Observation List
Get a list of Observation objects.
GET /api/v3/observations/?format=api&offset=5400
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These include cloud base height and also backscatter profiles. The backscatter profiles can allow detection of aerosol species such as volcanic ash where suitable instrumentation is deployed. The Met Offices operational LIDARNET network consists of Vaisala CT25k and CL31 ceilometers and the Jenoptik CHM15k Nimbus. In addition, this dataset also contains data from the development ceilometers operated by the Met Office." } ], "responsiblepartyinfo_set": [ 110299, 110300, 110301, 110302, 110304, 110305, 110303, 110298, 110306 ], "onlineresource_set": [ 25130, 25129 ] }, { "ob_id": 26284, "uuid": "17e18421f0304ee3b02a96abf344ebe9", "title": "Met Office LIDARNET Portglenone Raymetrics LR111-D300 lidar profile data", "abstract": "Range corrected lidar signal and volume depolarisation ratio data from the Met Office's Raymetrics LR111-D300 lidar located at the Met Office observations enclosure near Portglenone, County Antrim, Northern Ireland. Data available from June 2018 onwards, though the instrument is only operated sporadically (see below for further details).\r\n\r\nThis instrument is one of a suite of 10 Raman lidars deployed by the Met Office around the UK to complement a wider network of ceilometers within the \"LIDARNET\" upper air monitoring network. Returns from these instruments form a range of products for use in forecasting and hazard detection. The backscatter profiles can allow detection of aerosol species such as volcanic ash where suitable instrumentation is deployed.\r\n\r\nThe primary aim of the Raman lidar network is the detection and quantification of volcanic ash aerosols during a volcanic event, and the network is only test fired only for a few hours each week. Outside of these times the lidars may be fired if there is a mineral dust outbreak or other such aerosol event of interest. 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These include cloud base height and also backscatter profiles. The backscatter profiles can allow detection of aerosol species such as volcanic ash where suitable instrumentation is deployed. The Met Offices operational LIDARNET network consists of Vaisala CT25k and CL31 ceilometers and the Jenoptik CHM15k Nimbus. In addition, this dataset also contains data from the development ceilometers operated by the Met Office." } ], "responsiblepartyinfo_set": [ 110307, 110308, 110309, 110310, 110311, 110313, 110314, 110312, 110315 ], "onlineresource_set": [ 25131 ] }, { "ob_id": 26285, "uuid": "a2e00ed1f3724db2b5ff24f17adf3811", "title": "Met Office LIDARNET Rhyl Raymetrics LR111-D300 lidar profile data", "abstract": "Range corrected lidar signal and volume depolarisation ratio data from the Met Office's Raymetrics LR111-D300 lidar located at a Met Office instrument enclosure near Rhyl, Denbighshire, on the North Welsh coast. Data available from June 2018 onwards, though the instrument is only operated sporadically (see below for further details).\r\n\r\nThis instrument is one of a suite of 10 Raman lidars deployed by the Met Office around the UK to complement a wider network of ceilometers within the \"LIDARNET\" upper air monitoring network. Returns from these instruments form a range of products for use in forecasting and hazard detection. The backscatter profiles can allow detection of aerosol species such as volcanic ash where suitable instrumentation is deployed.\r\n\r\nThe primary aim of the Raman lidar network is the detection and quantification of volcanic ash aerosols during a volcanic event, and the network is only test fired only for a few hours each week. Outside of these times the lidars may be fired if there is a mineral dust outbreak or other such aerosol event of interest. The lidars will not fire if any precipitation is detected.\r\n\r\nRaman channel data are not presently available from this instrument in the CEDA archives.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2025-01-17T00:54:41", "updateFrequency": "asNeeded", "dataLineage": "Data were collected by the Met Office before processed through the Met Office's Hermes system. A copy of these data were then delivered by the Met Office to CEDA for archiving.", "removedDataReason": "", "keywords": "Met Office, lidar, ash, Raman, backscatter profiles", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2019-02-19T10:26:15", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2232, "bboxName": "Rhyl met station", "eastBoundLongitude": -3.508681, "westBoundLongitude": -3.508681, "southBoundLatitude": 53.259296, "northBoundLatitude": 53.259296 }, "verticalExtent": null, "result_field": { "ob_id": 26267, "dataPath": "/badc/ukmo-lidarnet/data/rhyl/raymetrics-lr111-d300/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 6314629641, "numberOfFiles": 13550, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 7100, "startTime": "2018-06-20T07:44:00", "endTime": null }, "resultQuality": { "ob_id": 3152, "explanation": "The data are checked to make sure there are data present, and all the channels are populated etc. However, there are no quality checks in place to catch problems with the machine. 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Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted." }, "timePeriod": { "ob_id": 7096, "startTime": "2018-05-16T23:00:00", "endTime": "2018-05-17T22:59:59" }, "resultQuality": { "ob_id": 3074, "explanation": "Data collected by flight participants before preparation for archival with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "FAAM to CEDA Data Quality Statement", "date": "2015-09-03" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 26291, "uuid": "63727e5732fc4ec882f7a6da316aaf33", "short_code": "acq", "title": "FAAM Flight C106 Acquisition", "abstract": "FAAM Flight C106 Acquisition" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 8 ], "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": 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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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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The global climatic impact of all clouds types is estimated as a cooling effect. This net cooling effect results from the opposing impacts from liquid clouds (which cool by reflecting sunlight back into space) and ice clouds (which warm through a \"greenhouse\" effect). Unfortunately there is a lack of understanding of many of the physical processes occurring in ice clouds due to the complexity of ice particle processes and interactions between atmospheric motions, water vapour, and aerosol particles. This means ice clouds are a source of significant uncertainty in climate simulations, and can lead to errors in weather forecasts. Establishing which models are the most accurate remains difficult due to the lack of observations of ice cloud properties. \r\n\r\nRemote sensing techniques (e.g. radar) can provide observations of ice clouds over large areas on a continuous basis, making them ideal for assessing model skill. 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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. 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Data from this site are availlable from August 2018 at present, although the radar has been producing augmented lrd scan data previous to this time. The radar is a C-band (5.3 cm wavelength) radar and data are received by the Nimrod system at 5 minute intervals.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-05-04T04:45:17", "updateFrequency": "continual", "dataLineage": "Data are acquired from rain radar network in the UK and Europe by the Met Office's NIMROD system. Single site data are then sent to the CEDA by the Met Office's DART operational dissemination system. Raw polar data from single sites are sent through from the RADARNET team at the Met Office. On arrival at CEDA the data are ingested after a 48h quarantine, having been compressed and then compiled into daily tar balls for each data type.", "removedDataReason": "", "keywords": "Met Office, NIMROD, rain radar", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2018-08-13T10:54:38", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2268, "bboxName": "Met Office High Moorsley Radar site", "eastBoundLongitude": -1.4745537, "westBoundLongitude": -1.4745537, "southBoundLatitude": 54.803407, "northBoundLatitude": 54.803407 }, "verticalExtent": null, "result_field": { "ob_id": 26421, "dataPath": "/badc/ukmo-nimrod/data/single-site/high-moorsley/raw-dual-polar/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 988801178574, "numberOfFiles": 838, "fileFormat": "Data are in NIMROD data format. See linked documentation for further details." }, "timePeriod": { "ob_id": 7134, "startTime": "2016-11-09T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 1497, "explanation": "Data are from the Met Office's operational NIMROD system", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2013-12-16" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 26422, "uuid": "5e03e5dfcc204db99cfbd1d65c35c826", "short_code": "acq", "title": "High Moorsley Rain Radar", "abstract": "High Moorsley Rain Radar" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2532, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "ukmo_wx", "label": "restricted: ukmo_wx group", "licence": { "ob_id": 12, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/ukmo_agreement.pdf", "licenceClassifications": [ { "ob_id": 4, "classification": "academic" } ] } }, { "ob_id": 2533, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "ukmo_wx_gov", "label": "restricted: ukmo_wx_gov group", "licence": { "ob_id": 13, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/ukmo_agreement_gov.pdf", "licenceClassifications": [ { "ob_id": 5, "classification": "policy" } ] } } ], "projects": [ { "ob_id": 5736, "uuid": "ced06d1e5a8eacb69bf0029bf5f0e17a", "short_code": "proj", "title": "Met Office NIMROD Database", "abstract": "The Met Office run the NIMROD system as a short-term forcasting tool, primarily used for collating observational data such as rain radar data from across Europe" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5709, "uuid": "82adec1f896af6169112d09cc1174499", "short_code": "coll", "title": "Met Office Rain Radar Data from the NIMROD System", "abstract": "A collection of products from rain radars operated by the Met Office and other European agencies for the UK and Europe. This collection includes rain composite plots and data for the UK and Europe, plus single site radar data including rain rate data, single and dual-polar data products. \r\n\r\nThese were produced by the Met Office's Nimrod system. Nimrod is a fully automated system for weather analysis and nowcasting based around a network of C-band rainfall radars. This dataset has the fine-resolution analyses of rain rate for the UK and Europe.\r\n\r\nThe UK has a network of C-band rainfall radars and data form these are processed by the Met Office NIMROD system. Four or five radar scans at different elevations at each site are processed to give the best possible estimate of rainfall at the ground. The main quality checking method is routine evaluation using rain gauges as ground truth.\r\n\r\nThe BADC holds the analyses of rainfall rate at a time resolution of 5 or 15 minutes. Data are available from late 2002. Images are available for the UK as well as a further image including neighbouring European countries from 1999. Data files are available on a 1 km and 5 km Cartesian grid. Single radar site data are available for 2 and 5 km Cartesian grids for various UK radar sites.\r\n\r\nThe value of radar-based data from the Nimrod system has been highlighted repeatedly. For example, in two severe flooding events during 1998 (at Easter over the Midlands and in late October over Wales), estimates of surface rainfall derived from radar data provided evidence of the extent and severity of the rainfall events. The 2 km data files reach to 100 km from the radar, the 5km files to 250 km.\r\n\r\nDetailed radar site location are given in the Met Office Weather Radar Factsheet.\r\n\r\nTime resolution is 5 or 15 minutes depending on the product.\r\n\r\nVarious scripts have been made available under the software directory to aid use of these data, including within GIS applications." } ], "responsiblepartyinfo_set": [ 110806, 110803, 110807, 110804, 110802, 110808, 110809, 110805, 110812 ], "onlineresource_set": [ 25235, 25237, 25239, 25236, 25238 ] }, { "ob_id": 26423, "uuid": "33592524dd9a4b2f897edbddb69b3381", "title": "Weighing trees with lasers project: terrestrial laser scanner data, French Guiana, Cayenne, Nourague Nature Reserve plot site (NOU-11), November 2015", "abstract": "This dataset is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations which, have been re-surveyed at different times. This plot site was situated in French Guiana, Cayenne, Nourague Nautre Reserve. The plot site had the following geographical features; Moisture type: Moist, Elevation: Lowland, Edaphic Type: Terra Firma, Substrate:Mixed, Geology: Pre-Quaternary,Forrestry: Old-growth.\r\n\r\nThe project scanned all trees in the permanent sample plot (PSP) spanning a range of soil fertility and productivity gradients (24 x 1 ha PSPs in total). The aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents, or whether they differ significantly, and require continental level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB (Above Ground Biomass) from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI (Global Ecosystem Dynamics Investigation Lidar) missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data including: UAVs (unmanned aerial vehicle) for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2018-07-04T14:17:06.533374", "updateFrequency": "", "dataLineage": "Provided by Andrew Burt UCL to CEDA for publication", "removedDataReason": "", "keywords": "Terrestrial Laser Scanner, French Guiana, Cayenne, Nourague Nautre Reserve, Moisture type:\tMoist, Elevation: Lowland, Edaphic Type: Terra Firma, Substrate:Mixed, Geology: Pre-Quaternary, Forestry: Old-growth", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2018-11-02T13:55:27", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2294, "bboxName": "NOU-11 French Guina plot site", "eastBoundLongitude": -52.68, "westBoundLongitude": -52.68, "southBoundLatitude": 4.08, "northBoundLatitude": 4.08 }, "verticalExtent": null, "result_field": { "ob_id": 26678, "dataPath": "/neodc/tls/data/raw/fguiana/NOU-11", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 68689279396, "numberOfFiles": 11145, "fileFormat": "Scan folder contain a number of file formats CSV, ASCII, text, PNG , RXP and PAT" }, "timePeriod": { "ob_id": 7197, "startTime": "2015-11-10T00:00:00", "endTime": "2015-11-11T00:00:00" }, "resultQuality": { "ob_id": 3184, "explanation": "validated by UCL", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-11-02" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 27253, "uuid": "fec1eb450b1145ccb3dd68f895625273", "short_code": "acq", "title": "French Guiana, Cayenne, Nourague Nature Reserve plot site", "abstract": "Weighing trees with lasers project: terrestrial laser scanner data; French Guiana, Cayenne, Nourague Nature Reserve plot site - 10/11/2015" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "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": 26683, "uuid": "70b2a6b0163747778ee85b4f7f86d8c0", "short_code": "proj", "title": "Weighing Trees with Lasers", "abstract": "Measuring the volume and structure of a tree accurately allows us to calculate the total above-ground carbon (C) stored in the tree, a very important property. Trees remove CO2 from the atmosphere during photosynthesis and can store this C for decades or even centuries until the tree dies, when some of it is released back to the atmosphere through decomposition. Tropical forests store around half of all above-ground terrestrial C, but are at particular risk due to deforestation and degradation, as well as from changing rainfall and temperature patterns. Surprisingly, our knowledge of tropical forest C stocks is quite poor, and errors in these stocks are large and uncertain. This uncertainty feeds into estimates of CO2 emissions due to deforestation, degradation and land use change. We will address this major uncertainty in the terrestrial C cycle by deploying a new, NERC-funded terrestrial laser scanner (TLS) to scan 1000s of trees in tropical forests on three continents: Amazonia, the Congo Basin and SE Asia. The laser data will allow us to measure 3D tree volume and biomass non-destructively to within a few percent of the best current estimates, made by destructive harvesting and weighing. The current, large uncertainties arise because weighing a tree is extremely difficult: tropical trees may be over 50m tall, and weigh 100 tonnes or more. Harvesting also precludes revisiting trees over time to measure change. In practice, a small sample of trees that have been harvested and weighed are related to easy-to-measure parameters of diameter and height, using empirical 'allometric' (size-to-mass) relationships. These relationships are then used to translate diameter and height measurements made over wider areas into estimates of biomass. Allometry is also the only way to infer biomass at very large (pan-tropical) scales, from remote sensing measurements. Unfortunately, the sample of harvested trees underpinning global allometric relationships is geographically limited, and contains very few large trees. Current estimates of tropical forest C stocks from satellite and ground data, all based on these very limited allometry samples, diverge significantly in size and pattern, leading to heated debate as to why this should be.\r\n\r\nThe project hopes to settle this debate, given that our lidar-derived estimates of biomass are completely independent of allometry and unbiased in terms of tree size. We will 'weigh' more trees than are currently included in all global pan-tropical allometries and quantify uncertainty in the allometry models. We will also test assumptions made in allometric models regarding tree shape and wood density. Our measurements will also answer fundamental questions about geographical differences in structural characteristics across tropical forests. Our data will be vital for testing new estimates of biomass from remote sensing; the UK-led ESA BIOMASS RADAR and NASA GEDI laser missions will both estimate pan-tropical C stocks by relying on allometric relationships between forest height and biomass. Our work will feed into these two missions through long-standing collaborations with the lead scientists. More generally, the large number of tree measurements we will collect would be of great interest to researchers in tropical ecology, forestry, biodiversity, remote sensing and C mapping, among others.\r\n\r\nA key aim of the project is to ensure the widest use of our results, by making our data and tools publicly available. We will work with partners to explore routes for commercial developments and input into government policy, particularly relating to forest management and C mapping and mitigation. Lastly, we will make our work accessible through a range of outreach activities, including developing links between a school in the Amazon and UK schools, to raise awareness of scientific, conservation and policy issues surrounding tropical forests.\r\n\r\nThis project was funded by NERC through grant: NE/N00373X/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 9691 ], "observationcollection_set": [ { "ob_id": 26677, "uuid": "ca0729ec30514a64a6ccd393eacff5f0", "short_code": "coll", "title": "Weighing trees with lasers project: Terrestrial Laser Scanner data collection", "abstract": "This dataset collection is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations which, have been re-surveyed at different times. The terrestrial laser scanner (TLS) was able to scan 1000s of trees in tropical forests on three continents: including Amazonia, the Congo Basin and SE Asia. The laser data measured 3D tree volume and biomass non-destructively to within a few percent of the best current estimates, made by destructive harvesting and weighing.\r\n\r\nThe project scanned all trees in multiple permanent sample plots (PSPs) spanning a range of soil fertility and productivity gradients (24 x 1 ha PSPs in total). The aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents, or whether they differ significantly, and require continental level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data including: UAVs for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy." }, { "ob_id": 30127, "uuid": "82b29f96b8c94db28ecc51a479f8c9c6", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Core datasets", "abstract": "This NCEO Core data set collection contains data generated by the National Centre for Earth Observation core scientific programmes. NCEO is a National Environment Research Council (NERC) research centre with more than 80 scientists distributed across leading UK universities and research organisations and led by Professor John Remedios at the University of Leicester.\r\n\r\nNCEO provides the UK with core expertise in Earth Observation science, data sets and merging techniques, and model evaluation to underpin Earth System research and the UK’s international contribution to environmental science. NCEO scientists work strategically with space agencies, play significant roles in mission planning, and generate internationally-recognised data products from 20 different satellite instruments." }, { "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": [ 112231, 112235, 112238, 112239, 112241, 112240, 112236, 112234, 112237, 112242, 112232, 112233 ], "onlineresource_set": [ 25667 ] }, { "ob_id": 26425, "uuid": "a78d998cc9714e0bbd4276c0bdd87703", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Along-Track Scanning Radiometer (ATSR) Level 3 Uncollated (L3U) Climate Data Record, version 2.0", "abstract": "This v2.0 SST_cci Along-Track Scanning Radiometer (ATSR) Level 3 Uncollated (L3U) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the Along Track Scanning Radiometer (ATSR) series of satellite instruments. It covers the period between 11/1991 and 04/2012. This Level 3 Uncollated (L3U) product provides these SST data on a 0.05 regular latitude-longitude grid with a single orbit per file.\r\n \r\nThe dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research. \r\n\r\nThis CDR Version 2.0 product is a later version of the Long Term product v1.1. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-03-09T03:06:49", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI SST project team and supplied to the Centre for Environmental Data Analysis (CEDA) for archiving in the context of the ESA CCI Open Data Portal project and the National Centre for Earth Observation (NCEO).", "removedDataReason": "", "keywords": "SST, CCI", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.05 degrees", "status": "superseded", "dataPublishedTime": "2019-08-02T22:12:44", "doiPublishedTime": "2019-08-22T12:23:24", "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 26426, "dataPath": "/neodc/esacci/sst/data/CDR_v2/ATSR/L3U/v2.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 536633837215, "numberOfFiles": 100630, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 7135, "startTime": "1991-11-01T00:00:00", "endTime": "2012-04-08T22:59:59" }, "resultQuality": { "ob_id": 3153, "explanation": "As provided by the CCI SST project", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-07-06" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 27432, "uuid": "d75a78a832c74476a4739c6dff0991c1", "short_code": "cmppr", "title": "CCI SST retrieval process from the (A)ATSR series of instruments", "abstract": "The ESA Climate Change Initiative Sea Surface Temperature (SST) product has retrieved sea surface temperature from the (A)ATSR series of satellite instruments." }, "imageDetails": [ 137 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2523, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 4, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_sst_terms_and_conditions_v2.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 11008, "uuid": "05fb7c9964b4172991a72082c46a3376", "short_code": "proj", "title": "Sea Surface Temperature Climate Change Initiative Project", "abstract": "The Sea Surface Temperature Climate Change Initiative (SST_cci) project is part of the European Space Agency's Climate Change Initiative programme, It aims to accurately mapping the surface temperature of the global oceans using observations from many satellites, and to independently quantify SST to a quality suitable for climate research.\r\n\r\nThe team brings together European expertise in creating climate quality records of ocean temperatures from satellite data, with expertise in climate applications and computer engineering. Through the ESA funded Climate Change Initiative, the team have created a climate record of global sea surface temperature (SST) for the period 1981 to 2016. Based on satellite data, this record is independent of thermometer based measurements from ships and buoys. The new climate SST record complements and challenges existing knowledge of how ocean temperatures have evolved. \r\n\r\nThe project started in August 2010. It is part of a wider initiative by the European Space Agency (ESA) addressing several essential climate variables in addition to SST." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50415, 50417, 52527, 52529, 52530, 52532, 52535, 52536, 52539, 52540, 52542, 52545, 57984, 57985, 57986, 57987, 57988, 57989, 66255, 66277, 74117 ], "vocabularyKeywords": [ { "ob_id": 10337, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/dataType/dtype_sstInt", "resolvedTerm": "sea surface temperature" } ], "identifier_set": [ 10574 ], "observationcollection_set": [ { "ob_id": 11005, "uuid": "1dc189bbf94209b48ed446c0e9a078af", "short_code": "coll", "title": "Collection of Sea Surface Temperature (SST) Data of the Global Oceans as part of the ESA Climate Change Initiative (CCI)", "abstract": "The ESA Sea Surface Temperature Climate Change Initiative (ESA SST_cci) datasets accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nThe latest version (v2.1) of the data are described in the data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ . To comply with the attribution aspect, please cite the above reference and the dataset citation given on the relevant dataset page." }, { "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": [ 110814, 110813, 110816, 110818, 110819, 110822, 110823, 110817, 110824, 115377 ], "onlineresource_set": [ 25241, 25242, 25248, 25246, 25249, 25245, 25243, 37110, 92529, 92530, 92531, 92532, 92533, 92534, 95027, 95028, 95029 ] }, { "ob_id": 26427, "uuid": "60476148c74148cb8cac1e47c501a178", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 2 Preprocessed (L2P) Climate Data Record, version 2.0", "abstract": "This v2.0 SST_cci Advanced Very High Resolution Radiometer (AVHRR) Level 2 Preprocessed (L2P) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the AVHRR series of satellite instruments. It covers the period between 08/1981 and 12/2016. This L2P product provides these SST data on the original satellite swath with a single orbit of data per file.\r\n\r\nThe dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST CCI accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2019-04-01T12:25:40", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI SST project team and supplied to the Centre for Environmental Data Analysis (CEDA) for archiving in the context of the ESA CCI Open Data Portal project and the National Centre for Earth Observation (NCEO).", "removedDataReason": "", "keywords": "SST, ESA Climate Change Initiative", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2019-08-02T22:04:58", "doiPublishedTime": "2019-08-22T12:23:59", "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 26428, "dataPath": "/neodc/esacci/sst/data/CDR_v2/AVHRR/L2P/v2.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 6171674744783, "numberOfFiles": 364921, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 7386, "startTime": "1981-08-23T23:00:00", "endTime": "2016-12-31T23:59:59" }, "resultQuality": { "ob_id": 3154, "explanation": "as provided by the CCI SST team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-07-06" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 27588, "uuid": "877515eb415a4797af7e06493f32e971", "short_code": "cmppr", "title": "CCI SST retrieval process from the AVHRR series of instruments", "abstract": "The ESA Climate Change Initiative Sea Surface Temperature (SST) product has retrieved sea surface temperature from the AVHRR series of satellite instruments." }, "imageDetails": [ 137 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2523, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 4, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_sst_terms_and_conditions_v2.