "short_code": "comp", "title": "Caption for Figure 9.15 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Antarctic sea ice historical records and Coupled Model Intercomparison Project Phase 6 (CMIP6) projections. (left) Absolute anomaly of observed monthly mean Antarctic sea ice area during the period 1979–2019 relative to the average monthly mean Antarctic sea ice area during the period 1979–2008. (right) Sea ice coverage in the Antarctic as given by the average of the three most widely used satellite-based estimates for September and February, which usually are the months of maximum and minimum sea ice coverage, respectively. First column: Mean sea ice coverage during the decade 1979–1988. Second column: Mean sea ice coverage during the decade 2010–2019. Third column: Absolute change in sea ice concentration between these two decades, with grid lines indicating non-significant differences. Fourth column: Number of available CMIP6 models that simulate a mean sea ice concentration above 15% for the decade 2045–2054. The average observational record of sea ice area is derived from the UHH sea ice area product (Doerr et al., 2021), based on the average sea ice concentration of OSISAF/CCI (OSI-450 for 1979–2015, OSI-430b for 2016–2019) (Lavergne et al., 2019), NASA Team (version 1, 1979–2019) (Cavalieri et al., 1996) and Bootstrap (version 3, 1979–2019) (Comiso, 2017) that is also used for the figure panels showing observed sea ice concentration. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 60438, 63599, 63600, 63601, 63602, 63603, 63604, 63605, 63606, 63607, 63608, 63609, 63610, 63611 ], "vocabularyKeywords": [], "identifier_set": [ 12500 ], "observationcollection_set": [ { "ob_id": 32725, "uuid": "d75f0692e2594df8af882c04db5ba3fe", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 9.3\r\n- data for Figure 9.4\r\n- data for Figure 9.5\r\n- data for Figure 9.6\r\n- data for Figure 9.7\r\n- data for Figure 9.9\r\n- data for Figure 9.10\r\n- data for Figure 9.11\r\n- data for Figure 9.12\r\n- data for Figure 9.13\r\n- data for Figure 9.14\r\n- data for Figure 9.15\r\n- data for Figure 9.22\r\n- data for Figure 9.24\r\n- data for Figure 9.26\r\n- data for Figure 9.28\r\n- data for Figure 9.29\r\n- data for Figure 9.30\r\n- data for Figure 9.32\r\n- data for Cross-Chapter Box 9.1, Figure 1\r\n- input data for Cross-Chapter Box 9.1, Figure 1" } ], "responsiblepartyinfo_set": [ 179935, 179936, 179937, 179938, 179939, 179940, 179941, 193292, 193293 ], "onlineresource_set": [ 52499, 52498, 52500, 52497, 82568, 88631, 94649 ] }, { "ob_id": 37725, "uuid": "e25c3cffd4ae4abc8b2ff9b755fce164", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 9.14 (v20220721)", "abstract": "Data for Figure 9.14 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 9.14 shows monthly mean March and September sea ice area as a function of global surface air temperature (GSAT) anomaly; cumulative anthropogenic CO2 emissions; year in CMIP6 model simulations and in observations. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nFox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 8 subpanels, with data provided for all panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- (a) Monthly mean March sea ice area as a function of global surface air temperature (GSAT) anomaly.\r\n- (b) Monthly mean March sea ice area as a function of cumulative anthropogenic CO2 emissions.\r\n- (c) Monthly mean March sea ice area as a function of year in CMIP6 model simulations and observations.\r\n- (d) Sensitivity of March sea ice loss to anthropogenic CO2 emissions as a function of modelled sensitivity of GSAT to anthropogenic CO2 emissions.\r\n- (e) Monthly mean September sea ice area as a function of global surface air temperature (GSAT) anomaly. \r\n- (f) Monthly mean September sea ice area as a function of cumulative anthropogenic CO2 emissions.\r\n- (g) Monthly mean September sea ice area as a function of year in CMIP6 model simulations and observations\r\n- (h) Sensitivity of September sea ice loss to anthropogenic CO2 emissions as a function of modelled sensitivity of GSAT to anthropogenic CO2 emissions.\r\n\r\nCO2 emissions are shown as CMIP6 model simulations (shading, ensemble mean as bold line) and observations (black dots).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 9.14\r\n \r\n - Data file: CMIP3_sensitivity_nh.nc\r\n - Data file: CMIP5_sensitivity_nh.nc\r\n - Data file: CMIP6_historical_nh.nc\r\n - Data file: CMIP6_sensitivity_nh.nc\r\n - Data file: CMIP6_ssp119_nh.nc\r\n - Data file: CMIP6_ssp126_nh.nc\r\n - Data file: CMIP6_ssp245_nh.nc\r\n - Data file: CMIP6_ssp585_nh.nc\r\n - Data file: CO2_CMIP6.nc\r\n - Data file: IPCC_GSAT.csv: Provisional time series for use in GSAT calculations. \r\n - Data file: SeaIceArea__NorthernHemisphere__monthly__UHH__v2019_fv0.01.nc\r\n - Data file: SeaIceArea__SouthernHemisphere__monthly__UHH__v2019_fv0.01.nc\r\n - Data file: obs_sensitivity_nh.nc\r\n\r\nCMIP3 is the third phase of the Coupled Model Intercomparison Project.\r\nCMIP5 is the fifth phase of Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nGSAT stands for Global Surface Air Temperature. \r\nSSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6.\r\nSSP245 is the Shared Socioeconomic Pathway which represents the median of radiative forcing and development scenarios, consistent with RCP4.5.\r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5.\r\nUHH is the University of Hamburg (Universität Hamburg).\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nSections of the figure in the report were plotted using standard matplotlib software - code is available via the link in the documentation. Some data has been converted to net-CDF format for archival, original .mat files used in the plotting script are archived on Zenodo at the link provided in the Related Document section of this catalogue record.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n- Link to the data and code used to produce this figure and others in Chapter 9, archived on Zenodo.\r\n- Link to the output data for this figure, contained in a dedicated GitHub repository.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-12-19T09:15:57", "latestDataUpdateTime": "2023-02-06T16:35:35", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).\r\nData converted to net-CDF format for archival by author.", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, sea ice, sea-ice area", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-02-06T16:52:06", "doiPublishedTime": "2023-05-16T15:51:46.623457", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38025, "dataPath": "/badc/ar6_wg1/data/ch_09/ch9_fig14/v20220721", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 41147764, "numberOfFiles": 17, "fileFormat": "Data are net-CDF and csv formatted" }, "timePeriod": { "ob_id": 1102, "startTime": "1850-01-01T00:00:00", "endTime": "2100-12-31T00:00:00" }, "resultQuality": { "ob_id": 4205, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39679, "uuid": "da64337eab4940e5a44825dd08726be2", "short_code": "comp", "title": "Caption for Figure 9.14 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Monthly mean March (a–d) and September (e–h) sea ice area as a function of global surface air temperature (GSAT) anomaly (a, e); cumulative anthropogenic CO2 emissions (b, f); year (c, g) in Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulations (shading, ensemble mean as bold line) and in observations (black dots). Panels (d) and (h) show the sensitivity of sea ice loss to anthropogenic CO2 emissions as a function of the modelled sensitivity of GSAT to anthropogenic CO2 emissions. In panels (d) and (h), the black dot denotes the observed sensitivity, while the shading around it denotes internal variability as inferred from CMIP6 simulations (after Notz and SIMIP Community, 2020). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 13238, 21859, 60438, 63032, 63033, 63034, 63035, 63036, 63037, 63038, 63039, 63040, 63041, 63042, 63043, 63044, 63045, 63046, 63047, 63048, 63602, 63604, 63606, 63608, 63610, 63611, 64068, 64069 ], "vocabularyKeywords": [], "identifier_set": [ 12498 ], "observationcollection_set": [ { "ob_id": 32725, "uuid": "d75f0692e2594df8af882c04db5ba3fe", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 9.3\r\n- data for Figure 9.4\r\n- data for Figure 9.5\r\n- data for Figure 9.6\r\n- data for Figure 9.7\r\n- data for Figure 9.9\r\n- data for Figure 9.10\r\n- data for Figure 9.11\r\n- data for Figure 9.12\r\n- data for Figure 9.13\r\n- data for Figure 9.14\r\n- data for Figure 9.15\r\n- data for Figure 9.22\r\n- data for Figure 9.24\r\n- data for Figure 9.26\r\n- data for Figure 9.28\r\n- data for Figure 9.29\r\n- data for Figure 9.30\r\n- data for Figure 9.32\r\n- data for Cross-Chapter Box 9.1, Figure 1\r\n- input data for Cross-Chapter Box 9.1, Figure 1" } ], "responsiblepartyinfo_set": [ 179948, 179949, 179950, 179951, 179952, 179953, 179954, 193295 ], "onlineresource_set": [ 52495, 52494, 52496, 52493, 82567 ] }, { "ob_id": 37726, "uuid": "6f6697fff85e42fdb87156ad34e4a24e", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 9.13 (v20220721)", "abstract": "Data for Figure 9.13 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 9.13 shows Arctic sea ice historical records and CMIP6 projections. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nFox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 2 subpanels, with data provided for both panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n- (Left panel) Absolute anomaly of monthly-mean Arctic sea ice area during the period 1979 to 2019 relative to the average monthly-mean Arctic sea ice area during the period 1979 to 2008. \r\n- (Right panel) Sea ice concentration in the Arctic for March and September, which usually are the months of maximum and minimum sea ice area, respectively. \r\n\r\nFirst column: Satellite-retrieved mean sea ice concentration during the decade 1979–1988. Second column: Satellite-retrieved mean sea ice concentration during the decade 2010-2019. \r\nThird column: Absolute change in sea ice concentration between these two decades, with grid lines indicating non-significant differences. \r\nFourth column: Number of available CMIP6 models that simulate a mean sea ice concentration above 15 % for the decade 2045–2054. \r\n\r\nThe average observational record of sea ice area is derived from the UHH sea ice area product (Doerr et al., 2021), based on the average sea ice concentration of OSISAF/CCI (OSI-450 for 1979–2015, OSI-430b for 2016–2019) (Lavergne et al., 2019), NASA Team (version 1, 1979–2019) (Cavalieri et al., 1996) and Bootstrap (version 3, 1979–2019) (Comiso, 2017) that is also used for the figure panels showing observed sea ice concentration. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 9.SM.9)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 9.13\r\n \r\n - Data file: NSIDC_polehole_big.nc\r\n - Data file: NSIDC_polehole_small.nc\r\n - Data file: SeaIceArea__NorthernHemisphere__monthly__UHH__v2019_fv0.01.nc\r\n - Data file: SeaIceArea__SouthernHemisphere__monthly__UHH__v2019_fv0.01.nc\r\n - Data file: cryo_div.txt\r\n - Data file: cryo_seq.txt\r\n\r\nDatafile 'mapplot_data.npz' included in the 'Plotted Data' folder of the dedicated GitHub repository is not archived here but on Zenodo at the link provided in the Related Documents section of this catalogue record.\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nNSIDC is the National Snow and Ice Data Center.\r\nUHH is the University of Hamburg (Universtität Hamburg).\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nBoth panels were plotted using standard matplotlib software - code is available via the link in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n- Link to the data and code used to produce this figure and others in Chapter 9, archived on Zenodo.\r\n- Link to the output data and scripts for this figure, contained in a dedicated GitHub repository.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-02-08T16:41:14", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, Arctic, sea ice", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-02-08T16:57:48", "doiPublishedTime": "2023-05-16T15:40:10.817396", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38024, "dataPath": "/badc/ar6_wg1/data/ch_09/ch9_fig13/v20220721", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4975844, "numberOfFiles": 9, "fileFormat": "NetCDF, txt" }, "timePeriod": { "ob_id": 10416, "startTime": "1979-01-01T00:00:00", "endTime": "2054-12-31T23:59:59" }, "resultQuality": { "ob_id": 4204, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39705, "uuid": "22beeb711b9d4f979a9a1f77f2de2869", "short_code": "comp", "title": "Caption for Figure 9.13 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Arctic sea ice historical records and Coupled Model Intercomparison Project Phase 6 (CMIP6) projections. (left) Absolute anomaly of monthly-mean Arctic sea ice area during the period 1979 to 2019 relative to the average monthly-mean Arctic sea ice area during the period 1979 to 2008. (right) Sea ice concentration in the Arctic for March and September, which usually are the months of maximum and minimum sea ice area, respectively. First column: Satellite-retrieved mean sea ice concentration during the decade 1979–1988. Second column: Satellite-retrieved mean sea ice concentration during the decade 2010–2019. Third column: Absolute change in sea ice concentration between these two decades, with grid lines indicating non-significant differences. Fourth column: Number of available CMIP6 models that simulate a mean sea ice concentration above 15 % for the decade 2045–2054. The average observational record of sea ice area is derived from the UHH sea ice area product (Doerr et al., 2021), based on the average sea ice concentration of OSISAF/CCI (OSI-450 for 1979–2015, OSI-430b for 2016–2019) (Lavergne et al., 2019), NASA Team (version 1, 1979–2019) (Cavalieri et al., 1996) and Bootstrap (version 3, 1979–2019) (Comiso, 2017) that is also used for the figure panels showing observed sea ice concentration. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 60438, 63599, 63600, 63601, 63602, 63603, 63604, 63605, 63606, 63607, 63608, 63609, 63610, 63611 ], "vocabularyKeywords": [], "identifier_set": [ 12497 ], "observationcollection_set": [ { "ob_id": 32725, "uuid": "d75f0692e2594df8af882c04db5ba3fe", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 9.3\r\n- data for Figure 9.4\r\n- data for Figure 9.5\r\n- data for Figure 9.6\r\n- data for Figure 9.7\r\n- data for Figure 9.9\r\n- data for Figure 9.10\r\n- data for Figure 9.11\r\n- data for Figure 9.12\r\n- data for Figure 9.13\r\n- data for Figure 9.14\r\n- data for Figure 9.15\r\n- data for Figure 9.22\r\n- data for Figure 9.24\r\n- data for Figure 9.26\r\n- data for Figure 9.28\r\n- data for Figure 9.29\r\n- data for Figure 9.30\r\n- data for Figure 9.32\r\n- data for Cross-Chapter Box 9.1, Figure 1\r\n- input data for Cross-Chapter Box 9.1, Figure 1" } ], "responsiblepartyinfo_set": [ 179961, 179962, 179963, 179964, 179965, 179966, 179967, 193405, 193406 ], "onlineresource_set": [ 52491, 52492, 52490, 52489, 82566, 88630 ] }, { "ob_id": 37727, "uuid": "b37501409dd641219dd7c57174acdc35", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 9.12 (v20220721)", "abstract": "Data for Figure 9.12 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 9.12 shows CMIP6 multi-model mean projected change contributions to relative sea level change in steric sea level anomaly, thermosteric sea level anomaly, and halosteric sea level anomaly between 1995–2014 and 2081–2100.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nFox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 9 subpanels, with data provided for all panels. \r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- (a, d) CMIP6 multi-model mean projected change contributions to relative sea level change in steric sea level anomaly between 1995-2014 and 2081-2100..\r\n- (b, e) CMIP6 multi-model mean projected change contributions to relative sea level change in thermosteric sea level anomaly between 1995-2014 and 2081-2100.\r\n- (c, f) CMIP6 multi-model mean projected change contributions to relative sea level change in halosteric sea level anomaly between 1995-2014 and 2081-2100.