Observation List
Get a list of Observation objects.
GET /api/v3/observations/?format=api&offset=7200
{ "count": 10256, "next": "https://api.catalogue.ceda.ac.uk/api/v3/observations/?format=api&limit=100&offset=7300", "previous": "https://api.catalogue.ceda.ac.uk/api/v3/observations/?format=api&limit=100&offset=7100", "results": [ { "ob_id": 32874, "uuid": "2787230b963942009e452255a3880609", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.5 (v20210809)", "abstract": "Data for Figure SPM.5 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure SPM.5 shows changes in annual mean surface temperatures, precipitation, and total column soil moisture.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n\r\nIPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels with 11 maps. All data is provided, except for panel a1.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n\r\n- Annual mean temperature change (°C) (relative to 1850-1900)\r\n- Annual mean precipitation change (%) (relative to 1850-1900)\r\n- Annual mean soil moisture change (standard deviation of interannual variability) (relative to 1850-1900)\r\n\r\n \r\nThe data is given for global warming levels (GWLs), namely +1.0°C (temperature only), +1.5°C, 2.0°C, and +4.0°C.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nPanel a:\r\n- Data file: Panel_a2_Simulated_temperature_change_at_1C.nc, simulated annual mean temperature change (°C) at 1°C global warming relative to 1850-1900 (right).\r\n\r\nPanel b:\r\n- Data file: Panel_b1_Simulated_temperature_change_at_1_5C.nc, simulated annual mean temperature change (°C) at 1.5°C global warming relative to 1850-1900 (left).\r\n- Data file: Panel_b2_Simulated_temperature_change_at_2C.nc, simulated annual mean temperature change (°C) at 2.0°C global warming relative to 1850-1900 (center).\r\n- Data file: Panel_b3_Simulated_temperature_change_at_4C.nc, simulated annual mean temperature change (°C) at 4.0°C global warming relative to 1850-1900 (right).\r\n\r\nPanel c:\r\n- Data file: Panel_c1_Simulated_precipitation_change_at_1_5C.nc, simulated annual mean precipitation change (%) at 1.5°C global warming relative to 1850-1900 (left).\r\n- Data file: Panel_c2_Simulated_precipitation_change_at_2C.nc, simulated annual mean precipitation change (%) at 2.0°C global warming relative to 1850-1900 (center).\r\n- Data file: Panel_c3_Simulated_precipitation_change_at_4C.nc, simulated annual mean precipitation change (%) at 4.0°C global warming relative to 1850-1900 (right).\r\n\r\nPanel d:\r\n- Data file: Figure_SPM5_d1_cmip6_SM_tot_change_at_1_5C.nc, simulated annual mean total column soil moisture change (standard deviation) at 1.5°C global warming relative to 1850-1900 (left).\r\n- Data file: Figure_SPM5_d2_cmip6_SM_tot_change_at_2C.nc, simulated annual mean total column soil moisture change (standard deviation) at 2.0°C global warming relative to 1850-1900 (center).\r\n- Data file: Figure_SPM5_d3_cmip6_SM_tot_change_at_4C.nc, simulated annual mean total column soil moisture change (standard deviation) at 4.0°C global warming relative to 1850-1900 (right).\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblink is provided in the Related Documents section of this catalogue record:\r\n\r\n- Link to the report webpage, which includes the component containing the figure (Summary for Policymakers), the Technical Summary (Figures TS.3 and TS.5) and the Supplementary Material for Chapters 1, 4 and 11, which contains details on the input data used in Tables 1.SM.1 (Figure 1.14), 4.SM.1 (Figures 4.31 and 4.32) and 11.SM.9 (Figure 11.19).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:24:24", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, SPM, Sixth Assessment Report, Working Group I, Physical Science Basis, Summary for Policymakers, Figure SPM.5, global warming level, temperature, precipitation, soil moisture, projection, CMIP6", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "2.5 Degrees", "status": "completed", "dataPublishedTime": "2021-08-09T08:04:40", "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": { "ob_id": 29, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "Kilometers" }, "result_field": { "ob_id": 32957, "dataPath": "/badc/ar6_wg1/data/spm/spm_05/v20210809", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 35754261, "numberOfFiles": 13, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 9027, "startTime": "1850-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": { "ob_id": 3709, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-07-16" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 32876, "uuid": "54e09534daab4b988933a7bbef02c204", "short_code": "comp", "title": "Caption for Figure SPM.5 from the Summary for Policymakers of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Panel a) Comparison of observed and simulated annual mean surface temperature change. The left map shows the observed changes in annual mean surface temperature in the period of 1850–2020 per °C of global warming (°C). The local (i.e., grid point) observed annual mean surface temperature changes are linearly regressed against the global surface temperature in the period 1850–2020. Observed temperature data are from Berkeley Earth, the dataset with the largest coverage and highest horizontal resolution. Linear regression is applied to all years for which data at the corresponding grid point is available. The regression method was used to take into account the complete observational time series and thereby reduce the role of internal variability at the grid point level. White indicates areas where time coverage was 100 years or less and thereby too short to calculate a reliable linear regression. The right map is based on model simulations and shows change in annual multi-model mean simulated temperatures at a global warming level of 1°C (20-year mean global surface temperature change relative to 1850–1900). The triangles at each end of the color bar indicate out-of-bound values, that is, values above or below the given limits. \r\n \r\nPanel b) Simulated annual mean temperature change (°C), panel c) precipitation change (%), and panel d) total column soil moisture change (standard deviation of interannual variability) at global warming levels of 1.5°C, 2°C and 4°C (20-yr mean global surface temperature change relative to 1850–1900). Simulated changes correspond to CMIP6 multi-model mean change (median change for soil moisture) at the corresponding global warming level, i.e. the same method as for the right map in panel a). In panel c), high positive percentage changes in dry regions may correspond to small absolute changes. In panel d), the unit is the standard deviation of interannual variability in soil moisture during 1850–1900. Standard deviation is a widely used metric in characterizing drought severity. A projected reduction in mean soil moisture by one standard deviation corresponds to soil moisture conditions typical of droughts that occurred about once every six years during 1850–1900. In panel d), large changes in dry regions with little interannual variability in the baseline conditions can correspond to small absolute change. The triangles at each end of the color bars indicate out-of-bound values, that is, values above or below the given limits. Results from all models reaching the corresponding warming level in any of the five illustrative scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) are averaged. Maps of annual mean temperature and precipitation changes at a global warming level of 3°C are available in Figure 4.31 and Figure 4.32 in Section 4.6.\r\nCorresponding maps of panels b), c) and d) including hatching to indicate the level of model agreement at grid-cell level are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in CC-box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability leading to an increase in robustness.\r\n\r\n{TS.1.3.2, Figure TS.3, Figure TS.5, Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1}" }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6019, 6020, 30956, 46705, 46706, 46707, 46708, 46709, 46710, 46711 ], "vocabularyKeywords": [], "identifier_set": [ 10913 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 146803, 146804, 146805, 146806, 146807, 146808, 146809, 146802, 146810, 168305 ], "onlineresource_set": [ 43306, 47461, 82815, 92699, 94672 ] }, { "ob_id": 32886, "uuid": "18f83caf9bdf4cb4803484d8dce19eef", "title": "Extreme precipitation return level changes at 1, 3, 6, 12, 24 hours for 2050 and 2070, derived from UKCP Local Projections on a 5km grid for the FUTURE-DRAINAGE Project", "abstract": "Extreme short-duration precipitation changes, derived from the UKCP Local projections at 5km resolution (Kendon et al 2021) have been estimated using a spatial statistical model as part of the NERC-funded Future-Drainage project. Future changes (\"\"uplifts\"\") are estimated for 2050 and 2070 for RCP8.5 compared to the baseline of 1990 for precipitation durations of 1-, 3-, 6-, 12-, 24-hours. 2070 is the central year for 2060-2080 (\"\"UKCP Local TS3\"\") time-slice, and 2050 value is an interpolation between TS3 and 2020-2040 (\"\"UKCP Local TS2\"\") time-slice. 2050 is an important date for the UK water industry in its delivery of Drainage and Wastewater Management Plans (DWMPs; Water UK, 2019). Return level changes are provided for 2, 30, and 100-year return periods. The data is on the OSGB 1936/EPSG:27700 projection at 5km resolution. The underlying statistical model is described in Youngman (2018, 2020) and is applied individually to each of the twelve UKCP Local ensemble members. Future changes plus their uncertainties from each ensemble member are then combined following the method described in Fosser et al (2020).\r\n\r\nTwo estimates of future changes are provided from this \"\"super-ensemble\"\" by estimating percentiles from the distribution obtained using the Fosser et al (2020) method - a central (50%) and high (95%) estimate. Values are rounded to nearest 5%. The future changes are available for each 5km grid point within the borders of the United Kingdom, provided as ERSI shapefiles and a CSV (comma-separated values) file, with separate files for different durations.\"", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2021-07-20T09:20:12", "updateFrequency": "notPlanned", "dataLineage": "Data is derived from updated OSGB-grid UKCP Local data (released July 2021; Kendon et al 2021; catalogue url: https://catalogue.ceda.ac.uk/uuid/e304987739e04cdc960598fa5e4439d0)", "removedDataReason": "", "keywords": "", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-07-20T09:09:59", "doiPublishedTime": "2022-10-31T14:28:29", "removedDataTime": null, "geographicExtent": { "ob_id": 2814, "bboxName": "", "eastBoundLongitude": 49.3144, "westBoundLongitude": -10.2262, "southBoundLatitude": 3.4038, "northBoundLatitude": 60.4165 }, "verticalExtent": null, "result_field": { "ob_id": 32887, "dataPath": "/badc/ukcp18/data/land-cpm/derived/future-extremes", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 80307963, "numberOfFiles": 151, "fileFormat": "Data are provided as Shapefiles and CSV" }, "timePeriod": { "ob_id": 9032, "startTime": "2040-01-01T00:00:00", "endTime": "2080-12-31T00:00:00" }, "resultQuality": { "ob_id": 3713, "explanation": "Derived directly from 5km UKCP Local data without any bias corrections.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-07-20" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 32889, "uuid": "c517236b3f3c41ef80522e20fc64bcd5", "short_code": "comp", "title": "Computation for extreme precipitation return level changes derived from UKCP Local Projections on a 5km grid for the FUTURE-DRAINAGE Project", "abstract": "A spatial statistical model was used to estimate extreme short-duration precipitation changes, derived from the UKCP Local projections at 5km resolution (Kendon et al 2021)." }, "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": [ { "ob_id": 32888, "uuid": "5f5f16cf7373468f802fa763c9a9c832", "short_code": "proj", "title": "FUTURE-DRAINAGE: Ensemble climate change rainfall estimates for sustainable drainage", "abstract": "The FUTURE-DRAINAGE project aims to provide revised rainfall uplifts for urban drainage modelling using the latest UKCP high resolution climate projections and develop new guidance for urban drainage design and modelling surface water flooding in urban areas. The project is funded by NERC. NE/S016678/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12282 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 146849, 146850, 146851, 146852, 146853, 146854, 146859, 146860, 146861, 146862, 146863 ], "onlineresource_set": [ 43066, 43069, 43070, 43071 ] }, { "ob_id": 32896, "uuid": "48e1c8759a034a7298ada6ab18ef5134", "title": "HYREX project: Data from Purpose-built Dense Raingauge Network", "abstract": "Data from the purpose-built dense raingauge network was established in Somerset as part of the HYREX project. HYREX (Hydrological Radar Experiment) was a NERC (Natural Environment Research Council) special topic running from May 1993 to April 1997. Field experiments with an emphasis on radar, plus related interpretation and modelling, were carried out to investigate the short term forecasting and hydrological implications of precipitation.