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 11008, "uuid": "05fb7c9964b4172991a72082c46a3376", "short_code": "proj", "title": "Sea Surface Temperature Climate Change Initiative Project", "abstract": "The Sea Surface Temperature Climate Change Initiative (SST_cci) project is part of the European Space Agency's Climate Change Initiative programme, It aims to accurately mapping the surface temperature of the global oceans using observations from many satellites, and to independently quantify SST to a quality suitable for climate research.\r\n\r\nThe team brings together European expertise in creating climate quality records of ocean temperatures from satellite data, with expertise in climate applications and computer engineering. Through the ESA funded Climate Change Initiative, the team have created a climate record of global sea surface temperature (SST) for the period 1981 to 2016. Based on satellite data, this record is independent of thermometer based measurements from ships and buoys. The new climate SST record complements and challenges existing knowledge of how ocean temperatures have evolved. \r\n\r\nThe project started in August 2010. It is part of a wider initiative by the European Space Agency (ESA) addressing several essential climate variables in addition to SST." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 52527, 52529, 52531, 52533, 52534, 52535, 52536, 52539, 52540, 52542, 52545, 57984, 57985, 57986, 57987, 57988, 57989, 66277, 74117 ], "vocabularyKeywords": [ { "ob_id": 10337, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/dataType/dtype_sstInt", "resolvedTerm": "sea surface temperature" } ], "identifier_set": [ 10575 ], "observationcollection_set": [ { "ob_id": 11005, "uuid": "1dc189bbf94209b48ed446c0e9a078af", "short_code": "coll", "title": "Collection of Sea Surface Temperature (SST) Data of the Global Oceans as part of the ESA Climate Change Initiative (CCI)", "abstract": "The ESA Sea Surface Temperature Climate Change Initiative (ESA SST_cci) datasets accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nThe latest version (v2.1) of the data are described in the data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ . To comply with the attribution aspect, please cite the above reference and the dataset citation given on the relevant dataset page." }, { "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": [ 110835, 110830, 110831, 110833, 110836, 110839, 110840, 110834, 110841, 115378 ], "onlineresource_set": [ 25251, 25252, 25256, 25255, 25258, 25259, 37111, 25253 ] }, { "ob_id": 26429, "uuid": "f194fe3a5c4f4d0180f133535096b43b", "title": "Copernicus Climate Change Service Dataset: Sea Surface Temperature Integrated Climate Data Record (ICDR) from the Advanced Very High Resolution Radiometer (AVHRR), Level 3C (L3C), version 2.0", "abstract": "This dataset provides gridded Sea Surface Temperature data derived from the Advance Very High Resolution Radiometer (AVHRR) series of satellites. Data is available separately for the AVHRR instruments on NOAA-19, METOP-A and METOP-B.\r\n\r\nThis dataset is produced as an Intermediate Climate Data Record for the Copernicus Climate Change Service (C3S). V2.0 extends from 2017-2021.\r\n\r\nA historic Climate Data Record (CDR) has also been produced under the ESA Climate Change Initiative Sea Surface Temperature (CCI_sst). This is available as a separate dataset in the CEDA catalgoue and through the ESA CCI Open Data Portal.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-04-28T00:00:00", "latestDataUpdateTime": "2023-04-28T00:00:00", "updateFrequency": "continual", "dataLineage": "Data were processed as part of the Copernicus Climate Change project", "removedDataReason": "", "keywords": "", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 26627, "dataPath": "/neodc/c3s_sst/data/ICDR_v2/AVHRR/L3C/v2.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 256809098822, "numberOfFiles": 5102, "fileFormat": "NetCDF format" }, "timePeriod": { "ob_id": 7167, "startTime": "2017-01-01T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 4253, "explanation": "For information on the data quality see the associated documentation", "passesTest": true, "resultTitle": "C3S SST data quality", "date": "2023-04-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 39977, "uuid": "78c216c6813a4f11be38d77797c36a55", "short_code": "cmppr", "title": "Composite process for Copernicus Climate Change Service Dataset: Sea Surface Temperature Integrated Climate Data Record (ICDR) from the Advanced Very High Resolution Radiometer (AVHRR), Level 3C (L3C), version 2", "abstract": "Data was derived from AVHRR data from the METOP-A, METOP-B and NOAA-19 satellites" }, "imageDetails": [ 137 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2556, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 28, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_sst_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 39992, "uuid": "e710f839faa3436ead72b6ff7efef32d", "short_code": "proj", "title": "Copernicus Climate Change Service: Sea Surface Temperature data production", "abstract": "The Copernicus Climate Change project (.....) produced Sea Surface Temperature datasets from the AVHRR and SLSTR instruments. \r\n\r\nTo be written...." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50415, 50417, 52527, 52529, 52530, 52532, 52535, 52536, 52539, 52540, 52542, 52543, 52545, 52547, 57984, 57985, 57986, 57987, 57988, 57989, 59109 ], "vocabularyKeywords": [ { "ob_id": 10337, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/dataType/dtype_sstInt", "resolvedTerm": "sea surface temperature" } ], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 110847, 110848, 110849, 110854, 110855, 110857, 110852 ], "onlineresource_set": [ 83375 ] }, { "ob_id": 26431, "uuid": "cfe46deca7324763b8989adaa2607f20", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Along-Track Scanning Radiometer (ATSR) Level 2 Preprocessed (L2P) Climate Data Record, version 2.0", "abstract": "This v2.0 SST_cci Along-Track Scanning Radiometer (ATSR) Level 2 Preprocessed (L2P) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the ATSR series of satellite instruments. It covers the period between 11/1991 - 04/2012. This L2P product provides these SST data on the original satellite swath with a single orbit of data per file.\r\n\r\nThe dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-03-09T03:06:23", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI SST project team and supplied to the Centre for Environmental Data Analysis (CEDA) for archiving in the context of the ESA CCI Open Data Portal project and the National Centre for Earth Observation (NCEO).", "removedDataReason": "", "keywords": "SST. CCI", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2019-08-02T22:00:26", "doiPublishedTime": "2019-08-22T12:24:15", "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 26432, "dataPath": "/neodc/esacci/sst/data/CDR_v2/ATSR/L2P/v2.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2784715590458, "numberOfFiles": 100630, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 7135, "startTime": "1991-11-01T00:00:00", "endTime": "2012-04-08T22:59:59" }, "resultQuality": { "ob_id": 3153, "explanation": "As provided by the CCI SST project", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-07-06" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 27432, "uuid": "d75a78a832c74476a4739c6dff0991c1", "short_code": "cmppr", "title": "CCI SST retrieval process from the (A)ATSR series of instruments", "abstract": "The ESA Climate Change Initiative Sea Surface Temperature (SST) product has retrieved sea surface temperature from the (A)ATSR series of satellite instruments." }, "imageDetails": [ 137 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2523, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 4, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_sst_terms_and_conditions_v2.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 11008, "uuid": "05fb7c9964b4172991a72082c46a3376", "short_code": "proj", "title": "Sea Surface Temperature Climate Change Initiative Project", "abstract": "The Sea Surface Temperature Climate Change Initiative (SST_cci) project is part of the European Space Agency's Climate Change Initiative programme, It aims to accurately mapping the surface temperature of the global oceans using observations from many satellites, and to independently quantify SST to a quality suitable for climate research.\r\n\r\nThe team brings together European expertise in creating climate quality records of ocean temperatures from satellite data, with expertise in climate applications and computer engineering. Through the ESA funded Climate Change Initiative, the team have created a climate record of global sea surface temperature (SST) for the period 1981 to 2016. Based on satellite data, this record is independent of thermometer based measurements from ships and buoys. The new climate SST record complements and challenges existing knowledge of how ocean temperatures have evolved. \r\n\r\nThe project started in August 2010. It is part of a wider initiative by the European Space Agency (ESA) addressing several essential climate variables in addition to SST." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 52527, 52529, 52531, 52533, 52534, 52535, 52536, 52539, 52540, 52542, 52543, 52545, 52547, 57984, 57985, 57986, 57987, 57988, 57989 ], "vocabularyKeywords": [ { "ob_id": 10337, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/dataType/dtype_sstInt", "resolvedTerm": "sea surface temperature" } ], "identifier_set": [ 10576 ], "observationcollection_set": [ { "ob_id": 11005, "uuid": "1dc189bbf94209b48ed446c0e9a078af", "short_code": "coll", "title": "Collection of Sea Surface Temperature (SST) Data of the Global Oceans as part of the ESA Climate Change Initiative (CCI)", "abstract": "The ESA Sea Surface Temperature Climate Change Initiative (ESA SST_cci) datasets accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nThe latest version (v2.1) of the data are described in the data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ . To comply with the attribution aspect, please cite the above reference and the dataset citation given on the relevant dataset page." }, { "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": [ 110864, 110865, 110867, 110869, 110870, 110873, 110875, 110868, 110874, 115379 ], "onlineresource_set": [ 25267, 25268, 25269, 25272, 25273, 25270, 25271, 37109, 92523, 92524, 92525, 92526, 92527, 92528, 95017, 95018, 95019, 95020 ] }, { "ob_id": 26433, "uuid": "3628cb2fdba443588155e15dee8e5352", "title": "ESA Fire Climate Change Initiative (Fire_cci): MODIS Fire_cci Burned Area Grid product, version 5.1", "abstract": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The MODIS Fire_cci v5.1 grid product described here contains gridded data on global burned area derived from the MODIS instrument onboard the TERRA satellite at 250m resolution for the period 2001 to 2019. This product supercedes the previously available MODIS v5.0 product. The v5.1 dataset was initially published for 2001-2017, and has later been periodically extended to include 2018 to 2022. \r\n\r\nThis gridded dataset has been derived from the MODIS Fire_cci v5.1 pixel product (also available) by summarising its burned area information into a regular grid covering the Earth at 0.25 x 0.25 degrees resolution and at monthly temporal resolution. Information on burned area is included in 23 individual quantities: sum of burned area, standard error, fraction of burnable area, fraction of observed area, number of patches and the burned area for 18 land cover classes, as defined by the Land_Cover_cci v2.0.7 product. For further information on the product and its format see the Fire_cci product user guide in the linked documentation.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-02-03T16:23:27", "updateFrequency": "unknown", "dataLineage": "Data was produced by the ESA Fire_CCI team as part of ESA's Climate Change Initiative (CCI) and is being held on the CEDA (Centre for Environmental Data Analysis) archive as part of the ESA CCI Open Data Portal Project.", "removedDataReason": "", "keywords": "ESA, CCI, GRID, Burned Area, Fire Disturbance, Climate Change, GCOS Essential Climate Variable", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.25 degree", "status": "completed", "dataPublishedTime": "2019-02-08T14:49:28", "doiPublishedTime": "2019-02-08T14:51:49", "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": 26985, "dataPath": "/neodc/esacci/fire/data/burned_area/MODIS/grid/v5.1/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 23711230101, "numberOfFiles": 295, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 8231, "startTime": "2001-01-01T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 1, "explanation": "See dataset associated documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2012-08-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 25857, "uuid": "4a6d41bfb75148b4aaa20ab513ddf72c", "short_code": "cmppr", "title": "ESA Fire_cci MODIS Burned Area product", "abstract": "The MODIS Fire_CCI Burned Area product has been produced from the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument on NASA's TERRA satellite, using an algorithm derived by the Fire_CCI team, as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI)" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" }, { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2539, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 19, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_fire_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13255, "uuid": "6c3584d985bd484e8beb23ff0df91292", "short_code": "proj", "title": "ESA Fire Climate Change Initiative Project (Fire CCI)", "abstract": "The European Space Agency (ESA) Fire Climate Change Initiative (Fire CCI) project, led by University of Alcala (Spain), is part of ESA's Climate Change Initiative (CCI) to produce long term datasets of Essential Climate Variables derived from global satellite data.\r\n\r\nThe Fire CCI focuses on the following issues relating to Fire Disturbance: Analysis and specification of scientific requirements relating to climate; Development and improvement of pre-processing and burned area algorithms; Inter-comparison and selection of burned area algorithms; System prototyping and production of burned area datasets; Product validation and product assessment\r\n" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 50416, 57314, 57317, 59231, 59233, 59234, 59235, 59236, 59237, 68093, 68094 ], "vocabularyKeywords": [ { "ob_id": 10683, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/freq/freq_mon", "resolvedTerm": "month" }, { "ob_id": 11026, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/product/prod_modTe", "resolvedTerm": "MODIS_TERRA" }, { "ob_id": 11105, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_modis", "resolvedTerm": "MODIS" }, { "ob_id": 10939, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platformProg/plat_eos", "resolvedTerm": "EOS" }, { "ob_id": 10746, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/org/org59", "resolvedTerm": "University of Alcala" }, { "ob_id": 10988, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/procLev/proc_level4", "resolvedTerm": "Level 4" }, { "ob_id": 10582, "vocabService": "nerc_skos_vocab", "uri": "http://vocab.nerc.ac.uk/collection/L22/current/TOOL1035/", "resolvedTerm": "Moderate Resolution Imaging Spectroradiometer" }, { "ob_id": 10663, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/ecv/cciecv_fire", "resolvedTerm": "fire" }, { "ob_id": 10213, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/ecv/cciecv_fire", "resolvedTerm": "fire" }, { "ob_id": 10602, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/dataType/dtype_ba", "resolvedTerm": "burned area" }, { "ob_id": 10923, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_terra", "resolvedTerm": "Terra" } ], "identifier_set": [ 10216, 10221 ], "observationcollection_set": [ { "ob_id": 12683, "uuid": "bcef9e87740e4cbabc743d295afbe849", "short_code": "coll", "title": "ESA Fire Climate Change Initiative (Fire CCI) Dataset Collection", "abstract": "The ESA Fire Climate Change Initiative (Fire_cci) project is producing long-term datasets of burned area information from satellites, as part of the ESA Climate Change Initiative. The data is of use for those interested in historical burned patterns, fire management and emissions analysis and climate change research, by providing a consistent burned area time series. \r\n\r\nCurrent datasets consist of maps of global burned area for the years 1982 to 2019. Products are available at different spatial resolutions: the Pixel product (at the original resolution of the sensor data) and the Grid product (0.25 degrees resolution), the latter of which is produced from the Pixel product. They are based upon spectral information from different sensors, and in many cases also thermal information from active fires.\r\n\r\nGlobal products: \r\n\r\nFireCCI41: Medium Resolution Imaging Spectrometer (MERIS) reflectance, on board the ENVISAT ESA satellite, 300m spatial resolution, and MODIS active fires. Temporal resolution: 2005 – 2011.\r\nFireCCI50: Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and active fires, on board the TERRA satellite, 250m spatial resolution, temporal resolution: 2001 – 2016.\r\nFireCCI51: Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and active fires, on board the TERRA satellite, 250m spatial resolution, temporal resolution: 2001 – 2019.\r\n\r\nFireCCILT10 (beta product): Advanced Very High Resolution Radiometer (AVHRR) Land Long Term Data Record (LTDR) reflectance. Provided only as grid product. Temporal resolution: 1982-2017.\r\n\r\nContinental products:\r\n\r\nFireCCISFD11: Multispectral Instrument (MSI) reflectance, on board the Sentinel-2A satellite, 20 spatial resolution, and MODIS active fires. Temporal resolution: 2016, spatial coverage: Sub-Saharan Africa." } ], "responsiblepartyinfo_set": [ 110881, 110882, 110883, 110885, 110910, 110908, 140427, 110884, 110909, 110886, 110887, 110899, 110891 ], "onlineresource_set": [ 25274, 25275, 41944, 25276, 89249, 89250, 89251, 89252, 89253, 89254, 89255, 89256, 89257, 88119 ] }, { "ob_id": 26434, "uuid": "58f00d8814064b79a0c49662ad3af537", "title": "ESA Fire Climate Change Initiative (Fire_cci): MODIS Fire_cci Burned Area Pixel product, version 5.1", "abstract": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. These MODIS Fire_cci v5.1 pixel products are distributed as 6 continental tiles and are based upon data from the MODIS instrument onboard the TERRA satellite at 250m resolution for the period 2001-2020. This product supersedes the previously available MODIS v5.0 product. The v5.1 dataset was initially published for 2001-2017, and has later been periodically extended to include 2018 to 2022.\r\n\r\nThe Fire_cci v5.1 Pixel product described here includes maps at 0.00224573-degrees (approx. 250m) resolution. Burned area(BA) information includes 3 individual files, packed in a compressed tar.gz file: date of BA detection (labelled JD), the confidence level (CL, a probability value estimating the confidence that a pixel is actually burned), and the land cover (LC) information as defined in the Land_Cover_cci v2.0.7 product.\r\n\r\nFiles are in GeoTIFF format using a geographic coordinate system based on the World Geodetic System (WGS84) reference ellipsoid and using Plate Carrée projection with geographical coordinates of equal pixel size. For further information on the product and its format see the Fire_cci Product User Guide in the linked documentation.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-02-03T16:34:17", "updateFrequency": "asNeeded", "dataLineage": "Data was produced by the ESA Fire CCI team as part of the ESA Climate Change Initiative (CCI) and is being held on the CEDA (Centre for Environmental Data Analysis) archive as part of the ESA CCI Open Data Portal project.", "removedDataReason": "", "keywords": "ESA, CCI, Pixel, Burned Area, Fire Disturbance, Climate Change, GCOS", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.00224573 degrees (~250m)", "status": "completed", "dataPublishedTime": "2018-11-01T13:24:27", "doiPublishedTime": "2018-11-01T13:24:27", "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": 26682, "dataPath": "/neodc/esacci/fire/data/burned_area/MODIS/pixel/v5.1/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 60713675075, "numberOfFiles": 1680, "fileFormat": "Data are in GeoTiff format and is composed of 4 files:\r\n...JD.tif: Day of first detection (Julian Day) of the burned area;\r\n...CL.tif: Confidence level of burned area detection;\r\n...LC.tif: Land cover of the pixel detected as burned as defined by the Land Cover CCI v2.0.7 product;\r\n....xml: Metadata of the product.\r\n\r\nThe Coordinate Reference System (CRS) used is a geographic coordinate system (GCS) based on the World Geodetic System 84 (WGS84) reference ellipsoid and using a Plate Carrée projection with geographical coordinates of equal pixel size." }, "timePeriod": { "ob_id": 9185, "startTime": "2001-01-01T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 1, "explanation": "See dataset associated documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2012-08-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 25857, "uuid": "4a6d41bfb75148b4aaa20ab513ddf72c", "short_code": "cmppr", "title": "ESA Fire_cci MODIS Burned Area product", "abstract": "The MODIS Fire_CCI Burned Area product has been produced from the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument on NASA's TERRA satellite, using an algorithm derived by the Fire_CCI team, as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI)" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" }, { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2539, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 19, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_fire_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13255, "uuid": "6c3584d985bd484e8beb23ff0df91292", "short_code": "proj", "title": "ESA Fire Climate Change Initiative Project (Fire CCI)", "abstract": "The European Space Agency (ESA) Fire Climate Change Initiative (Fire CCI) project, led by University of Alcala (Spain), is part of ESA's Climate Change Initiative (CCI) to produce long term datasets of Essential Climate Variables derived from global satellite data.\r\n\r\nThe Fire CCI focuses on the following issues relating to Fire Disturbance: Analysis and specification of scientific requirements relating to climate; Development and improvement of pre-processing and burned area algorithms; Inter-comparison and selection of burned area algorithms; System prototyping and production of burned area datasets; Product validation and product assessment\r\n" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [ { "ob_id": 10663, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/ecv/cciecv_fire", "resolvedTerm": "fire" }, { "ob_id": 10213, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/ecv/cciecv_fire", "resolvedTerm": "fire" }, { "ob_id": 11105, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_modis", "resolvedTerm": "MODIS" }, { "ob_id": 11026, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/product/prod_modTe", "resolvedTerm": "MODIS_TERRA" }, { "ob_id": 10923, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_terra", "resolvedTerm": "Terra" }, { "ob_id": 10316, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/dataType/dtype_ba", "resolvedTerm": "burned area" }, { "ob_id": 10602, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/dataType/dtype_ba", "resolvedTerm": "burned area" }, { "ob_id": 10333, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/product/prod_modTe", "resolvedTerm": "MODIS_TERRA" } ], "identifier_set": [ 9777, 9690 ], "observationcollection_set": [ { "ob_id": 12683, "uuid": "bcef9e87740e4cbabc743d295afbe849", "short_code": "coll", "title": "ESA Fire Climate Change Initiative (Fire CCI) Dataset Collection", "abstract": "The ESA Fire Climate Change Initiative (Fire_cci) project is producing long-term datasets of burned area information from satellites, as part of the ESA Climate Change Initiative. The data is of use for those interested in historical burned patterns, fire management and emissions analysis and climate change research, by providing a consistent burned area time series. \r\n\r\nCurrent datasets consist of maps of global burned area for the years 1982 to 2019. Products are available at different spatial resolutions: the Pixel product (at the original resolution of the sensor data) and the Grid product (0.25 degrees resolution), the latter of which is produced from the Pixel product. They are based upon spectral information from different sensors, and in many cases also thermal information from active fires.\r\n\r\nGlobal products: \r\n\r\nFireCCI41: Medium Resolution Imaging Spectrometer (MERIS) reflectance, on board the ENVISAT ESA satellite, 300m spatial resolution, and MODIS active fires. Temporal resolution: 2005 – 2011.\r\nFireCCI50: Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and active fires, on board the TERRA satellite, 250m spatial resolution, temporal resolution: 2001 – 2016.\r\nFireCCI51: Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and active fires, on board the TERRA satellite, 250m spatial resolution, temporal resolution: 2001 – 2019.\r\n\r\nFireCCILT10 (beta product): Advanced Very High Resolution Radiometer (AVHRR) Land Long Term Data Record (LTDR) reflectance. Provided only as grid product. Temporal resolution: 1982-2017.\r\n\r\nContinental products:\r\n\r\nFireCCISFD11: Multispectral Instrument (MSI) reflectance, on board the Sentinel-2A satellite, 20 spatial resolution, and MODIS active fires. Temporal resolution: 2016, spatial coverage: Sub-Saharan Africa." } ], "responsiblepartyinfo_set": [ 110912, 110913, 110914, 110915, 110938, 140425, 140426, 110911, 110916, 110939, 112221, 110929, 110921 ], "onlineresource_set": [ 25279, 25277, 25280, 25668, 87888, 87889, 89129, 89130, 89131, 89132, 89133, 89134, 89135, 89136, 89137, 89138, 89139, 89140, 89141, 89142, 89143, 89144, 89145, 89146, 89147, 89148, 89149, 89150, 89151, 89152, 87502, 87503, 87504, 87505, 87506, 87507, 87508, 92691, 92692, 95058, 95059, 95060, 95061 ] }, { "ob_id": 26435, "uuid": "c5a46c04c28f40f5a383043489325861", "title": "Global mean annual reconstructed surface temperature anomaly time series for merged land, air and sea-surface", "abstract": "This dataset contains global mean annual reconstructed surface temperature anomaly time series for merged land, air and sea-surface. This dataset was produced in 2004 for the period: 1880-2009. The reconstruction is separated into separate low and high-frequency components, which are added for the total reconstruction. To create the global mean the Northern and Southern Hemisphere averages are computed separately and then they are then averaged together (to stop the better sampled Northern Hemisphere from dominating the Global average). This dataset improved analysis and error estimation methods to analyze both sea surface temperature and land surface temperature anomalies, and to then merge them to form a global analysis.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2018-07-12T13:15:53", "updateFrequency": "", "dataLineage": "These data was delivered to the International Panel for Climate Change Data distribution Centre (IPCC DDC) for archiving.", "removedDataReason": "", "keywords": "IPCC DDC, temperature, land, sea, air", "publicationState": "working", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": null, "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": null, "timePeriod": { "ob_id": 7137, "startTime": "1880-01-01T00:00:00", "endTime": "2009-01-01T23:59:59" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 110970, 110972, 110973, 110974, 110976, 110977, 110978, 110975, 110971 ], "onlineresource_set": [] }, { "ob_id": 26437, "uuid": "a39add95a8dc49709f6e984c020c8dbc", "title": "MIDAS Open: UK daily temperature data, v201901", "abstract": "The UK daily temperature data contain maximum and minimum temperatures (air, grass and concrete slab) measured over a period of up to 24 hours. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM, DLY3208 or AWSDLY messages. The data span from 1853 to 2017. For details on measurement techniques, including calibration information and changes in measurements, see section 5.2 of the MIDAS User Guide linked to from this record. Soil temperature data may be found in the UK soil temperature datasets linked from this record.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Currently this represents approximately 95% of available daily temperature observations within the full MIDAS collection.