\r\n- (g–i) Standard deviation of ocean dynamic sea level change from (g) Aviso observations (10-day high-pass filter); (h) five-day mean of high-resolution Ocean Model Intercomparison Project phase 2 (OMIP-2) models forced with observed fluxes; and (i) five-day mean of low-resolution OMIP-2 models which are comparable in resolution to the models in (a–f).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 9.12\r\n \r\n - Data file: Fig9-12a_data.nc\r\n - Data file: Fig9-12b_data.nc\r\n - Data file: Fig9-12c_data.nc\r\n - Data file: Fig9-12d_data.nc\r\n - Data file: Fig9-12e_data.nc\r\n - Data file: Fig9-12f_data.nc\r\n - Data file: Fig9-12g_data.nc\r\n - Data file: Fig9-12h_data.nc\r\n - Data file: Fig9-12i_data.nc\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nOMIP-2 is the Ocean Model Intercomparison Project phase 2.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nPanels were plotted using standard matplotlib software - code is available via the link in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n - Link to the data and code used to produce this figure and others in Chapter 9, archived on Zenodo.\r\n - Link to the output data for this figure, contained in a dedicated GitHub repository along with code to plot the figure.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-02-01T15:07:27", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, sea level, sea level rise, sea surface height, standard deviation of sea surface height, steric, thermosteric, halosteric", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-02-03T09:26:13", "doiPublishedTime": "2023-05-16T15:33:50.084365", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38023, "dataPath": "/badc/ar6_wg1/data/ch_09/ch9_fig12/v20220721", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 8069034, "numberOfFiles": 12, "fileFormat": "NetCDF, txt" }, "timePeriod": { "ob_id": 10413, "startTime": "1995-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": { "ob_id": 4203, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39641, "uuid": "ddb402f172ed4bdd87d8fef1f703be30", "short_code": "comp", "title": "Caption for Figure 9.12 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "(a–f) Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model mean projected change contributions to relative sea level change in (a, d) steric sea level anomaly, (b, e) thermosteric sea level anomaly, and (c, f) halosteric sea level anomaly between 1995–2014 and 2081–2100 using a method that does not require a reference level (Landerer et al., 2007). Global mean change has been removed from these figures, consistent with the methods in Sections 9.6.3 and 9.SM.4 and the definitions of Gregory et al. (2019). (Gregory et al., 2019). See Figure 9.27 for global mean sea level (GMSL). (g–i) Standard deviation of ocean dynamic sea level change from (g) Aviso observations (10-day high-pass filter); (h) five-day mean of high-resolution Ocean Model Intercomparison Project phase 2 (OMIP-2) models forced with observed fluxes; and (i) five-day mean of low-resolution OMIP-2 models which are comparable in resolution to the models in (a–f). No overlay indicates regions with high model agreement, where ≥80% of models agree on the sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change (see Cross-Chapter Box Atlas.1 for more information). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 12600, 12601, 49849, 50314, 50315, 50317, 50319 ], "vocabularyKeywords": [], "identifier_set": [ 12496 ], "observationcollection_set": [ { "ob_id": 32725, "uuid": "d75f0692e2594df8af882c04db5ba3fe", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 9.3\r\n- data for Figure 9.4\r\n- data for Figure 9.5\r\n- data for Figure 9.6\r\n- data for Figure 9.7\r\n- data for Figure 9.9\r\n- data for Figure 9.10\r\n- data for Figure 9.11\r\n- data for Figure 9.12\r\n- data for Figure 9.13\r\n- data for Figure 9.14\r\n- data for Figure 9.15\r\n- data for Figure 9.22\r\n- data for Figure 9.24\r\n- data for Figure 9.26\r\n- data for Figure 9.28\r\n- data for Figure 9.29\r\n- data for Figure 9.30\r\n- data for Figure 9.32\r\n- data for Cross-Chapter Box 9.1, Figure 1\r\n- input data for Cross-Chapter Box 9.1, Figure 1" } ], "responsiblepartyinfo_set": [ 179976, 179977, 179978, 179979, 179980, 180179, 179974, 179975, 180180, 180181 ], "onlineresource_set": [ 52486, 52487, 52488, 52485, 82565, 88629, 94648 ] }, { "ob_id": 37728, "uuid": "88dc6a422faa4d0486d35088e3d1d78f", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 9.11 (v20220712)", "abstract": "Data for Figure 9.11 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 9.11 shows simulated barotropic streamfunction, surface speed and major current transport in CMIP5 and CMIP6. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nFox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 6 subpanels, with data provided for all panels in one central directory in the GitHub repository linked in the documentation.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- (a) Mean barotropic streamfunction (unit: 109 kgs–1; 1995–2014)\r\n- (b) Projected barotropic streamfunction change (109 kgs–1; 2018–2100 vs 1995–2014) under SSP5-8.5. \r\n- (d) Mean surface (0–100 m) speed (m s–1)\r\n- (e) Projected surface speed change (m s–1, 2081–2100) versus 1995–2014 under SSP5-8.5.\r\n- (c, f) Median and likely range of 1995–2014 and 2081–2100 transport of three currents with the largest transport change and four with the largest fractional change (Sen Gupta et al., 2016). (c) Deep currents: Agulhas Extension (ACx), Gulf Stream (GS), Gulf Stream Extension (GSx), Tasman Leakage (TASL), East Australia Current Extension (EACx), Indonesian Throughflow (ITF), and Brazil Current (BC). (f) Shallow currents: as for deep but with New Guinea Current (NGC), and without ACx. \r\n\r\n\r\nNo overlay indicates regions with high model agreement, where ≥80% of models agree on the sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change (see Cross-Chapter Box Atlas.1 for more information). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9). \r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 9.11\r\n \r\n - Data file: Fig9-11a_data.nc:\r\n - Data file: Fig9-11b_data.nc:\r\n - Data file: Fig9-11d_data.nc:\r\n - Data file: Fig9-11e_data.nc:\r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project. \r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nSpeed maps, stream function maps and transport panels were plotted using standard matplotlib software - code is available via the link in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n - Link to the data and code used to produce this figure and others in Chapter 9, archived on Zenodo.\r\n - Link to the output data for this figure, contained in a dedicated GitHub repository.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-02-01T15:03:56", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, streamfunction, surface speed, surface currents, transport, ocean transport", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-02-02T10:08:44", "doiPublishedTime": "2023-05-16T15:25:38.478957", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38016, "dataPath": "/badc/ar6_wg1/data/ch_09/ch9_fig11/v20220721", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3218973, "numberOfFiles": 7, "fileFormat": "NetCDF, txt" }, "timePeriod": { "ob_id": 10413, "startTime": "1995-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": { "ob_id": 4202, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39617, "uuid": "bdd989d212244327ace15c68499b7114", "short_code": "comp", "title": "Caption for Figure 9.11 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Simulated barotropic streamfunction, surface speed and major current transport in Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6). (a) Mean barotropic streamfunction (unit: 109kgs–1; 1995–2014) and projected barotropic streamfunction change (109kgs–1; 2018–2100 vs 1995–2014) under (b) SSP5-8.5. (d) Mean surface (0–100 m) speed (m s–1) and projected surface speed change (m s–1, 2081–2100) versus 1995–2014 under (e) SSP5-8.5. (c, f) Median and likely range of 1995–2014 and 2081–2100 transport of three currents with the largest transport change and four with the largest fractional change (Sen Gupta et al., 2016). (c) Deep currents: Agulhas Extension (ACx), Gulf Stream (GS), Gulf Stream Extension (GSx), Tasman Leakage (TASL), East Australia Current Extension (EACx), Indonesian Throughflow (ITF), and Brazil Current (BC). (f) Shallow currents: as for deep but with New Guinea Current (NGC), and without ACx. No overlay indicates regions with high model agreement, where ≥80% of models agree on the sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change (see Cross-Chapter Box Atlas.1 for more information). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 12600, 12601, 49849, 50309, 50310, 50311, 50313 ], "vocabularyKeywords": [], "identifier_set": [ 12495 ], "observationcollection_set": [ { "ob_id": 32725, "uuid": "d75f0692e2594df8af882c04db5ba3fe", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 9.3\r\n- data for Figure 9.4\r\n- data for Figure 9.5\r\n- data for Figure 9.6\r\n- data for Figure 9.7\r\n- data for Figure 9.9\r\n- data for Figure 9.10\r\n- data for Figure 9.11\r\n- data for Figure 9.12\r\n- data for Figure 9.13\r\n- data for Figure 9.14\r\n- data for Figure 9.15\r\n- data for Figure 9.22\r\n- data for Figure 9.24\r\n- data for Figure 9.26\r\n- data for Figure 9.28\r\n- data for Figure 9.29\r\n- data for Figure 9.30\r\n- data for Figure 9.32\r\n- data for Cross-Chapter Box 9.1, Figure 1\r\n- input data for Cross-Chapter Box 9.1, Figure 1" } ], "responsiblepartyinfo_set": [ 179981, 179982, 179983, 179984, 179985, 179986, 179987, 180176, 180177, 180178 ], "onlineresource_set": [ 52482, 52483, 52484, 52481, 82564, 88628 ] }, { "ob_id": 37729, "uuid": "260df0db210143dcbecf3182e24817a3", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 9.10 (v20220712)", "abstract": "Data for Figure 9.10 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 9.10 shows Atlantic Meridional Overturning Circulation (AMOC) strength in simulations and sensitivity to resolution and forcing. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nFox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 4 subpanels, with data for all panels contained in the code archived on Zenodo which is linked in the documentation. Data and code can also be found on the GitHub repository for chapter 9 linked in the documentation.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- AMOC magnitude (units: Sverdrup (Sv) = 109 kg s–1) in PMIP experiments (Top left). \r\n- Time series of AMOC from CMIP5 and CMIP6 based on (Menary et al., 2020b) (Top right). \r\n- Percent change in AMOC strength per year at different resolutions over the 1950–2050 period with colours for model families (Roberts et al., 2020) (Bottom left).\r\n- A compilation of percentage changes in the simulated AMOC after applying an additional freshwater flux in the subpolar North Atlantic at the surface for a limited time (de Vries and Weber, 2005; Stouffer et al., 2006; Yin and Stouffer, 2007; Jackson, 2013; Liu and Liu, 2013; Jackson and Wood, 2018; Haskins et al., 2019) (Bottom right). \r\n\r\nSymbols indicate whether the AMOC recovers within 200 years (circles), is starting to recover (upwards arrow), or does not recover within 200 years (downwards arrow). Symbol size indicates rate of freshwater input. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 9.SM.9).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nData provided for all panels in the code archived on Zenodo which is linked in the Related Documents section of this catalogue record. Data and code can also be found on the GitHub repository for chapter 9 also linked here.\r\n\r\nAMOC is the Atlantic Meridional Overturning Circulation.\r\nPMIP is the Paleoclimate Modelling Intercomparison Project.\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nAll panels were plotted using standard matplotlib software - code is available via the link in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n - Link to the data and code used to produce this figure and others in Chapter 9, archived on Zenodo.\r\n - Link to the dedicated GitHub repository for figure.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-07-12T11:03:07", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, meridional overturning circulation, AMOC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-02-09T14:20:36", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40274, "dataPath": "https://github.com/BrodiePearson/IPCC_AR6_Chapter9_Figures/tree/main/Plotting_code_and_data/Fig9_10_AMOC", "oldDataPath": [], "storageLocation": "external", "storageStatus": "online", "volume": 0, "numberOfFiles": 0, "fileFormat": "Data written into code on the Chapter 9 GitHub repository" }, "timePeriod": { "ob_id": 10415, "startTime": "1850-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": { "ob_id": 4201, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39616, "uuid": "c835cba84f8d42a0ba6b940f1a46be4b", "short_code": "comp", "title": "Caption for Figure 9.10 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Atlantic Meridional Overturning Circulation (AMOC) strength in simulations and sensitivity to resolution and forcing. (Top left) AMOC magnitude (units: Sverdrup (Sv) = 109kgs–1) in Paleoclimate Modelling Intercomparison Project (PMIP) experiments. (Top right) Time series of AMOC from Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) based on (Menary et al., 2020b). (Bottom left) Percent change in AMOC strength per year at different resolutions over the 1950–2050 period with colours for model families (Roberts et al., 2020). (Bottom right) A compilation of percentage changes in the simulated AMOC after applying an additional freshwater flux in the subpolar North Atlantic at the surface for a limited time (de Vries and Weber, 2005; Stouffer et al., 2006; Yin and Stouffer, 2007; Jackson, 2013; Liu and Liu, 2013; Jackson and Wood, 2018; Haskins et al., 2019). Symbols indicate whether the AMOC recovers within 200 years (circles), is starting to recover (upwards arrow), or does not recover within 200 years (downwards arrow). Symbol size indicates rate of freshwater input. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 32725, "uuid": "d75f0692e2594df8af882c04db5ba3fe", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 9.3\r\n- data for Figure 9.4\r\n- data for Figure 9.5\r\n- data for Figure 9.6\r\n- data for Figure 9.7\r\n- data for Figure 9.9\r\n- data for Figure 9.10\r\n- data for Figure 9.11\r\n- data for Figure 9.12\r\n- data for Figure 9.13\r\n- data for Figure 9.14\r\n- data for Figure 9.15\r\n- data for Figure 9.22\r\n- data for Figure 9.24\r\n- data for Figure 9.26\r\n- data for Figure 9.28\r\n- data for Figure 9.29\r\n- data for Figure 9.30\r\n- data for Figure 9.32\r\n- data for Cross-Chapter Box 9.1, Figure 1\r\n- input data for Cross-Chapter Box 9.1, Figure 1" } ], "responsiblepartyinfo_set": [ 180000, 180001, 180002, 180003, 180004, 180005, 180006, 192550 ], "onlineresource_set": [ 52478, 52479, 52480, 52477, 82563, 88627 ] }, { "ob_id": 37730, "uuid": "6b33327d0d0d4bcca872b431279086db", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 9.32 (v20220721)", "abstract": "Data for Figure 9.32 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 9.32 shows projected median frequency amplification factors for the 1% average annual probability extreme still water level in 2050 and 2100. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nFox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 6 subpanels, with data provided for panels a-f.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Projected median frequency amplification factors for 1% average annual probability extreme still water level in 2050 (a, c, e) and 2100 (b, d, f).\r\n- Regional projections for these under:\r\n(a, b) SSP5-85.\r\n(c, d) SSP2-45.\r\n(e, f) SSP1-26.\r\n\r\n1% average annual probability extreme still water level is defined as the 99th percentile of daily observed water levels over 1995–2014. Based on a peak-over-threshold (99.7%) method applied to the historical extreme still water levels of Global Extreme Sea Level Analysis version 2 (GESLA2) following Special Report on Ocean and Cryosphere in a Changing Climate (SROCC) and additionally fitting a Gumbel distribution between Mean Higher High Water (MHHW) and the threshold following Buchanan et al. (2016), using the regional sea level projections of Section 9.6.3.3 for (a-f).