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2002-12-20T10:19:18", "updateFrequency": "notPlanned", "dataLineage": "Latest HYREX data acquired in 1998 from CEH (on tape). Data was uploaded as is into the BADC archive as is from tapes.", "removedDataReason": "", "keywords": "HYREX, raingauge", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-07-26T11:36:13", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2815, "bboxName": "", "eastBoundLongitude": -2.35, "westBoundLongitude": -3.12, "southBoundLatitude": 50.88, "northBoundLatitude": 51.37 }, "verticalExtent": null, "result_field": { "ob_id": 32897, "dataPath": "/badc/hyrex/data/Common/drn", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 13601146, "numberOfFiles": 135, "fileFormat": "Text" }, "timePeriod": { "ob_id": 1284, "startTime": "1994-01-01T00:00:00", "endTime": "2000-04-29T23:00:00" }, "resultQuality": { "ob_id": 1143, "explanation": "unknown", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-01-26" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 32898, "uuid": "c2a39ba8284f461583d0c5ea5e592b07", "short_code": "acq", "title": "Dense Rainguage Network Purpose-built for HYREX project in Somerset.", "abstract": "Dense Rainguage Network Purpose-built for HYREX project in Somerset. deployed 1993-2000. \r\n\r\nStations Info:\r\n\r\nRAIN GAUGE DOWN LOAD DATE BY \r\nAWT NRA NAME LOCATION LOGGER GAUGE X Y \t HEIGHT (M)\r\n01 280 MEND MENDIP FARM 9999 99999 999999 999999 40\r\n02 281 KILK KILKENNY FIELDS 2361 22502 360990 135070 35\r\n03 282 ALFO ALFORD FIELDS 2360 22509 361060 133080 45\r\n04 283 DITC DITCHET HILL FARM 2359 22504 363000 137000 80\r\n05 284 KITE KITE LANE 2358 22603 362320 135780 50\r\n06 285 MEAD MEADOW BUILDINGS 2357 22518 363050 135550 50\r\n07 286 SNAG SNAGG FARM FIELDS 2341 22606 363690 135690 45\r\n08 287 BFRM BRIDGE FARM ORCHARD 2347 22520 362480 135000 39\r\n09 288 SCOT OPP SPRINGFIELD COTTS 2319 22507 363490 134990 45\r\n10 289 MOOR MOOR LANE PADDOCK 2364 22506 362310 134270 35\r\n11 290 JACO JACOBS LANE FIELD 2311 22522 363030 134450 53\r\n12 291 SHIE SHIELDSVILLE FARM 2349 22505 363620 134330 50\r\n13 292 LOWE LOWER FARM SUTTON 2331 22616 362690 133410 38\r\n14 293 CAST INDUST PARK CC 2314 22511 363300 132640 46\r\n15 294 SPRI SPRING STONY STRATTON 2363 22626 364990 140970 165\r\n16 295 EVER FIELDS IN EVERCREECH 2365 22524 364960 138950 90\r\n17 296 RODM RODMOOR FARM FIELDS 2352 22610 364970 136950 55\r\n18 297 WADD WADDON FARM ORCHARD 2327 22514 364620 135330 53\r\n19 298 EAST EAST HILL LANE FIELDS 9999 99999 365290 134660 50\r\n20 299 KNAP KNAPP HOLLOW WYKE LANE 2342 22622 365100 133000 72\r\n21 300 CHES CHESTERBLADE BANKS FM 2339 22615 367050 140950 155\r\n22 301 BADG BADGER SET MANOR FM 2332 22510 367070 139050 110\r\n23 302 MILT MILTON WOOD CORN FIELD 2330 22620 366660 137290 122\r\n24 303 GREE GREENSCOME FIELD 2326 22629 367330 136660 172\r\n25 304 CRAB CRABTREE LANE FIELD 2350 22503 366880 135070 95\r\n26 305 PITC PITCOMBE FIELD 2320 22526 367030 132980 75\r\n27 306 HADS HADSPEN HOUSE FIELDS 2310 22515 366710 131330 103\r\n28 307 FRAN FRANCOMBE LANE FIELDS 9999 99999 367270 130700 112\r\n29 308 BATC BATCOMBE HILL FIELDS 2354 22519 369000 141000 193\r\n30 309 QUAR QUARRY FIELD BATCOMBE 2317 22605 368660 139330 163\r\n31 310 SEAT SEAT LANE FIELD 2356 22627 369360 138660 105\r\n32 311 WHAD WHADDONN FARM FIELDS 2353 22623 369020 136950 124\r\n33 312 FLAG SEPTIC TANK COMPOUND 2351 22604 368900 135100 70\r\n34 313 NEWH NEW HOUSE FARM FIELD 9999 99999 368340 133680 87\r\n35 314 GODW GODMINSTER WOODS 2321 22608 368970 133520 97\r\n36 315 DROP DROPPING LANE FARM 2328 22499 369690 133690 157\r\n37 316 GODM GODMINSTER FARM 9999 99999 368480 133010 102\r\n38 317 REDL REDLYNCH FARM STUMP 9999 99999 369520 133050 108\r\n39 318 DODD DODDS CORNER FIELD 9999 99999 368260 132350 999\r\n40 319 TOWE TOWERS FIELD 9999 99999 369000 132490 999\r\n41 320 POND POND FIELD BY TOWERS 2344 22521 369630 132220 999\r\n42 321 KNOW KNOWLE FIELD BY STREAM 2316 22628 369000 131000 999\r\n43 322 UPTO UPTON NOBLE FIELD 9999 99999 999999 999999 999\r\n44 323 GOOD GOODEDGE FARM FIELDS 2348 22501 371850 136950 115\r\n45 324 COGL COGLY WOOD FIELDS 2355 22512 370780 135400 110\r\n46 325 HORS HORSLEY FARM FIELDS 2336 22517 371300 134720 125\r\n47 326 MOWO MOOR WOOD FIELDS 2321 22508 371120 132960 127\r\n48 327 WALT WALTERS FARM FIELD 2318 22621 372670 137300 123\r\n49 328 COOK COOKS FARM FIELD 2324 22523 373330 136670 110\r\n50 329 CRAW CRAWLY FARM FIELD 2335 22500 373000 135000 120\r\n51 330 GLAD GLADWELL FARM FIELD 2346 22609 375000 137000 148\r\nOTHER SITES OUTSIDE OF BRUE CATCHMENT (EA data)\r\n52\t PENR PENRIDGE 375500 132200\r\n53\t CHAR CHARD\t\t \t\t 333200 109500\r\n54 CATL CASTLETON\t\t\t 364600 116600\r\n55\t FULW FULWOOD\t\t\t\t 321300 119700\r\n56\t MAUN MAUNDOWN\t\t\t 305500 129100\r\n57 MOUN MOUNT ST NURSERIES 323100 124000\r\n58 RIVE RIVERS HOUSE\t\t\t 330100 137700\r\n59 NORT NORTH BREWHAM\t\t\t 372200 137000\r\n60\t DARS DARSHILL \t 360100 144000\r\n61\t GOLD GOLDS CORNER\t\t\t 336700 143100\r\n62\t GEOR ST GEORGE\t\t\t 337800 163100\r\n64\t SOME SOMERTON\t\t\t 348700 129500" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 18 ], "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": 4493, "uuid": "981cc26ce15811dc365158d480c21a0a", "short_code": "proj", "title": "Hydrological Radar Experiment (HYREX)", "abstract": "The Hydrological Radar Experiment (HYREX) was a UK Natural Environment Research Council (NERC) Special Topic which ran from May 1993 to April 1997. The broad aim of HYREX was to gain a better understanding of rainfall variability, as sensed by weather radar, and how this variability impacts on river flow at the catchment scale." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 146877, 146878, 146879, 146880, 146881, 146882, 146883, 146884, 146885 ], "onlineresource_set": [] }, { "ob_id": 32899, "uuid": "98af2184e13e4b91893ab72f301790db", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.8 (v20210809)", "abstract": "Data for Figure SPM.8 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure SPM.8 shows selected indicators of global climate change under the five core scenarios used in this report.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n\r\nIPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001.\r\n\r\n\r\n ---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has five panels, with data provided for all panels in subdirectories named panel_a, panel_b, panel_c, panel_d and panel_e.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n - Historical, SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 Global Surface Air Temperature (GSAT) anomalies relative to 1850-1900 (20 year means)\r\n - Historical, SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 September sea-ice area\r\n - Historical, SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 Global ocean surface pH\r\n - Historical sea level relative to 1900 from gauges (to 1992) and altimeters (1993 on) (offset 0.158 m vs. 1995-2014)\r\n - AR6 sea level projections relative to 1900 (offset 0.158 m vs. 1995-2014)\r\n - AR6 assessed global mean sea level at 2300 relative to 1900 (offset 0.158 m vs. 1995-2014)\r\n\r\nThe five illustrative SSP (Shared Socio-economic Pathway) scenarios are described in Box SPM.1 of the Summary for Policymakers and Section 1.6.1.1 of Chapter 1.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a: Near-Surface Air Temperature\r\n\r\n - Data file: panel_a/tas_global_Historical.csv (black line and grey shading)\r\n - Data file: panel_a/tas_global_SSP1_1_9.csv (cyan line)\r\n - Data file: panel_a/tas_global_SSP1_2_6.csv (blue line and blue shading)\r\n - Data file: panel_a/tas_global_SSP2_4_5.csv (orange line)\r\n - Data file: panel_a/tas_global_SSP3_7_0.csv (red line and red shading)\r\n - Data file: panel_a/tas_global_SSP5_8_5.csv (brown line)\r\n\r\n\r\nPanel b: Sea-Ice Area\r\n\r\n - Data file: panel_b/sia_arctic_september_Historical.csv (black line and grey shading)\r\n - Data file: panel_b/sia_arctic_september_SSP1_1_9.csv (cyan line)\r\n - Data file: panel_b/sia_arctic_september_SSP1_2_6.csv (blue line and blue shading)\r\n - Data file: panel_b/sia_arctic_september_SSP2_4_5.csv (orange line)\r\n - Data file: panel_b/sia_arctic_september_SSP3_7_0.csv (red line and red shading)\r\n - Data file: panel_b/sia_arctic_september_SSP5_8_5.csv (brown line)\r\n\r\n\r\nPanel c: Ocean Surface pH\r\n\r\n - Data file: panel_c/phos_global_Historical.csv (black line and grey shading\r\n - Data file: panel_c/phos_global_SSP1_1_9.csv (cyan line\r\n - Data file: panel_b/phos_global_SSP1_2_6.csv (blue line and blue shading)\r\n - Data file: panel_c/phos_global_SSP2_4_5.csv (orange line)\r\n - Data file: panel_c/phos_global_SSP3_7_0.csv (red line and red shading)\r\n - Data file: panel_c/phos_global_SSP5_8_5.csv (brown line)\r\n\r\n\r\nPanel d: Sea Level\r\n\r\n - Data file: panel_d/global_sea_level_observed.csv (black line)\r\n - Data file: panel_d/global_sea_level_projected.csv (cyan, blue, orange, red and brown lines, red and blue shading)\r\n\r\n\r\nPanel e: Sea Level\r\n\r\n - Data file: panel_e: global_sea_level_2300_assessed.csv (columns 2 and 3, SSP1-2.6 scenario; columns 4 to 6 SSP5-8.5 scenario)\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n\r\n - Link to the report component containing the figure (Summary for Policymakers)", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2021-08-09T07:29:26", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, SPM, Sixth Assessment Report, Working Group I, Physical Science Basis, Summary for Policymakers, Figure SPM.8, global temperature, Arctic sea ice, ocean acidification, sea level rise", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-08-09T08:04:27", "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": { "ob_id": 30, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "Kilometers" }, "result_field": { "ob_id": 32945, "dataPath": "/badc/ar6_wg1/data/spm/spm_08/v20210809", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 86756, "numberOfFiles": 24, "fileFormat": "Data are CSV formatted." }, "timePeriod": { "ob_id": 9036, "startTime": "1950-01-01T00:00:00", "endTime": "2300-12-31T23:59:59" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 32901, "uuid": "e6e6d5b2de9440ac98bcfca57a97264e", "short_code": "comp", "title": "Caption for Figure SPM.8 from the Summary for Policymakers of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Selected indicators of global climate change under the five illustrative scenarios used in this report. \r\n\r\nThe projections for each of the five scenarios are shown in colour. Shades represent uncertainty ranges – more detail is provided for each panel below. The black curves represent the historical simulations (panels a, b, c) or the observations (panel d). Historical values are included in all graphs to provide context for the projected future changes. \r\n\r\nPanel a) Global surface temperature changes in °C relative to 1850–1900. These changes were obtained by combining CMIP6 model simulations with observational constraints based on past simulated warming, as well as an updated assessment of equilibrium climate sensitivity (see Box SPM.1). Changes relative to 1850–1900 based on 20-year averaging periods are calculated by adding 0.85°C (the observed global surface temperature increase from 1850–1900 to 1995–2014) to simulated changes relative to 1995–2014. Very likely ranges are shown for SSP1-2.6 and SSP3-7.0.\r\n\r\nPanel b) September Arctic sea ice area in 10^6 km^2 based on CMIP6 model simulations. Very likely ranges are shown for SSP1-2.6 and SSP3-7.0. The Arctic is projected to be practically ice-free near mid-century under mid- and high GHG emissions scenarios.\r\n\r\nPanel c) Global ocean surface pH (a measure of acidity) based on CMIP6 model simulations. Very likely ranges are shown for SSP1-2.6 and SSP3-7.0.\r\n\r\nPanel d) Global mean sea level change in meters relative to 1900. The historical changes are observed (from tide gauges before 1992 and altimeters afterwards), and the future changes are assessed consistently with observational constraints based on emulation of CMIP, ice sheet, and glacier models. Likely ranges are shown for SSP1-2.6 and SSP3-7.0. Only likely ranges are assessed for sea level changes due to difficulties in estimating the distribution of deeply uncertain processes. The dashed curve indicates the potential impact of these deeply uncertain processes. It shows the 83rd percentile of SSP5-8.5 projections that include low-likelihood, high-impact ice sheet processes that cannot be ruled out; because of low confidence in projections of these processes, this curve does not constitute part of a likely range. Changes relative to 1900 are calculated by adding 0.158 m (observed global mean sea level rise from 1900 to 1995–2014) to simulated and observed changes relative to 1995–2014.\r\n\r\nPanel e): Global mean sea level change at 2300 in meters relative to 1900. Only SSP1-2.6 and SSP5-8.5 are projected at 2300, as simulations that extend beyond 2100 for the other scenarios are too few for robust results. The 17th–83rd percentile ranges are shaded. The dashed arrow illustrates the 83rd percentile of SSP5-8.5 projections that include low-likelihood, high-impact ice sheet processes that cannot be ruled out.\r\n\r\nPanels b) and c) are based on single simulations from each model, and so include a component of internal variability. Panels a), d) and e) are based on long-term averages, and hence the contributions from internal variability are small.\r\n\r\n{Figure TS.8, Figure TS.11, Box TS.4 Figure 1, Box TS.4 Figure 1, 4.3, 9.6, Figure 4.2, Figure 4.8, Figure 4.11, Figure 9.27}" }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 10916 ], "observationcollection_set": [ { "ob_id": 32729, "uuid": "ae4f1eb6fce24adcb92ddca1a7838a5c", "short_code": "coll", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nWhen using the datasets from this collection please use the citation indicated on each individual specific dataset, rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure SPM.1\r\n- data for Figure SPM.2\r\n- data for Figure SPM.3\r\n- data for Figure SPM.4\r\n- data for Figure SPM.5\r\n- data for Figure SPM.6\r\n- data for Figure SPM.7\r\n- data for Figure SPM.8\r\n- data for Figure SPM.9\r\n- data for Figure SPM.10" } ], "responsiblepartyinfo_set": [ 146887, 146888, 146889, 146890, 146891, 146892, 146920, 147452, 146921, 168318, 146922, 146923 ], "onlineresource_set": [ 43299, 47462, 82820, 88953, 88954, 88955, 88956, 88957, 88958, 88959, 88960, 88961, 88962, 88963, 88964, 87488, 95066, 95067, 95068 ] }, { "ob_id": 32906, "uuid": "93d1b84fbb144901809eaf67b35eb5c4", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.6 (v20210809)", "abstract": "Data for Figure SPM.6 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure SPM.6 shows projected changes in the intensity and frequency of extreme temperature, extreme precipitation and droughts.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n\r\nIPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with data provided for all panels in subdirectories named panel_a, panel_b, panel_c and panel_d.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\nThis dataset contains:\r\n- Changes in annual maximum temperature (TXx) extremes for intensity (°C) and frequency (-) for 1 in 10 year and 1 in 50 year events (relative to 1850-1900)\r\n- Changes in annual maximum 1-day precipitation (Rx1day) extremes for intensity (%) and frequency (-) for 1 in 10 year events (relative to 1850-1900)\r\n- Changes in soil moisture-based drought events for intensity (standard deviation) and frequency (-) for 1 in 10 year events (relative to 1850-1900)\r\n\r\n---------------------------------------------------\r\nData provided in relation to figure\r\n---------------------------------------------------\r\nPanel a:\r\n- Data file: panel_a/TXx_freq_change_10_year_event.csv ('Hot temperature extremes') [column 2 dark dots, columns 5 and 6 light dots]\r\n- Data file: panel_a/TXx_intens_change_10_year_event.csv ('Hot temperature extremes') [column 2 dark bars, columns 5 and 6 light bars]\r\nPanel b:\r\n- Data file: panel_b/TXx_freq_change_50_year_event.csv ('Hot temperature extremes') [column 2 dark dots, columns 5 and 6 light dots]\r\n- Data file: panel_b/TXx_intens_change_50_year_event.csv ('Hot temperature extremes') [column 2 dark bars, columns 5 and 6 light bars]\r\n \r\nPanel c:\r\n- Data file: panel_c/Rx1day_freq_change_10_year_event.csv ('Extreme precipitation over land') [column 2 dark dots, columns 5 and 6 light dots]\r\n- Data file: panel_c/Rx1day_intens_change_10_year_event.csv ('Extreme precipitation over land') [column 2 dark bars, columns 5 and 6 light bars]\r\nPanel d:\r\n- Data file: panel_d/drought_freq_change_10_year_event.csv ('Drought') [column 2 dark dots, columns 5 and 6 light dots]\r\n- Data file: panel_d/drought_intens_change_10_year_event.csv ('Drought') [column 2 dark bars, columns 5 and 6 light bars]\r\n\r\n---------------------------------------------------\r\nNotes on reproducing the figure from the provided data\r\n---------------------------------------------------\r\n- The 50th, 5th, and 95th percentiles are shown on the figure (lines on the bars).\r\n- The drought intensity shows 'drying' while the data file shows the change in soil moisture (i.e., a negative soil moisture change corresponds to a positive drying signal).