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-03-09T03:06:17", "updateFrequency": "notPlanned", "dataLineage": "Data collated by the Met Office and archived in the Met Office's MIDAS database. Data are extracted from a sub-set of available tables and delivered to Centre for Environmental Data Analysis (CEDA) approximately on a yearly basis.", "removedDataReason": "", "keywords": "Met Office, MIDAS, UK, meteorology, temperature, diurnal, daily, monthly", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2019-02-12T10:50:04", "doiPublishedTime": "2019-03-01T08:04:52", "removedDataTime": null, "geographicExtent": { "ob_id": 18, "bboxName": "MIDAS UK table s geographic domain", "eastBoundLongitude": 1.74002, "westBoundLongitude": -5.54236, "southBoundLatitude": 50.1172, "northBoundLatitude": 60.7592 }, "verticalExtent": null, "result_field": { "ob_id": 26897, "dataPath": "/badc/ukmo-midas-open/data/uk-daily-temperature-obs/dataset-version-201901/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2005442647, "numberOfFiles": 59586, "fileFormat": "Data are BADC-CSV formatted." }, "timePeriod": { "ob_id": 7293, "startTime": "1853-01-19T00:00:00", "endTime": "2017-12-31T23:59:59" }, "resultQuality": { "ob_id": 305, "explanation": "Data undergo quality checking by the Met Office. State of the data in the quality control process and level of data quality are indicated using version numbers and quality control flagging with the data. See documentation about how to use the quality control flagging and version numbers.\n\nThere are also some known data from commissioning trials in the data, which are given a src_id of 99999. These should be ignored by the user.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-09-11" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 1243, "uuid": "93916103ebf54c5fad1e24fee0255e91", "short_code": "acq", "title": "Acquisition Process for: UK Daily Temperature Data, Part of the Met Office Integrated Data Archive System (MIDAS)", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Thermometer; PLATFORMS: NCM (National Climate Message) Station Network, DLY3208 (Daily observations from Metform 3208) Station Network, AWSDLY (Automatic Weather Station Daily values) Station Network; " }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "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": 1186, "uuid": "245df050d57a500c183b88df509f5f5a", "short_code": "proj", "title": "Met Office Integrated Data Archive System (MIDAS)", "abstract": "Since the early days of this century the Met Office has been responsible for maintaining the public memory of the weather. All meteorological observations made in the UK and over neighbouring sea areas have been carefully recorded and placed in an archive where they may be accessed today by those with an interest in the weather and where they will also be available to those in future generations. The current climate database is MIDAS (Met Office Integrated Data Archive System) which has a relational structure. The MIDAS database contains the following general types of meteorological data: surface observations over land areas of the UK as far back as the digital record extends, a selection of global surface observations for the last 20 years, global surface marine observations from national and international sources as far back as the digital record extends, radiosonde observations over the UK, and at overseas stations operated by the Met Office, as far back as the digital record extends, a selection of global radiosonde observations for the last 10 years." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 68374, 68386, 68403, 68404, 68405, 68406, 68408, 68409, 68410, 68411, 68426, 68427, 68428, 68429, 68430, 68431, 68432, 68433, 68434, 69751, 69752, 69753, 69754, 69755, 69756, 69757, 69758, 69759, 69760, 69761, 69762, 69763, 69764, 69765, 69766 ], "vocabularyKeywords": [], "identifier_set": [ 10489 ], "observationcollection_set": [ { "ob_id": 26184, "uuid": "dbd451271eb04662beade68da43546e1", "short_code": "coll", "title": "Met Office MIDAS Open: UK Land Surface Stations Data (1853-current)", "abstract": "MIDAS Open is the open data version of the Met Office Integrated Data Archive System (MIDAS) containing land surface station data starting from 1853 and ending at the of the previous complete year. This collection comprises of hourly and daily weather measurements and observations of parameters relating to temperature, rainfall, sunshine, radiation, wind and weather observations such as present weather codes, cloud cover, snow etc.\r\n\r\nThe collection contains land surface observations data from those stations where the data have been designated as public sector information. Prior to version v202407 this consisted of stations operated by the Met Office only, but from version v202407, daily and hourly rainfall observations from stations with gauges owned by the Environment Agency (EA), Scottish Environment Protection Agency (SEPA) and Natural Resources Wales (NRW) have also been included in the collection. Since then, stations owned by other third-party organisations where approval for inclusion has been reached have also been added to the product.\r\n\r\nAll of these data are provided under an Open Government Licence. \r\n\r\nThe current collection contains the following proportions of the fuller MIDAS dataset collection:\r\n\r\n96% of daily temperature observations\r\n96% of daily weather observations\r\n92% of hourly weather observations\r\n94% of daily rainfall observations\r\n96% of hourly rainfall observations\r\n98% of soil temperature observations\r\n96% of solar radiation observations\r\n93% of mean wind observations\r\n\r\nDaily rainfall: Versions up until MIDAS Open v202407 only have about 13% coverage of observations. In version v202407, the coverage was increased to 58% with the inclusion of the third-party hydrological agency stations. In version v202507, the coverage was increased further to 94% with the inclusion of historic closed stations.\r\n\r\nThe fuller \"Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations Data (1853-current)\" collection is made available for academic use via the Centre for Environmental Data Analysis.\r\n\r\nThe MIDAS Open collection is updated annually in a delayed mode to ensure that data acquisition and quality control procedures have all been completed. Quality controlled (qc-version-1) and non-quality controlled (qc-version-0) data are available from 1853 where available, although this will vary by station depending on the operation period of the station. The collection includes stations which are currently operational as well as stations which were operational in the past and have since closed.\r\n\r\nEach version of the dataset will include data up until the end of the previous complete year relative to the year in the version number of the dataset (e.g. v202407 included data up until the end of 2023).\r\n\r\nNote: This collection does not supersede the full MIDAS collection which is also archived at CEDA." } ], "responsiblepartyinfo_set": [ 110988, 110990, 110989, 110987, 110986, 110985, 110983, 110984 ], "onlineresource_set": [ 25287, 25282, 42145, 26445, 25283, 91495, 91496, 91497, 91498, 91499, 91500, 91501, 91502, 91503, 91504, 91505, 91506, 91507, 91508, 91509, 91510, 91511, 91512, 91513, 91514, 91515, 91516, 91517, 26443 ] }, { "ob_id": 26438, "uuid": "ec54d5e5288a4ebb8c7ad2a1ef6aec42", "title": "MIDAS Open: UK daily rainfall data, v201901", "abstract": "The UK daily rainfall data contain rainfall accumulation and precipitation amounts over a 24 hour period. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSDLY, DLY3208 and SSER. The data spans from 1853 to 2017. Over time a range of rain gauges have been used - see section 5.6 and the relevant message type information in the linked MIDAS User Guide for further details.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. A large proportion of the UK raingauge observing network (associated with WAHRAIN, WADRAIN and WAMRAIN for hourly, daily and monthly rainfall measurements respectively) is operated by other agencies beyond the Met Office, and are consequently currently excluded from the Midas-open dataset. Currently this represents approximately 13% of available daily rainfall observations within the full MIDAS collection.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2019-02-08T06:05:43", "updateFrequency": "notPlanned", "dataLineage": "Data collated by the Met Office and archived in the Met Office's MIDAS database. Data are extracted from a sub-set of available tables and delivered to Centre for Environmental Data Analysis (CEDA) approximately on a yearly basis.", "removedDataReason": "", "keywords": "Met Office, MIDAS, UK, meteorology, rainfall, diurnal, daily, monthly", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2019-02-12T11:12:39", "doiPublishedTime": "2019-02-28T14:33:52", "removedDataTime": null, "geographicExtent": { "ob_id": 18, "bboxName": "MIDAS UK table s geographic domain", "eastBoundLongitude": 1.74002, "westBoundLongitude": -5.54236, "southBoundLatitude": 50.1172, "northBoundLatitude": 60.7592 }, "verticalExtent": null, "result_field": { "ob_id": 26889, "dataPath": "/badc/ukmo-midas-open/data/uk-daily-rain-obs/dataset-version-201901/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1061379834, "numberOfFiles": 50487, "fileFormat": "Data are BADC-CSV formatted" }, "timePeriod": { "ob_id": 7295, "startTime": "1853-01-02T00:00:00", "endTime": "2017-12-31T23:59:59" }, "resultQuality": { "ob_id": 297, "explanation": "Data undergo quality checking by the Met Office. State of the data in the quality control process and level of data quality are indicated using version numbers and quality control flagging with the data. See documentation about how to use the quality control flagging and version numbers.\n\nThere are also some known data from commissioning trials in the data, which are given a src_id of 99999. These should be ignored by the user.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-09-11" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 27187, "uuid": "037f3eff9db941a583b252e800a94979", "short_code": "acq", "title": "Acquisition Process for: UK Daily Rainfall Data, Part of the Met Office Integrated Data Archive System (MIDAS) Open - excludes WADRAIN and WAMRAIN", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Raingauge; PLATFORMS: DLY3208 (Daily observations from Metform 3208) Station Network, SSER (Solid State Event Recorder) Station Network, WADRAIN (Water Authorities Daily RAINfall) Station Network, WAMRAIN (Water Authorities Monthly RAINfall value) Station Network, NCM (National Climate Message) Station Network, AWSDLY (Automatic Weather Station Daily values) Station Network;" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "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": 1186, "uuid": "245df050d57a500c183b88df509f5f5a", "short_code": "proj", "title": "Met Office Integrated Data Archive System (MIDAS)", "abstract": "Since the early days of this century the Met Office has been responsible for maintaining the public memory of the weather. All meteorological observations made in the UK and over neighbouring sea areas have been carefully recorded and placed in an archive where they may be accessed today by those with an interest in the weather and where they will also be available to those in future generations. The current climate database is MIDAS (Met Office Integrated Data Archive System) which has a relational structure. The MIDAS database contains the following general types of meteorological data: surface observations over land areas of the UK as far back as the digital record extends, a selection of global surface observations for the last 20 years, global surface marine observations from national and international sources as far back as the digital record extends, radiosonde observations over the UK, and at overseas stations operated by the Met Office, as far back as the digital record extends, a selection of global radiosonde observations for the last 10 years." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 68374, 68379, 68386, 68403, 68404, 68405, 68406, 68407, 68408, 68409, 68410, 68411, 68426, 68427, 68428, 68429, 68430, 68431, 68432, 68433, 68434, 69743, 69744, 69746, 69747, 69748, 69749, 69750 ], "vocabularyKeywords": [], "identifier_set": [ 10488 ], "observationcollection_set": [ { "ob_id": 26184, "uuid": "dbd451271eb04662beade68da43546e1", "short_code": "coll", "title": "Met Office MIDAS Open: UK Land Surface Stations Data (1853-current)", "abstract": "MIDAS Open is the open data version of the Met Office Integrated Data Archive System (MIDAS) containing land surface station data starting from 1853 and ending at the of the previous complete year. This collection comprises of hourly and daily weather measurements and observations of parameters relating to temperature, rainfall, sunshine, radiation, wind and weather observations such as present weather codes, cloud cover, snow etc.\r\n\r\nThe collection contains land surface observations data from those stations where the data have been designated as public sector information. Prior to version v202407 this consisted of stations operated by the Met Office only, but from version v202407, daily and hourly rainfall observations from stations with gauges owned by the Environment Agency (EA), Scottish Environment Protection Agency (SEPA) and Natural Resources Wales (NRW) have also been included in the collection. Since then, stations owned by other third-party organisations where approval for inclusion has been reached have also been added to the product.\r\n\r\nAll of these data are provided under an Open Government Licence. \r\n\r\nThe current collection contains the following proportions of the fuller MIDAS dataset collection:\r\n\r\n96% of daily temperature observations\r\n96% of daily weather observations\r\n92% of hourly weather observations\r\n94% of daily rainfall observations\r\n96% of hourly rainfall observations\r\n98% of soil temperature observations\r\n96% of solar radiation observations\r\n93% of mean wind observations\r\n\r\nDaily rainfall: Versions up until MIDAS Open v202407 only have about 13% coverage of observations. In version v202407, the coverage was increased to 58% with the inclusion of the third-party hydrological agency stations. In version v202507, the coverage was increased further to 94% with the inclusion of historic closed stations.\r\n\r\nThe fuller \"Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations Data (1853-current)\" collection is made available for academic use via the Centre for Environmental Data Analysis.\r\n\r\nThe MIDAS Open collection is updated annually in a delayed mode to ensure that data acquisition and quality control procedures have all been completed. Quality controlled (qc-version-1) and non-quality controlled (qc-version-0) data are available from 1853 where available, although this will vary by station depending on the operation period of the station. The collection includes stations which are currently operational as well as stations which were operational in the past and have since closed.\r\n\r\nEach version of the dataset will include data up until the end of the previous complete year relative to the year in the version number of the dataset (e.g. v202407 included data up until the end of 2023).\r\n\r\nNote: This collection does not supersede the full MIDAS collection which is also archived at CEDA." } ], "responsiblepartyinfo_set": [ 110997, 110996, 110995, 110994, 110993, 110998, 110991, 110992, 117055 ], "onlineresource_set": [ 25298, 25295, 42147, 25293, 25296, 25290, 87706, 91518, 91519, 91520, 91521, 91522, 91523, 91524, 91525, 91526, 91527, 91528, 91529, 91530, 91531, 91532, 91533, 91534, 91535, 91536, 91537, 91538, 91539, 91540 ] }, { "ob_id": 26440, "uuid": "887a695bf0b24f5590097a16c42604d6", "title": "Sentinel 5P: Total column ozone (O3) level 2 data", "abstract": "Sentinel 5P total column ozone products contain total ozone, ozone temperature, and error information including averaging kernels. These data products are provided in a 7km x 3.5km resolution. \r\nOzone (O3) is of crucial importance for the equilibrium of the Earth's atmosphere. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentrations, it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change. Since the discovery of the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol regulating the production of chlorine-containing ozone-depleting substances, ozone has been routinely monitored from the ground and from space. For TROPOMI/S5P, there are two algorithms that will deliver total ozone: GDP for the near real-time and GODFIT for the offline products. GDP is currently being used for generating the operational total ozone products from GOME, SCIAMACHY and GOME-2; while GODFIT is being used in the ESA CCI and the Copernicus C3S projects.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2025-05-16T14:09:09", "updateFrequency": "", "dataLineage": "Data collected and prepared by European Space Agency (ESA). Downloaded from the Collaborative Hub for use by CEDA users.", "removedDataReason": "", "keywords": "Sentinel, O3, Ozone, ESA", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2021-05-27T12:43:33", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 32268, "dataPath": "/neodc/sentinel5p/data/L2_O3/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 10565012646038, "numberOfFiles": 67239, "fileFormat": "These data are provided in netCDF format." }, "timePeriod": { "ob_id": 8853, "startTime": "2018-06-27T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 1, "explanation": "See dataset associated documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2012-08-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 26458, "uuid": "c02e46f0ea3a445bac6fc819639ef2aa", "short_code": "cmppr", "title": "Composite Process for: Level 2 data from the Sentinel 5P TROPOspheric Monitoring Instrument (TROPOMI) for total Ozone (O3) column data.", "abstract": "Composite process for Level 2 data from the TROPOspheric Monitoring Instrument (TROPOMI) deployed on Sentinel 5P. This consists of the Acquisition process for raw imaging data from the Sentinel 5P TROPOMI and the computation component to produce processed Level 2 Ozone (O3) total column data." }, "imageDetails": [ 148 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2586, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 49, "licenceURL": "https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 12321, "uuid": "7896ea1117dc4fa9bb95485ca9b1c6be", "short_code": "proj", "title": "Copernicus Programme", "abstract": "Copernicus, formerly known as the Global Monitoring for Environment and Security (GMES) programme, is headed by the European Commission (EC) in partnership with the European Space Agency (ESA). Within the Copernicus Space Component, ESA is developing a series of Sentinel satellite missions. Data from the Sentinel missions, as well as from Contributing Missions from other space agencies, are made freely available through a unified ground segment. Each Sentinel mission is comprised of a constallation of two or more satellites to fulfil the timeliness and reliability requirements of the Copernicus Services environmental monitoring and civil security activities. As well as operational and monitoring capabilities, the Sentinel missions will provide a wealth of Earth Observation data for scientific exploitation. The Sentinel 1 mission provides all weather, day and night radar imagery with scientific applications in sea-ice measurements, biomass observations and earthquake analysis. Sentinel 2 is a high resolution imaging mission to provide imagery of vegetation, soil and water cover, inland waterways and coastal areas. Sentinel 3 is a multi-instrument mission to measure sea-surface topography, sea- and land-surface temperature, ocean colour and land colour with high-end accuracy and reliability. Sentinel 4 is devoted to atmospheric monitoring and will be flown on a Meteosat Third Generation-Sounder (MTG-S) satellite in geostationary orbit. Sentinel 5 will monitor the atmosphere from polar orbit on board a MetOp Second Generation satellite. The Sentinel 5 precursor satellite mission is being developed to reduce data gaps between Envisat, in particular the Sciamachy instrument, and the launch of Sentinel 5. The Sentinel 5 mission will be dedicated to atmospheric monitoring. Sentinel 6 carries a radar altimeter to measure global sea-surface height, primarily for operational oceanography and for climate studies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 55535, 66547, 66548, 66550, 66551, 66552, 66559, 66565, 66569, 66571, 66572, 66573, 66574, 66578, 66579, 66580, 66582, 66584, 66587, 66588, 66589, 66590, 66591, 66594, 66595, 66614, 66616, 66623, 66624, 66625, 66626, 66652, 66653, 66661, 66664, 66671, 66673, 66678, 66680, 66682, 66697, 66709, 66710, 66711, 66712, 66713, 66714, 66715, 66716, 66717, 66718, 66719, 66723, 66724, 66739, 66740, 66746, 67005, 67006, 67007, 67008, 67009, 67010, 67011, 67012, 67013, 67014, 67015, 67016, 67017, 67018, 67019, 67020, 67021, 67022, 67023, 67024, 67025, 67026, 67027, 67028, 67029, 67030, 67031 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 26459, "uuid": "6accd46663bc4669afaac418f2bf498e", "short_code": "coll", "title": "Sentinel 5 Precursor: Level 2 data", "abstract": "Need some info!" }, { "ob_id": 30129, "uuid": "3b0630c7fa264164868d4da5c9f90bed", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Third Party Data", "abstract": "The National Centre for Earth Observation (NCEO) Third Party data contains a broad range remotely sensed data acquired by satellite for use by the Earth Observation Scientific community supported by NCEO. The Centre for Environmental Data Analysis (CEDA) has archived and provides access to extensive Earth observation datasets under strict licensing conditions. Please see the individual dataset records for conditions of use." } ], "responsiblepartyinfo_set": [ 111152, 111147, 111149, 111154, 145347, 111148, 111155, 111153, 111151, 111150 ], "onlineresource_set": [ 25305, 25315, 25316, 25317 ] }, { "ob_id": 26441, "uuid": "e34eaffcf6bb4f3c87fffe0814f5c9bf", "title": "Sentinel 5P: Nitrogen Dioxide (NO2) Total Column level 2 data", "abstract": "Sentinel 5 Precursor (S5P) was launched on the 13th of October 2017 carrying the TROPOspheric Monitoring Instrument (TROPOMI). These data products provide geolocated total, tropospheric, or stratospheric Nitrogen dioxide concentrations. The TROPOMI NO2 data products pose an improvement over previous NO2 data sets, particularly in their unprecedented spatial resolution (7×3.5 km2), but also in the separation of the stratospheric and tropospheric contributions of the retrieved slant columns, and in the calculation of the air-mass factors used to convert slant to total columns.\r\n\r\nNitrogen dioxide (NO2) and nitrogen oxide (NO) together are usually referred to as nitrogen oxides (NOx = NO + NO2). They are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (notably fossil fuel combustion and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning). During the daytime, i.e. in the presence of sunlight, a photochemical cycle involving ozone (O3) converts NO into NO2 (and vice versa) on a timescale of minutes, so that NO2 is a robust measure for concentrations of nitrogen oxides. Tropospheric and stratospheric concentrations of NO2 are monitored all over the world by a variety of instruments either ground-based, in-situ (balloon, aircraft), or satellite-based each with its own specific advantages.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2025-06-30T15:00:52", "updateFrequency": "", "dataLineage": "Data collected and prepared by European Space Agency (ESA). Downloaded from the Collaborative Hub for use by CEDA users.", "removedDataReason": "", "keywords": "Sentinel 5P, Nitrogen Dioxide, NO2, ESA", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2021-05-21T09:54:46", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 27449, "dataPath": "/neodc/sentinel5p/data/L2_NO2/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 26308537342235, "numberOfFiles": 696601, "fileFormat": "These data are in NetCDF format." }, "timePeriod": { "ob_id": 7138, "startTime": "2018-05-06T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 1, "explanation": "See dataset associated documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2012-08-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 26462, "uuid": "77ee0e145c32414f87e877a619b4d812", "short_code": "cmppr", "title": "Composite Process for: Level 2 data from the Sentinel 5P TROPOspheric Monitoring Instrument (TROPOMI) for Nitrogen Dioxide (NO2) total column data.", "abstract": "Composite process for Level 2 data from the TROPOspheric Monitoring Instrument (TROPOMI) deployed on Sentinel 5P. This consists of the Acquisition process for raw imaging data from the Sentinel 5P TROPOMI and the computation component to produce processed Level 2 Nitrogen Dioxide (NO2) total column data.\r\n\r\nThe TROPOMI retrieval of total and tropospheric NO2 is based on the DOMINO-2 approach, a DOAS retrieval, a pre-calculated air-mass factor (AMF) look-up table, and a data assimilation/chemistry transport model for the separation of the stratospheric and tropospheric contributions to the NO2 column. This retrieval-assimilation-modelling system uses the 3-dimensional global TM5-MP chemistry transport model at a resolution of 1x1 degree as an essential element (see ATBD for details). It also includes many retrieval developments of the European Quality Assurance for Essential Climate Variables (QA4ECV) project." }, "imageDetails": [ 148 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2586, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 49, "licenceURL": "https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 12321, "uuid": "7896ea1117dc4fa9bb95485ca9b1c6be", "short_code": "proj", "title": "Copernicus Programme", "abstract": "Copernicus, formerly known as the Global Monitoring for Environment and Security (GMES) programme, is headed by the European Commission (EC) in partnership with the European Space Agency (ESA). Within the Copernicus Space Component, ESA is developing a series of Sentinel satellite missions. 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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." } ], "responsiblepartyinfo_set": [ 111297, 111298, 111299, 111302, 111303, 111304, 111305, 111306, 111300, 111301 ], "onlineresource_set": [ 25352, 25351 ] }, { "ob_id": 26488, "uuid": "0804621b8c414dd78800d5411d4d991d", "title": "Biodiversity and Land Use Impacts on Tropical Forest Ecosystem Function (BALI) : Atmospheric trace gas measurements", "abstract": "This dataset contains atmospheric trace gas measurements, including Volitile Organic Compunds (VOCs) for the Biodiversity and Land Use Impacts on Tropical Forest Ecosystem Function (BALI) project. The measurements were taken at the GLobal Atmosphere Watch (GAW) observatory at Burkit Atur, Danum Valley, Borneo. August 2015-August 2016", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-02-05T14:36:06", "updateFrequency": "", "dataLineage": "Data were collected by the project team and delivered to the Centre for Environmental data Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "Danum Valley, VOCs, BALI, gas", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2277, "bboxName": "Danum Valley GAW station", "eastBoundLongitude": 117.83, "westBoundLongitude": 117.83, "southBoundLatitude": 4.96, "northBoundLatitude": 4.96 }, "verticalExtent": null, "result_field": { "ob_id": 26486, "dataPath": "/badc/deposited2018/bali/data/york-gc-fid-field2", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 303866, "numberOfFiles": 2, "fileFormat": "Data are NASA Ames formatted" }, "timePeriod": { "ob_id": 7149, "startTime": "2015-08-24T23:00:00", "endTime": "2016-03-29T23:00:00" }, "resultQuality": { "ob_id": 3192, "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": "2018-11-22" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 26489, "uuid": "4612d6e808a34879abb647f92849a48d", "short_code": "acq", "title": "BALI: VOCs and Atmospheric gases", "abstract": "BALI Project : VOCs and Atmospheric gases" }, "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": 26485, "uuid": "742dfa25db5247c0a4306d297114e87d", "short_code": "proj", "title": "Biodiversity and Land Use Impacts on Tropical Forest Ecosystem Function (BALI)", "abstract": "Anthropogenic disturbance and land-use change in the tropics is leading to irrevocable changes in biodiversity and substantial shifts in ecosystem biogeochemistry. Yet, we still have a poor understanding of how human-driven changes in biodiversity feed back to alter biogeochemical processes. This knowledge gap substantially restricts our ability to model and predict the response of tropical ecosystems to current and future environmental change. There are a number of critical challenges to our understanding of how changes in biodiversity may alter ecosystem processes in the tropics; namely: (i) how the high taxonomic diversity of the tropics is linked to ecosystem functioning, (ii) how changes in the interactions among trophic levels and taxonomic groups following disturbance impacts upon functional diversity and biogeochemistry, and (iii) how plot-level measurements can be used to scale to whole landscapes. We have formed a consortium to address these critical challenges to launch a large-scale, replicated, and fully integrated study that brings together a multi-disciplinary team with the skills and expertise to study the necessary taxonomic and trophic groups, different biogeochemical processes, and the complex interactions amongst them.