\r\n\r\n Further details on data sources and processing are available in the chapter data table \r\n(Table 9.SM.9).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 9.32\r\n \r\n - Data file: Fig9-32a_facts_esl_af_allow_ssp585_2050.nc\r\n - Data file: Fig9-32b_facts_esl_af_allow_ssp585_2100.nc\r\n - Data file: Fig9-32c_facts_esl_af_allow_ssp245_2050.nc\r\n - Data file: Fig9-32d_facts_esl_af_allow_ssp245_2100.nc\r\n - Data file: Fig9-32e_facts_esl_af_allow_ssp126_2050.nc\r\n - Data file: Fig9-32f_facts_esl_af_allow_ssp126_2100.nc\r\n\r\nPython file 'facts_esl_output_to_NetCDF.py' included in the 'Plotted Data' folder of the dedicated GitHub repository is not archived here but on Zenodo at the link provided in the Related Documents section of this catalogue record. This is a script converting ESL results in CSV format to NetCDF.\r\n\r\nSSP stands for Shared Socioeconomic Pathway. \r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5.\r\nSSP245 is the Shared Socioeconomic Pathway which represents the median of radiative forcing and development scenarios, consistent with RCP4.5.\r\nSSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6.\r\nGESLA2 is the Global Extreme Sea Level Analysis version 2.\r\nSROCC stands for Ocean and Cryosphere in a Changing Climate. \r\nMHHW stands for Mean Higher High Water.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nMap and figure plots were plotted using standard matplotlib software - code is available via the link in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n - Link to the data and code used to produce this figure and others in Chapter 9, archived on Zenodo.\r\n - Link to the code and output data for this figure, contained in a dedicated GitHub repository.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T01:42:26", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, amplification, frequency amplification factors, extreme sea level, extreme still water level", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-02-15T16:13:41", "doiPublishedTime": "2023-05-17T10:14:38.343156", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38029, "dataPath": "/badc/ar6_wg1/data/ch_09/ch9_fig32/v20220721", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 431159, "numberOfFiles": 9, "fileFormat": "NetCDF, txt" }, "timePeriod": { "ob_id": 10420, "startTime": "2050-01-01T00:00:00", "endTime": "2100-01-01T23:59:59" }, "resultQuality": { "ob_id": 4212, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39689, "uuid": "90d27d3e388843499d26e994d59b59be", "short_code": "comp", "title": "Caption for Figure 9.32 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected median frequency amplification factors for the 1% average annual probability extreme still water level in 2050 (a, c, e) and 2100 (b, d, f). Based on a peak-over-threshold (99.7%) method applied to the historical extreme still water levels of Global Extreme Sea Level Analysis version 2 (GESLA2) following Special Report on Ocean and Cryosphere in a Changing Climate (SROCC) and additionally fitting a Gumbel distribution between Mean Higher High Water (MHHW) and the threshold following Buchanan et al. (2016), using the regional sea level projections of Section 9.6.3.3 for (a, b) SSP5-8.5, (c, d) SSP2-4.5 and (e, f) SSP1-2.6. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 18405, 18408, 50339, 50340, 50341 ], "vocabularyKeywords": [], "identifier_set": [ 12511 ], "observationcollection_set": [ { "ob_id": 32725, "uuid": "d75f0692e2594df8af882c04db5ba3fe", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 9.3\r\n- data for Figure 9.4\r\n- data for Figure 9.5\r\n- data for Figure 9.6\r\n- data for Figure 9.7\r\n- data for Figure 9.9\r\n- data for Figure 9.10\r\n- data for Figure 9.11\r\n- data for Figure 9.12\r\n- data for Figure 9.13\r\n- data for Figure 9.14\r\n- data for Figure 9.15\r\n- data for Figure 9.22\r\n- data for Figure 9.24\r\n- data for Figure 9.26\r\n- data for Figure 9.28\r\n- data for Figure 9.29\r\n- data for Figure 9.30\r\n- data for Figure 9.32\r\n- data for Cross-Chapter Box 9.1, Figure 1\r\n- input data for Cross-Chapter Box 9.1, Figure 1" } ], "responsiblepartyinfo_set": [ 180019, 180020, 180021, 180022, 180023, 180024, 180025, 180188, 180189, 180190, 180191 ], "onlineresource_set": [ 52476, 52474, 52475, 52473, 82575 ] }, { "ob_id": 37731, "uuid": "9374ee722fab464fb3ee8ea659b56546", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 9.30 (v20220712)", "abstract": "Data for Figure 9.30 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 9.30 shows global mean sea level (GMSL) commitment as a function of peak global surface air temperature. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nFox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 10 subpanels, with data provided for all panels in one central directory in the GitHub repository linked in the documentation.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Global mean sea level (GMSL) commitment as a function of peak global surface air temperature from models (Clark et al., 2016; DeConto and Pollard, 2016; Garbe et al., 2020; Van Breedam et al., 2020) and paleo data on 2000-year (lower row) and 10,000 year (upper row) time scales. \r\n- Different contributors to GMSL rise (from left to right panels: total GMSL change, Antarctic Ice Sheet, Greenland Ice Sheet, global mean thermosteric sea level rise, and glaciers). \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 9.SM.9)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 9.30\r\n \r\n - Data file: Fig9-30_data_Clark2016_UVic28_AIS_10000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic28_AIS_2000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic28_GIC_10000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic28_GIC_2000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic28_GMSL_10000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic28_GMSL_2000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic28_GMTE_10000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic28_GMTE_2000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic28_GrIS_10000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic28_GrIS_2000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic29_AIS_10000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic29_AIS_2000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic29_GIC_10000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic29_GIC_2000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic29_GMSL_10000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic29_GMSL_2000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic29_GMTE_10000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic29_GMTE_2000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic29_GrIS_10000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic29_GrIS_2000y.nc\r\n - Data file: Fig9-30_data_DeConto2016_AIS_2000y.nc\r\n - Data file: Fig9-30_data_Garbe2020_AIS.nc\r\n - Data file: Fig9-30_data_Gregory2020_GrIS_10000y.nc\r\n - Data file: Fig9-30_data_Gregory2020_GrIS_2000y.nc\r\n - Data file: Fig9-30_data_VB2020_AIS_10000y.nc\r\n - Data file: Fig9-30_data_VB2020_AIS_2000y.nc\r\n - Data file: Fig9-30_data_VB2020_GIC_10000y.nc\r\n - Data file: Fig9-30_data_VB2020_GIC_2000y.nc\r\n - Data file: Fig9-30_data_VB2020_GMSL_10000y.nc\r\n - Data file: Fig9-30_data_VB2020_GMSL_2000y.nc\r\n - Data file: Fig9-30_data_VB2020_GMTE_10000y.nc\r\n - Data file: Fig9-30_data_VB2020_GMTE_2000y.nc\r\n - Data file: Fig9-30_data_VB2020_GrIS_10000y.nc\r\n - Data file: Fig9-30_data_VB2020_GrIS_2000y.nc\r\n\r\nGMSL stands for Global Mean Sea Level.\r\n\r\n---------------------------------------------------\r\nTemporal Range of Paleoclimate Data\r\n---------------------------------------------------\r\nThis dataset covers a paleoclimate timespan from 10,000 years ago to present.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nSLR commitments were plotted using standard matplotlib software - code is available via the link in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to the output data and plotting code for this figure, contained in a dedicated GitHub repository.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:36", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, sea level, sea-level commitment", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-02-21T09:02:41", "doiPublishedTime": "2023-05-17T10:06:24.666200", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 39756, "dataPath": "/badc/ar6_wg1/data/ch_09/ch9_fig30/v20220721", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 392165, "numberOfFiles": 37, "fileFormat": "Data are Net-CDF formatted" }, "timePeriod": { "ob_id": 10990, "startTime": "0001-01-01T00:00:00", "endTime": "0001-01-01T00:00:00" }, "resultQuality": { "ob_id": 4211, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39688, "uuid": "1b5f43f3e12e414796c9ad2971cacccf", "short_code": "comp", "title": "Caption for Figure 9.30 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Global mean sea level (GMSL) commitment as a function of peak global surface air temperature. From models (Clark et al., 2016; DeConto and Pollard, 2016; Garbe et al., 2020; Van Breedam et al., 2020) and paleo data on 2000-year (lower row) and 10,000 year (upper row) time scales. Columns indicate different contributors to GMSL rise (from left to right: total GMSL change, Antarctic Ice Sheet, Greenland Ice Sheet, global mean thermosteric sea level rise, and glaciers). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 89723, 89724 ], "vocabularyKeywords": [], "identifier_set": [ 12510 ], "observationcollection_set": [ { "ob_id": 32725, "uuid": "d75f0692e2594df8af882c04db5ba3fe", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 9.3\r\n- data for Figure 9.4\r\n- data for Figure 9.5\r\n- data for Figure 9.6\r\n- data for Figure 9.7\r\n- data for Figure 9.9\r\n- data for Figure 9.10\r\n- data for Figure 9.11\r\n- data for Figure 9.12\r\n- data for Figure 9.13\r\n- data for Figure 9.14\r\n- data for Figure 9.15\r\n- data for Figure 9.22\r\n- data for Figure 9.24\r\n- data for Figure 9.26\r\n- data for Figure 9.28\r\n- data for Figure 9.29\r\n- data for Figure 9.30\r\n- data for Figure 9.32\r\n- data for Cross-Chapter Box 9.1, Figure 1\r\n- input data for Cross-Chapter Box 9.1, Figure 1" } ], "responsiblepartyinfo_set": [ 180026, 180027, 180028, 180029, 180030, 180031, 180032, 187090, 187091, 187092 ], "onlineresource_set": [ 52470, 52471, 52472, 52469, 82574 ] }, { "ob_id": 37732, "uuid": "ff28d78693f645aa820266d472a6e1b3", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 9.29 (v20221114)", "abstract": "Data for Figure 9.29 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 9.29 shows timing of when global mean sea level (GMSL) thresholds of 0.5, 1.0, 1.5 and 2.0 m are exceeded, based on four different ice-sheet projection methods informing post-2100 projections. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nFox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels, with data provided for panels SSP1-2.6 and SSP5-8.5 separately.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Timing of GMSL threshold exceedance at 0.5m, 1.0m, 1.5m, 2.0m based on four ice-sheet projection methods informing post-2100 methods. \r\n\r\nMethods are labelled based on their treatment of ice sheets. \r\n- ‘No acceleration’ assumes constant rates of mass change after 2100. \r\n- ‘Assessed ice sheet’ models post-2100 ice-sheet losses using a parametric fit (Supplementary Material 9.SM.4) extending to 2300 based on a multi-model assessment of contributions under RCP2.6 and RCP8.5 at 2300. \r\n- Marine ice-cliff instability (MICI) combines the parametric fit (Supplementary Material 9.SM3.4) for Greenland with Antarctic projections based on DeConto et al. (2021).\r\n- Structured expert judgement (SEJ) employs ice-sheet projections from Bamber et al. (2019). \r\n\r\nCircles, thick bars and thin bars represent the 50th, 17th–83rd and 5th–95th percentiles of the exceedance timing for the indicated projection method.\r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 9.SM.9).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 9.29\r\n \r\n - Data file: Fig9-29_ssp126_data.nc\r\n - Data file: Fig9-29_ssp585_data.nc\r\n\r\nGMSL stands for Global Mean Sea Level.\r\nRCP stands for Representative Concentration Pathway.\r\nSSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6. \r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nProjected exceedances were plotted using standard matplotlib software - code is available via the link in the documentation.\r\n\r\nThe provided R code for generating this plot uses relative paths. Be sure to set your session's working directory to the location of the R code before running the code.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n- Link to the data and code used to produce this figure and others in Chapter 9, archived on Zenodo.\r\n- Link to the output data for this figure, contained in a dedicated GitHub repository.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:18:10", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, sea-level, projection, milestone, exceedance, timing, sea level, sea-level rise", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-02-07T12:22:19", "doiPublishedTime": "2023-05-17T09:47:11", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38909, "dataPath": "/badc/ar6_wg1/data/ch_09/ch9_fig29/v20221114", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5457, "numberOfFiles": 4, "fileFormat": "Data are net-CDF formatted" }, "timePeriod": { "ob_id": 10419, "startTime": "2020-01-01T00:00:00", "endTime": "2300-12-31T23:59:59" }, "resultQuality": { "ob_id": 4210, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39687, "uuid": "847ea81176f84e648d101d79cb6b94b1", "short_code": "comp", "title": "Caption for Figure 9.29 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Timing of when global mean sea level (GMSL) thresholds of 0.5, 1.0, 1.5 and 2.0 m are exceeded, based on four different ice-sheet projection methods informing post-2100 projections. Methods are labelled based on their treatment of ice sheets. ‘No acceleration’ assumes constant rates of mass change after 2100. ‘Assessed ice sheet’ models post-2100 ice-sheet losses using a parametric fit (Supplementary Material 9.SM.4) extending to 2300 based on a multi-model assessment of contributions under RCP2.6 and RCP8.5 at 2300. Structured expert judgement (SEJ) employs ice-sheet projections from Bamber et al. (2019). Marine ice-cliff instability (MICI) combines the parametric fit (Supplementary Material 9.SM3.4) for Greenland with Antarctic projections based on DeConto et al. (2021). Circles, thick bars and thin bars represent the 50th, 17th–83rd and 5th–95th percentiles of the exceedance timing for the indicated projection method. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 63007, 63010, 63595, 63596, 63597, 63598 ], "vocabularyKeywords": [], "identifier_set": [ 12509 ], "observationcollection_set": [ { "ob_id": 32725, "uuid": "d75f0692e2594df8af882c04db5ba3fe", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 9.3\r\n- data for Figure 9.4\r\n- data for Figure 9.5\r\n- data for Figure 9.6\r\n- data for Figure 9.7\r\n- data for Figure 9.9\r\n- data for Figure 9.10\r\n- data for Figure 9.11\r\n- data for Figure 9.12\r\n- data for Figure 9.13\r\n- data for Figure 9.14\r\n- data for Figure 9.15\r\n- data for Figure 9.22\r\n- data for Figure 9.24\r\n- data for Figure 9.26\r\n- data for Figure 9.28\r\n- data for Figure 9.29\r\n- data for Figure 9.30\r\n- data for Figure 9.32\r\n- data for Cross-Chapter Box 9.1, Figure 1\r\n- input data for Cross-Chapter Box 9.1, Figure 1" } ], "responsiblepartyinfo_set": [ 180033, 180034, 180035, 180036, 180037, 180038, 180039, 180186, 180187 ], "onlineresource_set": [ 52465, 52466, 52467, 52468, 82573 ] }, { "ob_id": 37733, "uuid": "7f9c951b59ae44aeb6d745ed702c56dd", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 9.28 (v20220721)", "abstract": "Data for Figure 9.28 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 9.28 shows regional sea level change at 2100 for different scenarios (with respect to 1995–2014). \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nFox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 6 subpanels, with data provided for panels a-f. \r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Median regional relative sea level change from 1995–2014 up to 2100 for: (a) SSP1-1.9; (b) SSP1-2.6; (c) SSP2-4.5; (d) SSP3-7.0; (e) SSP5-8.5; and (f) width of the likely range for SSP3-7.0. \r\n\r\nThe high uncertainty in projections around Alaska and the Aleutian Islands arises from the tectonic contribution to vertical land motion, which varies greatly over short distances in this region. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 9.SM.9).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 9.28\r\n \r\n - Data file: Fig9-28a_data.nc\r\n - Data file: Fig9-28b_data.nc\r\n - Data file: Fig9-28c_data.nc\r\n - Data file: Fig9-28d_data.nc\r\n - Data file: Fig9-28e_data.nc\r\n - Data file: Fig9-28f_data.nc\r\n\r\nSSP119 is the Shared Socioeconomic Pathway which represents the lowest scenario of radiative forcing and development scenarios, consistent with RCP1.9.\r\nSSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6.\r\nSSP245 is the Shared Socioeconomic Pathway which represents the median of radiative forcing and development scenarios, consistent with RCP4.5.\r\nSSP370 is the Shared Socioeconomic Pathway which represents the upper-middle range of radiative forcing and development scenarios, consistent with RCP6.0.\r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5.\r\n\r\n------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nRSL scenarios were plotted using standard matplotlib software - code is available via the link in the documentation. Input data for each SSP scenario (.nc files in data/pb_1e) also provided.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n- Link to the data and code used to produce this figure and others in Chapter 9, archived on Zenodo.\r\n- Link to the code and output data for this figure, contained in a dedicated GitHub repository.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-02-07T12:06:08", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, projected regional sea level, sea level, sea-level change", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-02-15T16:03:38", "doiPublishedTime": "2023-05-17T09:42:12.913885", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38033, "dataPath": "/badc/ar6_wg1/data/ch_09/ch9_fig28/v20220721", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3229576, "numberOfFiles": 9, "fileFormat": "Data are net-CDF formatted" }, "timePeriod": { "ob_id": 10418, "startTime": "2100-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": { "ob_id": 4193, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39684, "uuid": "4cb88a7da37742318975dc2e5f0566ff", "short_code": "comp", "title": "Caption for Figure 9.28 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Regional sea level change at 2100 for different scenarios (with respect to 1995–2014). Median regional relative sea level change from 1995–2014 up to 2100 for: (a) SSP1-1.9; (b) SSP1-2.6; (c) SSP2-4.5; (d) SSP3-7.0; (e) SSP5-8.5; and (f) width of the likely range for SSP3-7.0. The high uncertainty in projections around Alaska and the Aleutian Islands arises from the tectonic contribution to vertical land motion, which varies greatly over short distances in this region. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 12600, 12601, 49847, 49848 ], "vocabularyKeywords": [], "identifier_set": [ 12508 ], "observationcollection_set": [ { "ob_id": 32725, "uuid": "d75f0692e2594df8af882c04db5ba3fe", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 9.3\r\n- data for Figure 9.4\r\n- data for Figure 9.5\r\n- data for Figure 9.6\r\n- data for Figure 9.7\r\n- data for Figure 9.9\r\n- data for Figure 9.10\r\n- data for Figure 9.11\r\n- data for Figure 9.12\r\n- data for Figure 9.13\r\n- data for Figure 9.14\r\n- data for Figure 9.15\r\n- data for Figure 9.22\r\n- data for Figure 9.24\r\n- data for Figure 9.26\r\n- data for Figure 9.28\r\n- data for Figure 9.29\r\n- data for Figure 9.30\r\n- data for Figure 9.32\r\n- data for Cross-Chapter Box 9.1, Figure 1\r\n- input data for Cross-Chapter Box 9.1, Figure 1" } ], "responsiblepartyinfo_set": [ 180040, 180041, 180042, 180043, 180044, 180045, 180046, 180184, 180185 ], "onlineresource_set": [ 52462, 52463, 52464, 52461, 82572 ] }, { "ob_id": 37734, "uuid": "64fa14764534431f805e747249786f88", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 9.26 (v20220721)", "abstract": "Data for Figure 9.26 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 9.26 shows median global mean and regional relative sea level projections (m) by contribution for the SSP1-2.6 and SSP5-8.5 scenarios. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nFox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 3 subpanels, with data provided for all panels in one central directory in the GitHub repository linked in the documentation.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Upper time series: Global mean contributions to sea level change as a function of time, relative to 1995–2014. \r\n\r\n- Lower maps: Regional projections of the sea level contributions in 2100 relative to 1995–2014 for SSP5-8.5 and SSP1-2.6. Vertical land motion is common to both SSPs.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 9.26\r\n\r\n - Data file: Fig9-26_2100likelyranges.nc\r\n - Data file: Fig9-26_data_landmotion_map.nc\r\n - Data file: Fig9-26_data_ssp126_Antarctic_timeseries.nc\r\n - Data file: Fig9-26_data_ssp126_Greenland_map.nc\r\n - Data file: Fig9-26_data_ssp126_Greenland_timeseries.nc\r\n - Data file: Fig9-26_data_ssp126_glacier_map.nc\r\n - Data file: Fig9-26_data_ssp126_glacier_timeseries.nc\r\n - Data file: Fig9-26_data_ssp126_landwater_map.nc\r\n - Data file: Fig9-26_data_ssp126_landwater_timeseries.nc\r\n - Data file: Fig9-26_data_ssp126_oceandynamics_map.nc\r\n - Data file: Fig9-26_data_ssp126_thermalexpansion_timeseries.nc\r\n - Data file: Fig9-26_data_ssp585_Antarctic_map.nc\r\n - Data file: Fig9-26_data_ssp585_Antarctic_timeseries.nc\r\n - Data file: Fig9-26_data_ssp585_Greenland_map.nc\r\n - Data file: Fig9-26_data_ssp585_Greenland_timeseries.nc\r\n - Data file: Fig9-26_data_ssp585_glacier_map.nc\r\n - Data file: Fig9-26_data_ssp585_glacier_timeseries.nc\r\n - Data file: Fig9-26_data_ssp585_landwater_map.nc\r\n - Data file: Fig9-26_data_ssp585_landwater_timeseries.nc\r\n - Data file: Fig9-26_data_ssp585_oceandynamics_map.nc\r\n - Data file: Fig9-26_data_ssp585_thermalexpansion_timeseries.nc\r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 9.SM.9).\r\n\r\nSSP stands for Shared Socioeconomic Pathway.\r\nSSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6.\r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nContribution maps and timeseries were plotted using standard matplotlib software - code is available via the link in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n- Link to the data and code used to produce this figure and others in Chapter 9, archived on Zenodo.\r\n- Link to the output data for this figure, contained in a dedicated GitHub repository.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T01:51:56", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, sea level, sea-level change, contributors to sea level change", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-02-08T12:24:22", "doiPublishedTime": "2023-05-17T09:37:27.245677", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38032, "dataPath": "/badc/ar6_wg1/data/ch_09/ch9_fig26/v20220721", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 6048943, "numberOfFiles": 25, "fileFormat": "Data are net-CDF formatted" }, "timePeriod": { "ob_id": 8813, "startTime": "2020-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": { "ob_id": 4209, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39682, "uuid": "2103ebc78e85483d86e2fcf74832a137", "short_code": "comp", "title": "Caption for Figure 9.26 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Median global mean and regional relative sea level projections (m) by contribution for the SSP1-2.6 and SSP5-8.5 scenarios. Upper time series: Global mean contributions to sea level change as a function of time, relative to 1995–2014. Lower maps: Regional projections of the sea level contributions in 2100 relative to 1995–2014 for SSP5-8.5 and SSP1-2.6. Vertical land motion is common to both Shared Socio-economic Pathways (SSPs). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1347, 12600, 12601, 49862, 49863, 49864, 49865, 49866, 49867, 49868, 49869, 49870, 49871, 49872, 49873, 49874, 49875, 49876, 49877, 49878, 49879, 49880, 49881, 49882, 49883, 49884, 49885, 49886, 49887, 49888, 49889, 49890, 49891, 49892 ], "vocabularyKeywords": [], "identifier_set": [ 12507 ], "observationcollection_set": [ { "ob_id": 32725, "uuid": "d75f0692e2594df8af882c04db5ba3fe", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 9.3\r\n- data for Figure 9.4\r\n- data for Figure 9.5\r\n- data for Figure 9.6\r\n- data for Figure 9.7\r\n- data for Figure 9.9\r\n- data for Figure 9.10\r\n- data for Figure 9.11\r\n- data for Figure 9.12\r\n- data for Figure 9.13\r\n- data for Figure 9.14\r\n- data for Figure 9.15\r\n- data for Figure 9.22\r\n- data for Figure 9.24\r\n- data for Figure 9.26\r\n- data for Figure 9.28\r\n- data for Figure 9.29\r\n- data for Figure 9.30\r\n- data for Figure 9.32\r\n- data for Cross-Chapter Box 9.1, Figure 1\r\n- input data for Cross-Chapter Box 9.1, Figure 1" } ], "responsiblepartyinfo_set": [ 180047, 180048, 180049, 180050, 180051, 180052, 180053, 180182, 180183 ], "onlineresource_set": [ 52458, 52459, 52460, 52457, 82571 ] }, { "ob_id": 37735, "uuid": "5806683122b74f4ca60e0d6c546583f9", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 9.24 (v20221114)", "abstract": "Data for Figure 9.24 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 9.24 shows simulated CMIP6 and observed snow cover extent (SCE). \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nFox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 2 subpanels, with data provided for both panels in one central directory in the GitHub repository linked in the documentation.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- (a) Simulated CMIP6 and observed (Mudryk et al., 2020) SCE (in millions of km2) for 1981–2014.\r\n- (b) Spring (March to May) Northern Hemisphere SCE against global surface air temperature (GSAT) (relative to the 1995–2014 average) for the CMIP6 Tier 1 scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5), with linear regressions. \r\n\r\n(a) Boxes and whiskers with outliers represent monthly mean values for the individual CMIP6 models averaged over 1981–2014, with the red bar indicating the median of the CMIP6 multi-model ensemble for that period. The observed interannual distribution over the period is represented in green, with the yellow bar indicating the median.\r\n\r\n(b) Each data point is the mean for one CMIP6 simulation (first ensemble member for each available model) in the corresponding temperature bin. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 9.SM.9).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 9.24\r\n \r\n - Data file: Mudryk_scf_1981-2014.txt\r\n - Data file: snc_clim_CMIP6_historical_1981-2014.txt\r\n - Data file: sncbin_BCC-CSM2-MR_historical_ssp126.nc\r\n - Data file: sncbin_BCC-CSM2-MR_historical_ssp245.nc\r\n - Data file: sncbin_BCC-CSM2-MR_historical_ssp370.nc\r\n - Data file: sncbin_BCC-CSM2-MR_historical_ssp585.nc\r\n - Data file: sncbin_CanESM5_historical_ssp119.nc\r\n - Data file: sncbin_CanESM5_historical_ssp126.nc\r\n - Data file: sncbin_CanESM5_historical_ssp245.nc\r\n - Data file: sncbin_CanESM5_historical_ssp370.nc\r\n - Data file: sncbin_CanESM5_historical_ssp585.nc\r\n - Data file: sncbin_CESM2_historical_ssp126.nc\r\n - Data file: sncbin_CESM2_historical_ssp245.nc\r\n - Data file: sncbin_CESM2_historical_ssp370.nc\r\n - Data file: sncbin_CESM2_historical_ssp585.nc\r\n - Data file: sncbin_CESM2-WACCM_historical_ssp126.nc\r\n - Data file: sncbin_CESM2-WACCM_historical_ssp245.nc\r\n - Data file: sncbin_CESM2-WACCM_historical_ssp370.nc\r\n - Data file: sncbin_CESM2-WACCM_historical_ssp585.nc\r\n - Data file: sncbin_CIESM_historical_ssp126.nc\r\n - Data file: sncbin_CIESM_historical_ssp245.nc\r\n - Data file: sncbin_CIESM_historical_ssp585.nc\r\n - Data file: sncbin_CMCC-CM2-SR5_historical_ssp126.nc\r\n - Data file: sncbin_CNRM-CM6-1_historical_ssp126.nc\r\n - Data file: sncbin_CNRM-CM6-1_historical_ssp245.nc\r\n - Data file: sncbin_CNRM-CM6-1_historical_ssp370.nc\r\n - Data file: sncbin_CNRM-CM6-1_historical_ssp585.nc\r\n - Data file: sncbin_CNRM-CM6-1-HR_historical_ssp126.nc\r\n - Data file: sncbin_CNRM-CM6-1-HR_historical_ssp245.nc\r\n - Data file: sncbin_CNRM-CM6-1-HR_historical_ssp370.nc\r\n - Data file: sncbin_CNRM-CM6-1-HR_historical_ssp585.nc\r\n - Data file: sncbin_CNRM-ESM2-1_historical_ssp119.nc\r\n - Data file: sncbin_CNRM-ESM2-1_historical_ssp126.nc\r\n - Data file: sncbin_CNRM-ESM2-1_historical_ssp245.nc\r\n - Data file: sncbin_CNRM-ESM2-1_historical_ssp370.nc\r\n - Data file: sncbin_CNRM-ESM2-1_historical_ssp585.nc\r\n - Data file: sncbin_EC-Earth3_historical_ssp126.nc\r\n - Data file: sncbin_EC-Earth3_historical_ssp245.nc\r\n - Data file: sncbin_EC-Earth3_historical_ssp370.nc\r\n - Data file: sncbin_EC-Earth3_historical_ssp585.nc\r\n - Data file: sncbin_EC-Earth3-Veg_historical_ssp126.nc\r\n - Data file: sncbin_EC-Earth3-Veg_historical_ssp245.nc\r\n - Data file: sncbin_EC-Earth3-Veg_historical_ssp370.nc\r\n - Data file: sncbin_EC-Earth3-Veg_historical_ssp585.nc\r\n - Data file: sncbin_FGOALS-f3-L_historical_ssp126.nc\r\n - Data file: sncbin_FGOALS-f3-L_historical_ssp245.nc\r\n - Data file: sncbin_FGOALS-f3-L_historical_ssp370.nc\r\n - Data file: sncbin_FGOALS-f3-L_historical_ssp585.nc\r\n - Data file: sncbin_GFDL-CM4_historical_ssp245.nc\r\n - Data file: sncbin_GFDL-CM4_historical_ssp585.nc\r\n - Data file: sncbin_GFDL-ESM4_historical_ssp126.nc\r\n - Data file: sncbin_GFDL-ESM4_historical_ssp245.nc\r\n - Data file: sncbin_GFDL-ESM4_historical_ssp370.nc\r\n - Data file: sncbin_GFDL-ESM4_historical_ssp585.nc\r\n - Data file: sncbin_GISS-E2-1-G_historical_ssp119.nc\r\n - Data file: sncbin_GISS-E2-1-G_historical_ssp126.nc\r\n - Data file: sncbin_GISS-E2-1-G_historical_ssp245.nc\r\n - Data file: sncbin_GISS-E2-1-G_historical_ssp370.nc\r\n - Data file: sncbin_GISS-E2-1-G_historical_ssp585.nc\r\n - Data file: sncbin_HadGEM3-GC31-LL_historical_ssp126.nc\r\n - Data file: sncbin_HadGEM3-GC31-LL_historical_ssp245.nc\r\n - Data file: sncbin_HadGEM3-GC31-LL_historical_ssp585.nc\r\n - Data file: sncbin_IPSL-CM6A-LR_historical_ssp119.nc\r\n - Data file: sncbin_IPSL-CM6A-LR_historical_ssp126.nc\r\n - Data file: sncbin_IPSL-CM6A-LR_historical_ssp245.nc\r\n - Data file: sncbin_IPSL-CM6A-LR_historical_ssp370.nc\r\n - Data file: sncbin_IPSL-CM6A-LR_historical_ssp585.nc\r\n - Data file: sncbin_MIROC6_historical_ssp119.nc\r\n - Data file: sncbin_MIROC6_historical_ssp126.nc\r\n - Data file: sncbin_MIROC6_historical_ssp245.nc\r\n - Data file: sncbin_MIROC6_historical_ssp370.nc\r\n - Data file: sncbin_MIROC6_historical_ssp585.nc\r\n - Data file: sncbin_MIROC-ES2L_historical_ssp119.nc\r\n - Data file: sncbin_MIROC-ES2L_historical_ssp126.nc\r\n - Data file: sncbin_MIROC-ES2L_historical_ssp245.nc\r\n - Data file: sncbin_MIROC-ES2L_historical_ssp370.nc\r\n - Data file: sncbin_MIROC-ES2L_historical_ssp585.nc\r\n - Data file: sncbin_MPI-ESM1-2-HR_historical_ssp126.nc\r\n - Data file: sncbin_MPI-ESM1-2-HR_historical_ssp245.nc\r\n - Data file: sncbin_MPI-ESM1-2-HR_historical_ssp370.nc\r\n - Data file: sncbin_MPI-ESM1-2-HR_historical_ssp585.nc\r\n - Data file: sncbin_MRI-ESM2-0_historical_ssp119.nc\r\n - Data file: sncbin_MRI-ESM2-0_historical_ssp126.nc\r\n - Data file: sncbin_MRI-ESM2-0_historical_ssp245.nc\r\n - Data file: sncbin_MRI-ESM2-0_historical_ssp370.nc\r\n - Data file: sncbin_MRI-ESM2-0_historical_ssp585.nc\r\n - Data file: sncbin_NorESM2-LM_historical_ssp126.nc\r\n - Data file: sncbin_NorESM2-LM_historical_ssp245.nc\r\n - Data file: sncbin_NorESM2-LM_historical_ssp370.nc\r\n - Data file: sncbin_NorESM2-LM_historical_ssp585.nc\r\n - Data file: sncbin_NorESM2-MM_historical_ssp126.nc\r\n - Data file: sncbin_NorESM2-MM_historical_ssp245.nc\r\n - Data file: sncbin_NorESM2-MM_historical_ssp370.nc\r\n - Data file: sncbin_NorESM2-MM_historical_ssp585.nc\r\n - Data file: sncbin_UKESM1-0-LL_historical_ssp119.nc\r\n - Data file: sncbin_UKESM1-0-LL_historical_ssp126.nc\r\n - Data file: sncbin_UKESM1-0-LL_historical_ssp245.nc\r\n - Data file: sncbin_UKESM1-0-LL_historical_ssp370.nc\r\n - Data file: sncbin_UKESM1-0-LL_historical_ssp585.nc\r\n\r\nIn the linked GitHub repository the filename convention differs slightly from the above, the final underscore is replaced with a '+'. \r\nFor example, 'sncbin_UKESM1-0-LL_historical_ssp585.nc' in the repository is called 'sncbin_UKESM1-0-LL_historical+ssp585.nc' in the GitHub repository.