\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblink is provided in the Related Documents section of this catalogue record:\r\n - - Link to the report webpage, which includes the report component containing the figure (Summary for Policymakers) and the Supplementary Material for Chapter 11, which contains details on the input data used in Table 11.SM.9. (Figures 11.15, 11.6, 11.7, 11.12, and 11.18)", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:36:45", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, SPM, Sixth Assessment Report, Working Group I, Physical Science Basis, Summary for Policymakers, Figure SPM.6, global warming level, hot temperature extremes, extreme precipitation, drought, projection, CMIP6, intensity, frequency", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "None", "status": "completed", "dataPublishedTime": "2021-08-09T08:04:18", "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": { "ob_id": 33, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "Kilometers" }, "result_field": { "ob_id": 32955, "dataPath": "/badc/ar6_wg1/data/spm/spm_06/v20210809", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 24076, "numberOfFiles": 12, "fileFormat": "Data are CSV formatted" }, "timePeriod": { "ob_id": 9035, "startTime": "1850-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": { "ob_id": 3717, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-07-26" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 32908, "uuid": "5e51855a28ff48babd9b881ad5b899d9", "short_code": "comp", "title": "Caption for Figure SPM.6 from the Summary for Policymakers of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected changes in the intensity and frequency of hot temperature extremes over land, extreme precipitation over land, and agricultural and ecological droughts in drying regions.\r\n \r\nProjected changes are shown at global warming levels of 1°C, 1.5°C, 2°C, and 4°C and are relative to 1850-1900 {Footnote } representing a climate without human influence. The figure depicts frequencies and increases in intensity of 10- or 50-year extreme events from the base period (1850-1900) under different global warming levels.\r\nHot temperature extremes are defined as the daily maximum temperatures over land that were exceeded on average once in a decade (10-year event) or once in 50 years (50-year event) during the 1850–1900 reference period. Extreme precipitation events are defined as the daily precipitation amount over land that was exceeded on average once in a decade during the 1850–1900 reference period. Agricultural and ecological drought events are defined as the annual average of total column soil moisture below the 10th percentile of the 1850–1900 base period. These extremes are defined on model grid box scale. For hot temperature extremes and extreme precipitation, results are shown for the global land. For agricultural and ecological drought, results are shown for drying regions only, which correspond to the AR6 regions in which there is at least medium confidence in a projected increase in agricultural/ecological drought at the 2°C warming level compared to the 1850–1900 base period in CMIP6. These regions include W. North-America, C. North-America, N. Central-America, S. Central-America, Caribbean, N. South-America, N.E. South-America, South-American-Monsoon, S.W. South-America, S. South-America, West & Central-Europe, Mediterranean, W. Southern-Africa, E. Southern-Africa, Madagascar, E. Australia, S. Australia (Caribbean is not included in the calculation of the figure because of the too small number of full land grid cells). The non-drying regions do not show an overall increase or decrease in drought severity. Projections of changes in agricultural and ecological droughts in the CMIP5 multi-model ensemble differ from those in CMIP6 in some regions, including in part of Africa and Asia. Assessments on projected changes in meteorological and hydrological droughts are provided in Chapter 11. {11.6, 11.9}\r\n \r\nIn the ‘frequency’ section, each year is represented by a dot. The dark dots indicate years in which the extreme threshold is exceeded, while light dots are years when the threshold is not exceeded. Values correspond to the medians (in bold) and their respective 5%-95% range based on the multi-model ensemble from simulations of CMIP6 under different SSP scenarios. For consistency, the number of dark dots is based on the rounded-up median. In the ‘intensity’ section, medians and their 5%-95% range, also based on the multi-model ensemble from simulations of CMIP6, are displayed as dark and light bars, respectively. Changes in the intensity of hot temperature extremes and extreme precipitations are expressed as degree celsius and percentage. As for agricultural and ecological drought, intensity changes are expressed as fractions of standard deviation of annual soil moisture.\r\n \r\n{11.1, 11.3, 11.4, 11.6, Figure 11.12, Figure 11.15, Figure 11.6, Figure 11.7, Figure 11.18}\r\n\r\nFootnote: The period 1850–1900 represents the earliest period of sufficiently globally complete observations to estimate global surface temperature and, consistent with AR5 and SR1.5, is used as an approximation for pre-industrial conditions." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1319, 1320, 4385, 6021, 6022, 6023, 6087, 6245, 6506, 6647, 6814, 6816, 7764, 7765, 7766, 7767, 7768, 7770, 7771, 7772, 7773, 7775, 7776, 7777, 7778, 7779, 7780, 7781, 7782, 7783, 7785, 7787, 7788, 7789, 7790, 7791, 7792, 7794, 7795, 7797, 7798, 7799, 7800, 7823, 7826, 7827, 7828, 7838, 7839, 7840, 7841, 7842, 7843, 7844, 7845, 7846, 7847, 7853, 7854, 7855, 7857, 7858, 7866, 7868, 7869, 7871, 7875, 7883, 7885, 7886, 7887, 7890, 7891, 7892, 7893, 7898, 7900, 7901, 7902, 7903, 7904, 7905, 7906, 7907, 7908, 7909, 7910, 7913, 7915, 7916, 7917, 7918, 7919, 7921, 7922, 7923, 7925, 7926, 7927, 7928, 7929, 7930, 7931, 7932, 7933, 7939, 7940, 7941, 7942, 7943, 7944, 7960, 7961, 7962, 7964, 7966, 7967, 7968, 7969, 7971, 7972, 7974, 7975, 7976, 7977, 7978, 7979, 7980, 7981, 7982, 7985, 7986, 7987, 7988, 7989, 7990, 7991, 7992, 7993, 7994, 8005, 8006, 8007, 8008, 8009, 8010, 8011, 8012, 8013, 8014, 8015, 8016, 8018, 8020, 8021, 8022, 8023, 8024, 8025, 8026, 8027, 8028, 8029, 8030, 8031, 8032, 8033, 8034, 8035, 8036, 8037, 8038, 8039, 8040, 8041, 8042, 8043, 8044, 8045, 8046, 8047, 8048, 8049, 8050, 8051, 8052, 8053, 8054, 8055, 8056, 8057, 8058, 8059, 8060, 8061, 8062, 8063, 8064, 8065, 8066, 8067, 8068, 8069, 8070, 8071, 8072, 8073, 8074, 8075, 8076, 8077, 8078, 8079, 8080, 8081, 8082, 8083, 8084, 8085, 8086, 8087, 8088, 8089, 8090, 8091, 8092, 8093, 8094, 8095, 8096, 8097, 8098, 8099, 8100, 8101, 8102, 8103, 8104, 8105, 8106, 8107, 8108, 8109, 8110, 8111, 8112, 8113, 8114, 8115, 8116, 8117, 8118, 8119, 8120, 8121, 8122, 8123, 8124, 8125, 8126, 8127, 8128, 8129, 8130, 8131, 8132, 8133, 8134, 8135, 8136, 8137, 8138, 8139, 8140, 8141, 8142, 8143, 8144, 25417, 25418, 25419, 25420, 25421, 25422, 25423, 25424, 25425, 25426, 25427 ], "vocabularyKeywords": [], "identifier_set": [ 10914 ], "observationcollection_set": [ { "ob_id": 32729, "uuid": "ae4f1eb6fce24adcb92ddca1a7838a5c", "short_code": "coll", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nWhen using the datasets from this collection please use the citation indicated on each individual specific dataset, rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure SPM.1\r\n- data for Figure SPM.2\r\n- data for Figure SPM.3\r\n- data for Figure SPM.4\r\n- data for Figure SPM.5\r\n- data for Figure SPM.6\r\n- data for Figure SPM.7\r\n- data for Figure SPM.8\r\n- data for Figure SPM.9\r\n- data for Figure SPM.10" } ], "responsiblepartyinfo_set": [ 146909, 146910, 146911, 146912, 146913, 146914, 146915, 146916, 146917, 168307 ], "onlineresource_set": [ 43307, 82817, 82819, 91192, 91193, 91194, 91195, 91196, 91197, 91198, 91199, 91200 ] }, { "ob_id": 32909, "uuid": "76cad0b4f6f141ada1c44a4ce9e7d4bd", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.1 (v20210809)", "abstract": "Data for Figure SPM.1 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure SPM.1 shows global temperature history and causes of recent warming.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n\r\nIPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu and B. Zhou (eds.)]. Cambridge University Press. In Press.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\nThe figure has two panels, with data provided for all panels in subdirectories named panel_a and panel_b.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\nPanel a\r\n\r\nThe dataset contains:\r\n\r\n - Estimated temperature during the warmest multi-century period in at least the last 100,000 years, which occurred around 6500 years ago (4500 BCE), multi-centennial average, from AR6 WGI Chapter 2\r\n - Global surface temperature change time series relative to 1850-1900 for 1-2020 from:\r\n• 1-2000 CE reconstruction from paleoclimate archives, decadal smoothed, from PAGES2k Consortium (2019, DOI: 10.1038/s41561-019-0400-0)\r\n• 1850-2020 CE, observations, decadal smoothed, from AR6 WGI Chapter 2 assessed mean\r\n\r\nPanel b:\r\n\r\nThe dataset contains global surface temperature change time series relative to 1850-1900 for 1850-2020 from simulations from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and observations:\r\n\r\n- CMIP6 historical+ssp245 simulations (simulations with human and natural forcing, 1850-2019)\r\n- CMIP6 hist-nat simulations (simulations with natural forcing, 1850-2019)\r\n- Global Surface Temperature Anomalies (GSTA) relative to 1850-1900 from observations assessed in IPCC AR6 WG1 Chapter 2 (1850-2020)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n---------------------------------------------------\r\nPanel a:\r\n\r\n- panel_a/SPM1_1-2000_recon.txt, 1-2000 time series, decadal smoothed, for years centered on 5-1996 CE [column 1 grey line, columns 2 and 3 grey shading]\r\n- panel_a/SPM1_1850-2020_obs.txt, 1850-2020 time series, decadal smoothed, for years centered on 1855-2016 CE [black line]\r\n- panel_a/SPM1_6500_recon.txt, bar for the warmest multi-century period in more than 100,000 years (around 6500 years ago: 4500 BCE) [grey bar]\r\n\r\nPanel b:\r\n\r\n- panel_b/gmst_changes_model_and_obs.csv. Global surface temperature change time series relative to 1850-1900 for 1850-2020 from:\r\n• CMIP6 historical+ssp245 simulations (1850-2019) [mean, brown line]\r\n• CMIP6 historical+ssp245 simulations (1850-2019) [5% range, brown shading, bottom]\r\n• CMIP6 historical+ssp245 simulations (1850-2019) [95% range, brown shading, top]\r\n• CMIP6 hist-nat simulations (1850-2019) [mean, green line]\r\n• CMIP6 hist-nat simulations (1850-2019) [5% range, green shading, bottom]\r\n• CMIP6 hist-nat simulations (1850-2019) [95% range, green shading, top]\r\n• Global Surface Temperature Anomalies (GSTA) relative to 1850-1900 from observations assessed in IPCC AR6 WG1 Chapter 2 (1850-2020) [black line]\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\nThe following weblinks are provided in the Related Documents section of this catalogue record:\r\n\r\n- Link to the report webpage, which includes the report component containing the figure (Summary for Policymakers), the Technical Summary (Cross-Section Box TS.1, Figure 1a) and the Supplementary Material for Chapters 2 and 3, which contains details on the input data used in Tables 2.SM.1 (Figure 2.11a) and 3.SM.1 (Figure 3.2c; FAQ 3.1, Figure 1).\r\n- Link to related publication for input data", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2021-08-06T18:52:00", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, SPM, Sixth Assessment Report, Working Group I, Physical Science Basis, Summary for Policymakers, Figure SPM.1, global surface temperature, pre-industrial temperature, Holocene", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-08-09T08:04:11", "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": { "ob_id": 34, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "Kilometers" }, "result_field": { "ob_id": 32959, "dataPath": "/badc/ar6_wg1/data/spm/spm_01/v20210809", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 66276, "numberOfFiles": 7, "fileFormat": "Data are CSV formatted." }, "timePeriod": { "ob_id": 9070, "startTime": "0001-01-01T00:00:00", "endTime": "2019-12-31T23:59:59" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 32911, "uuid": "b670de871d43428f83305a9b442d81b0", "short_code": "comp", "title": "Caption for Figure SPM.1 from the Summary for Policymakers of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "History of global temperature change and causes of recent warming\r\n\r\nPanel a): Changes in global surface temperature reconstructed from paleoclimate archives (solid grey line, 1–2000) and from direct observations (solid black line, 1850–2020), both relative to 1850–1900 and decadally averaged. The vertical bar on the left shows the estimated temperature (very likely range) during the warmest multi-century period in at least the last 100,000 years, which occurred around 6500 years ago during the current interglacial period (Holocene). The Last Interglacial, around 125,000 years ago, is the next most recent candidate for a period of higher temperature. These past warm periods were caused by slow (multi-millennial) orbital variations. The grey shading with white diagonal lines shows the very likely ranges for the temperature reconstructions. \r\n\r\nPanel b): Changes in global surface temperature over the past 170 years (black line) relative to 1850–1900 and annually averaged, compared to CMIP6 climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown), and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (see Figure SPM.2 for the assessed contributions to warming). \r\n\r\n{2.3.1, 3.3, Cross-Chapter Box 2.3, Cross-Section Box TS.1, Figure 1a, TS.2.2}" }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 61288, 80379, 80380, 80381, 80382, 80383, 80384, 80385 ], "vocabularyKeywords": [], "identifier_set": [ 10911 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 146924, 146925, 146926, 146927, 146928, 146929, 146931, 146935, 146932, 168309, 146933, 146934 ], "onlineresource_set": [ 95082, 43304, 43305, 47463, 82765, 87748, 87749, 91201, 91202, 91203, 91204 ] }, { "ob_id": 32913, "uuid": "35a7ee81a50c4b95ab59f9bd128f9b63", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.9 (v20210809)", "abstract": "Data for Figure SPM.9 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure SPM.9 provides a synthesis of the number of AR6 WGI reference regions where climatic impact-drivers are projected to change.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n\r\nIPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001.\r\n\r\n\r\n---------------------------------------------------\r\nTemporal range\r\n---------------------------------------------------\r\nChanges refer to a 20–30 year period centred around 2050 and/or consistent with 2°C global warming compared to a similar period within 1960-2014 or 1850-1900.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels, with data provided for all panels in a single file named consolidated_data_figure_SPM9.csv\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\nThis dataset contains the number of AR6 WGI regions where climatic impact-drivers are projected to change if a global warming level of 2°C is reached compared to a climatological reference period included within 1960-2014.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nData file: consolidated_data_figure_SPM.9.csv (count of regions with increasing or decreasing changes in climatic impact-drivers); relates to panel (a) and panel (b) and it's shown by the bars in the figure. The first row of data relates to the darker purple bars, the second row to the lighter purple bars, the third row to the lighter brown bars and the fourth row to the darker brown bars. Row 5 represents the maximum number of regions for which each climatic impact-driver is relevant. It is shown on the figure as the lighter-shaded ‘envelope’.