\r\n\r\nTo understand and quantify the effects of land-use change on the activity of focal biodiversity groups and how this impacts biogeochemistry, we will: (i) analyse pre-existing data on distributions of focal biodiversity groups; (ii) sample the landscape-scale treatments at the Stability of Altered Forest Ecosystems (SAFE) Project site (treatments include forest degradation, fragmentation, oil palm conversion) and key auxiliary sites (Maliau Basin - old growth on infertile soils, Lambir Hills - old growth on fertile soils, Sabah Biodiversity Experiment - rehabilitated forest, INFAPRO-FACE - rehabilitated forest); and (iii) implement new experiments that manipulate key components of biodiversity and pathways of belowground carbon flux. \r\n\r\nThe manipulations will focus on trees and lianas, mycorrhizal fungi, termites and ants, because these organisms are the likely agents of change for biogeochemical cycling in human-modified tropical forests. We will use a combination of cutting-edge techniques to test how these target groups of organisms interact each other to affect biogeochemical cycling. We will additionally collate and analyse archived data on other taxa, including vertebrates of conservation concern. The key unifying concept is the recognition that so-called 'functional traits' play a key role in linking taxonomic diversity to ecosystem function. We will focus on identifying key functional traits associated with plants, and how they vary in abundance along the disturbance gradient at SAFE. In particular, we propose that leaf functional traits (e.g. physical and chemical recalcitrance, nitrogen content, etc.) play a pivotal role in determining key ecosystem processes and also strongly influence atmospheric composition. Critically, cutting-edge airborne remote sensing techniques suggest it is possible to map leaf functional traits, chemistry and physiology at landscape-scales, and so we will use these novel airborne methods to quantify landscape-scale patterns of forest degradation, canopy structure, biogeochemical cycling and tree distributions. Process-based mathematical models will then be linked to the remote sensing imagery and ground-based measurements of functional diversity and biogeochemical cycling to upscale our findings over disturbance gradients. This project was funded under NERC grant NE/K016253/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 83761 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 111321, 111322, 111323, 111326, 111327, 111328, 111320, 111324, 111325 ], "onlineresource_set": [] }, { "ob_id": 26493, "uuid": "7b55fe21e239471595057f7711e02b3b", "title": "GloCAEM: Atmospheric electricity measurements at Nor-Amberd Research Station", "abstract": "Global Coordination of Atmospheric Electricity Measurements (GloCAEM) project brought these experts together to make the first steps towards an effective global network for FW atmospheric electricity monitoring by holding workshops to discuss measurement practises and instrumentation, as well as establish recording and archiving procedures to archive electric field data in a standardised, easily accessible format, then by creating a central data repository. This project was funded in the UK under NERC grant NE/N013689/1.\r\n\r\nThis dataset contains measurements of atmospheric electricity and electric potential gradient made using a Boltek EFM-100 Electric Field Monitor at Nor-Amberd Research Station.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-03-09T03:06:45", "updateFrequency": "unknown", "dataLineage": "Data collected by project team and sent to Centre for Environmental Data Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "GloCAEM, GEC, electric potential, electric field", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2020-07-09T14:16:22", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2278, "bboxName": "Nor-Amberd Research Station", "eastBoundLongitude": 44.26, "westBoundLongitude": 44.26, "southBoundLatitude": 40.37, "northBoundLatitude": 40.37 }, "verticalExtent": null, "result_field": { "ob_id": 26494, "dataPath": "/badc/glocaem/data/noramberd", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 155278891, "numberOfFiles": 1928, "fileFormat": "Data are csv formatted" }, "timePeriod": { "ob_id": 8633, "startTime": "2013-01-01T00:00:00", "endTime": "2017-08-31T23:59:59" }, "resultQuality": { "ob_id": 3068, "explanation": "Data provided by project group", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2016-10-18" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 26498, "uuid": "a313ff214a094e1cb677366ce93c77d1", "short_code": "acq", "title": "GloCAEM: Atmospheric electricity measurements at Nor-Amberd Research Station", "abstract": "GloCAEM: Atmospheric electricity measurements at Nor-Amberd Research Station" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2546, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 20068, "uuid": "6ee6e0a3f57c4ca79e8cbc0daaafe76f", "short_code": "proj", "title": "Global Coordination of Atmospheric Electricity Measurements (GloCAEM)", "abstract": "It is well established that Earth has a \"Global atmospheric Electric Circuit\" (GEC), through which charge separation in thunderstorms sustains large scale current flow around the planet. The GEC generates an atmospheric electric field which is present globally, and is typically 100V/m near the surface in fair weather conditions. Measurements of electric field have been shown to include information about global thunderstorm activity, local aerosol concentrations and cloud cover, as well as changes in the space weather environment. Recent work has also suggested that atmospheric electrical changes may be effective as earthquake precursors, as well as being sensitive to release of radioactivity, as evidenced by the Fukushima disaster in 2011. \r\n\r\nThe global nature of the GEC means that in order that truly global signals are considered in understanding the processes within the circuit, many validating measurements must be made at different locations around the world. To date, no genuinely global network of FW atmospheric electricity measurements has ever existed, therefore, given the growing number of groups now involved in atmospheric electricity monitoring, such a proposal is timely. \r\n\r\nThis project brought these experts together to make the first steps towards an effective global network for FW atmospheric electricity monitoring by holding workshops to discuss measurement practises and instrumentation, as well as establish recording and archiving procedures to archive electric field data in a standardised, easily accessible format, then by creating a central data repository. This project was funded in the UK under NERC grant NE/N013689/1." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 54175, 54176, 54178, 54179, 54180, 54185, 57990, 57991, 57992, 57993, 57994, 57995, 57996, 57997, 57998, 57999, 58000, 58001, 58002, 58003, 58004 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 24981, "uuid": "bffd0262439a4ecb8fadf0134c4a4a41", "short_code": "coll", "title": "GloCAEM: Atmospheric electric potential gradient measurements", "abstract": "Global Coordination of Atmospheric Electricity Measurements (GloCAEM) project brought these experts together to make the first steps towards an effective global network for FW atmospheric electricity monitoring by holding workshops to discuss measurement practises and instrumentation, as well as establish recording and archiving procedures to archive electric field data in a standardised, easily accessible format, then by creating a central data repository. This project was funded in the UK under NERC grant NE/N013689/1.\r\n\r\nThis dataset collection contains measurements of atmospheric electricity and electric potential gradient made using a Cambell Scientific CS110 electric-field mill." } ], "responsiblepartyinfo_set": [ 111349, 111341, 111342, 111343, 111345, 111346, 111347, 111348, 111344, 111357 ], "onlineresource_set": [ 25361 ] }, { "ob_id": 26499, "uuid": "aeb840c2e8994f12a22f3c49b46929d8", "title": "Sentinel 5P: Ultraviolet (UV) Aerosol Index level 2 data", "abstract": "Sentinel 5 Precursor (S5P) was launched on the 13th October 2017 carrying the TROPOspheric Monitoring Instrument (TROPOMI). The Aerosol Index (AI) is a well-established data product that has been calculated for several different satellite instruments spanning a period of nearly 40 years. The S5P/TROPOMI aerosol index is referred to as the Ultraviolet Aerosol Index (UVAI). The relatively simple calculation of the Aerosol Index is based on wavelength dependent changes in Rayleigh scattering in the UV spectral range where ozone absorption is very small. UVAI can also be calculated in the presence of clouds so that daily, global coverage is possible. This is ideal for tracking the evolution of episodic aerosol plumes from dust outbreaks, volcanic ash, and biomass burning.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": null, "updateFrequency": "", "dataLineage": "Data collected and prepared by European Space Agency (ESA). 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This consists of the Acquisition process for raw imaging data from the Sentinel 5P TROPOMI and the computation component to produce processed Level 2 UV Aerosol Index data." }, "imageDetails": [ 148 ], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 12321, "uuid": "7896ea1117dc4fa9bb95485ca9b1c6be", "short_code": "proj", "title": "Copernicus Programme", "abstract": "Copernicus, formerly known as the Global Monitoring for Environment and Security (GMES) programme, is headed by the European Commission (EC) in partnership with the European Space Agency (ESA). Within the Copernicus Space Component, ESA is developing a series of Sentinel satellite missions. Data from the Sentinel missions, as well as from Contributing Missions from other space agencies, are made freely available through a unified ground segment. Each Sentinel mission is comprised of a constallation of two or more satellites to fulfil the timeliness and reliability requirements of the Copernicus Services environmental monitoring and civil security activities. As well as operational and monitoring capabilities, the Sentinel missions will provide a wealth of Earth Observation data for scientific exploitation. The Sentinel 1 mission provides all weather, day and night radar imagery with scientific applications in sea-ice measurements, biomass observations and earthquake analysis. Sentinel 2 is a high resolution imaging mission to provide imagery of vegetation, soil and water cover, inland waterways and coastal areas. Sentinel 3 is a multi-instrument mission to measure sea-surface topography, sea- and land-surface temperature, ocean colour and land colour with high-end accuracy and reliability. Sentinel 4 is devoted to atmospheric monitoring and will be flown on a Meteosat Third Generation-Sounder (MTG-S) satellite in geostationary orbit. Sentinel 5 will monitor the atmosphere from polar orbit on board a MetOp Second Generation satellite. The Sentinel 5 precursor satellite mission is being developed to reduce data gaps between Envisat, in particular the Sciamachy instrument, and the launch of Sentinel 5. The Sentinel 5 mission will be dedicated to atmospheric monitoring. Sentinel 6 carries a radar altimeter to measure global sea-surface height, primarily for operational oceanography and for climate studies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 30129, "uuid": "3b0630c7fa264164868d4da5c9f90bed", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Third Party Data", "abstract": "The National Centre for Earth Observation (NCEO) Third Party data contains a broad range remotely sensed data acquired by satellite for use by the Earth Observation Scientific community supported by NCEO. The Centre for Environmental Data Analysis (CEDA) has archived and provides access to extensive Earth observation datasets under strict licensing conditions. Please see the individual dataset records for conditions of use." }, { "ob_id": 26459, "uuid": "6accd46663bc4669afaac418f2bf498e", "short_code": "coll", "title": "Sentinel 5 Precursor: Level 2 data", "abstract": "Need some info!" } ], "responsiblepartyinfo_set": [ 111358, 111359, 111360, 111361, 111362, 111363, 111364, 111365, 111366 ], "onlineresource_set": [ 25363, 25364, 25365, 25362 ] }, { "ob_id": 26505, "uuid": "5acca11ececb4d8283b7e633370b6751", "title": "Iceland Greenland seas Project (IGP): Upper air sounding: Profiles of temperature, pressure, humidity, wind speed and wind direction", "abstract": "This dataset contains upper air sounding profiles of temperature, pressure, humidity, wind speed and wind direction measurements from the NCAS Vaisala Sounding Station unit 2 radiosonde lauches. The radiosondes were launched over Greenland and Iceland from the Alliance research ship for the Iceland Greenland seas Project (IGP). \r\n\r\nThe Iceland Greenland seas Project (IGP) was an international project involving the UK, US a Norwegian research communities. The UK component was funded by NERC, under the Atmospheric Forcing of the Iceland Sea (AFIS) project (NE/N009754/1)", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2018-07-30T10:35:10", "updateFrequency": "", "dataLineage": "Data delivered to the Centre for Environmental Data Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "IGP, Upper air sounding, Profiles, temperature, pressure, humidity, wind speed, wind direction", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2019-08-01T07:30:50", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2279, "bboxName": "", "eastBoundLongitude": -16.1624, "westBoundLongitude": -22.4201, "southBoundLatitude": 64.1502, "northBoundLatitude": 70.5798 }, "verticalExtent": null, "result_field": { "ob_id": 26502, "dataPath": "/badc/igp/data/sonde", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 64178755, "numberOfFiles": 195, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 7150, "startTime": "2018-02-04T12:04:44", "endTime": "2018-03-19T12:49:26" }, "resultQuality": { "ob_id": 3155, "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": "2018-08-02" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 26504, "uuid": "a99e1ee00fa84c93a1afa4c66c68e337", "short_code": "acq", "title": "Iceland Greenland seas Project (IGP): Upper air sounding: Profiles of temperature, pressure, humidity, wind speed and wind direction", "abstract": "Iceland Greenland seas Project (IGP): Upper air sounding: Profiles of temperature, pressure, humidity, wind speed and wind direction" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "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": 24899, "uuid": "2780d047461c42f0a12534ccf42f487a", "short_code": "proj", "title": "Iceland Greenland seas Project (IGP) including the Atmospheric Forcing of the Iceland Sea (AFIS)", "abstract": "The Iceland Greenland seas Project (IGP) is an international project involving the UK, US a Norwegian research communities. The UK component was funded by NERC, under the Atmospheric Forcing of the Iceland Sea (AFIS) project (NE/N009754/1)\r\n\r\nThe Iceland Sea - to the north and east of Iceland - is arguably the least studied of the North Atlantic's subpolar seas. However new discoveries are forcing a redesign of our conceptual model of the North Atlantic's ocean circulation which places the Iceland Sea at the heart of this system and suggests that it requires urgent scientific focus. The recently discovered North Icelandic Jet is thought to be one of two pathways for dense water to pass through the Denmark Strait - the stretch of ocean between Iceland and Greenland - which is the main route for dense waters from the north to enter the Atlantic. Its discovery suggests a new paradigm for where dense water entering the North Atlantic originates. However at present the source of the North Icelandic Jet remains unknown. It is hypothesized that relatively warm Atlantic-origin water is modified into denser water in the Iceland Sea, although it is unclear precisely where, when or how this happens. \r\n\r\nThis project examined the wintertime atmosphere-ocean processes in the Iceland Sea by characterising its atmospheric forcing, i.e. observing the spatial structure and variability of surface heat, moisture and momentum fluxes in the region and the weather systems that dictate these fluxes. In situ observations of air-sea interaction processes from several platforms (an aircraft; and via project partners an unmanned airborne vehicle, a meteorological buoy and a research vessel) were made and used to evaluate meteorological analyses and reanalyses from operational weather forecasting centres. \r\n\r\nNumerical modelling experiments investigated the dynamics of selected weather systems which strongly influenced the region, but appear not to be well represented; for example, the boundary layers that develop over transitions between sea ice and the open ocean during cold-air outbreaks; or the jets and wakes that occur downstream of Iceland. The unique observations were used to improve model representation of these systems.\r\n\r\nThe project also carried out new high-resolution climate simulations. A series of experiments covered recent past and likely future situations; as well as some idealised situations such as no wintertime sea ice in the Iceland Sea region. This was done using a state-of-the-art atmospheric model with high resolution over the Iceland Sea to investigate changes in the atmospheric circulation and surface fluxes. \r\n\r\nFinally, in collaboration with the international partners, the project analysed new ocean observations and establish which weather systems are important for changing ocean properties in this region. The project used a range of ocean and atmospheric models to establish how current and future ocean circulation pathways function. In short, the project determined the role that atmosphere-ocean processes in the Iceland Sea play in creating the dense waters that flow through Denmark Strait and feed into the lower limb of the AMOC.\r\n\r\nThe subpolar region of the North Atlantic is crucial for the global climate system. It is where coupled atmosphere-ocean processes, on a variety of spatial scales, require an integrated approach for their improved understanding and prediction. This region has enhanced 'communication' between the atmosphere and ocean. Here large surface fluxes of heat and moisture make the surface waters colder, saltier and denser resulting in a convective overturning that contributes to the lower limb of the Atlantic Meridional Overturning Circulation (AMOC). The AMOC is an ocean circulation that carries warm water from the tropics northward with a return flow of cold water southwards at depth; it is instrumental in keeping Europe's climate relatively mild." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50512, 53936, 53937, 53938, 53941, 53942, 53943, 53944, 58078, 58079, 58080, 58081, 58082, 58083, 58084, 58085, 58086, 58087, 58088 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 27445, "uuid": "b3e807b8df824a8ca83468ce2e5b54e5", "short_code": "coll", "title": "In situ observations of air-sea interaction processes from the Iceland Greenland seas Project (IGP)", "abstract": "This collection contains a range of in situ observations of meteorological and air-sea interaction processes from a range of instruments on several platforms (buoy, ship , radiosonde) from the Iceland Greenland seas Project (IGP). \r\n\r\n\r\nThe Iceland Greenland seas Project (IGP) was an international project involving the UK, US and Norwegian research communities. The UK component was funded by NERC, under the Atmospheric Forcing of the Iceland Sea (AFIS) project (NE/N009754/1)" } ], "responsiblepartyinfo_set": [ 111373, 111374, 111375, 111376, 111377, 111379, 111380, 111381, 111378 ], "onlineresource_set": [] }, { "ob_id": 26507, "uuid": "4b34a2c6890f4e518cacc88911193354", "title": "EUSTACE: Coincident daily air temperature estimates and reference measurements, for validation, 1850-2015, v1.0", "abstract": "This dataset contains matched in situ reference and EUSTACE temperature estimates used in the validation of version 1.0 of the EUSTACE Globally gridded clear-sky daily air temperature estimates from satellites product and the version 1.0 EUSTACE Global daily air temperature product. The data are made available to allow traceability of the validation. This dataset has been compiled as part of the European Union Horizon 2020 EUSTACE (EU Surface Temperature for All Corners of Earth) project. \r\n\r\nNote that due to the licensing conditions of some of the validation datasets this data is made available for non-commercial purposes only. Additionally some of the datasets used in validation of the ice data do not allow onward redistribution, so only the ocean and land data have been included in this dataset.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2019-05-30T15:32:50", "updateFrequency": "notPlanned", "dataLineage": "Data are provided and archived for the EU EUSTACE project, which has received funding by the European Union's Horizon 2020 research and inovation programme under grant agreement no 640171.", "removedDataReason": "", "keywords": "EUSTACE, validation", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2019-05-30T16:56:52", "doiPublishedTime": "2019-05-31T09:46:31", "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": 27506, "dataPath": "/neodc/eustace/data/validation/v1.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1804364351, "numberOfFiles": 2321, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 5264, "startTime": "1850-01-01T00:00:00", "endTime": "2015-12-31T23:59:59" }, "resultQuality": { "ob_id": 3285, "explanation": "as provided by the data providers", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2019-05-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 27507, "uuid": "0b6dd27f98b848f2b4e92c248f86467a", "short_code": "comp", "title": "Derivation of the EUSTACE coincident daily air temperature estimates and reference measurements, for validation", "abstract": "To validate the EUSTACE globally gridded clear-sky daily air temperature estimates from satellites and the the EUSTACE global daily air temperature product a set of match-up datasets containing in-situ reference and EUSTACE temperature estimates have been produced. The matchups were produced separately for each test-reference dataset pairing. Therefore each file contains only matchups of one EUSTACE product to one in situ dataset.\r\n\r\nThe matchup process varied between platforms and as a consequence the filename format, file format and variables contained within each file depend on the matched datasets." }, "procedureCompositeProcess": null, "imageDetails": [ 214 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2561, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 32, "licenceURL": "http://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/version/2/", "licenceClassifications": [ { "ob_id": 6, "classification": "personal" }, { "ob_id": 4, "classification": "academic" }, { "ob_id": 5, "classification": "policy" } ] } } ], "projects": [ { "ob_id": 19556, "uuid": "a52b2cc065a847b8a77a93896880349f", "short_code": "proj", "title": "EUSTACE (EU Surface Temperature for All Corners of Earth)", "abstract": "The EUSTACE (EU Surface Temperature for All Corners of Earth) project is producing 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\nDay-to-day variations in surface air temperature affect society in many ways; however, daily surface air temperature measurements are not available everywhere. Satellite data can be used to estimate temperatures at locations where no ground (or in situ) observations are available. To achieve this, we must develop an understanding of the relationships between traditional (land and marine) surface air temperature measurements and satellite measurements, i.e. land surface temperature, ice surface temperature, sea surface temperature and lake surface water temperature. These relationships can be derived either empirically, or with the help of physical understanding.\r\n\r\nTo achieve this the EUSTACE project is using new statistical techniques to provide information on higher spatial and temporal scales than are currently available, making optimum use of the information in data-rich eras. The final and intermediate products of EUSTACE (e.g. satellite skin temperature retrievals over all domains with consistent uncertainty estimates; station time series with discontinuities identified; information on the relationship between skin and air temperature over different domains and different seasons) will be interesting for many applications.\r\n\r\nThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 640171." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1319, 1320, 25477, 25480, 25487, 25488, 25489, 25491, 25492, 25493, 25494, 25495, 25498, 25499, 25500, 25504, 25505, 25507, 25508, 25509, 25511, 25512, 25513, 25515, 25516, 25517, 25518, 25519, 25520, 25521, 25524, 25527, 25528, 25530, 69718, 69719, 69720, 69721, 69722, 69723, 69724, 69725, 69726, 69727, 69728, 69729, 69730, 69731, 69732, 69733, 69734, 69735, 69736, 69737, 69738, 69739, 69740, 69741, 69742 ], "vocabularyKeywords": [], "identifier_set": [ 10529 ], "observationcollection_set": [ { "ob_id": 20098, "uuid": "7ebff7be4c0149d082ca9a32ec8df4da", "short_code": "coll", "title": "EUSTACE: Collection of products from the EUSTACE (EU Surface Temperature for All Corners of Earth) project", "abstract": "The EUSTACE (EU Surface Temperature for All Corners of Earth) project is an EU Horizon 2020 project, producing 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\nThe data are publically available and consists of a number of different products: satellite skin temperature retrievals over all surface types; global surface air temperature derived from satellite skin temperature retrievals; homogonised surface meteorological station records for Europe and a European in filled analysis; global surface meteorological station records with discontinuities identified; and global analyses of daily surface air temperature going back to 1850, derived from both satellite and meteorological station data." }, { "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": [ 111386, 111385, 111383, 114551, 114987, 111384, 193348, 193349, 114986 ], "onlineresource_set": [ 25368, 26662, 94811, 94812 ] }, { "ob_id": 26509, "uuid": "0b4390871dce48ce91bc9dcf42137600", "title": "Sentinel 5P: TROPOspheric Monitoring Instrument (TROPOMI) level 1b Irradiance product UVN module data.", "abstract": "This dataset contains level 1B data from the TROPOspheric Monitoring Instrument (TROPOMI) aboard the European Space Agency (ESA) Sentinel 5P satellite. Sentinel 5P was launched on 13th October 2017. This satellite aims to provide atmospheric measurements relating to air quality, climate forcing, ozone and ultraviolet radiation. This data looks to build on the data from GOME, SCIAMACHY and OMI missions. Data are provided by the European Space Agency (ESA) and are made available via CEDA to any registered user.