\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nSSP stands for Shared Socioeconomic Pathway.\r\nSSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6.\r\nSSP245 is the Shared Socioeconomic Pathway which represents the median of radiative forcing and development scenarios, consistent with RCP4.5.\r\nSSP370 is the Shared Socioeonomic Pathway which represents the upper-middle range of radiative forcing and development scenarios, consistent with RCP6.0.\r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nPanels (a) and (b) were plotted using standard Python software - code is available via the link in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n- Link to the data and code used to produce this figure and others in Chapter 9, archived on Zenodo.\r\n- Link to the output data for this figure, contained in a dedicated GitHub repository. Note the difference in filenames mentioned above in the Data provided section.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-02-06T17:11:29", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, snow, snow cover, snow extent", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-02-06T17:12:57", "doiPublishedTime": "2023-05-17T09:31:04.054946", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38031, "dataPath": "/badc/ar6_wg1/data/ch_09/ch9_fig24/v20221114", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 999763, "numberOfFiles": 101, "fileFormat": "NetCDF, txt" }, "timePeriod": { "ob_id": 10426, "startTime": "1981-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": { "ob_id": 4207, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39681, "uuid": "643bfa33ef0549ddb1ec7dcf2ac2b036", "short_code": "comp", "title": "Caption for Figure 9.24 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Simulated Coupled Model Intercomparison Project Phase 6 (CMIP6) and observed snow cover extent (SCE). (a) Simulated CMIP6 and observed (Mudryk et al., 2020) SCE (in millions of km2) for 1981–2014. Boxes and whiskers with outliers represent monthly mean values for the individual CMIP6 models averaged over 1981–2014, with the red bar indicating the median of the CMIP6 multi-model ensemble for that period. The observed interannual distribution over the period is represented in green, with the yellow bar indicating the median. (b) Spring (March to May) Northern Hemisphere SCE against global surface air temperature (GSAT) (relative to the 1995–2014 average) for the CMIP6 Tier 1 scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5), with linear regressions. Each data point is the mean for one CMIP6 simulation (first ensemble member for each available model) in the corresponding temperature bin. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 62542, 62543, 62544 ], "vocabularyKeywords": [], "identifier_set": [ 12506 ], "observationcollection_set": [ { "ob_id": 32725, "uuid": "d75f0692e2594df8af882c04db5ba3fe", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 9.3\r\n- data for Figure 9.4\r\n- data for Figure 9.5\r\n- data for Figure 9.6\r\n- data for Figure 9.7\r\n- data for Figure 9.9\r\n- data for Figure 9.10\r\n- data for Figure 9.11\r\n- data for Figure 9.12\r\n- data for Figure 9.13\r\n- data for Figure 9.14\r\n- data for Figure 9.15\r\n- data for Figure 9.22\r\n- data for Figure 9.24\r\n- data for Figure 9.26\r\n- data for Figure 9.28\r\n- data for Figure 9.29\r\n- data for Figure 9.30\r\n- data for Figure 9.32\r\n- data for Cross-Chapter Box 9.1, Figure 1\r\n- input data for Cross-Chapter Box 9.1, Figure 1" } ], "responsiblepartyinfo_set": [ 180054, 180055, 180056, 180057, 180058, 180059, 180060, 193300 ], "onlineresource_set": [ 52455, 52456, 52454, 52453, 82570, 88633 ] }, { "ob_id": 37736, "uuid": "80475295b32f4df6879ad7d2a23a88c1", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 9.22 (v20220721)", "abstract": "Data for Figure 9.22 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 9.22 shows simulated versus observed permafrost extent and volume change by warming level. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nFox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 2 subpanels, with data provided for both panels in one central directory.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- (a) Diagnosed Northern Hemisphere permafrost extent (area with perennially frozen ground at 15 m depth, or at the deepest model soil level if this is above 15 m) for 1979–1998, for available CMIP5 and CMIP6 models, from the first ensemble member of the historical coupled run, and for CMIP6 AMIP (atmosphere+land surface, prescribed ocean) and land-hist (land only, prescribed atmospheric forcing) runs. \r\n\r\n- (b) Simulated global permafrost volume change between the surface and 3 m depth as a function of the simulated global surface air temperature (GSAT) change, from the first ensemble members of a selection of scenarios, for available CMIP6 models. \r\n\r\nEstimates of current permafrost extents based on physical evidence and reanalyses are indicated as black symbols – triangle: Obu et al. (2018); star: Zhang et al. (1999); circle: central value and associated range from Gruber (2012). \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 9.SM.9)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 9.22\r\n \r\n- Data file: pf15m_CMIP5historical_NH_1979-1998.txt\r\n- Data file: pf15m_amip_NH_1979-1998.txt\r\n- Data file: pf15m_historical_NH_1979-1998.txt\r\n- Data file: pf15m_land-hist_NH_1979-1998.txt\r\n- Data file: pfv_ACCESS-CM2_historical_ssp126.nc\r\n- Data file: pfv_ACCESS-CM2_historical_ssp245.nc\r\n- Data file: pfv_ACCESS-CM2_historical_ssp370.nc\r\n- Data file: pfv_ACCESS-CM2_historical_ssp585.nc\r\n- Data file: pfv_ACCESS-ESM1-5_historical_ssp126.nc\r\n- Data file: pfv_ACCESS-ESM1-5_historical_ssp245.nc\r\n- Data file: pfv_ACCESS-ESM1-5_historical_ssp370.nc\r\n- Data file: pfv_ACCESS-ESM1-5_historical_ssp585.nc\r\n- Data file: pfv_BCC-CSM2-MR_historical_ssp126.nc\r\n- Data file: pfv_BCC-CSM2-MR_historical_ssp245.nc\r\n- Data file: pfv_BCC-CSM2-MR_historical_ssp370.nc\r\n- Data file: pfv_BCC-CSM2-MR_historical_ssp585.nc\r\n- Data file: pfv_CAMS-CSM1-0_historical_ssp126.nc\r\n- Data file: pfv_CAMS-CSM1-0_historical_ssp245.nc\r\n- Data file: pfv_CAMS-CSM1-0_historical_ssp370.nc\r\n- Data file: pfv_CAMS-CSM1-0_historical_ssp585.nc\r\n- Data file: pfv_CESM2-WACCM_historical_ssp126.nc\r\n- Data file: pfv_CESM2-WACCM_historical_ssp245.nc\r\n- Data file: pfv_CESM2-WACCM_historical_ssp370.nc\r\n- Data file: pfv_CESM2-WACCM_historical_ssp585.nc\r\n- Data file: pfv_CESM2_historical_ssp126.nc\r\n- Data file: pfv_CESM2_historical_ssp245.nc\r\n- Data file: pfv_CESM2_historical_ssp370.nc\r\n- Data file: pfv_CESM2_historical_ssp585.nc\r\n- Data file: pfv_CNRM-CM6-1-HR_historical_ssp126.nc\r\n- Data file: pfv_CNRM-CM6-1-HR_historical_ssp245.nc\r\n- Data file: pfv_CNRM-CM6-1-HR_historical_ssp370.nc\r\n- Data file: pfv_CNRM-CM6-1-HR_historical_ssp585.nc\r\n- Data file: pfv_CNRM-CM6-1_historical_ssp126.nc\r\n- Data file: pfv_CNRM-CM6-1_historical_ssp245.nc\r\n- Data file: pfv_CNRM-CM6-1_historical_ssp370.nc\r\n- Data file: pfv_CNRM-CM6-1_historical_ssp585.nc\r\n- Data file: pfv_CNRM-ESM2-1_historical_ssp126.nc\r\n- Data file: pfv_CNRM-ESM2-1_historical_ssp245.nc\r\n- Data file: pfv_CNRM-ESM2-1_historical_ssp370.nc\r\n- Data file: pfv_CNRM-ESM2-1_historical_ssp585.nc\r\n- Data file: pfv_CanESM5-CanOE_historical_ssp126.nc\r\n- Data file: pfv_CanESM5-CanOE_historical_ssp245.nc\r\n- Data file: pfv_CanESM5-CanOE_historical_ssp370.nc\r\n- Data file: pfv_CanESM5-CanOE_historical_ssp585.nc\r\n- Data file: pfv_CanESM5_historical_ssp126.nc\r\n- Data file: pfv_CanESM5_historical_ssp245.nc\r\n- Data file: pfv_CanESM5_historical_ssp370.nc\r\n- Data file: pfv_CanESM5_historical_ssp585.nc\r\n- Data file: pfv_EC-Earth3_historical_ssp126.nc\r\n- Data file: pfv_EC-Earth3_historical_ssp245.nc\r\n- Data file: pfv_EC-Earth3_historical_ssp370.nc\r\n- Data file: pfv_EC-Earth3_historical_ssp585.nc\r\n- Data file: pfv_FGOALS-g3_historical_ssp126.nc\r\n- Data file: pfv_FGOALS-g3_historical_ssp245.nc\r\n- Data file: pfv_FGOALS-g3_historical_ssp370.nc\r\n- Data file: pfv_FGOALS-g3_historical_ssp585.nc\r\n- Data file: pfv_GFDL-CM4_historical_ssp245.nc\r\n- Data file: pfv_GFDL-CM4_historical_ssp585.nc\r\n- Data file: pfv_GFDL-ESM4_historical_ssp126.nc\r\n- Data file: pfv_GFDL-ESM4_historical_ssp245.nc\r\n- Data file: pfv_GFDL-ESM4_historical_ssp370.nc\r\n- Data file: pfv_GFDL-ESM4_historical_ssp585.nc\r\n- Data file: pfv_GISS-E2-1-G_historical_ssp126.nc\r\n- Data file: pfv_GISS-E2-1-G_historical_ssp245.nc\r\n- Data file: pfv_GISS-E2-1-G_historical_ssp370.nc\r\n- Data file: pfv_GISS-E2-1-G_historical_ssp585.nc\r\n- Data file: pfv_HadGEM3-GC31-LL_historical_ssp126.nc\r\n- Data file: pfv_HadGEM3-GC31-LL_historical_ssp245.nc\r\n- Data file: pfv_HadGEM3-GC31-LL_historical_ssp585.nc\r\n- Data file: pfv_IPSL-CM6A-LR_historical_ssp126.nc\r\n- Data file: pfv_IPSL-CM6A-LR_historical_ssp245.nc\r\n- Data file: pfv_IPSL-CM6A-LR_historical_ssp370.nc\r\n- Data file: pfv_IPSL-CM6A-LR_historical_ssp585.nc\r\n- Data file: pfv_KACE-1-0-G_historical_ssp126.nc\r\n- Data file: pfv_KACE-1-0-G_historical_ssp245.nc\r\n- Data file: pfv_KACE-1-0-G_historical_ssp370.nc\r\n- Data file: pfv_KACE-1-0-G_historical_ssp585.nc\r\n- Data file: pfv_MIROC-ES2L_historical_ssp126.nc\r\n- Data file: pfv_MIROC-ES2L_historical_ssp245.nc\r\n- Data file: pfv_MIROC-ES2L_historical_ssp370.nc\r\n- Data file: pfv_MIROC-ES2L_historical_ssp585.nc\r\n- Data file: pfv_MIROC6_historical_ssp126.nc\r\n- Data file: pfv_MIROC6_historical_ssp245.nc\r\n- Data file: pfv_MIROC6_historical_ssp370.nc\r\n- Data file: pfv_MIROC6_historical_ssp585.nc\r\n- Data file: pfv_MPI-ESM1-2-HR_historical_ssp126.nc\r\n- Data file: pfv_MPI-ESM1-2-HR_historical_ssp245.nc\r\n- Data file: pfv_MPI-ESM1-2-HR_historical_ssp370.nc\r\n- Data file: pfv_MPI-ESM1-2-HR_historical_ssp585.nc\r\n- Data file: pfv_MPI-ESM1-2-LR_historical_ssp126.nc\r\n- Data file: pfv_MPI-ESM1-2-LR_historical_ssp245.nc\r\n- Data file: pfv_MPI-ESM1-2-LR_historical_ssp370.nc\r\n- Data file: pfv_MPI-ESM1-2-LR_historical_ssp585.nc\r\n- Data file: pfv_MRI-ESM2-0_historical_ssp126.nc\r\n- Data file: pfv_MRI-ESM2-0_historical_ssp245.nc\r\n- Data file: pfv_MRI-ESM2-0_historical_ssp370.nc\r\n- Data file: pfv_MRI-ESM2-0_historical_ssp585.nc\r\n- Data file: pfv_NorESM2-LM_historical_ssp126.nc\r\n- Data file: pfv_NorESM2-LM_historical_ssp245.nc\r\n- Data file: pfv_NorESM2-LM_historical_ssp370.nc\r\n- Data file: pfv_NorESM2-LM_historical_ssp585.nc\r\n- Data file: pfv_NorESM2-MM_historical_ssp126.nc\r\n- Data file: pfv_NorESM2-MM_historical_ssp245.nc\r\n- Data file: pfv_NorESM2-MM_historical_ssp370.nc\r\n- Data file: pfv_NorESM2-MM_historical_ssp585.nc\r\n- Data file: pfv_UKESM1-0-LL_historical_ssp126.nc\r\n- Data file: pfv_UKESM1-0-LL_historical_ssp245.nc\r\n- Data file: pfv_UKESM1-0-LL_historical_ssp370.nc\r\n- Data file: pfv_UKESM1-0-LL_historical_ssp585.nc\r\n\r\nIn the GitHub repository the filenames differ from that listed above, the final underscore is replaced with a '+'.\r\nFor example, ' pfv_ACCESS-CM2_historical_ssp126.nc' in the repository is called ' pfv_ACCESS-CM2_historical+ssp126.nc'\r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nAMIP is the Atmospheric Modelling Intercomparison Project.\r\nGSAT stands for Global Surface Air Temperature.\r\nACCESS-CM2 is the Australian Community Climate and Earth System Simulator coupled climate model.\r\nACCESS-ESM1-5 is the Australian Community Climate and Earth System Simulator Earth system model version designed to participate in CMIP6 simulations.\r\nBCC-CSM2-MR is one of the Beijing Climate Center Climate System Models designed for use in CMIP6 simulations.\r\nCAMS-CSM1-0 is the Chinese Academy of Meteorological Sciences Climate System Model version 1.\r\nCESM is the Community Earth System Model. \r\nCESM2-WACCM is the Community System Model - Whole Atmosphere Community Climate Model.\r\nCNRM-CM6-1 is the Centre National de Recherches Météorologiques Climate Model for CMIP6.\r\nCNRM-CM6-1-HR is the Centre National de Recherches Météorologiques Climate Model for CMIP6 - altered Horizontal Resolution.\r\nCNRM-ESM2-1 is the Centre National de Recherches Météorologiques Earth System Model, derived from CNRM-CM6-1.\r\nCanESM5 is the Canadian Earth System Model version 5.\r\nCanESM5-CanOE is the Canadian Earth System Model version 5 - Canadian Ocean Ecosystem.\r\nEC-Earth3 is the European Community Earth-system model version 3.\r\nFGOALS-g3 is the Flexible Global Ocean-Atmosphere-Land System Model, Grid-point Version 3\r\nGFDL-ESM4 is the Geophysical Fluid Dynamics Laboratory - Earth System Model version 4.\r\nGISS-E2-1-G is the Goddard Institute for Space Studies - chemistry-climate model version E2.1, using the GISS Ocean v1 (G01) model.\r\nHadGEM3-GC31-LL is the Met Offfice Hadley Centre Global Environment Model - Global Coupled configuration 3.1 - using an atmosphere/ocean resolution for historical simulation N96/ORCA1.\r\nIPSL-CM6A-LR is the Institut Pierre-Simon Laplace Climate Model for CMIP6 - Low Resolution.\r\nKACE-1-0-G is the Korean Advanced Community Earth system model. \r\nMIROC-ES2L is the Model for Interdisciplinary Research on Climate - Earth System version 2 for Long-term simulations.\r\nMIROC6 is the Model for Interdisciplinary Research on Climate - version 6.\r\nMPI-ESM1-2-HR is the Max Planck Institute Earth System Model - version 2 - altered Horizontal Resolution.\r\nMPI-ESM1-2-LR is the Max Planck Institute Earth System Model - version 2 - Low Resolution.\r\nMRI-ESM2-0 is the Meteorological Research Institute Earth System Model version 2.0.\r\nNorESM2-LM is the Norwegian Earth System Model version 2 - 2 degree resolution for atmosphere and land components, 1 degree resolution for ocean and sea-ice components.\r\nNorESM2-MM is the Norwegian Earth System Model version 2 - 1 degree resolution for all model components.\r\nUKESM1-0-LL is the United Kingdom Earth System Modelling project - version 1 - 2 degree resolution for all model components.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nThe panels were plotted using Python and shell scripts (BASH files) - code is available via the link in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n - Link to the data and code used to produce this figure and others in Chapter 9, archived on Zenodo.\r\n - Link to the code and output data for this figure, contained in a dedicated GitHub repository.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-08-25T14:18:25", "updateFrequency": "", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, permafrost", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-02-15T16:01:01", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 38028, "dataPath": "/badc/ar6_wg1/data/ch_09/ch9_fig22/v20220721", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5175, "numberOfFiles": 6, "fileFormat": "txt" }, "timePeriod": { "ob_id": 10427, "startTime": "1979-01-01T00:00:00", "endTime": "2100-12-31T00:00:00" }, "resultQuality": { "ob_id": 4208, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39680, "uuid": "3c19a763a0564aa599355e87acee95fa", "short_code": "comp", "title": "Caption for Figure 9.22 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Simulated versus observed permafrost extent and volume change by warming level. (a) Diagnosed Northern Hemisphere permafrost extent (area with perennially frozen ground at 15 m depth, or at the deepest model soil level if this is above 15 m) for 1979–1998, for available Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) models, from the first ensemble member of the historical coupled run, and for CMIP6 Atmospheric Model Intercomparison Project (AMIP) (atmosphere+land surface, prescribed ocean) and land-hist (land only, prescribed atmospheric forcing) runs. Estimates of current permafrost extents based on physical evidence and reanalyses are indicated as black symbols – triangle: Obu et al. (2018); star: Zhang et al. (1999); circle: central value and associated range from Gruber (2012). (b) Simulated global permafrost volume change between the surface and 3 m depth as a function of the simulated global surface air temperature (GSAT) change, from the first ensemble members of a selection of scenarios, for available CMIP6 models. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 32725, "uuid": "d75f0692e2594df8af882c04db5ba3fe", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 9: Ocean, cryosphere, and sea level change.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 9.3\r\n- data for Figure 9.4\r\n- data for Figure 9.5\r\n- data for Figure 9.6\r\n- data for Figure 9.7\r\n- data for Figure 9.9\r\n- data for Figure 9.10\r\n- data for Figure 9.11\r\n- data for Figure 9.12\r\n- data for Figure 9.13\r\n- data for Figure 9.14\r\n- data for Figure 9.15\r\n- data for Figure 9.22\r\n- data for Figure 9.24\r\n- data for Figure 9.26\r\n- data for Figure 9.28\r\n- data for Figure 9.29\r\n- data for Figure 9.30\r\n- data for Figure 9.32\r\n- data for Cross-Chapter Box 9.1, Figure 1\r\n- input data for Cross-Chapter Box 9.1, Figure 1" } ], "responsiblepartyinfo_set": [ 180061, 180062, 180063, 180064, 180065, 180066, 180067, 193298 ], "onlineresource_set": [ 52450, 52449, 82569, 52451, 52452 ] }, { "ob_id": 37739, "uuid": "e02c8424657846468c1ff3a5acd0b1ab", "title": "Model output from 1/4° global JRA55-forced integration of GO8p7 global ocean-sea ice model from 1958 to 2021", "abstract": "Annual, monthly and 5-day ocean and ice output from an integration of the UK Global Ocean GO8p7 configuration, based on version 4.0.4 of the NEMO (Nucleus for European Modelling of the Ocean) ocean and sea-ice model, forced by the JRA-55 (Japanese 55-year atmospheric analysis, Tsujino et al., 2018) surface field dataset. The present integration is on the 1/4° eORCA025 global grid. The complete dataset includes: full monthly and annual mean ocean fields; monthly mean sea ice fields; monthly and annual mean global mean scalar quantities; and 5-day mean values of a subset of 2-dimensional fields, including surface fields and bottom pressure. The model is initialised from an average of years 1995-2014 of the EN4 climatology (Good et al., 2013), and is integrated from 1958 to 2021. The model was run on the Archer2 HPC platform. The integrations were funded by the Natural Environment Research Council (NERC) under the Atlantic Climate System Integrated Study (ACSIS) project (NE/N018044/1).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-10-31T02:06:04", "updateFrequency": "notPlanned", "dataLineage": "eORCA025 horizontal grid, with 1/4° resolution at the Equator. 75 levels in the vertical.\r\nGO8p7 configuration developed under JMMP collaborative programme, based on NEMO v4.0.4", "removedDataReason": "", "keywords": "Oceans, sea-ice, temperature, salinity", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-10-19T15:47:37", "doiPublishedTime": "2022-10-20T08:01:37", "removedDataTime": null, "geographicExtent": { "ob_id": 3555, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 37740, "dataPath": "/bodc/SOC220065/GO8p7_JRA55_eORCA25", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2160870689459, "numberOfFiles": 18982, "fileFormat": "Data are CF-Compliant NetCDF data files" }, "timePeriod": { "ob_id": 10422, "startTime": "1958-01-01T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 4018, "explanation": "Data as supplied to BODC - no additional quality checks performed", "passesTest": true, "resultTitle": "BODC Data Quality Statement", "date": "2022-07-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37741, "uuid": "883b1cb68d414c93b851de3a4a1f77a6", "short_code": "comp", "title": "UK Global Ocean GO8p7 configuration, based on version 4.0.4 of the NEMO (Nucleus for European Modelling of the Ocean) ocean and sea-ice model", "abstract": "GO8p7 configuration was developed under JMMP collaborative programme, based on NEMO v4.0.4" }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 24717, "uuid": "7e92f3a40afc494f9aaf92525ebb4779", "short_code": "proj", "title": "The North Atlantic Climate System Integrated Study: ACSIS", "abstract": "Major changes are occurring across the North Atlantic (NA) climate system: in ocean and atmosphere temperatures and circulation, in sea ice thickness and extent, and in key atmospheric constituents such as ozone, methane and aerosols. Many observed changes are unprecedented in instrumental records. Changes in the NA directly affect the UK’s climate, weather and air quality, with major economic impacts on agriculture, fisheries, water, energy, transport and health. The NA also has global importance, since changes here drive changes in climate, hazardous weather and air quality further afield, such as in North America, Africa and Asia.\r\n\r\nACSIS (the North Atlantic Climate System Integrated Study) was an integrated programme of sustained observations, synthesis, and numerical modelling designed to address the overarching objective of enhancing the UK's capability to detect, attribute and predict changes in the North Atlantic (NA) Climate System, comprising: the North Atlantic Ocean, the atmosphere above it including its composition, and interactions with Arctic Sea Ice and the Greenland Ice Sheet. Specific objectives are:\r\n1. To provide the UK science community with sustained observations, data syntheses, leading-edge numerical simulations, and analysis tools, to facilitate world-class research on changes in the NA climate system and their impacts.\r\n2. To provide a quantitative, multivariate, description of how the NA climate system is changing.\r\n3. To determine the primary drivers and processes that are shaping change in the NA climate system now and will shape change in the near future.\r\n4. To determine the extent to which future changes in the NA climate system are predictable.\r\nACSIS enabled and delivered research to address the following research questions:\r\nRQ1. How have changes in natural and anthropogenic emissions and atmospheric circulation combined to shape multiyear trends in NA atmospheric composition and radiative forcing?\r\nRQ2. How have natural variability and radiative forcing combined to shape multi- year trends in the NA physical climate system?\r\nRQ3. To what extent are changes in the NA climate system predictable on multi-year timescales?\r\nACSIS was a partnership between six NERC centres (NCAS, NOC, BAS, NCEO, CPOM, PML) and the UK Met Office, exploiting the partners' unique capabilities in observing and simulating the atmosphere including its composition, the ocean, the cryosphere, and the fully coupled climate system.\r\nThe observational component brought together records from Earth-based (e.g. Cape Verde observatory, FAAM missions, RAPID, Argo, OSNAP) and spacebased (e.g. Cryosat, MetOP) platforms with a focus on the sustained observations that are necessary to measure changes on multi-year timescales.\r\nACSIS worked closely with the NERC-Met Office UKESM programme on Earth System Modelling, and contributed to and benefited from UK participation in international observing programmes such as UK-US RAPID, EU ATLANTOS and Global Atmospheric Watch, and modelling programmes such as CMIP6 and EU PRIMAVERA.\r\nThe legacy of ACSIS includes: new long-term multivariate observational datasets and syntheses; new modelling capabilities and simulations with unprecedented fidelity. ACSIS provided advances in understanding and predicting changes in the NA climate system that can be exploited in further research and related activities, for example to assess the impact of these changes on the UK and other countries - e.g. in terms of the consequences for hazardous weather risk, the environment and businesses. ACSIS outputs will also inform policy on climate change adaptation and air quality.\r\nACSIS was fully funded for five years (2016-2021)by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) (grant NE/N018028/1) which aimed to encourage its research centres to work closely together to tackle major scientific and societal challenges. ACSIS is one of the projects funded through this new way of allocating national capability funding, designed to enable more ambitious science than any single research organisation could provide." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 9160, 9161, 9162, 10232, 10233, 22879, 27692, 27702, 27703, 36269, 36270, 36288, 36314, 36326, 40559, 40560, 40565, 40566, 40569, 40573, 40580, 40586, 40587, 40591, 40592, 40595, 40596, 41010, 41012, 41027, 41031, 41036, 41039, 49599, 49600, 49601, 49602, 49603, 49604, 49605, 49606, 49607, 49608, 49609, 49610, 49611, 49612, 49613, 49614, 49615, 49616, 49617, 49618, 49619, 49620, 49621, 49622, 49623, 49624, 49625, 49626, 49627, 49628, 49629, 49630, 49631, 49632, 49633, 49634, 49635, 49636, 49637, 49638, 49639, 49640, 49641, 49642, 49643, 49644, 49645, 49646, 49647, 49648, 49649, 49650, 49651, 49652, 49653, 49654, 49655, 49656, 49657, 49658, 49659, 49660, 49661, 49662, 49663, 49664, 49665, 49666, 49667, 49668, 49669, 49670, 49671, 49672, 49673, 49674, 49675, 49676, 49677, 49678, 49679, 49680, 49681, 49682, 49683, 49684, 49685, 49686, 49687, 49688, 49689, 49690, 49691, 49692, 49693, 49694, 49695, 49696, 49697, 49698, 49699, 49700, 49701, 49702, 49703, 49704, 49705 ], "vocabularyKeywords": [], "identifier_set": [ 12218 ], "observationcollection_set": [ { "ob_id": 33256, "uuid": "770a885a8bc34d51ad71e87ef346d6a8", "short_code": "coll", "title": "The North Atlantic Climate System Integrated Study: model run output", "abstract": "ACSIS (the North Atlantic Climate System Integrated Study) was an integrated programme of sustained observations, synthesis, and numerical modelling designed to address the overarching objective of enhancing the UK's capability to detect, attribute and predict changes in the North Atlantic (NA) Climate System, comprising: the North Atlantic Ocean, the atmosphere above it including its composition, and interactions with Arctic Sea Ice and the Greenland Ice Sheet. ACSIS was a partnership between six NERC centres (NCAS, NOC, BAS, NCEO, CPOM, PML) and the UK Met Office, exploiting the partners' unique capabilities in observing and simulating the atmosphere including its composition, the ocean, the cryosphere, and the fully coupled climate system.\r\nThis collection includes global ocean simulations generated within the project.\r\nACSIS was funded by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) grant NE/N018028/1" } ], "responsiblepartyinfo_set": [ 180083, 180086, 180087, 180080, 180084, 180082, 180081, 180085, 180088, 180089, 180090, 180091 ], "onlineresource_set": [ 52645, 52646, 52647, 52648, 52649 ] }, { "ob_id": 37744, "uuid": "399b0f762a004657a411a9ea7203493a", "title": "Model output from 1/12° global JRA55-forced integration of GO8p7 global ocean-sea ice model from 1958 to 2021", "abstract": "Annual, monthly and 5-day ocean and ice output from an integration of the UK Global Ocean GO8p7 configuration, based on version 4.0.4 of the NEMO (Nucleus for European Modelling of the Ocean) ocean and sea-ice model, forced by the JRA-55 (Japanese 55-year atmospheric analysis, Tsujino et al., 2018) surface field dataset. The complete dataset includes: full monthly and annual mean ocean fields; monthly mean sea ice fields; monthly and annual mean global mean scalar quantities; and 5-day mean values of a subset of 2-dimensional fields, including surface fields and bottom pressure. The present integration is on the 1/12° eORCA12 global grid. The model is initialised from an average of years 1995-2014 of the EN4 climatology (Good et al., 2013), and is integrated from 1958 to 2021. The model was run on the Archer2 HPC platform. The integrations were funded by the Natural Environment Research Council (NERC) under the Atlantic Climate System Integrated Study (ACSIS) project (NE/N018044/1).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:34", "updateFrequency": "notPlanned", "dataLineage": "Data are as given by the data provider, no quality control has been performed by British Oceanographic Data Centre (BODC)", "removedDataReason": "", "keywords": "Oceans, sea-ice, temperature, salinity", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-10-19T15:44:40", "doiPublishedTime": "2022-10-20T09:16:57", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 37745, "dataPath": "/bodc/SOC220065/GO8p7_JRA55_eORCA12", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 18019485068801, "numberOfFiles": 18993, "fileFormat": "Data are CF-Compliant NetCDF data files" }, "timePeriod": { "ob_id": 10424, "startTime": "1958-01-01T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 4020, "explanation": "Data as supplied to BODC - no additional quality checks performed", "passesTest": true, "resultTitle": "BODC Data Quality Statement", "date": "2022-07-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37741, "uuid": "883b1cb68d414c93b851de3a4a1f77a6", "short_code": "comp", "title": "UK Global Ocean GO8p7 configuration, based on version 4.0.4 of the NEMO (Nucleus for European Modelling of the Ocean) ocean and sea-ice model", "abstract": "GO8p7 configuration was developed under JMMP collaborative programme, based on NEMO v4.0.4" }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 24717, "uuid": "7e92f3a40afc494f9aaf92525ebb4779", "short_code": "proj", "title": "The North Atlantic Climate System Integrated Study: ACSIS", "abstract": "Major changes are occurring across the North Atlantic (NA) climate system: in ocean and atmosphere temperatures and circulation, in sea ice thickness and extent, and in key atmospheric constituents such as ozone, methane and aerosols. Many observed changes are unprecedented in instrumental records. Changes in the NA directly affect the UK’s climate, weather and air quality, with major economic impacts on agriculture, fisheries, water, energy, transport and health. The NA also has global importance, since changes here drive changes in climate, hazardous weather and air quality further afield, such as in North America, Africa and Asia.\r\n\r\nACSIS (the North Atlantic Climate System Integrated Study) was an integrated programme of sustained observations, synthesis, and numerical modelling designed to address the overarching objective of enhancing the UK's capability to detect, attribute and predict changes in the North Atlantic (NA) Climate System, comprising: the North Atlantic Ocean, the atmosphere above it including its composition, and interactions with Arctic Sea Ice and the Greenland Ice Sheet. Specific objectives are:\r\n1. To provide the UK science community with sustained observations, data syntheses, leading-edge numerical simulations, and analysis tools, to facilitate world-class research on changes in the NA climate system and their impacts.\r\n2. To provide a quantitative, multivariate, description of how the NA climate system is changing.\r\n3. To determine the primary drivers and processes that are shaping change in the NA climate system now and will shape change in the near future.\r\n4. To determine the extent to which future changes in the NA climate system are predictable.\r\nACSIS enabled and delivered research to address the following research questions:\r\nRQ1. How have changes in natural and anthropogenic emissions and atmospheric circulation combined to shape multiyear trends in NA atmospheric composition and radiative forcing?\r\nRQ2. How have natural variability and radiative forcing combined to shape multi- year trends in the NA physical climate system?\r\nRQ3. To what extent are changes in the NA climate system predictable on multi-year timescales?\r\nACSIS was a partnership between six NERC centres (NCAS, NOC, BAS, NCEO, CPOM, PML) and the UK Met Office, exploiting the partners' unique capabilities in observing and simulating the atmosphere including its composition, the ocean, the cryosphere, and the fully coupled climate system.\r\nThe observational component brought together records from Earth-based (e.g. Cape Verde observatory, FAAM missions, RAPID, Argo, OSNAP) and spacebased (e.g. Cryosat, MetOP) platforms with a focus on the sustained observations that are necessary to measure changes on multi-year timescales.\r\nACSIS worked closely with the NERC-Met Office UKESM programme on Earth System Modelling, and contributed to and benefited from UK participation in international observing programmes such as UK-US RAPID, EU ATLANTOS and Global Atmospheric Watch, and modelling programmes such as CMIP6 and EU PRIMAVERA.\r\nThe legacy of ACSIS includes: new long-term multivariate observational datasets and syntheses; new modelling capabilities and simulations with unprecedented fidelity. 