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n\r\n - Link to the report webpage, which includes the report component containing the figure (Summary for Policymakers)", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:24:25", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, SPM, Sixth Assessment Report, Working Group I, Physical Science Basis, Summary for Policymakers, Figure SPM.9, climatic impact-drivers, projection, AR6 regions", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-08-09T08:04:03", "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": { "ob_id": 35, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "Kilometers" }, "result_field": { "ob_id": 32962, "dataPath": "/badc/ar6_wg1/data/spm/spm_09/v20210809", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 6540, "numberOfFiles": 4, "fileFormat": "Data are CSV formatted." }, "timePeriod": null, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 32915, "uuid": "1c2ecbedc7754e4cbe92f12f8b4bfa21", "short_code": "comp", "title": "Caption for Figure SPM.9 from the Summary for Policymakers of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Synthesis of the number of AR6 WGI reference regions where climatic impact-drivers are projected to change\r\n\r\nA total of 35 climatic impact-drivers (CIDs) grouped into seven types are shown: heat and cold, wet and dry, wind, snow and ice, coastal, open ocean and other. For each CID, the bar in the graph below displays the number of AR6 WGI reference regions where it is projected to change. The colours represent the direction of change and the level of confidence in the change: purple indicates an increase while brown indicates a decrease; darker and lighter shades refer to high and medium confidence, respectively. Lighter background colours represent the maximum number of regions for which each CID is broadly relevant.\r\n\r\nPanel a) shows the 30 CIDs relevant to the land and coastal regions while panel b) shows the 5 CIDs relevant to the open ocean regions. Marine heatwaves and ocean acidity are assessed for coastal ocean regions in panel a) and for open ocean regions in panel b). Changes refer to a 20–30 year period centred around 2050 and/or consistent with 2°C global warming compared to a similar period within 1960-2014, except for hydrological drought and agricultural and ecological drought which is compared to 1850-1900. Definitions of the regions are provided in Atlas.1 and the Interactive Atlas (see interactive-atlas.ipcc.ch). \r\n\r\n{Table TS.5, Figure TS.22, Figure TS.25, 11.9, 12.2, 12.4, Atlas.1} (Table SPM.1)" }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 10909 ], "observationcollection_set": [ { "ob_id": 32729, "uuid": "ae4f1eb6fce24adcb92ddca1a7838a5c", "short_code": "coll", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nWhen using the datasets from this collection please use the citation indicated on each individual specific dataset, rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure SPM.1\r\n- data for Figure SPM.2\r\n- data for Figure SPM.3\r\n- data for Figure SPM.4\r\n- data for Figure SPM.5\r\n- data for Figure SPM.6\r\n- data for Figure SPM.7\r\n- data for Figure SPM.8\r\n- data for Figure SPM.9\r\n- data for Figure SPM.10" } ], "responsiblepartyinfo_set": [ 146939, 146940, 146941, 146942, 146943, 146944, 146946, 146950, 146947, 168310, 146948, 146949 ], "onlineresource_set": [ 43303, 43302, 47464, 82821, 87744 ] }, { "ob_id": 32916, "uuid": "cfe938e70f8f4e98b0622296743f7913", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.10 (v20210809)", "abstract": "Data for Figure SPM.10 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure SPM.10 shows global warming as a function of cumulative emissions of carbon dioxide.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\nWhen citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n\r\nIPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels that are closely linked. Data files for the top panel are labelled with 'Top_panel' while data files for the bottom panel are labelled with 'Bottom_panel'. \r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n---------------------------------------------------\r\nThis dataset contains:\r\n\r\nTop panel:\r\n\r\n- Cumulative global total anthropogenic carbon dioxide emissions (1850-2019)\r\n- Global surface temperature increase relative to 1850-1900 (1850-2019)\r\n- Estimated human-caused warming relative to 1850-1900 (1850-2019)\r\n- Projected global total anthropogenic carbon dioxide emissions for the five scenarios of the AR6 WGI core set of scenarios (2015-2050)\r\n- Assessed global surface temperature increase relative to 1850-1900 for the five scenarios of the AR6 WGI core set of scenarios (2015-2050)\r\n\r\nBottom panel:\r\n\r\n- Cumulative global total anthropogenic carbon dioxide emissions (1850-2019)\r\n- Projected global total anthropogenic carbon dioxide emissions for the five scenarios of the AR6 WGI core set of scenarios (2015-2050)\r\n\r\nThe illustrative SSP (Shared Socio-economic Pathway) scenarios (referred to here as core scenarios) are described in Box SPM.1 of the Summary for Policymakers and Section 1.6.1.1 of Chapter 1.\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nTop panel: \r\n•\tTop_panel_HISTORY.csv: historical CO2 emissions, global surface temperature increase since 1850-1900 for the 1850-2019 period, estimated human-caused warming since 1850-1900 over the 1850-2019 period. [row 1 for black line, grey line and grey range, row 2 for black line, row 3 to 5 range and central grey range]\r\n•\tTop_panel_SSP1-19.csv: projected CO2 emissions, assessed projections of global surface temperature increase relative to the 1850-1900 period for the period 2015-2050 [row 1 and 2 for central lines, row 1, 3, and 4 for ranges]\r\n•\tTop_panel_SSP1-26.csv: projected CO2 emissions, assessed projections of global surface temperature increase relative to the 1850-1900 period for the period 2015-2050 [row 1 and 2 for central lines, row 1, 3, and 4 for ranges]\r\n•\tTop_panel_SSP2-45.csv: projected CO2 emissions, assessed projections of global surface temperature increase relative to the 1850-1900 period for the period 2015-2050 [row 1 and 2 for central lines, row 1, 3, and 4 for ranges]\r\n•\tTop_panel_SSP3-70.csv: projected CO2 emissions, assessed projections of global surface temperature increase relative to the 1850-1900 period for the period 2015-2050 [row 1 and 2 for central lines, row 1, 3, and 4 for ranges]\r\n•\tTop_panel_SSP5-85.csv: projected CO2 emissions, assessed projections of global surface temperature increase relative to the 1850-1900 period for the period 2015-2050 [row 1 and 2 for central lines, row 1, 3, and 4 for ranges]\r\n\r\nBottom panel: \r\n•\tBottom_panel_GtCO2_historical.csv: historical CO2 emissions [grey bars]\r\n•\tBottom_panel_GtCO2_projections.csv; projected CO2 emissions for the five scenarios in the core set of IPCC AR6 WG1 scenarios [coloured bars]\r\n\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n\r\n - Link to the report webpage, which includes the report component containing the figure (Summary for Policymakers) the Technical Summary (Section TS.3.3). and the Supplementary Material for Chapter 5, which contains details on the input data used in Table 5.SM.6 (Figure 5.31)\r\n - Link to related publications for input data", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:24:35", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, SPM, Sixth Assessment Report, Working Group I, Physical Science Basis, Summary for Policymakers, Figure SPM.10, carbon dioxide, projection, carbon budgets, shared socio-economic pathways, SSP", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-08-09T08:03:55", "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": { "ob_id": 36, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "Kilometers" }, "result_field": { "ob_id": 32963, "dataPath": "/badc/ar6_wg1/data/spm/spm_10/v20210809", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 46050, "numberOfFiles": 11, "fileFormat": "Data are CSV formatted." }, "timePeriod": { "ob_id": 9037, "startTime": "1850-01-01T00:00:00", "endTime": "2050-12-31T23:59:59" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 32918, "uuid": "b6e6f3d33acf46979c6291b8c6255311", "short_code": "comp", "title": "Caption for Figure SPM.10 from the Summary for Policymakers of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Near-linear relationship between cumulative CO2 emissions and the increase in global surface temperature. \r\n\r\nTop panel: Historical data (thin black line) shows observed global surface temperature increase in °C since 1850–1900 as a function of historical cumulative carbon dioxide (CO2) emissions in GtCO2 from 1850 to 2019. The grey range with its central line shows a corresponding estimate of the historical human-caused surface warming (see Figure SPM.2). Coloured areas show the assessed very likely range of global surface temperature projections, and thick coloured central lines show the median estimate as a function of cumulative CO2 emissions from 2020 until year 2050 for the set of illustrative scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, see Figure SPM.4). Projections use the cumulative CO2 emissions of each respective scenario, and the projected global warming includes the contribution from all anthropogenic forcers. The relationship is illustrated over the domain of cumulative CO2 emissions for which there is high confidence that the transient climate response to cumulative CO2 emissions (TCRE) remains constant, and for the time period from 1850 to 2050 over which global CO2 emissions remain net positive under all illustrative scenarios as there is limited evidence supporting the quantitative application of TCRE to estimate temperature evolution under net negative CO2 emissions.\r\n\r\nBottom panel: Historical and projected cumulative CO2 emissions in GtCO2 for the respective scenarios.\r\n\r\n{Figure TS.18, Figure 5.31, Section 5.5}" }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 10912 ], "observationcollection_set": [ { "ob_id": 32729, "uuid": "ae4f1eb6fce24adcb92ddca1a7838a5c", "short_code": "coll", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nWhen using the datasets from this collection please use the citation indicated on each individual specific dataset, rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure SPM.1\r\n- data for Figure SPM.2\r\n- data for Figure SPM.3\r\n- data for Figure SPM.4\r\n- data for Figure SPM.5\r\n- data for Figure SPM.6\r\n- data for Figure SPM.7\r\n- data for Figure SPM.8\r\n- data for Figure SPM.9\r\n- data for Figure SPM.10" } ], "responsiblepartyinfo_set": [ 146951, 146952, 146953, 146954, 146955, 146956, 146958, 146959, 147405, 168311, 147406, 147407, 147408, 147409, 147410, 147411 ], "onlineresource_set": [ 43309, 43310, 43311, 43312, 43308, 47465, 82822, 94782, 94783 ] }, { "ob_id": 32921, "uuid": "bd65331b1d344ccca44852e495d3a049", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.4 (v20210809)", "abstract": "Data for Figure SPM.4 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure SPM.4 panel a shows global emissions projections for CO2 and a set of key non-CO2 climate drivers, for the core set of five IPCC AR6 scenarios. Figure SPM.4 panel b shows attributed warming in 2081-2100 relative to 1850-1900 for total anthropogenic, CO2, other greenhouse gases, and other anthropogenic forcings for five Shared Socio-economic Pathway (SSP) scenarios.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n\r\nIPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n---------------------------------------------------\r\nThe figure has two panels, with data provided for all panels in subdirectories named panel_a and panel_b.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n---------------------------------------------------\r\n This dataset contains:\r\n\r\n - Projected emissions from 2015 to 2100 for the five scenarios of the AR6 WGI core scenario set (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5)\r\n - Projected warming for all anthropogenic forcers, CO2 only, non-CO2 greenhouse gases (GHGs) only, and other anthropogenic components for 2081-2100 relative to 1850-1900, for SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5.\r\n\r\nThe five illustrative SSP (Shared Socio-economic Pathway) scenarios are described in Box SPM.1 of the Summary for Policymakers and Section 1.6.1.1 of Chapter 1.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a:\r\n\r\n\r\n The first column includes the years, while the next columns include the data per scenario and per climate forcer for the line graphs.\r\n\r\n - Data file: Carbon_dioxide_Gt_CO2_yr.csv. relates to Carbon dioxide emissions panel\r\n - Data file: Methane_Mt_CO2_yr.csv. relates to Methane emissions panel\r\n - Data file: Nitrous_oxide_Mt N2O_yr.csv. relates to Nitrous oxide emissions panel\r\n - Data file: Sulfur_dioxide_Mt SO2_yr.csv. relates to Sulfur dioxide emissions panel\r\n\r\n Panel b:\r\n\r\n - Data file: ts_warming_ranges_1850-1900_base_panel_b.csv. [Rows 2 to 5 relate to the first bar chart (cyan). Rows 6 to 9 relate to the second bar chart (blue). Rows 10 to 13 relate to the third bar chart (orange). Rows 14 to 17 relate to the fourth bar chart (red). Rows 18 to 21 relate to the fifth bar chart (brown).].\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblink are provided in the Related Documents section of this catalogue record:\r\n- Link to the report webpage, which includes the report component containing the figure (Summary for Policymakers) and the Supplementary Material for Chapter 1, which contains details on the input data used in Table 1.SM.1..(Cross-Chapter Box 1.4, Figure 2).\r\n- Link to related publication for input data used in panel a.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:51:28", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, SPM, Sixth Assessment Report, Working Group I, Physical Science Basis, Summary for Policymakers, Figure SPM.4, global temperature, attribution, projections, scenarios, shared socioeconomic pathways, SSP", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-08-09T08:03:47", "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": { "ob_id": 38, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "Kilometers" }, "result_field": { "ob_id": 32965, "dataPath": "/badc/ar6_wg1/data/spm/spm_04/v20210809", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 19280, "numberOfFiles": 8, "fileFormat": "Data are CSV formatted." }, "timePeriod": { "ob_id": 9039, "startTime": "2015-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": { "ob_id": 3722, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-07-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 32923, "uuid": "ddad23c4850f407e81e7b847eb8e5ef0", "short_code": "comp", "title": "Caption for Figure SPM.4 from the Summary for Policymakers of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Future anthropogenic emissions of key drivers of climate change and warming contributions by groups of drivers for the five illustrative scenarios used in this report.\r\n\r\nThe five scenarios are SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5.\r\n\r\nPanel a) Annual anthropogenic (human-caused) emissions over the 2015–2100 period. Shown are emissions trajectories for carbon dioxide (CO2) from all sectors (GtCO2/yr) (left graph) and for a subset of three key non-CO2 drivers considered in the scenarios: methane (CH4, MtCH4/yr, top-right graph), nitrous oxide (N2O, MtN2O/yr, middle-right graph) and sulfur dioxide (SO2, MtSO2/yr, bottom-right graph, contributing to anthropogenic aerosols in panel b).