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": null, "updateFrequency": "", "dataLineage": "Data collected and prepared by European Space Agency (ESA). Downloaded from the Collaborative Hub for use by registered users of CEDA.", "removedDataReason": "", "keywords": "", "publicationState": "working", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "planned", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": null, "timePeriod": null, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 148 ], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 12321, "uuid": "7896ea1117dc4fa9bb95485ca9b1c6be", "short_code": "proj", "title": "Copernicus Programme", "abstract": "Copernicus, formerly known as the Global Monitoring for Environment and Security (GMES) programme, is headed by the European Commission (EC) in partnership with the European Space Agency (ESA). Within the Copernicus Space Component, ESA is developing a series of Sentinel satellite missions. Data from the Sentinel missions, as well as from Contributing Missions from other space agencies, are made freely available through a unified ground segment. Each Sentinel mission is comprised of a constallation of two or more satellites to fulfil the timeliness and reliability requirements of the Copernicus Services environmental monitoring and civil security activities. As well as operational and monitoring capabilities, the Sentinel missions will provide a wealth of Earth Observation data for scientific exploitation. The Sentinel 1 mission provides all weather, day and night radar imagery with scientific applications in sea-ice measurements, biomass observations and earthquake analysis. Sentinel 2 is a high resolution imaging mission to provide imagery of vegetation, soil and water cover, inland waterways and coastal areas. Sentinel 3 is a multi-instrument mission to measure sea-surface topography, sea- and land-surface temperature, ocean colour and land colour with high-end accuracy and reliability. Sentinel 4 is devoted to atmospheric monitoring and will be flown on a Meteosat Third Generation-Sounder (MTG-S) satellite in geostationary orbit. Sentinel 5 will monitor the atmosphere from polar orbit on board a MetOp Second Generation satellite. The Sentinel 5 precursor satellite mission is being developed to reduce data gaps between Envisat, in particular the Sciamachy instrument, and the launch of Sentinel 5. The Sentinel 5 mission will be dedicated to atmospheric monitoring. Sentinel 6 carries a radar altimeter to measure global sea-surface height, primarily for operational oceanography and for climate studies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 26538, "uuid": "d491b9e869d845a3a727b569f95dde60", "short_code": "coll", "title": "Sentinel 5 Precursor: Level 1 data", "abstract": "Level 1 radiance and irradiance data from the TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel 5P satellite. Sentinel 5P was launched on 13th October 2017. This satellite aims to provide atmospheric measurements relating to air quality, climate forcing, ozone and ultraviolet radiation. This data looks to build on the data from GOME, SCIAMACHY and OMI missions." }, { "ob_id": 30129, "uuid": "3b0630c7fa264164868d4da5c9f90bed", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Third Party Data", "abstract": "The National Centre for Earth Observation (NCEO) Third Party data contains a broad range remotely sensed data acquired by satellite for use by the Earth Observation Scientific community supported by NCEO. The Centre for Environmental Data Analysis (CEDA) has archived and provides access to extensive Earth observation datasets under strict licensing conditions. Please see the individual dataset records for conditions of use." } ], "responsiblepartyinfo_set": [ 111391, 111392, 111393, 111395, 111396, 111397, 111394, 204864, 111398 ], "onlineresource_set": [ 25369 ] }, { "ob_id": 26512, "uuid": "92c7a6a0824f4d18afa70aa1b86c103f", "title": "Tropical Rainfall Measuring Mission (TRMM) Rainfall Estimate L3 3 hour 0.25 degree x 0.25 degree (TRMM_3B42)", "abstract": "This dataset contains output from the TMPA (TRMM Multi-satellite Precipitation) Algorithm, and provides precipitation estimates in the TRMM regions that have the (nearly-zero) bias of the ”TRMM Combined Instrument” precipitation estimate and the dense sampling of high-quality microwave data with fill-in using microwave-calibrated infrared estimates. The granule size is 3 hours.\r\n\r\nThe Tropical Rainfall Measuring Mission (TRMM) was a joint mission between NASA and the Japan Aerospace Exploration (JAXA) Agency to study rainfall for weather and climate research.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-03-09T03:07:11", "updateFrequency": "monthly", "dataLineage": "Data taken as is from National Aeronautics and Space Administration (NASA). https://disc.gsfc.nasa.gov/datasets/TRMM_3B42_V7/summary?keywords=trmm_3b42", "removedDataReason": "", "keywords": "TRMM, TMPA, rainfall, tropical", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2018-09-10T09:13:59", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2280, "bboxName": "TRMM", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -50.0, "northBoundLatitude": 50.0 }, "verticalExtent": null, "result_field": { "ob_id": 26524, "dataPath": "/badc/trmm/data/TRMM_3B42", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 55468077361, "numberOfFiles": 252245, "fileFormat": "Data are HDF formatted" }, "timePeriod": { "ob_id": 7151, "startTime": "1997-12-31T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 3157, "explanation": "Data is 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": "2018-08-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 26510, "uuid": "bebcc2d6c59f4167a406c87e08106122", "short_code": "comp", "title": "TRMM Multi-Satellite Precipitation Analysis", "abstract": "The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) provides a calibration-based sequential scheme for combining precipitation estimates from multiple satellites, as well as gauge analyses where feasible, at fine scales (0.25° × 0.25° and 3 hourly). TMPA is available both after and in real time, based on calibration by the TRMM Combined Instrument and TRMM Microwave Imager precipitation products, respectively." }, "procedureCompositeProcess": null, "imageDetails": [ 128 ], "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": 26511, "uuid": "ae00040a3c1543e09eb6c4e47297662b", "short_code": "proj", "title": "Tropical Rainfall Measuring Mission (TRMM)", "abstract": "The Tropical Rainfall Measuring Mission (TRMM) was a joint mission between NASA and the Japan Aerospace Exploration (JAXA) Agency to study rainfall for weather and climate research. The TRMM satellite ended collecting data on April 15, 2015. Launched in late November 1997, with a design lifetime of 3 years, the TRMM satellite produced over 17 years of valuable scientific data. TRMM carried 5 instruments: a 3-sensor rainfall suite (PR, TMI, VIRS) and 2 related instruments (LIS and CERES). TRMM delivered a unique 17-year dataset of global tropical rainfall and lightning. The TRMM dataset became the space standard for measuring precipitation, and led to research that improved our understanding of tropical cyclone structure and evolution, convective system properties, lightning-storm relationships, climate and weather modeling, and human impacts on rainfall. The data also supported operational applications such as flood and drought monitoring and weather forecasting." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 64837, 64838, 64839, 64840, 64841, 64842, 64843, 64844, 64845 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 111402, 111403, 111404, 111405, 111407, 111408, 111409, 111448, 111406, 168825 ], "onlineresource_set": [ 25371, 25372, 25373 ] }, { "ob_id": 26515, "uuid": "ff725747de574f7dbb8236a9c31984e5", "title": "Global Precipitation Measurements (GPM) Integrated Multi-satellitE Retrievals (IMERG) L3 Half Hourly 0.1 degree x 0.1 degree v5", "abstract": "This dataset contains Global Precipitation Measurements (GPM) Integrated Multi-satellitE Retrievals (IMERG) v5. The Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the Day-1 multi-satellite precipitation product. 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This technique uses precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively, and whose features are transported via spatial propagation information that is obtained entirely from geostationary satellite IR data. At present we incorporate precipitation estimates derived from the passive microwaves aboard the DMSP 13, 14 & 15 (SSM/I), the NOAA-15, 16, 17 & 18 (AMSU-B), and AMSR-E and TMI aboard NASA's Aqua and TRMM spacecraft, respectively. These estimates are generated by algorithms of Ferraro (1997) for SSM/I, Ferraro et al. (2000) for AMSU-B and Kummerow et al. (2001) for TMI. Note that this technique is not a precipitation estimation algorithm but a means by which estimates from existing microwave rainfall algorithms can be combined. 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New evidence suggests that our estimates of how this fraction of CO2 that stays in the atmosphere is changing, and will continue to change, may be too high, meaning that there may be more hope that we can prevent atmospheric CO2 concentrations rising too high than was previously thought.\r\n\r\nWhilst we can estimate how much CO2 we are presently emitting, and can measure the concentration of CO2 in the atmosphere, and therefore work out how strong these sinks are (i.e. how much CO2 they are taking out of the atmosphere), we must calculate how this number will change in the future if we are to determine how much CO2 we can emit as a society without exceeding dangerous CO2 concentrations in the atmosphere. 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This project extended this work from the North Atlantic to the full ocean, and by correcting for the biases that cause the models to under-predict this change, produced new and improved future estimates of ocean CO2 absorption." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 22883, 50623, 50624, 52949, 58090, 58091, 58092, 58093, 58256, 63050 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 26567, "uuid": "1f850b0d4d6f4c4fbb59fdc5fe46307e", "short_code": "coll", "title": "CURBCO2: Coupled physical-biogeochemical ocean simulations model output", "abstract": "This dataset collection contains coupled physical-biogeochemical ocean simulations model output using the 1 degree NEMO-HadOCC model. The model output contains 3D Digital Image Correlation (DIC), alkalinity, temperature and salinity datasets at annualy-averaged frequency and monthly averaged surface ocean CO2 fugacities and fluxes.\r\n\r\nThis data was collected in support of CURBCO2: Carbon Uptake Revisited - Biases Corrected using Ocean Observations, a Natural Environment Research Council (NERC) funded project (NERC Grant NE/P015042/1). The overarching aim of this project was to provide UK and international governments with the best possible impartial information from which they can plan how best to work towards the global warming targets (the 'Paris Agreement') set at the Paris Climate Conference in December 2015." } ], "responsiblepartyinfo_set": [ 111582, 111583, 111584, 111585, 111587, 111588, 111589, 111590, 111586, 111591, 168840, 111592 ], "onlineresource_set": [] }, { "ob_id": 26559, "uuid": "de6591c3d5d44b08b4d954410f353c6e", "title": "A simulated Northern Hemisphere terrestrial climate dataset for the past 60,000 years", "abstract": "We present a continuous land climate reconstruction dataset extending from 60 kyr before present to the pre-industrial period at 0.5deg resolution on a monthly timestep for 0degN to 90degN. It has been generated from 42 discrete snapshot simulations using the HadCM3B-M2.1 coupled general circulation model. We incorporate Dansgaard-Oeschger (DO) and Heinrich events to represent millennial scale variability, based on a temperature reconstruction from Greenland ice-cores, with a spatial fingerprint based\r\n on a freshwater hosing simulation with HadCM3B-M2.1. Interannual variability is also added and derived from the initial snapshot simulations. Model output has been downscaled to 0.5deg resolution (using simple bilinear interpolation) and bias corrected using either the University of East Anglia, Climate Research Unit observational data (for temperature, precipitation, windchill, and minimum monthly temperature), or the EWEMBI dataset (for incoming shortwave energy). Here we provide datasets for; surface air temperature, precipitation, incoming shortwave energy, wind-chill, snow depth (as snow water equivalent), number of rainy days per month, minimum monthly temperature, and the land-sea mask and ice fractions used in the simulations. The datasets are in the form of NetCDF files. The variables are represented by a set of 24 files that have been compressed into nine folders: temp, precip, down_sw, wind_chill, snow, rainy_days, tempmonmin, landmask and icefrac. Each file represents 2500 years. The landmask and ice fraction are provided annually, whereas the climate variables are given as monthly files equivalent to 30000 months, between the latitudes 0deg to 90degN at 0.5deg resolution. Each of the climate files therefore have the dimensions 180 (lat) x 720 (lon) x 30000 (month). We also provide an example subset of the temperature dataset, which gives decadal averages for each month for 0-2500 years.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2019-09-19T13:29:38", "updateFrequency": "unknown", "dataLineage": "The dataset has been produced using the HadCM3B coupled climate model, a version of the more commonly known HadCM3 that has been developed at the University of Bristol, and is outlined in detail in the study of Valdes et al., (2017). The original HadCM3 originated from the UK Met Office Hadley Centre. Data provided to Centre for Environmental Data Analysis (CEDA) for archiving. Files with extra metadata to make them compliant with the CF conventions and using compressed to make them more manageable overwrote the originals in June 2019. Additional variable added Sep 2019.", "removedDataReason": "", "keywords": "Climate, Model, HadCM3B, air temperature, precipitation", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "underDevelopment", "dataPublishedTime": "2019-09-20T09:04:41", "doiPublishedTime": "2019-10-25T07:52:09.713004", "removedDataTime": null, "geographicExtent": { "ob_id": 2284, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": 0.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 26557, "dataPath": "/badc/deposited2018/HadCM3B_60Kyr_Climate/data", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 725297620051, "numberOfFiles": 223, "fileFormat": "NetCDF" }, "timePeriod": null, "resultQuality": { "ob_id": 3183, "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": "2018-10-24" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 26560, "uuid": "8a1587e2f3034eab8ef78e6b8ea4c5b8", "short_code": "comp", "title": "HadCM3B coupled climate model", "abstract": "The Hadley Centre Climate Model 3 Bristol (HadCM3B) is a coupled climate model consisting of a 3D dynamical atmosphere26 and ocean27 component. HadCM3B is a version of the more commonly known HadCM3 that has been developed at the University of Bristol" }, "procedureCompositeProcess": null, "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": [], "inspireTheme": [], "topicCategory": [], "phenomena": [ 4385, 11306, 11307, 13238, 50542, 50543, 54336, 54715, 58111, 58112, 58113, 58114, 58115, 58116, 58117, 58118 ], "vocabularyKeywords": [], "identifier_set": [ 10633 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 111594, 111596, 111597, 111598, 111599, 111608, 111595, 193353, 111600, 111601 ], "onlineresource_set": [ 25427, 25428, 25429, 92481, 92482, 92483, 92484, 92485, 92486, 92487, 92488, 92489, 92490, 92491, 92492, 92493, 92494, 92495, 92496, 92497, 92498, 92499, 92500, 92501, 92502 ] }, { "ob_id": 26561, "uuid": "ddf1032d626b45f78ce1c5e94f289a66", "title": "ICOZA: Atmospheric species measurements of OH, HONO, HO2, NO, NO2, NOy, O3, SO2 and CO from Weybourne Atmosphere Observatory July 2015", "abstract": "This dataset contains concentration measurements of atmospheric species performed at the Weybourne Atmospheric Observatory (WAO) on the North Norfolk coast. Measurements were all performed at ground level. Data was collected from 30th June till the 31st July 2015. This data was collected as part of Integrated Chemistry of Ozone in the Atmosphere (ICOZA) project, which aimed to apply in situ measurements to examine the response of the local atmospheric ozone production rate to NOx and VOC levels. The following instruments measured these species: LOPAP – Nitrous Acid (HONO) FAGE – OH, HO2, total organic RO2 Trace gases - NO, NO2, NOy, O3, SO2 and CO Gas chromatography (GC) - volatile organic compounds The measurements and data processing were performed by researchers from the Universities of Birmingham, East Anglia, Leeds and York. In-situ measurements of the ozone production rate are still in progress.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2018-09-24T12:11:37.812744", "updateFrequency": "", "dataLineage": "Each individual instrument data underwent appropriate QA/QC by it respective authors. This data was then delivered to the Centre for Environmental Data Analysis for archiving.", "removedDataReason": "", "keywords": "ICOZA, WAO, Weybourne, Chemistry, Ozone, Radicals", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2018-10-26T09:02:02", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 893, "bboxName": "Weybourne Atmospheric Observatory", "eastBoundLongitude": 1.1219, "westBoundLongitude": 1.1219, "southBoundLatitude": 52.9504, "northBoundLatitude": 52.9504 }, "verticalExtent": null, "result_field": { "ob_id": 32370, "dataPath": "/badc/deposited2018/icoza/data/weybourne/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4488585, "numberOfFiles": 7, "fileFormat": "NASA-Ames" }, "timePeriod": { "ob_id": 7158, "startTime": "2015-06-28T23:00:00", "endTime": "2015-07-31T22:59:59" }, "resultQuality": { "ob_id": 3164, "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": "2018-09-21" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 26564, "uuid": "596de6816fd444b887058aeca6f45504", "short_code": "acq", "title": "ICOZA: Atmospheric species measurements of OH, HONO, HO2, NO, NO2, NOy, O3, SO2 and CO from Weybourne Atmosphere Observatory July 2015", "abstract": "ICOZA: Atmospheric species measurements of OH, HONO, HO2, NO, NO2, NOy, O3, SO2 and CO from Weybourne Atmosphere Observatory July 2015" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "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": 12407, "uuid": "b4a5480f3b1d4a41b5e5262424686f6d", "short_code": "proj", "title": "ICOZA - Integrated Chemistry of Ozone in the Atmosphere", "abstract": "The ICOZA (Integrated Chemistry of Ozone in the Atmosphere) project was funded by the Natural Environment Research Council (NERC) with the grant references; NE/K012169/1, NE/K012398/1 and NE/K012029/1. The grants were led by Dr William Bloss (University of Birmingham), Professor Claire Reeves (University of East Anglia) and Professor Dwayne Ellis Heard (University of Leeds) respectively. \r\n\r\nTropospheric ozone is an important air pollutant, harmful to human health, agricultural crops and vegetation. It is the main precursor to the atmospheric oxidants which initiate the degradation of most reactive gases emitted to the atmosphere, and is an important greenhouse gas in its own right. As a consequence of this central role in atmospheric chemistry and air pollution, the capacity to understand, predict and manage tropospheric ozone levels is a key goal for atmospheric science research. This goal is hard to achieve, as ozone is a secondary pollutant, formed in the atmosphere from the complex oxidation of VOCs in the presence of NOx and sunlight, and the timescale of ozone production is such that a combination of in situ chemical processes, deposition and transport govern ozone levels. Uncertainties in all of these factors affect the accuracy of numerical models used to predict current and future ozone levels, and so hinder development of optimal air quality policies to mitigate ozone exposure. The project addressed this problem by measuring the local chemical ozone production rate, and (for the first time) perform measurements of the response of the local atmospheric ozone production rate to NOx and VOC levels - directly determining the ozone production regime. \r\n\r\nThe project aimed to achieve this by building upon an existing instrument for the measurement of atmospheric ozone production rates (funded through a NERC Technology Proof-of-Concept grant, and deployed in the recent ClearfLo 'Clean Air for London' NERC Urban Atmospheric Science programme). In addition to directly measuring ozone production, by perturbing the ambient chemical conditions (for example, through addition of NOx or VOCs to the sampled airflow), and measuring the effect of this change upon the measured ozone production rate, the ozone control regime (extent of NOx vs VOC limitation) may be directly determined. Within the project, they developed an existing ozone production instrument to include this capability, and validated the measurements, through comparison with ozone production from VOC oxidation in a large simulation chamber, and by measurement of the key oxidant OH radicals, and their precursors, within the system. \r\n\r\nICOZA then applied the instrument to compare the measured ozone production rates with those calculated using other observational and model approaches, and to characterise the ozone control regime, in two contrasting environments: In the outflow of a European megacity (at Weybourne Atmospheric Observatory, WAO, in the UK), and in a rural continental location (at Hohenpeissenberg, HPB, in southern Germany). At WAO, the project compared the measured ozone production rate with that calculated through co-located measurements of HO2 and RO2 radicals (using a newly developed approach to distinguish between these closely related species), and with that simulated using a constrained photochemical box model. They then compared the NOx-dependence of the ozone production rate with that predicted using indicator approaches, based upon observations of other chemical species. At HPB, they focused upon the VOC-dependence of the ozone production rate, and assess the error in model predictions of ozone production, which arise from the presence of unmeasured VOCs.\r\n\r\nThe project developed and demonstrated a new measurement approach, and applied this to improve the understanding of a fundamental aspect of atmospheric chemical processing. Future applications have considerable potential both to support atmospheric science research, but also as an important air quality tool, alongside existing measurement and modelling approaches, to inform the most effective emission controls to reduce ozone production in a given location. In the context of global crop yield reductions arising from ozone exposure of 7 - 12 % (wheat), 6 - 16 % (soybean) and 3 - 4 % (rice), this is an important societal as well as scientific impact. \r\n\r\nThe aim of the ICOZA project was to develop, refine and apply in situ measurements of the local chemical ozone production rate and its response to NOx and VOC levels. They aimed to assess the accuracy of model predictions of ozone production rates, and the systematic errors which arises from the presence of unmeasured species and/or limitations in our understanding of the atmospheric chemistry, through field observations in two contrasting environments. \r\n\r\nThe specific objectives were :\r\n\r\n1. To establish and validate the Perturbed Ozone Production Rate (POPR) concept, through laboratory development and simulation chamber testing\r\n\r\n2. To compare the directly measured ozone production rate with that calculated from measured peroxy radical abundance, and deduced from photochemical model predictions.\r\n\r\n3. To characterise the local ozone control regime in the contrasting environments of the outflow from a European megacity, and a continental rural location, for comparison with indicator approaches and zero dimensional photochemical model predictions\r\n\r\n4. To quantify the error in model-derived dP(O3)/d[VOC] due to unmeasured VOCs, and explore the scope for empirical determination of an expression for the chemical ozone production term, as a computationally cheap integration of tropospheric ozone chemistry." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 66080 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 111610, 111611, 111612, 111614, 111615, 111616, 111609, 111613, 111630, 111631, 111632, 111633, 111634, 111635, 111636, 111637 ], "onlineresource_set": [] }, { "ob_id": 26569, "uuid": "cbc7f00cde514dfebfb8d75913ee4862", "title": "CURBCO2: Coupled Physical-biogeochemical ocean NEMO-HadOCC simulation output forced by GFDL-ESM2M atmosphere", "abstract": "This dataset contains coupled physical-biogeochemical ocean second generation Geophysical Fluid Dynamics Laboratory (GFDL-ESM2M) simulation outputs using the 1 degree NEMO-HadOCC model. The model output contains 3D Digital Image Correlation (DIC), alkalinity, temperature and salinity datasets at annualy-averaged frequency and monthly averaged surface ocean CO2 fugacities and fluxes.\r\n\r\n Job IDs included in this dataset are:\r\n GFDL-ESM2M surface fluxes (started on 19th July ~14h):\r\n RCP85: u-ao541 (copy from u-ao419, change model names, restart + reduce walltime for nemo to test )\r\n RCP26: u-ao551 (copy from u-ao541 and change rcp26 surface fluxes)\r\n Constant atm CO2:\r\n RCP85: u-ao552 (copy from u-ao541 with cst atm changes)\r\n RCP26: u-ao554 (copy from u-ao551 with cst atm changes)\r\n\r\nThis data was collected in support of CURBCO2: Carbon Uptake Revisited - Biases Corrected using Ocean Observations, a Natural Environment Research Council (NERC) funded project (NERC Grant NE/P015042/1). The overarching aim of this project was to provide UK and international governments with the best possible impartial information from which they can plan how best to work towards the global warming targets (the 'Paris Agreement') set at the Paris Climate Conference in December 2015.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2018-09-26T15:55:33", "updateFrequency": "", "dataLineage": "Simulations were undertaken with the NEMO-HadOCC model ocean at 1 degree with HadOCC biogeochemistry and delivered to the Centre for Environmental Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "CURB, CO2, Oceans, alkalinity, temperature, salinity", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2019-01-21T14:32:07", "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": 26570, "dataPath": "/badc/deposited2018/curb/data/GFDL-ESM2M", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 105149493142, "numberOfFiles": 277, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 7157, "startTime": "2002-01-01T00:00:00", "endTime": "2100-12-30T23:59:59" }, "resultQuality": { "ob_id": 3163, "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": "2018-09-19" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 26555, "uuid": "b8ec9aef5b104c2f84c5192663af342e", "short_code": "comp", "title": "NEMO-HadOCC model ocean at 1 degree with HadOCC biogeochemistry.", "abstract": "Simulations were undertaken with the NEMO-HadOCC model ocean at 1 degree with HadOCC biogeochemistry." }, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "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": 26554, "uuid": "fb3577716c0b4e3ca361089f0f2a74c0", "short_code": "proj", "title": "CURB CO2: Carbon Uptake Revisited - Biases Corrected using Ocean Observations", "abstract": "CURB CO2: Carbon Uptake Revisited - Biases Corrected using Ocean Observations was a Natural Environment Research Council (NERC) funded project (NERC Grant NE/P015042/1). The overarching aim of this project was to provide UK and international governments with the best possible impartial information from which they can plan how best to work towards the global warming targets (the 'Paris Agreement') set at the Paris Climate Conference in December 2015.\r\n\r\nWhen we emit carbon dioxide (CO2) to the atmosphere through industrial activity, only around half of that CO2 remains in the atmosphere, with the remainder being taken up approximately equally through photosynthesis by plants on land and being absorbed by the oceans. These anthropogenic CO2 'sinks' are essentially saving us from a large part of the global warming that we would otherwise be experiencing. New evidence suggests that our estimates of how this fraction of CO2 that stays in the atmosphere is changing, and will continue to change, may be too high, meaning that there may be more hope that we can prevent atmospheric CO2 concentrations rising too high than was previously thought.\r\n\r\nWhilst we can estimate how much CO2 we are presently emitting, and can measure the concentration of CO2 in the atmosphere, and therefore work out how strong these sinks are (i.e. how much CO2 they are taking out of the atmosphere), we must calculate how this number will change in the future if we are to determine how much CO2 we can emit as a society without exceeding dangerous CO2 concentrations in the atmosphere. This project aimed to give us a better understanding of what this future change in the fraction of CO2 staying in the atmosphere is, by correcting a bias we have identified in the models we use to make these projections.\r\n\r\nThis project made projections of how the land and ocean CO2 sinks may change in the future using increasingly comprehensive Earth System Models (which are climate modes which also contain a representation of additional processes such as the carbon cycle). While these models are the best possible tools to simulate future climate change, they are still far from perfect. In the North Atlantic, which is the most intense ocean CO2 sink, these models underestimate how quickly the CO2 absorption is increasing, and have identified what the models are doing wrong. This project extended this work from the North Atlantic to the full ocean, and by correcting for the biases that cause the models to under-predict this change, produced new and improved future estimates of ocean CO2 absorption." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 22883, 50623, 50624, 50625, 52949, 58090, 58091, 58092, 58093, 58256 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 26567, "uuid": "1f850b0d4d6f4c4fbb59fdc5fe46307e", "short_code": "coll", "title": "CURBCO2: Coupled physical-biogeochemical ocean simulations model output", "abstract": "This dataset collection contains coupled physical-biogeochemical ocean simulations model output using the 1 degree NEMO-HadOCC model. The model output contains 3D Digital Image Correlation (DIC), alkalinity, temperature and salinity datasets at annualy-averaged frequency and monthly averaged surface ocean CO2 fugacities and fluxes.