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ACSIS was a partnership between six NERC centres (NCAS, NOC, BAS, NCEO, CPOM, PML) and the UK Met Office, exploiting the partners' unique capabilities in observing and simulating the atmosphere including its composition, the ocean, the cryosphere, and the fully coupled climate system.\r\nThis collection includes global ocean simulations generated within the project.\r\nACSIS was funded by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) grant NE/N018028/1" } ], "responsiblepartyinfo_set": [ 180109, 180112, 180113, 180106, 180110, 180108, 180107, 180111, 180114, 180115, 180116, 180117 ], "onlineresource_set": [ 52632, 52633, 52634, 52635, 52636 ] }, { "ob_id": 37752, "uuid": "26b934d2731944dd945bc05406e40bee", "title": "MOYA: In flight Methane samples, Llanos de Moxos, Bolivia 2019 with supporting GEOS-Chem and NAME model output", "abstract": "This dataset contains isotopic sampling of methane taken on board the British Antarctic Survey (BAS) twin-otter aircraft during a flight campaign over the Llanos de Moxos wetland near Trinidad, Bolivia in 2019 and supporting model simulations for the Methane Observations and Yearly Assessments (MOYA) project. Air samples were collected in tedlar bags during flights over the region and subsequently analysed at the Greenhouse Gas Laboratory, Royal Holloway University (RHUL). These are supported with data from a nested GEOS-Chem model simulation at 0.25° x 0.3125° which was used to map the relationship between emissions and aircraft measurements in a regional domain bounded by 24 - 0 °S and 75 – 55 °W. In addition, a footprint of the air source was simulated for each minute of aircraft sampling to capture using the Met Office NAME model at of 0.14° × 0.09° and temporal resolution of 3 hourly.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-09-11T13:08:05", "updateFrequency": "notPlanned", "dataLineage": "Data were collected locally and sent to RHUL for analysis and deposited at the Centre for Environmental Data Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "Methane, NAME, GEOS-Chem, aircraft", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-07-26T14:29:41", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3559, "bboxName": "", "eastBoundLongitude": -64.98, "westBoundLongitude": -66.37, "southBoundLatitude": -14.77, "northBoundLatitude": -12.81 }, "verticalExtent": null, "result_field": { "ob_id": 37753, "dataPath": "/badc/moya/data/aircraft/Boliva_BAS-Flights_and_Model", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 372672338, "numberOfFiles": 9, "fileFormat": "Data are BADC-CSV and NetCDF formatted." }, "timePeriod": { "ob_id": 10429, "startTime": "2019-03-08T00:00:00", "endTime": "2019-03-09T23:59:59" }, "resultQuality": { "ob_id": 4022, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-07-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 37757, "uuid": "64f9a34007ef425880561a49913aa85f", "short_code": "cmppr", "title": "Composite process for Geochem model and NAME model for MOYA Bolivia BAS twin Otter Flights", "abstract": "Aircraft measurements Geochem model and NAME model for MOYA Bolivia BAS twin Otter Flights" }, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 24718, "uuid": "dd2b03d085c5494a8cbfc6b4b99ca702", "short_code": "proj", "title": "Methane Observations and Yearly Assessments (MOYA)", "abstract": "MOYA was a NERC funded research programme which began in May 2016 and will run for four years. Sixteen research partners make up the MOYA consortium.\r\n\r\nThe central objective of the MOYA project is to move towards closing the global methane budget through undertaking new observations and further analysis of existing data." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 25598, 52192, 52193, 63572, 63573, 63574, 63575, 63576, 63577, 63578, 63579, 63580, 63581, 63582, 63583, 63584, 63585, 63586, 63587, 63588, 63589, 63590, 63591, 63592, 63593, 63594 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 180142, 180143, 180144, 180145, 180146, 180147, 180148, 180149, 180150, 180151, 180152, 180153, 180192, 180193 ], "onlineresource_set": [ 52526 ] }, { "ob_id": 37758, "uuid": "8d769bddaddc4e10bdd6f5428a3a0af5", "title": "Chapter 8 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Box 8.2, Figure 1 (v20220718)", "abstract": "Data for Box 8.2, figure 1 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nBox 8.2, figure 1 shows projected long-term changes in precipitation seasonality. \r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Douville, H., K. Raghavan, J. Renwick, R.P. Allan, P.A. Arias, M. Barlow, R. Cerezo-Mota, A. Cherchi, T.Y. Gan, J. Gergis, D. Jiang, A. Khan, W. Pokam Mba, D. Rosenfeld, J. Tierney, and O. Zolina, 2021: Water Cycle Changes. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1055–1210, doi:10.1017/9781009157896.010.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has multiple panels. Data is provided in panel-specific sub-directories.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n - Global simulated 1995–2014 precipitation climatology\r\n - Global maps of projected changes in precipitation seasonality averaged across 31 to 33 CMIP6 models in the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios\r\n \r\n All changes are estimated in 2081–2100 relative to 1995–2014.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n There are two NetCDF files per panel, except for panel a which has only the first one :\r\n - one for the main field, which is represented with colors and has 'rchange' or 'rmeans' or 'mean' in the filename\r\n - the other for the confidence information, based on fraction of models which agree about signal change sign, which is represented in figures by diagonal lines as specified by the so called AR6 simple hatching scheme; it has 'agreemeent' or 'slashes' in the filename\r\n \r\n Each datafile has NetCDF attributes which clearly describe the data.\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nSSP stands for Shared Socioeconomic Pathway.\r\nSSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6.\r\nSSP245 is the Shared Socioeconomic Pathway which represents the median of radiative forcing and development scenarios, consistent with RCP4.5.\r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 8)\r\n - Link to the Supplementary Material for Chapter 8, which contains details on the input data used in Table 8.SM.1\r\n - Link to the code for all figures in Chapter 8, archived on Zenodo.\r\n - Link to the documentation for CAMMAC, the tool used for AR6 analysis.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:40", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, precipitation, seasonality, long-term change, scenarios, multi-model", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-25T15:49:34", "doiPublishedTime": "2023-05-15T12:39:25.900350", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 158, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37759, "dataPath": "/badc/ar6_wg1/data/ch_08/ch8_box8_2_fig1/v20220718", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2046842, "numberOfFiles": 10, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10430, "startTime": "1995-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4023, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-07-18" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37760, "uuid": "72c2436b889842eca00ec230276de8ce", "short_code": "comp", "title": "Caption for Box 8.2, figure 1 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected long-term changes in precipitation seasonality. Global maps of projected changes in precipitation seasonality (simply defined as the sum of the absolute deviations of mean monthly rainfalls from the overall monthly mean, divided by the mean annual rainfall as in Walsh and Lawler, 1981) averaged across 31 to 33 CMIP6 models in the SSP1-2.6 (b), SSP2-4.5 (c) and SSP5-8.5 (d) scenario respectively. The simulated 1995–2014 climatology is shown in panel (a). All changes are estimated in 2081–2100 relative to 1995–2014. Uncertainty is represented using the simple approach. No overlay indicates regions with high model agreement, where ≥80% of models agree on sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 63031 ], "vocabularyKeywords": [], "identifier_set": [ 12487 ], "observationcollection_set": [ { "ob_id": 32723, "uuid": "ab437cee56cb405285aac2bb59cc36d6", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 8: Water cycle changes", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 8: Water cycle changes.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 8.13\r\n- data for Figure 8.14\r\n- data for Figure 8.15\r\n- input data for Figure 8.16\r\n- data for Figure 8.17\r\n- data for Figure 8.18\r\n- data for Figure 8.21\r\n- data for Figure 8.25\r\n- data for Figure 8.26\r\n- data for Box 8.2, Figure 1" } ], "responsiblepartyinfo_set": [ 180194, 180195, 180196, 180197, 180198, 180199, 180200, 180201 ], "onlineresource_set": [ 52571, 52561, 52527, 82556, 52528 ] }, { "ob_id": 37761, "uuid": "bbf5ae3b78c44bf28ccb17b487d58a94", "title": "Chapter 8 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 8.14 (v20220718)", "abstract": "Data for Figure 8.14 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 8.14 shows projected long-term relative changes in seasonal mean precipitation. \r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Douville, H., K. Raghavan, J. Renwick, R.P. Allan, P.A. Arias, M. Barlow, R. Cerezo-Mota, A. Cherchi, T.Y. Gan, J. Gergis, D. Jiang, A. Khan, W. Pokam Mba, D. Rosenfeld, J. Tierney, and O. Zolina, 2021: Water Cycle Changes. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1055–1210, doi:10.1017/9781009157896.010.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has multiple panels. Data is provided in panel-specific sub-directories.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n \r\n - Global maps of projected relative changes (%) in seasonal mean of precipitation averaged across 29 CMIP6 models in the SSP2-4.5 scenario (2081–2100 relative to the 1995–2014).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n There are two NetCDF files per panel: one for the main field (*pr_means*.nc), which is represented with colors, the other for the confidence information (*pr_agreement-fraction-on-sign*.nc), based on agreement fraction of models about signal change sign, which is represented by diagonal lines as specified by the so called AR6 simple hatching scheme.\r\n \r\n Each datafile has NetCDF attributes which clearly describe the data.\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n SSP245 is the Shared Socioeconomic Pathway which represents the median of radiative forcing and development scenarios, consistent with RCP4.5.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 8)\r\n - Link to the Supplementary Material for Chapter 8, which contains details on the input data used in Table 8.SM.1\r\n - Link to the code for all figures in Chapter 8, archived on Zenodo.\r\n - Link to the documentation for CAMMAC, the tool used for AR6 analysis.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:18:08", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, precipitation, seasonal, change, long-term, hydrology, water budget, scenario, multi-model", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-25T12:02:21", "doiPublishedTime": "2023-05-15T08:42:56.658219", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 159, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37762, "dataPath": "/badc/ar6_wg1/data/ch_08/ch8_fig14/v20220718", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2328856, "numberOfFiles": 11, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10431, "startTime": "2081-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4024, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-07-18" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37763, "uuid": "ac6b2a9b2517438cb37081d9c3278e6f", "short_code": "comp", "title": "Caption for Figure 8.14 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected long-term relative changes in seasonal mean precipitation. Global maps of projected relative changes (%) in seasonal mean of precipitation averaged across 29 CMIP6 models in the SSP2-4.5 scenario. All changes are estimated for 2081–2100 relative to the 1995–2014 base period. Uncertainty is represented using the simple approach. No overlay indicates regions with high model agreement, where ≥80% of models agree on sign of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 63029, 63030 ], "vocabularyKeywords": [], "identifier_set": [ 12479 ], "observationcollection_set": [ { "ob_id": 32723, "uuid": "ab437cee56cb405285aac2bb59cc36d6", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 8: Water cycle changes", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 8: Water cycle changes.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 8.13\r\n- data for Figure 8.14\r\n- data for Figure 8.15\r\n- input data for Figure 8.16\r\n- data for Figure 8.17\r\n- data for Figure 8.18\r\n- data for Figure 8.21\r\n- data for Figure 8.25\r\n- data for Figure 8.26\r\n- data for Box 8.2, Figure 1" } ], "responsiblepartyinfo_set": [ 180204, 180205, 180206, 180207, 180208, 180209, 180210, 180211 ], "onlineresource_set": [ 52570, 52560, 52543, 82548, 52544, 88619 ] }, { "ob_id": 37764, "uuid": "2d67a9f7631247d7bb6130ddc033ba7a", "title": "Chapter 8 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 8.15 (v20220718)", "abstract": "Data for Figure 8.15 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 8.15 shows projected long-term relative changes in daily precipitation statistics.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Douville, H., K. Raghavan, J. Renwick, R.P. Allan, P.A. Arias, M. Barlow, R. Cerezo-Mota, A. Cherchi, T.Y. Gan, J. Gergis, D. Jiang, A. Khan, W. Pokam Mba, D. Rosenfeld, J. Tierney, and O. Zolina, 2021: Water Cycle Changes. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1055–1210, doi:10.1017/9781009157896.010.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has multiple panels. Data is provided in panel-specific sub-directories.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains global data of projected seasonal mean relative changes (%) averaged across CMIP6 models in the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios, in:\r\n \r\n - Number of dry days (e.g. days with less than 1 mm of rain)\r\n - Daily precipitation intensity (in mm/ day–1, estimated as the mean daily precipitation amount on wet days – e.g. days with intensity above 1 mm/ day–1)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n There are two NetCDF files per panel :\r\n - One for the main field, which is represented with colors and has 'rchange' or 'rmeans' in the filename\r\n - The other for the confidence information, based on fraction of models which agree about signal change sign, which is represented in figures by diagonal lines as specified by the so called AR6 simple hatching scheme; it has 'agreement' or 'slashes' in the filename\r\n Each datafile has NetCDF attributes which clearly describe the data.\r\n\r\n CMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n SSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6.\r\n SSP245 is the Shared Socioeconomic Pathway which represents the median of radiative forcing and development scenarios, consistent with RCP4.5.\r\n SSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 8)\r\n - Link to the Supplementary Material for Chapter 8, which contains details on the input data used in Table 8.SM.1\r\n - Link to the code for all figures in Chapter 8, archived on Zenodo.\r\n - Link to the documentation for CAMMAC, the tool used for AR6 analysis.