\r\n\r\nPanel b) Warming contributions by groups of anthropogenic drivers and by scenario are shown as change in global surface temperature (°C) in 2081–2100 relative to 1850–1900, with indication of the observed warming to date. Bars and whiskers represent median values and the very likely range, respectively. Within each scenario bar plot, the bars represent total global warming (°C; total bar) (see Table SPM.1) and warming contributions (°C) from changes in CO2 (CO2 bar), from non-CO2 greenhouse gases (non-CO2 GHGs bar; comprising well-mixed greenhouse gases and ozone) and net cooling from other anthropogenic drivers (aerosols and land-use bar; anthropogenic aerosols, changes in reflectance due to land-use and irrigation changes, and contrails from aviation; see Figure SPM.2, panel c, for the warming contributions to date for individual drivers). The best estimate for observed warming in 2010–2019 relative to 1850–1900 (see Figure SPM.2, panel a) is indicated in the darker column in the total bar. Warming contributions in panel b are calculated as explained in Table SPM.1 for the total bar. For the other bars the contribution by groups of drivers are calculated with a physical climate emulator of global surface temperature which relies on climate sensitivity and radiative forcing assessments.\r\n\r\n{Cross-Chapter Box 1.4, 4.6, Figure 4.35, 6.7, Figure 6.18, 6.22 and 6.24, Cross-Chapter Box 7.1, 7.3, Figure 7.7, Box TS.7, Figures TS.4 and TS.15}" }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 10910 ], "observationcollection_set": [ { "ob_id": 32729, "uuid": "ae4f1eb6fce24adcb92ddca1a7838a5c", "short_code": "coll", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nWhen using the datasets from this collection please use the citation indicated on each individual specific dataset, rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure SPM.1\r\n- data for Figure SPM.2\r\n- data for Figure SPM.3\r\n- data for Figure SPM.4\r\n- data for Figure SPM.5\r\n- data for Figure SPM.6\r\n- data for Figure SPM.7\r\n- data for Figure SPM.8\r\n- data for Figure SPM.9\r\n- data for Figure SPM.10" } ], "responsiblepartyinfo_set": [ 146966, 146967, 146968, 146969, 146970, 146971, 146972, 146973, 146974, 168312, 147412, 147413, 147414, 147418, 147419 ], "onlineresource_set": [ 43301, 43300, 47466, 82814, 94785, 94784, 94786 ] }, { "ob_id": 32924, "uuid": "b1ad4c02319b438884a72fea34cb5a18", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.7 (v20210809)", "abstract": "Data for Figure SPM.7 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure SPM.7 shows the cumulative anthropogenic CO2 emissions taken up by land and ocean sinks by 2100 under the five core scenarios.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n---------------------------------------------------\r\nWhen citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n\r\nIPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n---------------------------------------------------\r\nThis dataset contains cumulative anthropogenic (human-caused) carbon dioxide (CO2) emissions taken up by the land and ocean sinks under the five core scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5), simulated from 1850 to 2100 by Earth System Models that contributed to the sixth phase of the Coupled Model Intercomparison Project (CMIP6).\r\n\r\nThe five illustrative SSP (Shared Socio-economic Pathway) scenarios are described in Box SPM.1 of the Summary for Policymakers and Section 1.6.1.1 of Chapter 1.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n---------------------------------------------------\r\nData file: SPM7_data.csv: each column corresponds to a single scenario, in which rows 2-7 are the bar values, rows 8-10 are the pie chart values and row 11 is the central value in the pie chart.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n---------------------------------------------------\r\nThe following weblink is provided in the Related Documents section of this catalogue record:\r\n- Link to the report webpage, which includes the report component containing the figure (Summary for Policymakers).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:24:23", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, SPM, Sixth Assessment Report, Working Group I, Physical Science Basis, Summary for Policymakers, Figure SPM.7, CO2 emissions, cumulative CO2 emissions, carbon sinks, airborne fraction", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-08-09T08:03:38", "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": { "ob_id": 39, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "Kilometers" }, "result_field": { "ob_id": 32966, "dataPath": "/badc/ar6_wg1/data/spm/spm_07/v20210809", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4587, "numberOfFiles": 4, "fileFormat": "Data are CSV formatted." }, "timePeriod": { "ob_id": 9040, "startTime": "1850-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": { "ob_id": 3723, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-07-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 32926, "uuid": "fb0ba8552b244598a7594c102121172c", "short_code": "comp", "title": "Caption for Figure SPM.7 from the Summary for Policymakers of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Cumulative anthropogenic CO2 emissions taken up by land and ocean sinks by 2100 under the five illustrative scenarios. The cumulative anthropogenic (human-caused) carbon dioxide (CO2) emissions taken up by the land and ocean sinks under the five illustrative scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) are simulated from 1850 to 2100 by CMIP6 climate models in the concentration-driven simulations. Land and ocean carbon sinks respond to past, current and future emissions, therefore cumulative sinks from 1850 to 2100 are presented here. During the historical period (1850-2019) the observed land and ocean sink took up 1430 GtCO2 (59% of the emissions). The bar chart illustrates the projected amount of cumulative anthropogenic CO2 emissions (GtCO2) between 1850 and 2100 remaining in the atmosphere (grey part) and taken up by the land and ocean (coloured part) in the year 2100. The doughnut chart illustrates the proportion of the cumulative anthropogenic CO2 emissions taken up by the land and ocean sinks and remaining in the atmosphere in the year 2100. Values in % indicate the proportion of the cumulative anthropogenic CO2 emissions taken up by the combined land and ocean sinks in the year 2100. The overall anthropogenic carbon emissions are calculated by adding the net global land use emissions from CMIP6 scenario database to the other sectoral emissions calculated from climate model runs with prescribed CO2 concentrations*FOOTNOTE. Land and ocean CO2 uptake since 1850 is calculated from the net biome productivity on land, corrected for CO2 losses due to land-use change by adding the land-use change emissions, and net ocean CO2 flux. {Box TS.5, Box TS.5, Figure 1, 5.2.1, Table 5.1, 5.4.5, Figure 5.25}*Footnote: The other sectoral emissions are calculated as the residual of the net land and ocean CO2 uptake and the prescribed atmospheric CO2 concentration changes in the CMIP6 simulations. These calculated emissions are net emissions and do not separate gross anthropogenic emissions from removals, which are included implicitly." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 10908 ], "observationcollection_set": [ { "ob_id": 32729, "uuid": "ae4f1eb6fce24adcb92ddca1a7838a5c", "short_code": "coll", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nWhen using the datasets from this collection please use the citation indicated on each individual specific dataset, rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure SPM.1\r\n- data for Figure SPM.2\r\n- data for Figure SPM.3\r\n- data for Figure SPM.4\r\n- data for Figure SPM.5\r\n- data for Figure SPM.6\r\n- data for Figure SPM.7\r\n- data for Figure SPM.8\r\n- data for Figure SPM.9\r\n- data for Figure SPM.10" } ], "responsiblepartyinfo_set": [ 146977, 146978, 146979, 146980, 146981, 146982, 146983, 146984, 146985, 168314, 146986, 146987, 146988, 146989, 146990, 146991 ], "onlineresource_set": [ 43298, 47467, 82818, 94787, 94788, 94789 ] }, { "ob_id": 32927, "uuid": "b69d7604f031485bb515eff2de322468", "title": "ESA Cloud Climate Change Initiative (Cloud_cci): ATSR2-AATSR cloud properties on the satellite swath (L2), version 3.0", "abstract": "The Cloud_cci ATSR2-AATSRv3 Level 2 dataset (covering 1995-2012) was generated within the Cloud_cci project, which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. \r\n\r\nThis dataset is based on measurements from the ATSR2 and AATSR instruments (onboard the ERS2 and ENVISAT satellites) and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework. The core cloud properties contained in the Cloud_cci ATSR2-AATSRv3 dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. This particular dataset contains Level 2 data on the satellite swath. Level-3U (globally gridded, unaveraged data fields) and L3C (monthly gridded) is also available as a separate dataset. Pixel-based uncertainty estimates come along with all properties.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-07-14T19:01:58", "updateFrequency": "notPlanned", "dataLineage": "This dataset has been produced as part of the ESA Climate Change Initiative (CCI) cloud project with additional support from the National Centre for Earth Observation (NCEO)", "removedDataReason": "", "keywords": "ESA, CCI, Clouds", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "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": { "ob_id": 32928, "dataPath": "/neodc/esacci/cloud/data/version3/L2/ATSR2-AATSR/CLOUD/v3.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 56169148862303, "numberOfFiles": 253085, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 8164, "startTime": "1995-06-01T00:00:00", "endTime": "2012-04-30T23:59:59" }, "resultQuality": { "ob_id": 3375, "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": "2020-02-04" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 32286, "uuid": "eba971fa045f46f99dd082dc1c103de8", "short_code": "cmppr", "title": "ESA Cloud Climate Change Initiative ATSR2-AATSR version 3 data", "abstract": "The ATSR2-AATSR datasets from the ESA Cloud Climate Change Initiative project have been derived from the ATSR-2 (Along Track Scanning Radiometer-2) instrument on the ERS-2 satellite and the AATSR (Advanced Along Track Scanning Radiometer) on the ENVISAT satellite. They were derived using the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework." }, "imageDetails": [ 147 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2524, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 5, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_cloud_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 68911, 87637, 87638, 87639, 87640, 87641, 87642, 87643, 87644, 87645, 87646, 87647, 87648, 87649, 87650, 87651, 87652, 87653, 87654, 87655, 87656, 87657, 87658, 87659, 87660, 87661, 87662, 87663, 87664, 87665, 87666, 87667, 87668, 87669, 87670, 87671, 87672, 87673, 87674, 87675, 87676, 87677, 87678, 87679, 87680, 87681, 87682, 87683, 87684, 87685, 87686, 87687, 87688, 87689, 87690, 87691, 87692, 87693, 87694, 87695 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 147024, 147023, 147025, 147026, 147027, 147028, 147029, 147030, 147031, 147032, 147033, 147034, 147035, 147036, 147037, 147038, 147039 ], "onlineresource_set": [ 43102, 43103, 43104, 43105, 43106, 43107, 43108, 43109, 43111, 43112, 43113, 43110 ] }, { "ob_id": 32929, "uuid": "420feb0e4f364ed2bdf0bc92171a1374", "title": "ESA Cloud Climate Change Initiative (Cloud_cci): ATSR2-AATSR flux data on the satellite swath (L2), version 3.0", "abstract": "The Cloud_cci ATSR2-AATSRv3 Level 2 dataset (covering 1995-2012) was generated within the Cloud_cci project, which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. \r\n\r\nThis dataset is based on measurements from the ATSR2 and AATSR instruments (onboard the ERS2 and ENVISAT satellites) and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework. This particular dataset contains Level 2 flux data on the satellite swath.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2021-11-04T14:23:23", "updateFrequency": "notPlanned", "dataLineage": "This dataset has been produced as part of the ESA Climate Change Initiative (CCI) cloud project with additional support from the National Centre for Earth Observation (NCEO)", "removedDataReason": "", "keywords": "ESA, CCI, Clouds", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "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": { "ob_id": 32930, "dataPath": "/neodc/esacci/cloud/data/version3/L2/ATSR2-AATSR/FLUX/v3.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 99143962759941, "numberOfFiles": 246683, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 8164, "startTime": "1995-06-01T00:00:00", "endTime": "2012-04-30T23:59:59" }, "resultQuality": { "ob_id": 3375, "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": "2020-02-04" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 32286, "uuid": "eba971fa045f46f99dd082dc1c103de8", "short_code": "cmppr", "title": "ESA Cloud Climate Change Initiative ATSR2-AATSR version 3 data", "abstract": "The ATSR2-AATSR datasets from the ESA Cloud Climate Change Initiative project have been derived from the ATSR-2 (Along Track Scanning Radiometer-2) instrument on the ERS-2 satellite and the AATSR (Advanced Along Track Scanning Radiometer) on the ENVISAT satellite. They were derived using the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework." }, "imageDetails": [ 147 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2524, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 5, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_cloud_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [ 82965, 82973, 82979, 82987, 82991, 82994, 83000, 87728, 87729, 87730, 87731, 87732, 87733, 87734, 87735, 87736, 87737, 87738, 87739, 87740, 87741, 87742, 87743, 87744, 87745, 87746, 87747, 87748 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 147042, 147040, 147041, 147043, 147044, 147045, 147046, 147047, 147048, 147049, 147050, 147051, 147052, 147053, 147054, 147055, 147056 ], "onlineresource_set": [ 43117, 43119, 43120, 43114, 43115, 43116, 43118, 43121, 43122, 43123, 43124, 43125 ] }, { "ob_id": 32932, "uuid": "48ecd3eb90434cc3a843544065c69d47", "title": "ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): 3D vertically resolved water vapour, version 1", "abstract": "To be written", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": null, "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "underDevelopment", "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": 8796, "startTime": "2002-07-01T00:00:00", "endTime": "2017-12-31T23:59:59" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 32934, "uuid": "24186d13405b4000aef0ba577b9031aa", "short_code": "proj", "title": "ESA Water Vapour Climate Change Initiative Project", "abstract": "The Water Vapour Climate Change Initiatve Project (Water_Vapour_cci) is part of the European Space Agency's Climate Change Initiative Programme. The project aims to generate new global high-quality climate data records of both total column and vertically resolved water vapour." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 147065, 147066, 147067, 147068, 147063, 147064 ], "onlineresource_set": [] }, { "ob_id": 32933, "uuid": "92824e3ec2e44a58b10048df3209b99c", "title": "ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Vertically resolved water vapour - stratosphere (CCI WV-strato, CDR-3), v3.3", "abstract": "This water vapour (WV) climate data record (CCI WV-strato or WV_cci CDR-3) has been generated within the European Space Agency (ESA) Water Vapour Climate Change Initiative (Water Vapour_cci). CCI WV-strato is a merged product based on a number of WV datasets obtained from limb and solar occultation satellite instruments. CCI WV-strato features zonal monthly mean vertically resolved water vapour in the stratosphere, covers a pressure range from 300 to 0.1 hPa at 5 degree latitudinal resolution, and spans the time period between 1985 and 2019.\r\n\r\nThis version of the data is v3.3.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-10-21T17:14:39", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "stratospheric water vapour, ESA Climate Change Initiative, CCI", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-10-21T16:24:05", "doiPublishedTime": "2022-11-22T14:00:29.327907", "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": 38319, "dataPath": "/neodc/esacci/water_vapour/data/WV-strato/L3S/v3.3", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 8483130, "numberOfFiles": 2, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 10409, "startTime": "1985-01-01T00:00:00", "endTime": "2019-12-31T23:59:59" }, "resultQuality": { "ob_id": 4101, "explanation": "For information on the data quality see the water vapour CCI documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-10-14" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 37713, "uuid": "83951253e83b4d4dadc4b07a79a76b0e", "short_code": "comp", "title": "Derivation of ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Vertically resolved water vapour - stratosphere (CCI WV-strato, CDR-3), v3.3", "abstract": "CCI WV-strato is based on multiple limb satellite sounders (SAGE II, UARS-MLS, HALOE, POAM III, SMR, SAGE III, SCIAMACHY, MIPAS, ACE-FTS, ACE-MAESTRO, Aura-MLS and SAGE III/ISS) merged using a chemistry-climate model in specified dynamics mode as transfer function following the methodology established in Hegglin et al. (2014)." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2529, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 9, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_watervapour_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32934, "uuid": "24186d13405b4000aef0ba577b9031aa", "short_code": "proj", "title": "ESA Water Vapour Climate Change Initiative Project", "abstract": "The Water Vapour Climate Change Initiatve Project (Water_Vapour_cci) is part of the European Space Agency's Climate Change Initiative Programme. The project aims to generate new global high-quality climate data records of both total column and vertically resolved water vapour." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6023, 50415, 60450, 92024, 92025, 92026, 92027, 92028, 92029 ], "vocabularyKeywords": [], "identifier_set": [ 12296 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 147069, 147070, 147071, 147072, 147073, 147074, 179800, 179802, 179801, 179803, 179804, 179805, 179806, 179807, 179808 ], "onlineresource_set": [ 52443, 52444, 53048, 53049, 53069, 53070 ] }, { "ob_id": 32935, "uuid": "c1eb6dad1598427f8f9f3eae346ece2f", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.2 (v20210809)", "abstract": "Data for Figure SPM.2 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure SPM.2 relates to assessed contributions to observed warming.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n---------------------------------------------------\r\nWhen citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n\r\nIPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n---------------------------------------------------\r\nThe figure has three panels, with data provided for all panels in subdirectories named panel_a, panel_b and panel_c. \r\n\r\n---------------------------------------------------\r\n List of data provided\r\n---------------------------------------------------\r\n This data set contains:\r\n\r\n- Observed warming (2010-2019 relative to 1850-1900)\r\n - Aggregated contributions to 2010-2019 warming relative 1850 -1900, assessed from attribution studies\r\n - Contributions to 2010-2019 warming relative to 1850-1900, assessed from radiative studies\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n---------------------------------------------------\r\n Panel a:\r\n\r\n- Data file: panel_a/SPM2a.csv (Observed warming). Mean value is used for the bar plot and top and bottom values are used for the error bars and they represent borders of the very likely range.\r\n\r\nPanel b:\r\n\r\n - Data file: panel_b/SPM2b.csv (Aggregated contributions assessed from attribution studies). Mean values are used for the bar plot and top and bottom values are used for the error bars and represent the borders of the very likely range\r\n\r\nPanel c:\r\n\r\n - Data file: panel_c/SPM2c_data.csv (Contributions assessed from radiative studies). Total global surface air temperature (GSAT) effect values are used for the bar plots and 5% and 95% very likely limit values are used for the error bars.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n---------------------------------------------------\r\n The following weblink is provided in the Related Documents section of this catalogue record:\r\n\r\n- Link to the report webpage, which includes the report component containing the figure (Summary for Policymakers) and the Supplementary Material for Chapters 3, 6 and 7, which contain details on the input data used in Tables 3.SM.1 (Figure 3.8), 6.SM.1 (Figure 6.12) and 7.SM.14 (Figure 7.7).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2021-08-06T19:01:39", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, SPM, Sixth Assessment Report, Working Group I, Physical Science Basis, Summary for Policymakers, Figure SPM.2, emissions, drivers, historical, attribution, human influence, global mean surface temperature, GMST, CMIP6, HadCRUT4.6, global warming", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-08-09T08:03:29", "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": { "ob_id": 40, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "Kilometers" }, "result_field": { "ob_id": 32967, "dataPath": "/badc/ar6_wg1/data/spm/spm_02/v20210809", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 10279, "numberOfFiles": 6, "fileFormat": "Data are CSV formatted." }, "timePeriod": { "ob_id": 9066, "startTime": "2010-01-01T00:00:00", "endTime": "2019-12-31T23:59:59" }, "resultQuality": { "ob_id": 3725, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-07-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 32937, "uuid": "a3773efdf0c149c5af0ba322e52e8320", "short_code": "comp", "title": "Caption for Figure SPM.2 from the Summary for Policymakers of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Assessed contributions to observed warming in 2010–2019 relative to 1850–1900. \r\nPanel a): Observed global warming (increase in global surface temperature) and its very likely range {3.3.1, Cross-Chapter Box 2.3}.\r\nPanel b): Evidence from attribution studies, which synthesize information from climate models and observations. The panel shows temperature change attributed to total human influence, changes in well-mixed greenhouse gas concentrations, other human drivers due to aerosols, ozone and land-use change (land-use reflectance), solar and volcanic drivers, and internal climate variability. Whiskers show likely ranges {3.3.1}. \r\nPanel c): Evidence from the assessment of radiative forcing and climate sensitivity. The panel shows temperature changes from individual components of human influence, including emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails. Whiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers. For aerosols, both direct (through radiation) and indirect (through interactions with clouds) effects are considered. {6.4.2, 7.3}" }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 10907 ], "observationcollection_set": [ { "ob_id": 32729, "uuid": "ae4f1eb6fce24adcb92ddca1a7838a5c", "short_code": "coll", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nWhen using the datasets from this collection please use the citation indicated on each individual specific dataset, rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure SPM.1\r\n- data for Figure SPM.2\r\n- data for Figure SPM.3\r\n- data for Figure SPM.4\r\n- data for Figure SPM.5\r\n- data for Figure SPM.6\r\n- data for Figure SPM.7\r\n- data for Figure SPM.8\r\n- data for Figure SPM.9\r\n- data for Figure SPM.10" } ], "responsiblepartyinfo_set": [ 147081, 147082, 147083, 147084, 147085, 147086, 147087, 147088, 147089, 168316, 147090, 147091, 147092, 147093, 147094, 147095 ], "onlineresource_set": [ 43296, 47468, 82812 ] }, { "ob_id": 32938, "uuid": "a1c7ae7387af4f048346d05ba36babd8", "title": "ESA Ozone Climate Change Initiative (Ozone_cci): ACE-FTS Level 3 monthly mean zonal mean ozone profiles on an altitude grid, v0001", "abstract": "The dataset provides monthly zonal mean (MZM) ozone profiles from the ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer) instrument onboard SCISAT, created by the ESA Ozone Climate Change Initiative project. The data are provided as ozone molar concentration (mol m-3) in 10 degree latitude bands from 90S to 90N, in the altitude range from 6 to 94 km. \r\n\r\nThe ACE-FTS Level 2 profiles (Bernath et al., 2005; Bernath, 2017), which are used for the MZM data, are retrieved with the University of Toronto processor UoT v3.5/3.6 and included into the new version of the HARMonized datasets of OZone profiles (HARMOZ_ALT, Sofieva et al., 2013). A more detailed description of the MZM data processing and dataset parameters can be found in README and in (Sofieva et al., 2017).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": null, "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "Climate Change Initiative, CCI, Ozone, ACE", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "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": 9042, "startTime": "2004-02-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3726, "explanation": "For information on the data quality see the linked documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-07-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 32941, "uuid": "a0b041d6de0341b1a6218014d3964417", "short_code": "cmppr", "title": "Composite process for the ESA Ozone Climate Change Initiative: ACE-FTS Level 3 monthly mean zonal mean ozone profiles on an altitude grid, v0001", "abstract": "The ACE-FTS L3 Monthly Zonal Mean (MZM) data was derived from the ACE-FTS instrument on SCISAT. The data was derived as detailed in the computation section." }, "imageDetails": [ 111 ], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 14147, "uuid": "10b36f5715274b1d985c569501ceed68", "short_code": "proj", "title": "ESA Ozone Climate Change Initiative Project", "abstract": "The European Space Agency Ozone Climate Change Initiative (Ozone CCI) project is one of several projects of ESA's Climate Change Initiative (CCI), which will deliver various Essential Climate Variables (ECVs). \r\nOzone_cci aims at generating new high-quality satellite data sets that are essential to assess the fate of atmospheric ozone and better understand its link with anthropogenic activities." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [ { "ob_id": 10669, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/ecv/cciecv_ozone", "resolvedTerm": "ozone" } ], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 147098, 147099, 147100, 147101, 147102, 147103, 147104, 147105 ], "onlineresource_set": [ 43290, 43291, 43292, 43293, 43294, 43295 ] }, { "ob_id": 32944, "uuid": "30eca31abb0244efa68e05dd28b2e1de", "title": "ESA Ozone Climate Change Initiative (Ozone_cci): GOMOS Level 3 monthly mean zonal mean ozone profiles on an altitude grid, v0001", "abstract": "This dataset provides monthly zonal mean (MZM) ozone profiles from the GOMOS (Global Ozone Monitoring by Occultation of Stars) instrument onboard ENVISAT, created by the ESA Ozone Climate Change Initiative project. The data are provided as ozone molar concentration (mol m-3) in 10 degree latitude bands from 90S to 90N, in the altitude range from 10 to 105 km. \r\n\r\nThe GOMOS Level 2 profiles, which are used for the MZM data, are retrieved with the scientific ALGOM2s v1 processor (Sofieva et al., 2017a) and included into the new version of the HARMonized datasets of OZone profiles (HARMOZ_ALT, Sofieva et al., 2013). \r\n\r\nA more detailed description of the GOMOS Monthly zonal mean data can be found in the README and in (Sofieva et al., 2017b).\r\n\r\n\r\n\r\n\r\n.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": null, "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "Climate Change Initiative, CCI, Ozone, GOMOS", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "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": 9043, "startTime": "2002-08-01T00:00:00", "endTime": "2011-12-31T23:59:59" }, "resultQuality": { "ob_id": 3726, "explanation": "For information on the data quality see the linked documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-07-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 32947, "uuid": "48aa2b83890b49c8bde874553141555a", "short_code": "cmppr", "title": "Composite process for the ESA Ozone Climate Change Initiative: GOMOS Level 3 monthly mean zonal mean ozone profiles on an altitude grid, v0001", "abstract": "The GOMOS L3 Monthly Zonal Mean (MZM) data was derived from the GOMOS instrument on the ENVISAT. The data was derived as detailed in the computation section." }, "imageDetails": [ 111 ], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 14147, "uuid": "10b36f5715274b1d985c569501ceed68", "short_code": "proj", "title": "ESA Ozone Climate Change Initiative Project", "abstract": "The European Space Agency Ozone Climate Change Initiative (Ozone CCI) project is one of several projects of ESA's Climate Change Initiative (CCI), which will deliver various Essential Climate Variables (ECVs). \r\nOzone_cci aims at generating new high-quality satellite data sets that are essential to assess the fate of atmospheric ozone and better understand its link with anthropogenic activities." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 147115, 147116, 147117, 147118, 147119, 147120, 147121, 147122 ], "onlineresource_set": [ 43283, 43284, 43285, 43286, 43287 ] }, { "ob_id": 32948, "uuid": "8e930bdbd3d34e91995ca631da982c52", "title": "ESA Ozone Climate Change Initiative (Ozone_cci): MIPAS Level 3 monthly mean zonal mean ozone profiles on an altitude grid, v0001", "abstract": "This dataset provides monthly zonal mean (MZM) ozone profiles from the MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) instrument onboard ENVISAT, created by the ESA Ozone Climate Change Initiative project. The data are provided as ozone molar concentration (mol m-3) in 10 degree latitude bands from 90S to 90N, in the altitude range from 6 to 70 km. \r\n\r\nThe MIPAS Level 2 profiles, which are used for the MZM data, are retrieved with the Karlsruhe Institute of Technology processor KIT/IAA V7R_O3_240 (von Clarmann et al., 2003; 2009) and included into the new version of the HARMonized datasets of Ozone profiles (HARMOZ_ALT, Sofieva et al., 2013). \r\n\r\nA more detailed description of the MIPAS Monthly zonal mean data can be found in the README and in (Sofieva et al., 2017).