\r\n\r\nThis data was collected in support of CURBCO2: Carbon Uptake Revisited - Biases Corrected using Ocean Observations, a Natural Environment Research Council (NERC) funded project (NERC Grant NE/P015042/1). The overarching aim of this project was to provide UK and international governments with the best possible impartial information from which they can plan how best to work towards the global warming targets (the 'Paris Agreement') set at the Paris Climate Conference in December 2015." } ], "responsiblepartyinfo_set": [ 111650, 111651, 111652, 111653, 111654, 111655, 111656, 111658, 111657, 111659, 168843, 111660 ], "onlineresource_set": [] }, { "ob_id": 26571, "uuid": "70440cf97bd04a9890800566f9d31399", "title": "CURBCO2: Coupled Physical-biogeochemical ocean NEMO-HadOCC simulation output forced by HadGEM2-ES atmosphere", "abstract": "This dataset contains coupled physical-biogeochemical ocean second generation Met Office (HadGEM2-ES) simulation outputs using the 1 degree NEMO-HadOCC model. The model output contains 3D Digital Image Correlation (DIC), alkalinity, temperature and salinity datasets at annualy-averaged frequency and monthly averaged surface ocean CO2 fugacities and fluxes.\r\n\r\n Job IDs included in this dataset are:\r\n HadGEM2-ES surface fluxes (2099 for rcp85 not resolved yet) (runs started on 26th July)\r\n RCP85: u-ao789 (nov 2099)(copy from u-ao559,change model name, restart path, and surface fluxes files)\r\n RCP26: u-ao790 (nov 2099) (copy from u-ao789,change rcp26 surface fluxes)\r\n Constant atm CO2\r\n RCP85: u-ao791 (nov 2099) (copy from u-ao789 with cst atm changes)\r\n RCP26: u-ao793 (nov 2099) copy from u-ao790 with cst atm changes)\r\n\r\nThis data was collected in support of CURBCO2: Carbon Uptake Revisited - Biases Corrected using Ocean Observations, a Natural Environment Research Council (NERC) funded project (NERC Grant NE/P015042/1). The overarching aim of this project was to provide UK and international governments with the best possible impartial information from which they can plan how best to work towards the global warming targets (the 'Paris Agreement') set at the Paris Climate Conference in December 2015.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-03-09T03:07:12", "updateFrequency": "", "dataLineage": "Simulations were undertaken with the NEMO-HadOCC model ocean at 1 degree with HadOCC biogeochemistry and delivered to the Centre for Environmental Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "CURB, CO2, Oceans, alkalinity, temperature, salinity", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2019-01-21T14:32:21", "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": 26572, "dataPath": "/badc/deposited2018/curb/data/HadGEM2-ES", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 10629346743, "numberOfFiles": 28, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 7157, "startTime": "2002-01-01T00:00:00", "endTime": "2100-12-30T23:59:59" }, "resultQuality": { "ob_id": 3163, "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": "2018-09-19" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 26555, "uuid": "b8ec9aef5b104c2f84c5192663af342e", "short_code": "comp", "title": "NEMO-HadOCC model ocean at 1 degree with HadOCC biogeochemistry.", "abstract": "Simulations were undertaken with the NEMO-HadOCC model ocean at 1 degree with HadOCC biogeochemistry." }, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "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": 26554, "uuid": "fb3577716c0b4e3ca361089f0f2a74c0", "short_code": "proj", "title": "CURB CO2: Carbon Uptake Revisited - Biases Corrected using Ocean Observations", "abstract": "CURB CO2: Carbon Uptake Revisited - Biases Corrected using Ocean Observations was a Natural Environment Research Council (NERC) funded project (NERC Grant NE/P015042/1). The overarching aim of this project was to provide UK and international governments with the best possible impartial information from which they can plan how best to work towards the global warming targets (the 'Paris Agreement') set at the Paris Climate Conference in December 2015.\r\n\r\nWhen we emit carbon dioxide (CO2) to the atmosphere through industrial activity, only around half of that CO2 remains in the atmosphere, with the remainder being taken up approximately equally through photosynthesis by plants on land and being absorbed by the oceans. These anthropogenic CO2 'sinks' are essentially saving us from a large part of the global warming that we would otherwise be experiencing. New evidence suggests that our estimates of how this fraction of CO2 that stays in the atmosphere is changing, and will continue to change, may be too high, meaning that there may be more hope that we can prevent atmospheric CO2 concentrations rising too high than was previously thought.\r\n\r\nWhilst we can estimate how much CO2 we are presently emitting, and can measure the concentration of CO2 in the atmosphere, and therefore work out how strong these sinks are (i.e. how much CO2 they are taking out of the atmosphere), we must calculate how this number will change in the future if we are to determine how much CO2 we can emit as a society without exceeding dangerous CO2 concentrations in the atmosphere. This project aimed to give us a better understanding of what this future change in the fraction of CO2 staying in the atmosphere is, by correcting a bias we have identified in the models we use to make these projections.\r\n\r\nThis project made projections of how the land and ocean CO2 sinks may change in the future using increasingly comprehensive Earth System Models (which are climate modes which also contain a representation of additional processes such as the carbon cycle). While these models are the best possible tools to simulate future climate change, they are still far from perfect. In the North Atlantic, which is the most intense ocean CO2 sink, these models underestimate how quickly the CO2 absorption is increasing, and have identified what the models are doing wrong. This project extended this work from the North Atlantic to the full ocean, and by correcting for the biases that cause the models to under-predict this change, produced new and improved future estimates of ocean CO2 absorption." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 22883, 50623, 50624, 52949, 58089, 58090, 58091, 58092, 58093, 63050 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 26567, "uuid": "1f850b0d4d6f4c4fbb59fdc5fe46307e", "short_code": "coll", "title": "CURBCO2: Coupled physical-biogeochemical ocean simulations model output", "abstract": "This dataset collection contains coupled physical-biogeochemical ocean simulations model output using the 1 degree NEMO-HadOCC model. The model output contains 3D Digital Image Correlation (DIC), alkalinity, temperature and salinity datasets at annualy-averaged frequency and monthly averaged surface ocean CO2 fugacities and fluxes.\r\n\r\nThis data was collected in support of CURBCO2: Carbon Uptake Revisited - Biases Corrected using Ocean Observations, a Natural Environment Research Council (NERC) funded project (NERC Grant NE/P015042/1). The overarching aim of this project was to provide UK and international governments with the best possible impartial information from which they can plan how best to work towards the global warming targets (the 'Paris Agreement') set at the Paris Climate Conference in December 2015." } ], "responsiblepartyinfo_set": [ 111666, 111661, 111662, 111663, 111664, 111667, 111668, 111669, 111665, 168844, 111670, 111671 ], "onlineresource_set": [] }, { "ob_id": 26573, "uuid": "5a819e8e510a47b7bd8b1356caed43cb", "title": "CURBCO2: Coupled Physical-biogeochemical ocean NEMO-HadOCC simulation output forced by IPSL-CM5A-LR atmosphere", "abstract": "This dataset contains coupled physical-biogeochemical ocean second generation Institut Pierre-Simon Laplace (IPSL-CM5A-LR) simulation outputs using the 1 degree NEMO-HadOCC model. The model output contains 3D Digital Image Correlation (DIC), alkalinity, temperature and salinity datasets at annualy-averaged frequency and monthly averaged surface ocean CO2 fugacities and fluxes.\r\n\r\n Job IDs included in this dataset are:\r\n IPSL-CM5A-LR surface fluxes:\r\n RCP85: u-ao559 (copy from u-ao419, change model names, restart + reduce walltime for nemo to test)\r\n Failed in nemo_cice 20431201: v10 not found in y2044 (and same for the years after) => download,merge,transfer,re-run => fixed\r\n RCP26: u-ao562 (copy from u-ao559 and change rcp26 surface fluxes)\r\n Constant atm CO2:\r\n RCP85: u-ao563 (copy from u-ao559 with cst atm changes)\r\n RCP26: u-ao564 (copy from u-ao562 with cst atm changes)\r\n\r\nThis data was collected in support of CURBCO2: Carbon Uptake Revisited - Biases Corrected using Ocean Observations, a Natural Environment Research Council (NERC) funded project (NERC Grant NE/P015042/1). The overarching aim of this project was to provide UK and international governments with the best possible impartial information from which they can plan how best to work towards the global warming targets (the 'Paris Agreement') set at the Paris Climate Conference in December 2015.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-03-09T03:07:11", "updateFrequency": "", "dataLineage": "Simulations were undertaken with the NEMO-HadOCC model ocean at 1 degree with HadOCC biogeochemistry and delivered to the Centre for Environmental Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "CURB, CO2, Oceans, alkalinity, temperature, salinity", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2019-01-21T14:32:29", "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": 26574, "dataPath": "/badc/deposited2018/curb/data/IPSL-CM5A-LR", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 94119107925, "numberOfFiles": 249, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 7157, "startTime": "2002-01-01T00:00:00", "endTime": "2100-12-30T23:59:59" }, "resultQuality": { "ob_id": 3163, "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": "2018-09-19" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 26555, "uuid": "b8ec9aef5b104c2f84c5192663af342e", "short_code": "comp", "title": "NEMO-HadOCC model ocean at 1 degree with HadOCC biogeochemistry.", "abstract": "Simulations were undertaken with the NEMO-HadOCC model ocean at 1 degree with HadOCC biogeochemistry." }, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "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": 26554, "uuid": "fb3577716c0b4e3ca361089f0f2a74c0", "short_code": "proj", "title": "CURB CO2: Carbon Uptake Revisited - Biases Corrected using Ocean Observations", "abstract": "CURB CO2: Carbon Uptake Revisited - Biases Corrected using Ocean Observations was a Natural Environment Research Council (NERC) funded project (NERC Grant NE/P015042/1). The overarching aim of this project was to provide UK and international governments with the best possible impartial information from which they can plan how best to work towards the global warming targets (the 'Paris Agreement') set at the Paris Climate Conference in December 2015.\r\n\r\nWhen we emit carbon dioxide (CO2) to the atmosphere through industrial activity, only around half of that CO2 remains in the atmosphere, with the remainder being taken up approximately equally through photosynthesis by plants on land and being absorbed by the oceans. These anthropogenic CO2 'sinks' are essentially saving us from a large part of the global warming that we would otherwise be experiencing. New evidence suggests that our estimates of how this fraction of CO2 that stays in the atmosphere is changing, and will continue to change, may be too high, meaning that there may be more hope that we can prevent atmospheric CO2 concentrations rising too high than was previously thought.\r\n\r\nWhilst we can estimate how much CO2 we are presently emitting, and can measure the concentration of CO2 in the atmosphere, and therefore work out how strong these sinks are (i.e. how much CO2 they are taking out of the atmosphere), we must calculate how this number will change in the future if we are to determine how much CO2 we can emit as a society without exceeding dangerous CO2 concentrations in the atmosphere. This project aimed to give us a better understanding of what this future change in the fraction of CO2 staying in the atmosphere is, by correcting a bias we have identified in the models we use to make these projections.\r\n\r\nThis project made projections of how the land and ocean CO2 sinks may change in the future using increasingly comprehensive Earth System Models (which are climate modes which also contain a representation of additional processes such as the carbon cycle). While these models are the best possible tools to simulate future climate change, they are still far from perfect. In the North Atlantic, which is the most intense ocean CO2 sink, these models underestimate how quickly the CO2 absorption is increasing, and have identified what the models are doing wrong. This project extended this work from the North Atlantic to the full ocean, and by correcting for the biases that cause the models to under-predict this change, produced new and improved future estimates of ocean CO2 absorption." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 22883, 50623, 50624, 52949, 58089, 58090, 58091, 58092, 58093, 63050 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 26567, "uuid": "1f850b0d4d6f4c4fbb59fdc5fe46307e", "short_code": "coll", "title": "CURBCO2: Coupled physical-biogeochemical ocean simulations model output", "abstract": "This dataset collection contains coupled physical-biogeochemical ocean simulations model output using the 1 degree NEMO-HadOCC model. The model output contains 3D Digital Image Correlation (DIC), alkalinity, temperature and salinity datasets at annualy-averaged frequency and monthly averaged surface ocean CO2 fugacities and fluxes.\r\n\r\nThis data was collected in support of CURBCO2: Carbon Uptake Revisited - Biases Corrected using Ocean Observations, a Natural Environment Research Council (NERC) funded project (NERC Grant NE/P015042/1). The overarching aim of this project was to provide UK and international governments with the best possible impartial information from which they can plan how best to work towards the global warming targets (the 'Paris Agreement') set at the Paris Climate Conference in December 2015." } ], "responsiblepartyinfo_set": [ 111680, 111679, 111672, 111673, 111674, 111675, 111676, 111677, 111678, 111681, 168845, 111682 ], "onlineresource_set": [] }, { "ob_id": 26584, "uuid": "e6edf4076e7e42f681f495ccf0ec22cb", "title": "20 and 30 year climatologies from CMIP3 climate model output as used in the 2007 IPCC Fourth Assessment Report (AR4)", "abstract": "Climatology data used in Climate Change 2007, the Fourth Assessment Report (AR4) of the United Nations Intergovernmental Panel on Climate Change (IPCC) calculated from Coupled Model Intercomparison Project Phase 3 (CMIP3) model output.\r\n\r\nSimulations of global climate models were run by various climate modelling groups coordinated by the World Climate Research Programme (WCRP) on behalf of the IPCC.\r\n\r\nClimatology data was calculated from global climate model simulations of experiments representative of Special Report on Emission Scenarios (SRES) scenarios: A1b, A2, B1, the commitment scenario experiment (COMMIT), the twentieth century experiment (20C3M), the pre-industrial control (PICTL) and the idealised experiments 1PCTO2X and 1PCTO4X.\r\n\r\nThe AR4 climatologies are 20-year period averages. 30-year period averages have also been calculated for comparison with the IPCC Third Assessment Report (TAR). The monthly climatology data covers the period from 1850-2100. The climatologies are of global scope and are provided on latitude-longitude grids.\r\n\r\nThe period averages are:\r\n20x: 20-year averages [+20-39, +46-65, +80-99, +180-199] as used in chapter 10 of IPCC 2007, \r\n30a: 30-year averages [+01-30, +31-60, +61-90] as used in the observed climatologies, \r\n30b: 30-year averages [+10-39, +40-69, +70-99] for compatibility with the IPCC 3rd Assessment Report (TAR).\r\n\r\nThe climatology period averages listed above are relative to AD2000 for SRES scenarios A1B, A2, B1, and the commitment scenario COMMIT, relative to AD1900 for the twentieth century run (20C3M) and relative to the start of the experiment for the pre-industrial control (PICTL) and the 1PCTO2X and 1PCTO4X runs. \r\n\r\nData files are 'tar' files with names of the form [model]_[experiment]_[variable]_{climatology}.tar. \r\n\r\nThe experiments in this dataset include the SRES future scenarios: \r\nA1B Globalisation with rapid economic growth, \r\nA2 Regionalisation with regionally oriented economic development, \r\nB1 Globalisation with global environmental sustainability.\r\nAlso:\r\nCOMMIT simulation of climate change we are committed to given the current loading of greenhouse gasses and zero future emissions. \r\nPICTL the pre-industrial control. \r\n20C3M the 20th century climate simulaiton. \r\n1PTO2X and 1PTO4X idealised simulaitons where the concentration of atmospheric carbon dioxide (CO2) is increased by 1 percent per year from pre-industrial concentration until reaching 2x and 4x the original concentration.\r\n\r\nThe dataset is comprised of monthly mean surface data for the following atmospheric variables: \r\nspecific humidity, \r\nprecipitation flux, \r\nair pressure at sea level, \r\nsurface downwelling shortwave flux in air, \r\nair temperature, \r\nair temperature daily max, \r\nair temperature daily min, \r\neastward wind, \r\nnorthward wind.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2011-03-07T11:39:19", "updateFrequency": "", "dataLineage": "Climatology data calculated from global climate model simulations for a variety of experiments from the Coupled Model Intercomparison Project Phase 3 (CMIP3).", "removedDataReason": "", "keywords": "IPCC, DDC, AR4, CMIP3, SRES, COMMIT, PICTL, 20C3M, 1PCTO2X, 1PCTO4X, Climate Change", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-06-01T20:39:47", "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": 26583, "dataPath": "/badc/ipcc-ddc/data/netcdf/ar4_v2", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 13488312498, "numberOfFiles": 16002, "fileFormat": "netcdf" }, "timePeriod": { "ob_id": 7159, "startTime": "1850-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "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": 26576, "uuid": "0940f94c73bd4a63a7e126b3c23b60ec", "short_code": "proj", "title": "IPCC Fourth Assessment Report: Climate Change 2007 (AR4)", "abstract": "The Fourth Assessment Report (AR4) of the United Nations Intergovernmental Panel on Climate Change (IPCC).\r\n\r\nThe Working Group I contribution describes progress in understanding of the human and natural drivers of climate change, observed climate change, climate processes and attribution, and estimates of projected future climate change.\r\nThe Working Group II contribution assesses current scientific understanding of the impacts of climate change on natural, managed and human systems, the capacity of these systems to adapt and their vulnerability.\r\nThe Working Group III contribution focuses on the scientific, technological, environmental, economic and social aspects of mitigation of climate change." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 8030, 8031, 8142, 50512, 50559, 50561, 54336, 55250, 55251, 55252, 55253, 55254, 55255, 55256, 55257, 55258, 55259, 55260, 55261, 55262, 55263, 55264, 55265, 55266 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 26582, "uuid": "3a9c171d7f1c4441ba8ffb9c2c22b81e", "short_code": "coll", "title": "Model output described in the 2007 IPCC Fourth Assessment Report (AR4), 20 and 30 year climatologies", "abstract": "Data used in Climate Change 2007, the Fourth Assessment Report (AR4) of the United Nations Intergovernmental Panel on Climate Change (IPCC).\r\n\r\nSimulations of global climate models were run by various climate modelling groups coordinated by the World Climate Research Programme (WCRP) on behalf of the United Nations Intergovernmental Panel on Climate Change (IPCC).\r\nClimatology data calculated from global climate model simulations of experiments representative of Special Report on Emission Scenarios (SRES) scenarios: A1b, A2, B1, the commitment scenario experiment (COMMIT), the twentieth century experiment (20C3M), the pre-industrial control (PICTL) and the idealised experiments 1PCTO2X and 1PCTO4X.\r\nThe AR4 climatologies are 20-year averages, 30-year averages have also been calculated for comparison with the IPCC Third Assessment Report (TAR). The monthly climatology data covers the period from 1850-2100. The climatologies are of global scope and are provided on latitude-longitude grids." }, { "ob_id": 5525, "uuid": "a0913dba885f1b1c0e3eb6dc0c04c188", "short_code": "coll", "title": "Intergovernmental Panel on Climate Change Data Distribution Centre (IPCC DDC) holdings", "abstract": "The Intergovernmental Panel on Climate Change (IPCC) Data Distribution Centre provides four main types of data and guidance:\r\n1. Observed Climate Data Sets;\r\n2. Global Climate Model Data;\r\n3. Socio-economic data and scenarios;\r\n4. Data and scenarios for other environmental changes." } ], "responsiblepartyinfo_set": [ 111718, 143531, 143535, 143534, 143533, 111715, 143544, 143545, 143532, 111716, 111717 ], "onlineresource_set": [ 25446, 25462, 25461 ] }, { "ob_id": 26585, "uuid": "eca40c1addba4d0782aa7fd45e998c81", "title": "30 year climatologies from model output as used in the 2001 IPCC Third Assessment Report (TAR)", "abstract": "Data used in Climate Change 2001, the Third Assessment Report (TAR) of the United Nations Intergovernmental Panel on Climate Change (IPCC).\r\n\r\nSimulations of global climate models were run by various climate modelling groups coordinated by the World Climate Research Programme (WCRP) on behalf of the United Nations Intergovernmental Panel on Climate Change (IPCC).\r\nClimatology data calculated from global climate model simulations of experiments representative of Special Report on Emission Scenarios (SRES) scenarios: A1F, A1T, A1a, A2a, A2b, A2c, B1a, B2b.\r\nThe climatologies are 30-year averages. Climate anomalies are expressed relative to the period 1961-1990. The monthly climatology data covers the period from 1961-2100. The climatologies are of global scope and are provided on latitude-longitude grids.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2007-09-14T11:45:29", "updateFrequency": "", "dataLineage": "Climatology data calculated from global climate model simulations for SRES experiments A1F, A1T, A1a, A2a, A2b, A2c, B1a, B2b.\r\nThe climatologies are 30-year averages. Climate anomalies are expressed relative to the period 1961-1990. \r\n\r\nThe Models.\r\nCCCma: The atmospheric component AGCM2 is a spectral model with triangular truncation at wave no. 32 and 10 vertical levels. The ocean model component based on the GFDL MOM 1.1 code with 29 vertical levels and has a isopycnal / eddy stirring parameterization. \r\nCSIRO: The Mark 2 CSIRO coupled global climate model consists of the atmospheric component which has 9 vertical levels with a R21 horizontal resolution and the ocean component which was based on the GFDL code. \r\nECHAM4: The model consists of the atmospheric component which based on the weather forecast model of ECMWF. The atmosphere component is the standard model version of a 19-level hybrid sigma-pressure coordinate system. The ocean component is a model which computes with isopycnal coordinates.\r\nGFDL: The atmospheric component solves the primitive equations using a spectral transform method, GFDL_R30 means a rhomboidal 30 truncation and the ocean component solves the primitive equation of motion using the Boussinesq, rigid-lid and hydrostatic approximation and based on GFDL Modular Ocean Model version 1.\r\nHadCM3: The Hadley Centre Circulation Model is a 3-dim AOGCM. The atmospheric component has a 19 levels, the horizontal resolution is comparable with a spectral resolution of T42, while the ocean component has a 20 levels resolution. \r\nNIES: The model developed by the Center for Climate System Resaerch/ National Institute for Enviromental Studies in Tokyo consists of the atmospheric component which has vertical resolution of 20 levels and the triangular truncation at wavenumber 21 (T21). The ocean model has 17 vertical levels and the same resolution. .\r\n\r\nThe SRES Experiments.\r\nA1B Globalisation with rapid economic growth and a balanced emphasis on all energy sources. \r\nA1T Globalisation with rapid economic growth and an emphasis on non-fossil energy sources. \r\nA1FI Globalisation with rapid economic growth and an emphasis on fossil fuels (Fossil Intensive). \r\nA2 Regionalisation with regionally oriented economic development. A2a, A2b and A2c follow the same experiment design and historic and future forcings but start from different initial conditions.\r\nB1 Globalisation with global environmental sustainability. \r\nB2 Regionalisation with local environmental sustainability. B2a and B2b follow the same experiment design and historic and future forcings but start from different initial conditions.\r\n\r\nNotes.\r\n * TAR uses CMIP2 simulations.\r\n * Only two models have run all the experiments.", "removedDataReason": "", "keywords": "IPCC, DDC, TAR, SRES, Climate Change", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-06-01T21:15:29", "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": 26586, "dataPath": "/badc/ipcc-ddc/data/netcdf/sres", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 649046392, "numberOfFiles": 3194, "fileFormat": "netcdf" }, "timePeriod": { "ob_id": 7160, "startTime": "1961-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 195 ], "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": 26577, "uuid": "b244465e39cb4ab7a086e3cab285e619", "short_code": "proj", "title": "IPCC Third Assessment Report: Climate Change 2001 (TAR)", "abstract": "The Third Assessment Report (TAR) of the United Nations Intergovernmental Panel on Climate Change (IPCC).\r\n\r\nThe Working Group I contribution focuses on the science of climate change. It covers the physical climate system, the factors that drive climate change, analyses of past climate and projections of future climate change, and detection and attribution of human influences on recent climate.\r\nThe Working Group II contribution focuses on the environmental, social, and economic consequences of climate change and potential adaptation responses. It covers the sensitivity, adaptive capacity, and vulnerability of natural and human systems to climate change, and the potential impacts and adaptation options at regional and global scales.\r\nThe Working Group III contribution focuses on the mitigation of climate change. It covers the technological and biological options to mitigate climate change, their costs and ancillary benefits, the barriers to their implementation, and policies, measures and instruments to overcome these barriers." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1319, 1320, 4385, 58119, 58120, 58121, 58122, 58123, 58124, 58125, 58126, 58127, 58128, 58129, 58130, 58131, 58132, 58133, 58134, 58135, 58136, 58137, 58138, 58139, 58140, 58141, 58142, 58143, 58144, 58145, 58146, 58147, 58148, 58149, 58150, 58151, 58152, 58153, 58154, 58155, 58156, 58157, 58158, 58159, 58160, 58161, 58162, 58163, 58164, 58165, 58166, 58167, 58168, 58169, 58170, 58171, 58172, 58173 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 26581, "uuid": "d31ec9ec0411447e9056d14b6f941026", "short_code": "coll", "title": "Data used in Climate Change 2001, the Third Assessment Report (TAR) of the United Nations Intergovernmental Panel on Climate Change (IPCC)", "abstract": "Data used in Climate Change 2001, the Third Assessment Report (TAR) of the United Nations Intergovernmental Panel on Climate Change (IPCC).\r\n\r\nSimulations of global climate models were run by various climate modelling groups coordinated by the World Climate Research Programme (WCRP) on behalf of the United Nations Intergovernmental Panel on Climate Change (IPCC).\r\nClimatology data calculated from global climate model simulations of experiments representative of Special Report on Emission Scenarios (SRES) scenarios: A1F, A1T, A1a, A2a, A2b, A2c, B1a, B2b.