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:39", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, daily precipitation, change, multi-model, scenario, dry days, long term", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-25T12:09:20", "doiPublishedTime": "2023-05-15T12:11:00.756756", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 160, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37765, "dataPath": "/badc/ar6_wg1/data/ch_08/ch8_fig15/v20220718", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3455876, "numberOfFiles": 15, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10432, "startTime": "2081-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4025, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-07-18" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37766, "uuid": "44765428d93547b3a7bc3f77d411f8db", "short_code": "comp", "title": "Caption for Figure 8.15 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected long-term relative changes in daily precipitation statistics. Global maps of projected seasonal mean relative changes (%) in the number of dry days (for examplei.e., days with less than 1 mm of rain) and daily precipitation intensity (in mm/ day–1, estimated as the mean daily precipitation amount at wet days –- for examplei.e., days with intensity above 1 mm/ day–1) averaged across CMIP6 models in the SSP1-2.6 (a, b), SSP2-4.5 (c, d) and SSP5-8.5 (e, f) scenario respectively. Uncertainty is represented using the simple approach.: No overlay indicates regions with high model agreement, where ≥80% of models agree on sign of change.; Ddiagonal lines indicate regions with low model agreement, where <80% of models agree on sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 63027, 63028 ], "vocabularyKeywords": [], "identifier_set": [ 12480 ], "observationcollection_set": [ { "ob_id": 32723, "uuid": "ab437cee56cb405285aac2bb59cc36d6", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 8: Water cycle changes", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 8: Water cycle changes.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 8.13\r\n- data for Figure 8.14\r\n- data for Figure 8.15\r\n- input data for Figure 8.16\r\n- data for Figure 8.17\r\n- data for Figure 8.18\r\n- data for Figure 8.21\r\n- data for Figure 8.25\r\n- data for Figure 8.26\r\n- data for Box 8.2, Figure 1" } ], "responsiblepartyinfo_set": [ 180214, 180215, 180216, 180217, 180218, 180219, 180220, 180221 ], "onlineresource_set": [ 52569, 52559, 52542, 52541, 82549, 88620, 94645 ] }, { "ob_id": 37767, "uuid": "92dc7ae089d84a43a28099ae49633383", "title": "Chapter 8 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 8.16 (v20220718)", "abstract": "Input Data for Figure 8.16 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 8.16 shows rate of change in mean and variability across increasing global warming levels. \r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Douville, H., K. Raghavan, J. Renwick, R.P. Allan, P.A. Arias, M. Barlow, R. Cerezo-Mota, A. Cherchi, T.Y. Gan, J. Gergis, D. Jiang, A. Khan, W. Pokam Mba, D. Rosenfeld, J. Tierney, and O. Zolina, 2021: Water Cycle Changes. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1055–1210, doi:10.1017/9781009157896.010.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has multiple panels. Input data is provided in one single file. \r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n A single json file provides in a structured way the data for each graph point. Details are provided under 'notes on reproducing the figure'.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Datafile : 'Fig8-16_data.json'\r\n\r\n The relation between provided data and figure elements is essentially described in field 'List of data' above, 'Notes on reproducing the figure' below, and in caption.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The figure can be reproduced using the software linked in the Related Documents section of this catalogue record\r\n \r\n For additional details about data description, please refer to 'Fig8-16_input_data.README.txt'\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 8)\r\n - Link to the Supplementary Material for Chapter 8, which contains details on the input data used in Table 8.SM.1\r\n - Link to the code for all figures in Chapter 8, archived on Zenodo.\r\n - Link to the the script for generating figure on GitHub\r\n - Link to the documentation for CAMMAC, the tool used for AR6 analysis.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:30", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, precipitation, precipitable water, runoff, change, variability, global warming, tropics, extra-tropics", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-25T12:18:10", "doiPublishedTime": "2023-05-15T12:16:57.388679", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 161, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37768, "dataPath": "/badc/ar6_wg1/data/ch_08/inputdata_ch8_fig16/v20220718", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 360894, "numberOfFiles": 4, "fileFormat": "Data are json formatted" }, "timePeriod": { "ob_id": 10433, "startTime": "2021-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4026, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-07-18" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37769, "uuid": "a86f91aeb6804a7f81ae8ee5e43b36b3", "short_code": "comp", "title": "Caption for Figure 8.16 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Rate of change in mean and variability across increasing global warming levels. Relative change (%) in seasonal mean total precipitable water (grey line), precipitation (red dashed lines), runoff (blue dashed lines), as well as in standard deviation of precipitation (red dashed lines) and runoff (blue dashed lines) averaged over extratropical land in (c) summer and (d) winter, and tropical land in (a) June–July–August (JJA) and (b) December–January–February (DJF) as a function of global mean surface temperature for the CMIP6 multi-model mean across the SSP5-8.5 scenario. Extratropical winter refers to DJF for Northern Hemisphere and JJA for Southern Hemisphere (and the reverse for extratropical summer). Each marker indicates a 21-year period centred on consecutive decades between 2015 and 2085 relative to the 1995–2014 base period. Precipitation and runoff variability are estimated by their standard deviation after removing linear trends from each time series. Error bars show the 5–95% confidence interval for the warmest 5°C global warming level. Figure adapted fromPendergrass et al. (2017) and updated with CMIP6 models. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12481 ], "observationcollection_set": [ { "ob_id": 32723, "uuid": "ab437cee56cb405285aac2bb59cc36d6", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 8: Water cycle changes", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 8: Water cycle changes.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 8.13\r\n- data for Figure 8.14\r\n- data for Figure 8.15\r\n- input data for Figure 8.16\r\n- data for Figure 8.17\r\n- data for Figure 8.18\r\n- data for Figure 8.21\r\n- data for Figure 8.25\r\n- data for Figure 8.26\r\n- data for Box 8.2, Figure 1" } ], "responsiblepartyinfo_set": [ 180224, 180225, 180226, 180227, 180228, 180229, 180230, 180231 ], "onlineresource_set": [ 52568, 52558, 52539, 52540, 82550, 82880, 88621, 94643 ] }, { "ob_id": 37770, "uuid": "7da00222bbb345c99ce14e358cde9f6d", "title": "Chapter 8 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 8.17 (v20220718)", "abstract": "Data for Figure 8.17 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 8.17 shows projected long-term relative changes in seasonal mean evapotranspiration.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Douville, H., K. Raghavan, J. Renwick, R.P. Allan, P.A. Arias, M. Barlow, R. Cerezo-Mota, A. Cherchi, T.Y. Gan, J. Gergis, D. Jiang, A. Khan, W. Pokam Mba, D. Rosenfeld, J. Tierney, and O. Zolina, 2021: Water Cycle Changes. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1055–1210, doi:10.1017/9781009157896.010.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has multiple panels. Data is provided in panel-specific sub-directories.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains global data of projected relative changes (%) in seasonal mean of surface evapotranspiration for\r\n - December–January–February (DJF; left panels)\r\n - June–July–August (JJA; right panels)\r\n \r\n The data are averaged across 29 or 30 CMIP6 models for SSP1.2-6, SSP2-4.5, and SSP5-8.5 scenarios. All changes are estimated in 2081–2100 relative to 1995–2014.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n There are two NetCDF files per panel :\r\n - one for the main field, which is represented with colors and has 'rchange' or 'rmeans' in the filename\r\n - the other for the confidence information, based on fraction of models which agree about signal change sign, which is represented in figures by diagonal lines as specified by the so called AR6 simple hatching scheme; it has 'agreemeent' or 'slashes' in the filename\r\n Each datafile has NetCDF attributes which clearly describe the data.\r\n\r\n CMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n SSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6.\r\n SSP245 is the Shared Socioeconomic Pathway which represents the median of radiative forcing and development scenarios, consistent with RCP4.5.\r\n SSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 8)\r\n - Link to the Supplementary Material for Chapter 8, which contains details on the input data used in Table 8.SM.1\r\n - Link to the code for all figures in Chapter 8, archived on Zenodo.\r\n - Link to the documentation for CAMMAC, the tool used for AR6 analysis.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:39", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, multi-model, seasonal, mean, evapotranspiration, change, long-term, scenario", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-25T12:37:46", "doiPublishedTime": "2023-05-15T12:20:25.166219", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 162, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37771, "dataPath": "/badc/ar6_wg1/data/ch_08/ch8_fig17/v20220718", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3494963, "numberOfFiles": 15, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10434, "startTime": "2081-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4027, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-07-18" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37772, "uuid": "8466f565e78142d9a94d99e99920dce1", "short_code": "comp", "title": "Caption for Figure 8.17 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected long-term relative changes in seasonal mean evapotranspiration. Global maps of projected relative changes (%) in seasonal mean of surface evapotranspiration for December–January–February (DJF; left panels) and June–July–August (JJA; right panels) averaged across 29 or 30 CMIP6 models for SSP1.2-6 (a, b) SSP2-4.5 (c, d) and SSP5-8.5 (e, f) scenario respectively. All changes are estimated in 2081–2100 relative to 1995–2014. Uncertainty is represented using the simple approach. No overlay indicates regions with high model agreement, where ≥80% of models agree on sign of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 63025, 63026 ], "vocabularyKeywords": [], "identifier_set": [ 12482 ], "observationcollection_set": [ { "ob_id": 32723, "uuid": "ab437cee56cb405285aac2bb59cc36d6", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 8: Water cycle changes", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 8: Water cycle changes.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 8.13\r\n- data for Figure 8.14\r\n- data for Figure 8.15\r\n- input data for Figure 8.16\r\n- data for Figure 8.17\r\n- data for Figure 8.18\r\n- data for Figure 8.21\r\n- data for Figure 8.25\r\n- data for Figure 8.26\r\n- data for Box 8.2, Figure 1" } ], "responsiblepartyinfo_set": [ 180234, 180235, 180236, 180237, 180238, 180239, 180240, 180241 ], "onlineresource_set": [ 52567, 52537, 52538, 82551, 52557, 88622, 94644 ] }, { "ob_id": 37773, "uuid": "6ed1539e8fe84caea089a0d6a7ffcdbd", "title": "Chapter 8 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 8.13 (v20220718)", "abstract": "Data for Figure 8.13 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 8.13 shows zonal and annual mean projected long-term changes in the atmospheric water budget.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Douville, H., K. Raghavan, J. Renwick, R.P. Allan, P.A. Arias, M. Barlow, R. Cerezo-Mota, A. Cherchi, T.Y. Gan, J. Gergis, D. Jiang, A. Khan, W. Pokam Mba, D. Rosenfeld, J. Tierney, and O. Zolina, 2021: Water Cycle Changes. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1055–1210, doi:10.1017/9781009157896.010.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n There are 9 sub-panels, with data provided for all panels in one single directory.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains zonal and annual-mean projected long-term changes in the atmospheric water budget in:\r\n \r\n - Modelled global precipitation (CMIP6 simulations, ssp126, ssp245 and ssp585 scenarios, 1650 -2100)\r\n - Modelled global evaporation (CMIP6 simulations, ssp126, ssp245 and ssp585 scenarios, 1650 -2100)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n There is one NetCDF file per sub-panel, named by the scenario and variable of the sub-panels: 3 variables (precipitation : 'pr', evaporation : 'evspsbl' and their difference : 'P-E') times 3 scenarios (ssp126, ssp245 and ssp585).\r\n\r\n Each sub-panel NetCDF file has 6 variables. The variable names have suffix:\r\n - mean for multi-model change mean (thick coloured line)\r\n - land_mean for multi-model change mean over land (thick black line),\r\n - pctl5 and pctl95 for multi-model 5 and 95 percentiles (coloured shaded area),\r\n - variab5 and variab95 for 5 and 95 percentiles of the internal variability (grey shaded area).\r\n\r\n As an example Fig8-13_pr_ssp126.nc (precipitation, scenario SSP1-2.6), relates to the upper panel (left).\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n SSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6.\r\n SSP245 is the Shared Socioeconomic Pathway which represents the median of radiative forcing and development scenarios, consistent with RCP4.5.\r\n SSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 8)\r\n - Link to the Supplementary Material for Chapter 8, which contains details on the input data used in Table 8.SM.1\r\n - Link to the code for all figures in Chapter 8, archived on Zenodo.\r\n - Link to the documentation for CAMMAC, the tool used for AR6 analysis.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:17:38", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, precipitation, hydrology, water budget, projection, scenario", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-25T11:57:19", "doiPublishedTime": "2023-05-15T08:38:16.986675", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 163, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 37774, "dataPath": "/badc/ar6_wg1/data/ch_08/ch8_fig13/v20220718", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 409873, "numberOfFiles": 12, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10435, "startTime": "1650-01-01T12:00:00", "endTime": "2100-12-31T12:00:00" }, "resultQuality": { "ob_id": 4028, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-07-18" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37775, "uuid": "df421c953c5746e3a8a7ff6bc14ccaab", "short_code": "comp", "title": "Caption for Figure 8.13 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Zonal and annual-mean projected long-term changes in the atmospheric water budget. Zonal and annual mean projected changes (mm day–1) in P (precipitation, left column), E (evaporation, middle column), and P–E (right column) over both land and ocean areas (thick line) and over land only (dashed line) averaged across 36 to 38 CMIP6 models in the SSP1-2.6 (top row), SSP2-4.5 (middle row) and SSP5-8.5 (bottom row) scenario, respectively. Shading denotes confidence intervals estimated from the CMIP6 ensemble under a normal distribution hypothesis. Colour shading denotes changes over both land and ocean. Grey shading represents internal variability derived from the pre-industrial control simulations. All changes are estimated for 2081–2100 relative to the 1995–2014 base period. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 83767, 83768, 83769 ], "vocabularyKeywords": [], "identifier_set": [ 12478 ], "observationcollection_set": [ { "ob_id": 32723, "uuid": "ab437cee56cb405285aac2bb59cc36d6", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 8: Water cycle changes", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 8: Water cycle changes.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 8.13\r\n- data for Figure 8.14\r\n- data for Figure 8.15\r\n- input data for Figure 8.16\r\n- data for Figure 8.17\r\n- data for Figure 8.18\r\n- data for Figure 8.21\r\n- data for Figure 8.25\r\n- data for Figure 8.26\r\n- data for Box 8.2, Figure 1" } ], "responsiblepartyinfo_set": [ 180251, 180244, 180245, 180246, 180247, 180248, 180249, 180250 ], "onlineresource_set": [ 52566, 82546, 52545, 52556, 52546, 88618 ] } ] }