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": null, "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "Climate Change Initiative, CCI, Ozone, GOMOS", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "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": 9043, "startTime": "2002-08-01T00:00:00", "endTime": "2011-12-31T23:59:59" }, "resultQuality": { "ob_id": 3726, "explanation": "For information on the data quality see the linked documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-07-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 32951, "uuid": "101d3d9c256d4b259725cf05d11afc87", "short_code": "cmppr", "title": "Composite process for the ESA Ozone Climate Change Initiative: MIPAS Level 3 monthly mean zonal mean ozone profiles on an altitude grid, v0001", "abstract": "The MIPAS L3 Monthly Zonal Mean (MZM) data was derived from the MIPAS instrument on the ENVISAT satellite. The data was derived as detailed in the computation section." }, "imageDetails": [ 111 ], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 14147, "uuid": "10b36f5715274b1d985c569501ceed68", "short_code": "proj", "title": "ESA Ozone Climate Change Initiative Project", "abstract": "The European Space Agency Ozone Climate Change Initiative (Ozone CCI) project is one of several projects of ESA's Climate Change Initiative (CCI), which will deliver various Essential Climate Variables (ECVs). \r\nOzone_cci aims at generating new high-quality satellite data sets that are essential to assess the fate of atmospheric ozone and better understand its link with anthropogenic activities." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 147127, 147128, 147132, 147129, 147130, 147131, 147133, 147134 ], "onlineresource_set": [ 43279, 43280, 43281, 43282, 43288, 43289 ] }, { "ob_id": 32961, "uuid": "27d14e4b9ecb426bb22c3c8a93391d4e", "title": "ESA Ozone Climate Change Initiative (Ozone_cci): OSIRIS Level 3 monthly mean zonal mean ozone profiles on an altitude grid, v0002", "abstract": "The dataset provides monthly zonal mean (MZM) ozone profiles from the OSIRIS (Optical Spectrograph and InfraRed Imaging System) instrument onboard Odin, created as part of the ESA Ozone Climate Change Initiative project. The data are provided as ozone molar concentration (mol m-3) in 10 degree latitude bands from 90S to 90N, in the altitude range from 10 to 59 km. \r\n\r\nThe OSIRIS Level 2 profiles, which are used for the MZM data, are retrieved with the University of Saskatchewan processor USask v.5.10 (Bourassa et al., 2017; Degenstein et al., 2009) and included into the new version of the HARMonized datasets of Ozone profiles (HARMOZ_ALT, Sofieva et al., 2013). \r\n\r\nA more detailed description of the data processing and dataset parameters can be found in the README and in (Sofieva et al., 2017).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": null, "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "Climate Change Initiative, CCI, Ozone, OSIRIS", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "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": 9045, "startTime": "2001-11-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3726, "explanation": "For information on the data quality see the linked documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-07-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 32960, "uuid": "6afb33b6bc8145d49ade2e2cc7784993", "short_code": "cmppr", "title": "Composite process for the ESA Ozone Climate Change Initiative: OSIRIS Level 3 monthly mean zonal mean ozone profiles on an altitude grid, v0002", "abstract": "The OSIRIS L3 Monthly Zonal Mean (MZM) data was derived from the OSIRIS instrument on the Odin satellite. The data was derived as detailed in the computation section." }, "imageDetails": [ 111 ], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 14147, "uuid": "10b36f5715274b1d985c569501ceed68", "short_code": "proj", "title": "ESA Ozone Climate Change Initiative Project", "abstract": "The European Space Agency Ozone Climate Change Initiative (Ozone CCI) project is one of several projects of ESA's Climate Change Initiative (CCI), which will deliver various Essential Climate Variables (ECVs). \r\nOzone_cci aims at generating new high-quality satellite data sets that are essential to assess the fate of atmospheric ozone and better understand its link with anthropogenic activities." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 147153, 147154, 147155, 147156, 147157, 147158, 147159, 147160 ], "onlineresource_set": [ 43273, 43274, 43275, 43276, 43277, 43278 ] }, { "ob_id": 32964, "uuid": "876e3b41a2d6482db13887914b38a96c", "title": "ESA Ozone Climate Change Initiative (Ozone_cci): SCIAMACHY Level 3 monthly mean zonal mean ozone profiles on an altitude grid, v0001", "abstract": "This dataset provides monthly zonal mean (MZM) ozone profiles from the SCIAMACHY (SCanning Imaging Spectrometer for Atmospheric CHartographY) instrument onboard ENVISAT, created as part of the ESA Ozone Climate Change Initiative project. The data are provided as ozone molar concentration (mol m-3) in 10 degree latitude bands from 90S to 90N, in the altitude range from 5 to 65 km. \r\n\r\nThe SCIAMACHY Level 2 profiles, which are used for the MZM data, are retrieved with the University of Bremen processor UBr v3.5 (Jia et al., 2015) and included into the new version of the HARMonized datasets of Ozone profiles (HARMOZ_ALT, Sofieva et al., 2013). \r\n\r\nA more detailed description of the data processing and dataset parameters can be found in the README and in (Sofieva et al., 2017).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": null, "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "Climate Change Initiative, CCI, Ozone, SCIAMACHY", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "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": 9046, "startTime": "2002-08-01T00:00:00", "endTime": "2012-03-31T23:59:59" }, "resultQuality": { "ob_id": 3726, "explanation": "For information on the data quality see the linked documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-07-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 32953, "uuid": "dcc20903b5214fc0973cb00450a8cabc", "short_code": "cmppr", "title": "Composite process for the ESA Ozone Climate Change Initiative: SCIAMACHY Level 3 monthly mean zonal mean ozone profiles on an altitude grid, v0001", "abstract": "The SCIAMACHY L3 Monthly Zonal Mean (MZM) data was derived from the SCIAMACHY instrument on the ENVISAT. The data was derived as detailed in the computation section." }, "imageDetails": [ 111 ], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 14147, "uuid": "10b36f5715274b1d985c569501ceed68", "short_code": "proj", "title": "ESA Ozone Climate Change Initiative Project", "abstract": "The European Space Agency Ozone Climate Change Initiative (Ozone CCI) project is one of several projects of ESA's Climate Change Initiative (CCI), which will deliver various Essential Climate Variables (ECVs). \r\nOzone_cci aims at generating new high-quality satellite data sets that are essential to assess the fate of atmospheric ozone and better understand its link with anthropogenic activities." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 147161, 147162, 147163, 147164, 147165, 147166, 147167, 147168 ], "onlineresource_set": [ 43268, 43269, 43270, 43271, 43272 ] }, { "ob_id": 32968, "uuid": "92e823b277cc4f439803a87f5246db5f", "title": "MIDAS Open: UK daily temperature data, v202107", "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 2020. 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 version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. Of particular note, however, is that as well as including data for 2020, historical data recovery has added further data for Eskdalemuir (1915-1948) and Eastbourne (1887-1910).\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 the 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", "latestDataUpdateTime": "2021-08-12T20:43:07", "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": "2021-08-26T10:06:54", "doiPublishedTime": "2021-09-08T10:27:30", "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": 32979, "dataPath": "/badc/ukmo-midas-open/data/uk-daily-temperature-obs/dataset-version-202107/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2120207136, "numberOfFiles": 62229, "fileFormat": "Data are BADC-CSV formatted." }, "timePeriod": { "ob_id": 9053, "startTime": "1853-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "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": [ 31142, 31143, 31144, 31145, 31146, 31147, 31148, 31149, 31150, 31151, 31152, 31153, 31154, 31155, 31156, 31157, 31158, 31159, 31160, 31161, 31162, 31163, 31164, 31165, 31166, 31167, 31168, 31169, 31170, 31171, 31172, 31173, 31174, 31175, 31176 ], "vocabularyKeywords": [], "identifier_set": [ 10925 ], "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": [ 147174, 147175, 147169, 147176, 147173, 147172, 147171, 147170, 147177 ], "onlineresource_set": [ 43159, 43161, 43156, 43162, 43163, 43157, 89462, 89463, 89464, 89465, 89466, 89467, 89468, 89469, 89470, 43158, 87516, 87517, 43160 ] }, { "ob_id": 32969, "uuid": "cabc37d867fa4f2a84302350df908693", "title": "MIDAS Open: UK soil temperature data, v202107", "abstract": "The UK soil temperature data contain daily and hourly values of soil temperatures at depths of 5, 10, 20, 30, 50, and 100 centimetres. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM or DLY3208 messages. The data spans from 1900 to 2020.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nAt many stations temperatures below the surface are measured at various depths. The depths used today are 5, 10, 20, 30 and 100cm, although measurements are not necessarily made at all these depths at a station and exceptionally measurements may be made at other depths. When imperial units were in general use, typically before 1961, the normal depths of measurement were 4, 8, 12, 24 and 48 inches.\r\n\r\nLiquid-in-glass soil thermometers at a depth of 20 cm or less are unsheathed and have a bend in the stem between the bulb and the lowest graduation. At greater depths the thermometer is suspended in a steel tube and has its bulb encased in wax.\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 the 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.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2021-08-12T18:25:23", "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, soil temperatures", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2021-08-26T09:51:08", "doiPublishedTime": "2021-09-08T10:27:52", "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": 32982, "dataPath": "/badc/ukmo-midas-open/data/uk-soil-temperature-obs/dataset-version-202107/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3953950041, "numberOfFiles": 23308, "fileFormat": "Data are BADC-CSV formatted." }, "timePeriod": { "ob_id": 9051, "startTime": "1900-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 301, "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": 1227, "uuid": "0c787dde18584b3293d4f55daf6ef431", "short_code": "acq", "title": "Acquisition Process for: UK Soil 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, HCM (Hourly Climate Messages) Station Network, DLY3208 (Daily observations from Metform 3208) Station Network, AWSHRLY (Automatic Weather Station Hourly 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": [ 31142, 31144, 31146, 31150, 31161, 31162, 31164, 31165, 31166, 31167, 31217, 31218, 31219, 31220, 31221, 31222, 31223, 31224, 31225, 31226, 31227, 31228, 31229, 31230, 31231, 31232, 31233, 31234, 31235, 31236, 31237 ], "vocabularyKeywords": [], "identifier_set": [ 10926 ], "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": [ 147178, 147185, 147184, 147183, 147182, 147181, 147179, 147180, 147186 ], "onlineresource_set": [ 43165, 43166, 43168, 43170, 43171, 43164, 43167, 43169, 91116, 91117, 91118, 91119, 91120, 91121, 91122, 91123, 91124, 91125, 91126, 91127, 91128, 91129 ] }, { "ob_id": 32970, "uuid": "f7ae919f96b44a1c9695f40a9cf988dd", "title": "MIDAS Open: UK hourly rainfall data, v202107", "abstract": "The UK hourly rainfall data contain the rainfall amount (and duration from tilting syphon gauges) during the hour (or hours) ending at the specified time. The data also contains precipitation amounts, however precipitation measured over 24 hours are not stored. Over time a range of rain gauges have been used - see the linked MIDAS User Guide for further details.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nThe data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSHRLY, DLY3208, SREW and SSER. The data spans from 1915 to 2020.\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 the 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.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2021-08-12T17:28:46", "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, hourly", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2021-08-26T09:54:50", "doiPublishedTime": "2021-09-08T10:28:14", "removedDataTime": null, "geographicExtent": { "ob_id": 17, "bboxName": "", "eastBoundLongitude": 1.74002, "westBoundLongitude": -8.5636, "southBoundLatitude": 49.914, "northBoundLatitude": 60.8562 }, "verticalExtent": null, "result_field": { "ob_id": 32978, "dataPath": "/badc/ukmo-midas-open/data/uk-hourly-rain-obs/dataset-version-202107/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4952556253, "numberOfFiles": 15263, "fileFormat": "Data are BADC-CSV formatted." }, "timePeriod": { "ob_id": 9047, "startTime": "1915-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 308, "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": 27188, "uuid": "b0418372412d4d79ba08d65681fc6f5d", "short_code": "acq", "title": "Acquisition Process for: UK Hourly Rainfall Data, Part of the Met Office Integrated Data Archive System (MIDAS) Open (excludes WAHRAIN)", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Raingauge; PLATFORMS: SSER (Solid State Event Recorder) Station Network, AWSHRLY (Automatic Weather Station Hourly values) Station Network, SREW (Synoptic Rainfall Europe West) Station Network, DLY3208 (Daily observations from Metform 3208) Station Network, NCM (National Climate Message) 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": [ 68435, 68437, 68439, 68443, 68454, 68455, 68457, 68458, 68459, 68460, 68804, 68810, 68813, 68820, 68821, 68823, 68825, 68826, 83906 ], "vocabularyKeywords": [], "identifier_set": [ 10927 ], "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": [ 147187, 147192, 147191, 147193, 147190, 147194, 147189, 147188, 147195 ], "onlineresource_set": [ 43179, 43172, 43173, 43176, 43178, 43175, 43174, 43177, 90458, 90459, 87734, 90460, 90461, 90462, 90463, 90464, 90465, 90466, 90467, 90468, 90469, 90470, 90471, 90472, 90473, 90474, 90475, 90476, 90477, 90478, 90479 ] }, { "ob_id": 32971, "uuid": "d6bcf4171c2f4754a7455d00deda0f72", "title": "MIDAS Open: UK daily rainfall data, v202107", "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 2020. 