\r\nThe climatologies are 30-year averages. Climate anomalies are expressed relative to the period 1961-1990. The monthly climatology data covers the period from 1961-2100. The climatologies are of global scope and are provided on latitude-longitude grids." }, { "ob_id": 5525, "uuid": "a0913dba885f1b1c0e3eb6dc0c04c188", "short_code": "coll", "title": "Intergovernmental Panel on Climate Change Data Distribution Centre (IPCC DDC) holdings", "abstract": "The Intergovernmental Panel on Climate Change (IPCC) Data Distribution Centre provides four main types of data and guidance:\r\n1. Observed Climate Data Sets;\r\n2. Global Climate Model Data;\r\n3. Socio-economic data and scenarios;\r\n4. Data and scenarios for other environmental changes." } ], "responsiblepartyinfo_set": [ 111719, 111723, 146576, 146577, 146578, 146579, 146580, 146581, 111724 ], "onlineresource_set": [ 25448, 25449, 25457, 25458, 25459, 25460, 25456 ] }, { "ob_id": 26588, "uuid": "ea4424762e754ae5971b911084bad162", "title": "30 year climatologies from model output as used in the 1995 IPCC Second Assessment Report (SAR)", "abstract": "Data used in Climate Change 1995, the Second Assessment Report (SAR) of the United Nations Intergovernmental Panel on Climate Change (IPCC).\r\n\r\nSimulations of global climate models were run by various climate modelling groups coordinated by the World Climate Research Programme (WCRP) on behalf of the United Nations Intergovernmental Panel on Climate Change (IPCC).\r\nClimatology data calculated from global climate model simulations of experiments representative of IPCC Scenarios IS92 a and d.\r\nThe climatologies are 30-year averages. Climate anomalies are expressed relative to the period 1961-1990. The monthly climatology data covers the period from 1961-2100. The climatologies are of global scope and are provided on latitude-longitude grids.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2025-05-25T01:52:33", "updateFrequency": "", "dataLineage": "Climatology data calculated from global climate model simulations for IS92 experiments a and d.\r\nThe climatologies are 30-year averages. Climate anomalies are expressed relative to the period 1961-1990. \r\n\r\nModels: \r\n\r\nCCCma/CGCM1: Canadian Global Coupled Model, CGCM1. A spectral model with triangular truncation at wave number 32 (roughly 3.7ºx3.7º latitude/longitude) and 10 vertical levels. The ocean component is based on the GFDL MOM1.1 with a resolution of approximately 1.8ºx1.8º and 29 vertical levels. \r\n\r\nCCSR/NIES: A coupled ocean-atmosphere model that consists of the CCSR/NIES atmospheric GCM, the CCSR ocean GCM, a thermodynamic sea-ice model, and a river routing model. The spatial resolution is T21 spectral truncation (roughly 5.6º latitude/longitude) and 20 vertical levels for the atmospheric part, and roughly 2.8º horizontal grid and 17 vertical levels for the oceanic part. \r\n\r\nCSIRO-Mk2b: The CSIRO Atmospheric Research Mark 2b climate model atmosphere has 9 levels in the vertical and horizontal resolution of spectral R21 (roughly 5.6 by 3.2 degrees). The ocean model has the same horizontal resolution with 21 levels. \r\n\r\nECHAM-4: The ECHAM-4 model is a spectral transform model with 19 atmospheric layers and the results used here derive from experiments performed with spatial resolution T42 (roughly 2.8º longitude/latitude).\r\n\r\nGFDL-R15: The model has interactive clouds and seasonally varying solar insolation. The atmospheric component has nine finite difference (sigma) levels in the vertical. This version has a rhomboidal resolution of 15 waves (R15) roughly 4.5º latitude by 7.5º longitude. The model has global geography consistent with its computational resolution and seasonal (but not diurnal) variation of insolation.\r\n\r\nHadCM2: HadCM2 has a spatial resolution of 2.5° x 3.75° (latitude by longitude) and the representation produces a grid box resolution of 96 x 73 grid cells. This produces a surface spatial resolution of about 417km x 278 km reducing to 295 x 278km at 45 degrees North and South (comparable to a spectral resolution of T42).\r\n\r\nNCAR-DoE: The NCAR-DoE global coupled model (sometimes referred to as the Washington-Meehl coupled model) used in these experiments has an atmospheric component with rhomboidal 15 (R15) resolution (roughly 4.5º latitude by 7.5º longitude) with 9 levels, mass flux convection and a cloud albedo feedback scheme; a global 1º by 1º 20-level ocean; and dynamic and thermodynamic sea ice.\r\n\r\nExperiments: \r\n\r\n1% per annum forcing greenhouse gases only \"GG\", 1% per annum forcing greenhouse gasses and sulphates \"GS\" - equivalent to scenario IS92a: Population rises to 11.3 billion by 2100 and economic growth averages 2.3 % per annum between 1990 and 2100, with a mix of conventional and renewable energy sources being used. \r\n\r\n0.5% per annum forcing greenhouse gases only \"GG\", 0.5% per annum forcing greenhouse gasses and sulphates \"GS\" - equivalent to scenario IS92d: Population rises to 7.6 billion in 2025 then falls to 6.4 billion by 2100 and economic growth averages 2.0% per annum between 1990 and 2100 with increasing use of renewable energy.", "removedDataReason": "", "keywords": "IPCC, DDC, SAR, IS92, Climate Change", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-06-01T21:12:21", "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": 26587, "dataPath": "/badc/ipcc-ddc/data/netcdf/is92", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 366763121, "numberOfFiles": 166, "fileFormat": "netcdf" }, "timePeriod": null, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 195 ], "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": 26578, "uuid": "576c6c9260c84ee2bc23988712665035", "short_code": "proj", "title": "IPCC Second Assessment Report: Climate Change 1995 (SAR)", "abstract": "The Second Assessment Report (SAR) of the United Nations Intergovernmental Panel on Climate Change (IPCC).\r\n\r\nThe contribution from Working Group I focuses on the science of climate change. It covers the physical climate system, the factors that drive climate change, analyses of past climate and projections of future climate change, and detection and attribution of human influence on recent climate.\r\nThe contribution from Working Group II focuses on potential impacts of climate change, adaptive responses, and measures that could mitigate future emissions. It covers a wide range of ecological systems and socioeconomic sectors and activities.\r\nThe contribution from Working Group III focuses on the socioeconomic aspects of climate change. It covers the scope of the analysis, decision making under uncertainty, equity issues, intertemporal equity and discounting, applicability of cost and benefit assessments to climate change, social costs of climate change, response options, conceptual issues related to estimating mitigation costs, review of mitigation cost studies, integrated assessment of climate change, and an economic assessment of policy options to address climate change." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1319, 1320, 4385, 56257, 58174, 58175, 58176, 58177, 58178, 58179, 58180, 58181, 58182, 58183, 58184, 58185, 58186, 58187, 58188, 58189, 58190, 58191, 58192, 58193, 58194, 58195, 58196, 58197, 58198, 58199, 58200, 58201, 58202, 58203, 58204, 58205, 58206, 58207, 58208, 58209, 58210, 58211, 58212, 58213, 58214 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 26580, "uuid": "35829fb8a0f94cdea5859905bdae2e55", "short_code": "coll", "title": "Model output described in the 1995 IPCC Second Assessment Report (IS92 scenarios), 30-year means", "abstract": "Data used in Climate Change 1995, the Second Assessment Report (SAR) of the United Nations Intergovernmental Panel on Climate Change (IPCC).\r\n\r\nSimulations of global climate models were run by various climate modelling groups coordinated by the World Climate Research Programme (WCRP) on behalf of the United Nations Intergovernmental Panel on Climate Change (IPCC).\r\nClimatology data calculated from global climate model simulations of experiments representative of IPCC Scenarios IS92 a and d.\r\nThe climatologies are 30-year averages. Climate anomalies are expressed relative to the period 1961-1990. The monthly climatology data covers the period from 1961-2100. The climatologies are of global scope and are provided on latitude-longitude grids." }, { "ob_id": 5525, "uuid": "a0913dba885f1b1c0e3eb6dc0c04c188", "short_code": "coll", "title": "Intergovernmental Panel on Climate Change Data Distribution Centre (IPCC DDC) holdings", "abstract": "The Intergovernmental Panel on Climate Change (IPCC) Data Distribution Centre provides four main types of data and guidance:\r\n1. Observed Climate Data Sets;\r\n2. Global Climate Model Data;\r\n3. Socio-economic data and scenarios;\r\n4. Data and scenarios for other environmental changes." } ], "responsiblepartyinfo_set": [ 111725, 111727, 146570, 146571, 146572, 146573, 146574, 146575, 111726 ], "onlineresource_set": [ 25454, 25455 ] }, { "ob_id": 26598, "uuid": "9d0f61dc7a1b4017b22d88f9d38ab398", "title": "IPCC AR5 Seasonal Mean Temperature and Precipitation in IPCC Regions for CMIP3 and CMIP5", "abstract": "Projected regional average change in seasonal and annual mean temperature and precipitation for the IPCC SREX regions for CMIP5 and CMIP3. The data were produced in 2014 by the Intergovernmental Panel on Climate Change (IPCC) Working Group II (WGII) Chapter 21 author team for the IPCC Fifth Assessment Report (AR5). \r\n\r\nRegional average seasonal and annual mean temperature and precipitation for the period 2071-2100 are compared to a baseline of 1961-1990 for CMIP5 and CMIP3 General Circulation Model (GCM) projections.\r\nThe data compare the range of projections from 35 Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble under four Representative Concentration Pathway (RCP) scenarios compared with GCM projections from 22 CMIP3 ensemble under three Special Report on Emission Scenarios (SRES) scenarios. \r\n\r\nRegional averages are based on SREX regions defined by the IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (IPCC, 2012: also known as \"SREX\").", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-03-09T03:06:59", "updateFrequency": "", "dataLineage": "Temperature and Precipitation in IPCC Regions for CMIP3 and CMIP5 prepared from CMIP5 projections for the IPCC 5th Assessment Report WGII; reformatted for the IPCC DDC by CEDA;\r\n\r\nOriginal data generated for the IPCC AR5 WGII team by Carol McSweeney (UK Met Office).\r\n\r\nscatter_headings.csv headings correspond to the data in IPCC-AR5-WGII-Chap21SM_scatter.csv\r\n\r\nvar_IPCC-AR5-WGII-Chap21SM_scatter.csv has the same data in a different organisation .. more columns, fewer rows.", "removedDataReason": "", "keywords": "IPCC AR5 IPCC-DDC regional CMIP CMIP3 CMIP5 SREX RCP25 RCP45 RCP60 RCP85 A1B A2 B1", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2019-09-11T15:01:08", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2411, "bboxName": "SREX Regions", "eastBoundLongitude": 180.0, "westBoundLongitude": -168.0, "southBoundLatitude": -56.7, "northBoundLatitude": 75.0 }, "verticalExtent": null, "result_field": { "ob_id": 27739, "dataPath": "/badc/ipcc-ddc-regions/data/ar5/seasonal-means", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 6785403, "numberOfFiles": 8, "fileFormat": ".xls, .csv, .txt" }, "timePeriod": { "ob_id": 7404, "startTime": "2071-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": { "ob_id": 3329, "explanation": "See dataset associated documentation.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2019-09-11" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 195 ], "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": 426, "uuid": "5b9dbe341d2fb169922d36e7c0cf8805", "short_code": "proj", "title": "The World Climate Research Programme's (WCRP) Coupled Model Intercomparison Project phase 3 (CMIP3)", "abstract": "In response to a proposed activity of the World Climate Research Programme (WCRP) Working Group on Coupled Modelling (WGCM), PCMDI volunteered to collect model output contributed by leading modelling centres around the world. Climate model output from simulations of the past, present and future climate was collected by PCMDI mostly during the years 2005 and 2006, and this archived data constitutes phase 3 of the Coupled Model Intercomparison Project (CMIP3). In part, the WGCM organized this activity to enable those outside the major modelling centres to perform research of relevance to climate scientists preparing the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC). The IPCC was established by the World Meteorological Organization and the United Nations Environmental Program to assess scientific information on climate change. The IPCC publishes reports that summarize the state of the science." }, { "ob_id": 11975, "uuid": "d2d8f982d66cce55bb59fc769ca39264", "short_code": "proj", "title": "WCRP CMIP5: Coupled Model Intercomparison Project Phase 5", "abstract": "The WCRP Coupled Model Intercomparison Project, Phase 5 (CMIP5), was a global climate model intercomparison project, coordinated by PCMDI (Program For Climate Model Diagnosis and Intercomparison) on behalf of the World Climate Research Program (WCRP) and provided input for the Intergovernmental Panel on Climate Change (IPCC) 5th Assessment Report (AR5).\r\n\r\nThe CMIP5 archive is managed via the Earth System Grid Federation, a globally distributed archive, with various \"gateways\" with advanced faceted search capabilities provided by a number of participating organisations. Full details are available from the PCMDI CMIP5 pages (see linked documentation on this record).\r\n\r\nCEDA provides access to the UK Met Office contribution to the CMIP5 archive to UK researchers within the full distributed archive infrastructure and additionally holds a copy of the CMIP5 archive within its infrastructure to aid local use." }, { "ob_id": 26579, "uuid": "2a88cc098f9c4f1ba44b098972ca49f8", "short_code": "proj", "title": "IPCC Fifth Assessment Report: Climate Change 2013 (AR5)", "abstract": "The Fifth Assessment Report (AR5) of the United Nations Intergovernmental Panel on Climate Change (IPCC).\r\n\r\nThe decision to prepare a Fifth Assessment Report (AR5) was taken by the members of the IPCC at its 28th Session (09-10 April 2008, Budapest, Hungary). Following the election of the new IPCC Bureau at the 29th Session of the IPCC (31 August - 04 September 2008, Geneva, Switzerland) and discussions about future IPCC activities at the 30th Session of the IPCC (21-23 April 2009, Antalya, Turkey), a Scoping Meeting was held (13-17 July 2009, Venice, Italy) to develop the scope and outline of the AR5. The resulting outlines for the three Working Group contributions to the AR5 were approved by the 31st Session of the IPCC in Bali (26-29 October 2009)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 58094, 58095, 58096, 58097, 58098, 58099, 58100, 58101, 58102, 58103, 58104 ], "vocabularyKeywords": [ { "ob_id": 6571, "vocabService": "nerc_skos_vocab", "uri": "http://vocab.nerc.ac.uk/collection/P07/current/CFSN0453/", "resolvedTerm": "precipitation_flux" }, { "ob_id": 6525, "vocabService": "nerc_skos_vocab", "uri": "http://vocab.nerc.ac.uk/collection/P07/current/CFSN0023/", "resolvedTerm": "air_temperature" } ], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5525, "uuid": "a0913dba885f1b1c0e3eb6dc0c04c188", "short_code": "coll", "title": "Intergovernmental Panel on Climate Change Data Distribution Centre (IPCC DDC) holdings", "abstract": "The Intergovernmental Panel on Climate Change (IPCC) Data Distribution Centre provides four main types of data and guidance:\r\n1. Observed Climate Data Sets;\r\n2. Global Climate Model Data;\r\n3. Socio-economic data and scenarios;\r\n4. Data and scenarios for other environmental changes." } ], "responsiblepartyinfo_set": [ 115958, 115949, 115950, 115952, 115953, 115954, 115956, 115957, 115955, 115951 ], "onlineresource_set": [ 25491, 26796, 26797, 25490 ] }, { "ob_id": 26599, "uuid": "b8141fa5842b45e8863816da536def5a", "title": "Along Track Scanning Radiometers Arctic combined Surface Temperature (AAST) daily mean Level 3 (L3S) dataset, v2.1", "abstract": "The AAST L3S Combined Surface Temperature (CST) products here primarily provide data on surface temperatures in the Arctic, across all Arctic surfaces, and their associated uncertainties. This dataset consists of CSTs with uncertainty estimates produced from the ATSR-2 and AATSR instruments on ESA's ERS-2 and Enivsat satellite respectively. The CSTs in this dataset are surface temperatures across ocean, snow/ice and land surfaces in the Arctic region, where the Arctic is defined as the area at, or north of, 60°N. Please note, the data here provide daily temperature data, not daily mean data as indicated by the dataset title. For more information see the paper linked under the details tab The data were produced for ATSR Satellite Dataset project, which was funded by The UK Dept. for Business, Energy & Industrial Strategy.\r\n\r\nIce (and snow) Surface Temperatures (ISTs) and Land Surface Temperatures (LSTs) used to produce this dataset are sourced from the GlobTemperature Level 2 LST V2.1 product. Sea Surface Temperature (SSTs) are from the ATSR SST L2P V3.0 product. This version of the CST dataset is v2.1, with an earlier version previously provided via the GlobTemperature data portal. ATSR data was sourced from the GlobTemperature data portal and the CEDA archive. It consists of a complete set of CST and accompanying auxiliary (AUX) datafiles for the ATSR-2 and AATSR instruments separately. ATSR-2 data are available for 01/08/1995 - 22/06/2003 while AATSR data are available for 20/05/2002 – 08/04/2012.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-03-09T03:07:35", "updateFrequency": "notPlanned", "dataLineage": "Data were produced for ATSR Satellite Dataset project, which was funded by The UK Dept. for Business, Energy & Industrial Strategy. ATSR data was sourced from the GlobTemperature data portal and the CEDA archive.", "removedDataReason": "", "keywords": "Arctic, Surface Temperature, ATSR-2, AATSR", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2018-10-12T11:12:31", "doiPublishedTime": "2018-10-12T11:35:44", "removedDataTime": null, "geographicExtent": { "ob_id": 2286, "bboxName": "AAST", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": 60.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 26623, "dataPath": "/neodc/aast_leicester/data/CST_L3S_V2.1/daily/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 180086262350, "numberOfFiles": 11819, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 7161, "startTime": "1995-07-31T23:00:00", "endTime": "2012-04-08T22:59:59" }, "resultQuality": { "ob_id": 3169, "explanation": "Data as provided by the project team (see product user guide for further quality information)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-10-12" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 26605, "uuid": "cc12ffa2c20e4c6ba01b6e828dcd56d8", "short_code": "cmppr", "title": "Along Track Scanning Radiometers Arctic combined Surface Temperature (AAST) Retrieval Process", "abstract": "The AAST L3S Combined Surface Temperature (CST) products have been produced using an algorithm developed by the University of Leicester.\r\n\r\nThe Ice (and snow) Surface Temperatures (ISTs) and Land Surface Temperatures (LSTs) used to produce this dataset are sourced from the GlobTemperature Level 2 LST V2.1 product. Sea Surface Temperature (SSTs) are from the ATSR SST L2P V3.0 product. This version of the CST dataset is v2.1, with an earlier version previously provided via the GlobTemperature data portal. ATSR data was sourced from the GlobTemperature data portal and the CEDA archive." }, "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": [], "inspireTheme": [], "topicCategory": [], "phenomena": [ 54670, 54671, 54677, 57594, 57599, 69219, 69220, 69806, 69807, 69808, 69809, 69810, 69811, 69812, 69813, 69814, 69815, 69816, 69817, 69818, 69819, 69820, 69821 ], "vocabularyKeywords": [], "identifier_set": [ 9685 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 111793, 111796, 111797, 111799, 111802, 111800, 111798, 193354, 111794, 111801, 111795 ], "onlineresource_set": [ 25977, 27075, 94685 ] }, { "ob_id": 26600, "uuid": "39ff3d8b84b341b8af6be78aaaaa5cdb", "title": "Ground based measurements of carbon dioxide and methane taken from Tres Cruces (montane grasslands) in the Peruvian Andes (seasonal campaigns)", "abstract": "The dataset contains concentrations of carbon dioxide and methane which were collected in discrete air samples during intensive seasonal campaigns in November 2011 and August 2012 by the University of St Andrews Thermo TRACE Gas Chromatograph Ultra at Tres Cruces, a montane grassland ecosystem ground site, in the Peruvian Andes.\r\n\r\nData were collected for the NERC project: 'Are tropical uplands regional hotspots for methane and nitrous oxide?' (NERC grant awards: NE/H007849/1, NE/H006753/1 and NE/H006583/2).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2018-10-05T09:54:12.585308", "updateFrequency": "notPlanned", "dataLineage": "Data collected, processed and checked by the project team prior to delivery to the Centre for Environmental Data Analysis (CEDA) for ingestion into the CEDA archives", "removedDataReason": "", "keywords": "methane, nitrous oxide, Andes, Peru, tropical ecosystems, rainforest, greenhouse gases, carbon dioxide, soil-atmosphere gas exchange, forest, grassland", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2018-11-07T09:58:18", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2289, "bboxName": "Tres Cruces", "eastBoundLongitude": 13.12194, "westBoundLongitude": 13.12194, "southBoundLatitude": -71.615, "northBoundLatitude": -71.615 }, "verticalExtent": null, "result_field": { "ob_id": 26602, "dataPath": "/badc/ch4-n2o-hotspots/data/montane-grasslands-seasonal", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 156163, "numberOfFiles": 2, "fileFormat": "Data are BADC-CSV formatted" }, "timePeriod": { "ob_id": 7163, "startTime": "2011-11-12T15:15:37", "endTime": "2012-08-21T09:45:38" }, "resultQuality": { "ob_id": 3047, "explanation": "These data have been archived as delivered by the project participants and have no quality control has been applied by the archive.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2016-07-12" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 26604, "uuid": "2cdc64d5c1084fc4942748e1ee5cb7c4", "short_code": "acq", "title": "Ground based measurements of carbon dioxide, methane and nitrous oxide taken from Tres Cruces (montane grasslands) in the Peruvian Andes (2011-2012)", "abstract": "Ground based measurements of carbon dioxide, methane and nitrous oxide taken from Tres Cruces (montane grasslands) in the Peruvian Andes (2011-2012)" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "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": 19561, "uuid": "13897d37fa144915bd29fd0573b57217", "short_code": "proj", "title": "NERC project: Are tropical uplands regional hotspots for methane and nitrous oxide?", "abstract": "This project was funded by the Natural Environment Research Council (NERC) with the following grant references; NE/H007849/1, NE/H006753/1 and NE/H006583/2. These grants were led by Professor Pete Smith, Professor Patrick Meir and Dr Yit Arn Teh respectively.\r\n\r\nTropical ecosystems are major sources of the greenhouse gases (GHGs) methane (CH4) and nitrous oxide (N2O), which are 25 and 298 times more effective than carbon dioxide (CO2), respectively, in trapping long-wave radiation in the atmosphere. Increases in CH4 and N2O concentrations since the start of the Industrial Revolution are responsible for over one-third of global warming, and future changes in the atmospheric budgets of these GHGs have implications for the Earth's climate and environmental conditions. N2O emissions, in particular, are projected to rise in the future due to agricultural expansion and enhanced atmospheric nitrogen deposition.\r\n\r\nRecent studies of the global budgets of CH4 and N2O using satellite imagery, atmospheric measurements, and modelling suggest that significantly more CH4 and N2O are released from the tropics than previously thought due to unaccounted sources of CH4 and N2O. It is critical for us to identify and characterise these 'missing' sources if we wish to understand the current contribution of the tropics to GHG budgets. Knowledge of these 'missing' sources is also necessary for predicting how tropical GHG emissions are likely to respond to future environmental or climatic change. One strong potential candidate for these 'missing' sources of CH4 and N2O are tropical uplands. Tropical uplands have been conspicuously absent from existing atmospheric budgets, because scientific attention has largely focused on CH4 and N2O emissions from lowland forests, savannas, or wetlands. Studies from tropical uplands suggest that they are potentially large sources of CH4 and N2O, with emissions that are equal to or greater than those from lowland environments. Upland rainforests in Puerto Rico, for example, emit more CH4 than lowland forests, with emission rates that are on par with northern wetlands, the largest natural sources of CH4 worldwide. \r\n\r\nTo address these gaps in knowledge, a comprehensive study of CH4 and N2O cycling in the Peruvian Andes, using a mixture of field measurements, controlled environment studies, and mathematical modelling was undertaken. \r\n\r\nThe projects aims were to: \r\n1. 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Determine if GHG emissions from the Andes are able to account for some of the 'missing' tropical sources of CH4 and N2O by extrapolating our field observations to the regional scale using a combination of mathematical modelling, satellite imagery, and land cover databases (i.e., GIS). \r\n\r\nThis research greatly advanced our understanding of CH4 and N2O emissions for an important but understudied region, and helped to determine the relative contribution of Andean ecosystems to the CH4 and N2O budgets for South America. Knowledge of the emission rates and environmental controls on CH4 and N2O fluxes from upland Andean ecosystems also helped evaluate whether other tropical uplands are likely to be sources of CH4 and N2O, and assess their potential contributions to the global atmospheric budgets of CH4 and N2O. Lastly, the development and adaptation of mathematical models that accurately simulate tropical CH4 and N2O fluxes allowed the project to predict the likely response of tropical uplands to future environmental or climatic change.\r\n\r\nThe goal of this research was to use the Andes as a model system for understanding the rates, spatial and temporal patterns, and environmental controls on methane (CH4) and nitrous oxide (N2O) fluxes in upland tropical environments. They aimed to quantify CH4 and N2O fluxes along a 3000 m environmental gradient in the Andes, spanning lowland rainforest (220 masl) to upper montane rainforest (3000 masl). \r\n\r\nEmpirical data were used to parameterise or adapt models to extrapolate CH4 and N2O fluxes to larger spatial scales, and to predict the response of trace gas fluxes to environmental change. \r\n\r\nThe project also aimed to evaluate the importance of upland Andean ecosystems as regional sources of CH4 and N2O, exploring the extent to which CH4 and N2O emissions from these environments account for discrepancies in regional CH4 and N2O budgets. They used an integrated, multiscale approach, combining field measurements, controlled environment studies, modelling, and spatially weighted upscaling. \r\n\r\nSpecifically, objectives include: \r\n1. Determine spatial and temporal patterns of CH4 and N2O exchange from key ecosystems along an altitudinal gradient in the Andes. \r\n2. Investigate how natural variations in key driving variables (plant productivity, climate, soil moisture, soil C, soil N, and oxygen) influence rates of CH4 and N2O exchange using field observations and controlled environment studies. \r\n3. Parameterise or adapt existing models (ECOSSE, DNDC) with empirical data from a lowland to upland gradient in order to accurately simulate CH4 and N2O flux processes in tropical ecosystems. \r\n4. Use a combination of modelling and remote sensing imagery to upscale our observations to evaluate the importance of Andean uplands as regional sources of CH4 and N2O." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 52300, 52301, 55825, 55826, 55827, 55836, 58257, 58258, 58259, 58260, 58261, 58262, 58263, 58264, 58265, 58266, 58267, 58268, 58269, 58270, 58271, 58272, 58273, 58274, 58275, 58276, 58277, 58278, 58279, 58280, 58281, 58282, 58283, 58284 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 19563, "uuid": "93fdb48b713b4dbc93a28d695771312d", "short_code": "coll", "title": "NERC Project: Are tropical uplands regional hotspots for methane and nitrous oxide?: in-situ ground based atmospheric flux measurements and model output", "abstract": "'Are tropical uplands regional hotspots for methane and nitrous oxide?' was a NERC (Natural Environment Research Council) funded project from 2010-2015 with the following grant references NE/H007849/1, NE/H006753/1 and NE/H006583/2.\r\n\r\nThis dataset collection contains in-situ ground based soil-atmosphere flux and soil condition measurements from 4 different ecosystems located in the Peruvian Andes over ~2.5 years between 2010-2013. 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(NERC grant awards: NE/H007849/1, NE/H006753/1 and NE/H006583/2).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2018-10-05T09:59:14", "updateFrequency": "notPlanned", "dataLineage": "Data collected, processed and checked by the project team prior to delivery to the Centre for Environmental Data Analysis (CEDA) for ingestion into the CEDA archives", "removedDataReason": "", "keywords": "methane, nitrous oxide, Andes, Peru, tropical ecosystems, rainforest, greenhouse gases, carbon dioxide, soil-atmosphere gas exchange, forest, grassland", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2019-02-13T09:59:54", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2289, "bboxName": "Tres Cruces", "eastBoundLongitude": 13.12194, "westBoundLongitude": 13.12194, "southBoundLatitude": -71.615, "northBoundLatitude": -71.615 }, "verticalExtent": null, "result_field": { "ob_id": 26609, "dataPath": "/badc/ch4-n2o-hotspots/data/montane-grasslands", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 64582, "numberOfFiles": 2, "fileFormat": "Data are BADC-CSV formatted" }, "timePeriod": { "ob_id": 7164, "startTime": "2011-01-05T00:00:00", "endTime": "2013-07-04T22:59:59" }, "resultQuality": { "ob_id": 3047, "explanation": "These data have been archived as delivered by the project participants and have no quality control has been applied by the archive.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2016-07-12" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 26604, "uuid": "2cdc64d5c1084fc4942748e1ee5cb7c4", "short_code": "acq", "title": "Ground based measurements of carbon dioxide, methane and nitrous oxide taken from Tres Cruces (montane grasslands) in the Peruvian Andes (2011-2012)", "abstract": "Ground based measurements of carbon dioxide, methane and nitrous oxide taken from Tres Cruces (montane grasslands) in the Peruvian Andes (2011-2012)" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "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": 19561, "uuid": "13897d37fa144915bd29fd0573b57217", "short_code": "proj", "title": "NERC project: Are tropical uplands regional hotspots for methane and nitrous oxide?", "abstract": "This project was funded by the Natural Environment Research Council (NERC) with the following grant references; NE/H007849/1, NE/H006753/1 and NE/H006583/2. These grants were led by Professor Pete Smith, Professor Patrick Meir and Dr Yit Arn Teh respectively.\r\n\r\nTropical ecosystems are major sources of the greenhouse gases (GHGs) methane (CH4) and nitrous oxide (N2O), which are 25 and 298 times more effective than carbon dioxide (CO2), respectively, in trapping long-wave radiation in the atmosphere. Increases in CH4 and N2O concentrations since the start of the Industrial Revolution are responsible for over one-third of global warming, and future changes in the atmospheric budgets of these GHGs have implications for the Earth's climate and environmental conditions. 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They aimed to quantify CH4 and N2O fluxes along a 3000 m environmental gradient in the Andes, spanning lowland rainforest (220 masl) to upper montane rainforest (3000 masl). \r\n\r\nEmpirical data were used to parameterise or adapt models to extrapolate CH4 and N2O fluxes to larger spatial scales, and to predict the response of trace gas fluxes to environmental change. \r\n\r\nThe project also aimed to evaluate the importance of upland Andean ecosystems as regional sources of CH4 and N2O, exploring the extent to which CH4 and N2O emissions from these environments account for discrepancies in regional CH4 and N2O budgets. They used an integrated, multiscale approach, combining field measurements, controlled environment studies, modelling, and spatially weighted upscaling. \r\n\r\nSpecifically, objectives include: \r\n1. Determine spatial and temporal patterns of CH4 and N2O exchange from key ecosystems along an altitudinal gradient in the Andes. \r\n2. 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Use a combination of modelling and remote sensing imagery to upscale our observations to evaluate the importance of Andean uplands as regional sources of CH4 and N2O." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 18434, 18435, 18436, 18437, 18438, 18439, 18440, 18441, 18442, 18443, 18444, 18445, 18446, 18447, 18448, 18449, 18450, 18451, 18452 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 19563, "uuid": "93fdb48b713b4dbc93a28d695771312d", "short_code": "coll", "title": "NERC Project: Are tropical uplands regional hotspots for methane and nitrous oxide?: in-situ ground based atmospheric flux measurements and model output", "abstract": "'Are tropical uplands regional hotspots for methane and nitrous oxide?' was a NERC (Natural Environment Research Council) funded project from 2010-2015 with the following grant references NE/H007849/1, NE/H006753/1 and NE/H006583/2.\r\n\r\nThis dataset collection contains in-situ ground based soil-atmosphere flux and soil condition measurements from 4 different ecosystems located in the Peruvian Andes over ~2.5 years between 2010-2013. 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This data was collected as part of the Climate Communication Project which was a collaboration between academics and practitioners working on public engagement with climate change. NERC Grant NE/R011974/1.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2018-10-05T12:25:46.466109", "updateFrequency": "notPlanned", "dataLineage": "This data has been fully anonymised by the author and delivered to the Centre for Environmental Data Anaylsis for archiving.", "removedDataReason": "", "keywords": "Climate change, questionnaire, attitudes", "publicationState": "citable", "nonGeographicFlag": true, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2018-10-08T13:49:51", "doiPublishedTime": "2018-10-09T08:00:40", "removedDataTime": null, "geographicExtent": null, "verticalExtent": null, "result_field": { "ob_id": 26612, "dataPath": "/badc/deposited2018/science-communication/data", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 68155, "numberOfFiles": 3, "fileFormat": "Data are csv formatted" }, "timePeriod": null, "resultQuality": { "ob_id": 3167, "explanation": "Data is 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": "2018-10-05" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "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": 26610, "uuid": "363d3b6833084bca91621e62e19b2ea9", "short_code": "proj", "title": "Climate Communication Project", "abstract": "The Climate Communication Project aimed to provide the 'infrastructure' to ensure that efforts to engage the public on climate change are coordinated, effective and based on the most up-to-date evidence from academic studies and tried-and-tested practitioner expertise. The project team included internationally recognised specialists on public engagement, high profile science communicators and a blend of climate science, social science and communications expertise. Using our networks and experience, we ensured that the Climate Consortium was a inclusive one year programme, producing evidence-based but accessible resources to catalyse public engagement with climate change at a national scale.\r\n \r\nNERC grant: NE/R011974/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 9682 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 111850, 111851, 111852, 111854, 111855, 111856, 111857, 111853 ], "onlineresource_set": [ 25499, 87742 ] }, { "ob_id": 26615, "uuid": "b36ed14d0a204f4fa6b1d884a516b26e", "title": "Methane Observations and Yearly Assessments (MOYA): Methane measurements by the British Antarctic Survey from the Halley Research Station in Antarctica", "abstract": "This dataset contains methane concentration measurements from the British Antarctic Survey's Halley Research Station in Antarctica. The Picarro G2301 analyser was used for the measurement of Methane and located at the Clean Air Sector Laboratory (CASLab). Data times were averaged from the 1 minute data to provide hourly data sets. 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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": [ 58105, 58106, 58107, 58108, 58109, 58110 ], "vocabularyKeywords": [], "identifier_set": [ 9693 ], "observationcollection_set": [ { "ob_id": 24762, "uuid": "d309a5ab60b04b6c82eca6d006350ae6", "short_code": "coll", "title": "MOYA: ground station and in-situ airborne observations by the FAAM BAE-146 aircraft", "abstract": "This dataset collection contains ground observations and in-situ airborne observations by the FAAM BAE-146 aircraft for Methane Observations and Yearly Assessments: MOYA.\r\n\r\nThis data was collected as part of the Methane Observations and Yearly Assessments (MOYA) project funded by the Natural Environment Research Council (NERC) (NE/N016211/1)." } ], "responsiblepartyinfo_set": [ 111862, 111863, 111864, 111866, 111867, 111868, 111869, 111865, 111870, 168375 ], "onlineresource_set": [] }, { "ob_id": 26620, "uuid": "84e30a69df5a4cd2b34eddb647c5c3d9", "title": "Southern OceaN optimal Approach To Assess the carbon state, variability and climatic drivers (SONATA): Carbon Dioxide measurements by the British Antarctic Survey from the Halley Research Station in Antarctica", "abstract": "This dataset contains carbon dioxide concentration measurements from the British Antarctic Survey's Halley Research Station in Antarctica. 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It is believed also to be among the largest 'sink' for atmospheric CO2, accounting for about one third of the uptake of CO2 by the global ocean and nearly one tenth of the global emissions of CO2 on average each year. Thus the evolution of the SO carbon sink has the potential to alter the rate and extent of climate change.\r\n\r\nIn spite of its importance, we don't know the state, variability, or climatic drivers of the contemporary SO carbon sink and there is much controversy over its recent evolution. The climate of the SO has been changing over recent decades: in particular, winds have intensified, (attributed in part to the depletion of stratospheric ozone and in part to increasing temperature gradients arising from climate change), ocean acidification is occurring, and there is a long term decline in krill stocks. These effects take place on top of large natural variability and poorly quantified climatic trends.\r\n\r\nSONATA will achieve a step change in our understanding of the contemporary SO carbon sink by delivering new data and new insights, integrating observations from the ocean, from the atmosphere, and model results. We will develop three complementary streams of research, an 'Oceanic', an 'Atmospheric', and a 'Processes and drivers' view, and will bring them together using advanced mathematical frameworks to provide a single assessment with multiple constraints and reduction of uncertainties.\r\n\r\nThe Oceanic view will use existing and new observations of ocean carbon. We will undertake a new calibration experiment to better assess the large number of pH measurements now being made by about 200 sophisticated profiling floats introduced by the US SOCCOM programme. These have the potential to greatly increase the number of observations that can be used to calculate air-sea CO2 fluxes, but only if adequately calibrated. In addition we will develop and use a new technique to construct estimates of the seasonal and temporal evolution of the air-sea flux, using a model of the upper water column constrained with available hydrographic and carbon-system observations. \r\n\r\nThe Atmospheric view will collect new atmospheric CO2 data in remote SO locations comprising Halley Station (75S), the Falkland Islands (51S), and aboard the BAS research ship James Clark Ross; new atmospheric O2 data will come from a ship track that repeats a SO transect every 8 weeks, as well as from Halley Station in coastal Antarctica. Using these data and an inverse framework approach, SONATA will provide an independent assessment of the SO carbon sink, which will deliver particularly on the geographic distribution of the changes, with O2 data helping to inform the drivers.\r\n\r\nThe Processes and drivers view will use two climate-scale carbon models and a series of hindcast simulations to identify the relative contributions of (a) atmospheric CO2 concentration, (b) natural climate variability, (c) climate change, and (d) stratospheric ozone depletion to recent SO carbon trends and variability. Ocean and atmosphere observations, including new data from SONATA and SOCCOM, will be used to optimise the model and validate the results. Idealised forcing with climate models will provide the 'fingerprints' of climatic drivers that are needed to understand the observed patterns of change.\r\n\r\nFinally the three streams of research will be integrated using a Bayesian fusion mathematical approach that considers the strengths and weaknesses of each stream of information and minimises the joint uncertainty. The SO ocean carbon sink will be assessed annually in this way. We will then test the added value of including new streams of observations in the future, including from floats, gliders, drifters, Autonomous Surface Vehicles, additional ground-based observations and satellite CO2 data.\r\n\r\nGrant Ref: NE/P021417/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 58106, 58107, 58108, 58109, 58110, 58386 ], "vocabularyKeywords": [], "identifier_set": [ 9694 ], "observationcollection_set": [ { "ob_id": 27293, "uuid": "ab029e4d61e94aaba2ecbf40779ad42d", "short_code": "coll", "title": "Southern OceaN optimal Approach To Assess the carbon state, variability and climatic drivers (SONATA): Atmospheric carbon dioxide, oxygen and atmospheric potential oxygen", "abstract": "This dataset contains atmospheric carbon dioxide, oxygen and atmospheric potential oxygen data from the Southern OceaN optimal Approach To Assess the carbon state, variability and climatic drivers (SONATA) was funded by the Natural Environment Research Council (NERC, grant: NE/P021360/1)." } ], "responsiblepartyinfo_set": [ 111876, 111877, 111878, 111879, 111880, 111882, 111883, 111881, 168849, 111884 ], "onlineresource_set": [] }, { "ob_id": 26628, "uuid": "656b9c8effd0470080a8797e90c5ac44", "title": "CMORPH 0.25 degree 3-hourly precipitation estimates", "abstract": "This dataset contains 0.25 degree resolution 3-hourly global Climate Prediction Center morphing method (CMORPH) precipitation data. CMORPH is the CPC Morphing technique which derives precipitation estimates from low orbiter satellite microwave observations.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2019-11-11T14:47:18.018149", "updateFrequency": "", "dataLineage": "Data taken as is from National Oceanic and Atmospheric Administration. https://ftp.cpc.ncep.noaa.gov/precip/CMORPH_V1.0/CRT/0.25deg-3HLY/", "removedDataReason": "", "keywords": "CMORPH, rainfall, precipitation", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2018-10-12T15:56:54", "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": 26629, "dataPath": "/badc/cmorph/data/0.25deg-3HLY", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 13893690603, "numberOfFiles": 8032, "fileFormat": "Data are binary formatted" }, "timePeriod": { "ob_id": 7154, "startTime": "1998-01-01T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 3160, "explanation": "Data is 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": "2018-08-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 26528, "uuid": "accf62d297264f2e94d6299be7ba7333", "short_code": "comp", "title": "Climate Prediction Center morphing method (CMORPH)", "abstract": "CMORPH (CPC MORPHing technique) produces global precipitation analyses at very high spatial and temporal resolution. 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This technique uses precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively, and whose features are transported via spatial propagation information that is obtained entirely from geostationary satellite IR data. At present we incorporate precipitation estimates derived from the passive microwaves aboard the DMSP 13, 14 & 15 (SSM/I), the NOAA-15, 16, 17 & 18 (AMSU-B), and AMSR-E and TMI aboard NASA's Aqua and TRMM spacecraft, respectively. These estimates are generated by algorithms of Ferraro (1997) for SSM/I, Ferraro et al. (2000) for AMSU-B and Kummerow et al. (2001) for TMI. Note that this technique is not a precipitation estimation algorithm but a means by which estimates from existing microwave rainfall algorithms can be combined. 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The state of the Greenland Ice Sheet is of global importance, and has consequently been included in the ESA CCI Programme as a monitored Essential Climate Variable (ECV).\r\n\r\nThe project is producing data products of the following five parameters, which are important in characterizing the Greenland Ice Sheet as an Essential Climate Variable: Surface Elevation Change (SEC) gridded data from radar altimetry; Ice Velocity (IV) gridded data from synthetic aperture radar interferometry and feature tracking; Calving Front Location (CFL) time series of marine-terminating glaciers; Grounding Line Location (GLL) time series of marine-terminating glaciers; Gravimetry Mass Balance (GMB) maps and time series." } ], "responsiblepartyinfo_set": [ 111917, 111911, 111910, 111912, 111913, 111914, 111916, 111915, 112739 ], "onlineresource_set": [ 25506, 25507, 25508, 25504, 25505, 25823 ] }, { "ob_id": 26632, "uuid": "ef5c6596cae548c6aea9dea181c7624c", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Upernavik Glacier for 2014-2017 from Sentinel-1 data, v1.1", "abstract": "This dataset contains a time series of ice velocities for the Upernavik Glacier in Greenland, derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between October 2014 and March 2017. 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The data set provides surface elevation changes (SEC) for the Greenland Ice sheet derived from Cryosat 2 satellite radar altimetry, for the time period between 2010 and 2017.\r\n \r\nThe surface elevation change data are provided as 2-year means (2011-2012, 2012-2013, 2013-2014, 2014-2015, 2015-2016, and 2016-2017), and five-year means are also provided (2011-2015, 2012-2016, 2013-2017), along with their associated errors. Data are provided in both NetCDF and gridded ASCII format, as well as png plots.\r\n\r\nThe algorithm used to devive the product is described in the paper “Implications of changing scattering properties on the Greenland ice sheet volume change from Cryosat-2 altimetry” by S.B. Simonsen and L.S. Sørensen, Remote Sensing of the Environment, 190,pp.207-216, doi:10.1016/j.rse.2016.12.012", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2018-11-20T12:12:46.183516", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI Greenland Ice Sheet project team and supplied to CEDA in the context of the ESA CCI Open Data Portal Project.", "removedDataReason": "", "keywords": "Greenland Ice Sheet, ESA, CCI", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2018-11-22T11:59:39", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 895, "bboxName": "", "eastBoundLongitude": -10.0, "westBoundLongitude": -80.0, "southBoundLatitude": 60.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 26726, "dataPath": "/neodc/esacci/ice_sheets_greenland/data/greenland_surface_elevation_change/cryosat2/v2.2/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 93901837, "numberOfFiles": 42, "fileFormat": "Contains three different types of data files:\r\n1) png plots of the surface elevation changes.\r\n2) gridded ASCII files of the surface elevation changes and their associated errors\r\n3) NetCDF files containing the surface elevation changes and their associated errors" }, "timePeriod": { "ob_id": 7209, "startTime": "2011-01-01T00:00:00", "endTime": "2017-12-31T23:59:59" }, "resultQuality": { "ob_id": 3187, "explanation": "As provided by the CCI Greenland Ice Sheets team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-11-20" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 26728, "uuid": "b38bf6b4aa1549efb16edbc39b8f48c9", "short_code": "cmppr", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Retrieval of Greenland Surface Elevation Change from Cryosat-2", "abstract": "Surface elevation changes (SEC) for the Greenland Ice sheet have been derived from Cryosat 2 satellite radar altimetry, based on ESA's Baseline C product\r\n \r\nThe surface elevation change data are provided as 2-year means, and five-year means are also provided, along with their associated errors. Data are provided in both NetCDF and gridded ASCII format, as well as png plots.\r\n\r\nThe algorithm used to devive the product is described in the paper “Implications of changing scattering properties on the Greenland ice sheet volume change from Cryosat-2 altimetry” by S.B. Simonsen and L.S. Sørensen, Remote Sensing of the Environment, 190,pp.207-216, doi:10.1016/j.rse.2016.12.012" }, "imageDetails": [ 147 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" }, { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2553, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 25, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_icesheets_greenland_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 14317, "uuid": "362f66a7e09a4a59be2a40af6b41d0a6", "short_code": "proj", "title": "ESA Greenland Ice Sheet Climate Change Initiative Project", "abstract": "The Greenland Ice Sheet CCI project aims to maximize the impact of ESA satellite data on climate research, by analysing data from ESA Earth Observation missions such as ERS, Envisat, CryoSat, GRACE and the new Sentinel series of satellites. Over the last decade, the Greenland Ice Sheet has shown rapid change, characterized by rapid thinning along the margins, accelerating outlet glaciers, and overall increasing mass loss. The state of the Greenland Ice Sheet is of global importance, and has consequently been included in the ESA CCI Programme as a monitored Essential Climate Variable (ECV).\r\n\r\nThe project is producing data products of the following five parameters, which are important in characterizing the Greenland Ice Sheet as an Essential Climate Variable: Surface Elevation Change (SEC) gridded data from radar altimetry; Ice Velocity (IV) gridded data from synthetic aperture radar interferometry and feature tracking; Calving Front Location (CFL) time series of marine-terminating glaciers; Grounding Line Location (GLL) time series of marine-terminating glaciers; Gravimetry Mass Balance (GMB) maps and time series." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 12752, 18453, 18454, 18455, 18456, 18457, 18459, 18544, 18549, 18550, 18551, 18552 ], "vocabularyKeywords": [ { "ob_id": 10187, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/ecv/cciecv_iceSheet", "resolvedTerm": "ice sheets" }, { "ob_id": 10666, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/ecv/cciecv_iceSheet", "resolvedTerm": "ice sheets" }, { "ob_id": 11055, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_siral", "resolvedTerm": "SIRAL" }, { "ob_id": 10787, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_cryosat2", "resolvedTerm": "CryoSat-2" } ], "identifier_set": [], "observationcollection_set": [ { "ob_id": 14316, "uuid": "394464f9c39445d3b6445d8e305841d7", "short_code": "coll", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland Ice Sheet CCI) Dataset Collection", "abstract": "The Greenland Ice Sheet CCI project aims to maximize the impact of ESA satellite data on climate research, by analysing data from ESA Earth Observation missions such as ERS, Envisat, CryoSat, GRACE and the new Sentinel series of satellites. 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This version of the dataset (v1.3) has been extended with grounding lines for 2017. Data was produced as part of the ESA Greenland Ice Sheets Climate Change Initiative (CCI) project by ENVEO, Austria. \r\n\r\nThe grounding line is the separation point between the floating and grounded parts of the glacier. Processes at the grounding lines of floating marine termini of glaciers and ice streams are important for understanding the response of the ice masses to changing boundary conditions and for establishing realistic scenarios for the response to climate change. The grounding line location product is derived from InSAR data by mapping the tidal flexure and is generated for a selection of the few glaciers in Greenland, which have a floating tongue. In general, the true location of the grounding line is unknown, and therefore validation is difficult for this product.\r\n\r\nRemote sensing observations do not provide direct measurement on the transition from floating to grounding ice (the grounding line). The satellite data deliver observations on ice surface features (e.g. tidal deformation by InSAR, spatial changes in texture and shading in optical images) that are indirect indicators for estimating the position of the grounding line. Due to the plasticity of ice these indicators spread out over a zone upstream and downstream of the grounding line, the tidal flexure zone (also called grounding zone).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2018-11-21T14:28:09", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI Greenland Ice Sheet project team and supplied to CEDA in the context of the ESA CCI Open Data Portal Project.", "removedDataReason": "", "keywords": "Greenland, Ice Sheet, CCI, ESA", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2018-11-28T16:54:15", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1650, "bboxName": "Greenland", "eastBoundLongitude": -10.0, "westBoundLongitude": -80.0, "southBoundLatitude": 60.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 26763, "dataPath": "/neodc/esacci/ice_sheets_greenland/data/greenland_grounding_line_locations/v1.3/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 283357, "numberOfFiles": 74, "fileFormat": "Shape files" }, "timePeriod": { "ob_id": 7216, "startTime": "1995-08-27T23:00:00", "endTime": "2017-03-16T23:59:59" }, "resultQuality": { "ob_id": 3190, "explanation": "As provided by the CCI Greenland Ice Sheets team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-11-21" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" }, { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2553, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 25, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_icesheets_greenland_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 14317, "uuid": "362f66a7e09a4a59be2a40af6b41d0a6", "short_code": "proj", "title": "ESA Greenland Ice Sheet Climate Change Initiative Project", "abstract": "The Greenland Ice Sheet CCI project aims to maximize the impact of ESA satellite data on climate research, by analysing data from ESA Earth Observation missions such as ERS, Envisat, CryoSat, GRACE and the new Sentinel series of satellites. Over the last decade, the Greenland Ice Sheet has shown rapid change, characterized by rapid thinning along the margins, accelerating outlet glaciers, and overall increasing mass loss. The state of the Greenland Ice Sheet is of global importance, and has consequently been included in the ESA CCI Programme as a monitored Essential Climate Variable (ECV).\r\n\r\nThe project is producing data products of the following five parameters, which are important in characterizing the Greenland Ice Sheet as an Essential Climate Variable: Surface Elevation Change (SEC) gridded data from radar altimetry; Ice Velocity (IV) gridded data from synthetic aperture radar interferometry and feature tracking; Calving Front Location (CFL) time series of marine-terminating glaciers; Grounding Line Location (GLL) time series of marine-terminating glaciers; Gravimetry Mass Balance (GMB) maps and time series." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [ { "ob_id": 10187, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/ecv/cciecv_iceSheet", "resolvedTerm": "ice sheets" }, { "ob_id": 10666, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/ecv/cciecv_iceSheet", "resolvedTerm": "ice sheets" }, { "ob_id": 11047, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_amisar", "resolvedTerm": "AMI-SAR" }, { "ob_id": 11139, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_sarcS1", "resolvedTerm": "SAR-C (Sentinel-1)" }, { "ob_id": 10809, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_ers1", "resolvedTerm": "ERS-1" }, { "ob_id": 10810, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_ers2", "resolvedTerm": "ERS-2" }, { "ob_id": 10907, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_sentinel1a", "resolvedTerm": "Sentinel-1A" }, { "ob_id": 10908, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_sentinel1b", "resolvedTerm": "Sentinel-1B" } ], "identifier_set": [], "observationcollection_set": [ { "ob_id": 14316, "uuid": "394464f9c39445d3b6445d8e305841d7", "short_code": "coll", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland Ice Sheet CCI) Dataset Collection", "abstract": "The Greenland Ice Sheet CCI project aims to maximize the impact of ESA satellite data on climate research, by analysing data from ESA Earth Observation missions such as ERS, Envisat, CryoSat, GRACE and the new Sentinel series of satellites. 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