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 version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\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", "latestDataUpdateTime": "2021-08-12T21:16:08", "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": "2021-08-26T09:57:30", "doiPublishedTime": "2021-09-08T10:25:29", "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": 32976, "dataPath": "/badc/ukmo-midas-open/data/uk-daily-rain-obs/dataset-version-202107/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1086544333, "numberOfFiles": 51431, "fileFormat": "Data are BADC-CSV formatted" }, "timePeriod": { "ob_id": 9048, "startTime": "1853-01-02T00:00:00", "endTime": "2020-12-31T23:59:59" }, "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": [ 31142, 31144, 31146, 31150, 31161, 31162, 31164, 31165, 31166, 31167, 31209, 31210, 31211, 31212, 31213, 31214, 31215, 31216, 31217 ], "vocabularyKeywords": [], "identifier_set": [ 10922 ], "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": [ 147196, 147203, 147200, 147199, 147198, 147197, 147201, 147202, 147204 ], "onlineresource_set": [ 43180, 43183, 43184, 43185, 43186, 43187, 43181, 43182, 91169, 91170, 91171, 91172, 91173, 91174, 91175, 91176, 91177, 91178, 91179, 91180, 91181, 91182, 91183, 91184, 91185, 91186, 91187, 91188, 91189, 91190, 91191 ] }, { "ob_id": 32972, "uuid": "4d48efaaeb7f47a7963df75d6d1dbdc5", "title": "MIDAS Open: UK mean wind data, v202107", "abstract": "The UK mean wind data contain the mean wind speed and direction, and the direction, speed and time of the maximum gust, all during 1 or more hours, ending at the stated time and date. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, DLY3208, HWNDAUTO and HWND6910. The data spans from 1949 to 2020.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nFor further details on observing practice, including measurement accuracies for the message types, see relevant sections of the MIDAS User Guide linked from this record (e.g. section 3.3 details the wind network in the UK, section 5.5 covers wind measurements in general and section 4 details message type information).\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 the 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.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2021-08-12T17:58:21", "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, wind speed, wind direction, gust", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2021-08-26T10:06:13", "doiPublishedTime": "2021-09-08T10:25:54", "removedDataTime": null, "geographicExtent": { "ob_id": 17, "bboxName": "", "eastBoundLongitude": 1.74002, "westBoundLongitude": -8.5636, "southBoundLatitude": 49.914, "northBoundLatitude": 60.8562 }, "verticalExtent": null, "result_field": { "ob_id": 32981, "dataPath": "/badc/ukmo-midas-open/data/uk-mean-wind-obs/dataset-version-202107/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 7589141537, "numberOfFiles": 14406, "fileFormat": "Data are BADC-CSV formatted." }, "timePeriod": { "ob_id": 9050, "startTime": "1949-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 296, "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": 1194, "uuid": "38b9698404d64725bc8a0b9b6b6f15ae", "short_code": "acq", "title": "Acquisition Process for: UK Mean Wind Data, Part of the Met Office Integrated Data Archive System (MIDAS)", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Anemometer; PLATFORMS: Land SYNOP (surface synoptic observations) Station Network, HCM (Hourly Climate Messages) Station Network, AWSHRLY (Automatic Weather Station Hourly values) Station Network, DLY3208 (Daily observations from Metform 3208) Station Network, HWND6910 (Hourly WIND from Metform 6910) Station Network, HWNDAUTO (Hourly WiND from AUTOmatic recording devices) 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": [ 68435, 68437, 68439, 68443, 68454, 68455, 68457, 68458, 68459, 68460, 68803, 68804, 68805, 68806, 68807, 68808, 68809, 68810, 68811, 68812, 68813, 68814, 68815, 68816, 68817, 68818, 68819, 68820 ], "vocabularyKeywords": [], "identifier_set": [ 10923 ], "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": [ 147205, 147206, 147211, 147212, 147210, 147209, 147208, 147207, 147213 ], "onlineresource_set": [ 43195, 43188, 43189, 43191, 43193, 43194, 43192, 43190, 91147, 91148, 91149, 91150, 91151, 91152, 91153, 91154, 91155, 91156, 91157, 91158, 91159, 91160, 91161, 91162, 91163, 91164, 91165, 91166, 91167, 91168 ] }, { "ob_id": 32973, "uuid": "d399794d81fa41779a925b6d4758a5cd", "title": "MIDAS Open: UK daily weather observation data, v202107", "abstract": "The UK daily weather observation data contain meteorological values measured on a 24 hour time scale. The measurements of sunshine duration, concrete state, snow depth, fresh snow depth, and days of snow, hail, thunder and gail were attained by observation stations operated by the Met Office across the UK operated and transmitted within DLY3208, NCM, AWSDLY and SYNOP messages. The data span from 1887 to 2020. For details of observations see the relevant sections of the MIDAS User Guide linked from this record for the various message types.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. Of particular note, however, is that as well as including data for 2020, historical data recovery has added further data for Eastbourne (1887-1910).\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 the 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 weather observations within the full MIDAS collection.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2021-08-12T20:47:23", "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, daily, diurnal, monthly", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2021-08-26T10:03:59", "doiPublishedTime": "2021-09-08T10:28:40", "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": 32977, "dataPath": "/badc/ukmo-midas-open/data/uk-daily-weather-obs/dataset-version-202107/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3006018865, "numberOfFiles": 46758, "fileFormat": "Data are BADC-CSV formatted." }, "timePeriod": { "ob_id": 9049, "startTime": "1887-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 306, "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": 1246, "uuid": "1efa07092762447189d030fe5d41e1fa", "short_code": "acq", "title": "Acquisition Process for: UK Daily Weather Observation Data, Part of the Met Office Integrated Data Archive System (MIDAS)", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Station Observer, Sunshine Recorder, Cloud Recorder, Snow Depth Sensor, Present (and Past) Weather Sensor; PLATFORMS: Land SYNOP (surface synoptic observations) Station Network, 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": [ 68435, 68437, 68439, 68443, 68454, 68455, 68457, 68458, 68459, 68460, 68810, 68820, 86989, 87012, 89205, 89206, 89207, 89208, 89209, 89210, 89211, 89212, 89213, 89214, 89215, 89216, 89217, 89218, 89219, 89220, 89221, 89222, 89223, 89224, 89225, 89226, 89227, 89228 ], "vocabularyKeywords": [], "identifier_set": [ 10928 ], "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": [ 147214, 147219, 147220, 147221, 147218, 147217, 147216, 147215, 147222 ], "onlineresource_set": [ 43196, 43199, 43200, 43202, 43203, 43201, 43198, 90454, 90455, 90456, 90457, 87733, 90446, 90447, 90448, 90449, 90450, 90451, 90452, 90453, 43197 ] }, { "ob_id": 32974, "uuid": "3bd7221d4844435dad2fa030f26ab5fd", "title": "MIDAS Open: UK hourly weather observation data, v202107", "abstract": "The UK hourly weather observation data contain meteorological values measured on an hourly time scale. The measurements of the concrete state, wind speed and direction, cloud type and amount, visibility, and temperature were recorded by observation stations operated by the Met Office across the UK and transmitted within SYNOP, DLY3208, AWSHRLY and NCM messages. The sunshine duration measurements were transmitted in the HSUN3445 message. The data spans from 1875 to 2020.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. Of particular note, however, is that as well as including data for 2020, historical data recovery has added further data for Eskdalemuir (1914-1944) and Eastbourne (1887-1910).\r\n\r\nFor details on observing practice see the message type information in the MIDAS User Guide linked from this record and relevant sections for parameter types.\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. Note, METAR message types are not included in the Open version of this dataset. Those data may be accessed via the full MIDAS hourly weather data.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2021-08-15T09:24:24", "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, hourly", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2021-08-26T10:03:41", "doiPublishedTime": "2021-09-08T10:29:11", "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": 32980, "dataPath": "/badc/ukmo-midas-open/data/uk-hourly-weather-obs/dataset-version-202107/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 30652786376, "numberOfFiles": 50518, "fileFormat": "Data are BADC-CSV formatted." }, "timePeriod": { "ob_id": 9052, "startTime": "1875-01-01T09:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 300, "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": 27186, "uuid": "336b071077af4805a8394f1402c5c26b", "short_code": "acq", "title": "Acquisition Process for: UK Hourly Weather Observation Data, Part of the Met Office Integrated Data Archive System (MIDAS) Open version (excluded METARS)", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Thermometer, Visiometer, Station Observer, Sunshine Recorder, Raingauge, Cloud Recorder, Snow Depth Sensor, Present (and Past) Weather Sensor; PLATFORMS: Land SYNOP (surface synoptic observations) Station Network, METAR (MEteorological Terminal Aviation Routine Weather Report) Station Network, NCM (National Climate Message) Station Network, DLY3208 (Daily observations from Metform 3208) Station Network, AWSHRLY (Automatic Weather Station Hourly values) Station Network, HSUN3445 (Hourly values of SUNshine duration from Metform 3445) 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": [ 68435, 68439, 68443, 68454, 68455, 68457, 68458, 68459, 68460, 68461, 68462, 68463, 68464, 68465, 68466, 68467, 68468, 68469, 68810, 68820, 68833, 86927, 86928, 86929, 86930, 86931, 86932, 86933, 86934, 86935, 86936, 86937, 86938, 86939, 86940, 86941, 86942, 86943, 86944, 86945, 86946, 86947, 86948, 86949, 86950, 86951, 86952, 86953, 86954, 86955, 86956, 86957, 86958, 86959, 86960, 86961, 86962, 86963, 86964, 86965, 86966, 86967, 86968, 86969, 86970, 86971, 86972, 86973, 86974, 86975, 86976, 86977, 86978, 86979, 86980, 86981, 86982, 86983, 86984, 86985, 86986, 86987, 86988, 86989, 86990, 86991, 86992, 86993, 86994, 86995, 86996, 86997, 86998, 86999, 87000, 87001, 87002, 87003, 87004, 87005, 87006, 87007, 87008, 87009, 87010, 87011, 87012, 87013, 87014, 87015, 87016, 87017, 87018, 87019, 87020, 87021, 87022 ], "vocabularyKeywords": [], "identifier_set": [ 10929 ], "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": [ 147226, 147228, 147223, 147230, 147229, 147227, 147225, 147224, 147231 ], "onlineresource_set": [ 43209, 43204, 43210, 43211, 43206, 43205, 89550, 89551, 89552, 89553, 89554, 89555, 89556, 89557, 89558, 89559, 89560, 89561, 89562, 43207, 87648, 87649, 43208 ] }, { "ob_id": 32975, "uuid": "625f5ea4ddac4578a2aacf47bcf39657", "title": "MIDAS Open: UK hourly solar radiation data, v202107", "abstract": "The UK hourly solar radiation data contain the amount of solar irradiance received during the hour ending at the specified time. All sites report 'global' radiation amounts. This is also known as 'total sky radiation' as it includes both direct solar irradiance and 'diffuse' irradiance as a result of light scattering. Some sites also provide separate diffuse and direct irradiation amounts, depending on the instrumentation at the site. For these the sun's path is tracked with two pyrometers - one where the path to the sun is blocked by a suitable disc to allow the scattered sunlight to be measured to give the diffuse measurement, while the other has a tube pointing at the sun to measure direct solar irradiance whilst blanking out scattered sun light.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nThe data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, MODLERAD, ESAWRADT and DRADR35 messages. The data spans from 1947 to 2020.\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 the 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.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2021-08-02T11:13:50", "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, solar irradiance, hourly, global, diffuse, direct", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2021-08-26T09:46:24", "doiPublishedTime": "2021-09-08T10:26:57", "removedDataTime": null, "geographicExtent": { "ob_id": 17, "bboxName": "", "eastBoundLongitude": 1.74002, "westBoundLongitude": -8.5636, "southBoundLatitude": 49.914, "northBoundLatitude": 60.8562 }, "verticalExtent": null, "result_field": { "ob_id": 32983, "dataPath": "/badc/ukmo-midas-open/data/uk-radiation-obs/dataset-version-202107/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2947770418, "numberOfFiles": 6781, "fileFormat": "Data are BADC-CSV formatted." }, "timePeriod": { "ob_id": 9054, "startTime": "1947-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 308, "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": 1240, "uuid": "051afeb5c56f46469683e9b8b0bb38b1", "short_code": "acq", "title": "Acquisition Process for: Global Radiation Observations, Part of the Met Office Integrated Data Archive System (MIDAS)", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Pyranometer, Sunshine Recorder; PLATFORMS: DRADR35 (Daily Radiation Form 35) Station Network, AWSHRLY (Automatic Weather Station Hourly values) Station Network, MODLERAD (Hourly radiation values from Met Office Data Logging Equipment) Station Network, ESAWRADT (Enhanced Synoptic Automatic Weather station RADiaTion) Station Network, HCM (Hourly Climate Messages) Station Network, Land SYNOP (surface synoptic observations) 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": [ 31142, 31144, 31146, 31150, 31161, 31162, 31164, 31165, 31166, 31167, 31217, 31231, 31334, 31335, 31338, 31339, 31340, 31341, 31342, 31343, 31344, 31345, 31346, 31347, 31348, 31349 ], "vocabularyKeywords": [], "identifier_set": [ 10924 ], "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": [ 147233, 147234, 147232, 147239, 147238, 147237, 147236, 147235, 147240 ], "onlineresource_set": [ 43216, 43218, 43219, 43217, 43213, 43214, 43212, 43215, 91146, 91140, 91141, 91142, 91143, 91144, 91145, 91130, 91131, 91132, 91133, 91134, 91135, 91136, 91137, 91138, 91139 ] }, { "ob_id": 32984, "uuid": "0cb035c7598a4dcb8aecb6b6558c83e9", "title": "HadUK-Grid Climate Observations by UK river basins, v1.0.3.0 (1862-2020)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK river basins consistent with data from UKCP18 climate projections. The dataset spans the period from 1862 to 2020, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-08-02T10:52:18", "updateFrequency": "notPlanned", "dataLineage": "Data provided by the UK Met Office for archiving in the Centre for Environmental Data Analysis (CEDA) archives.", "removedDataReason": "", "keywords": "Met Office, UKCP18, BEIS, Defra, land surface, climate observations, hadobs", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2021-08-26T09:32:33", "doiPublishedTime": "2021-09-08T10:30:24", "removedDataTime": null, "geographicExtent": { "ob_id": 2308, "bboxName": "", "eastBoundLongitude": 1.76, "westBoundLongitude": -8.84, "southBoundLatitude": 49.86, "northBoundLatitude": 60.86 }, "verticalExtent": null, "result_field": { "ob_id": 32992, "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.0.3.0/river", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 25938984, "numberOfFiles": 133, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 9062, "startTime": "1862-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3344, "explanation": "Data quality control details for the HadUK-Grid version 1.0 datasets is available in section 2.2. of Hollis et al. (2018). See linked documentation for further details.", "passesTest": true, "resultTitle": "HadUK-Grid v1 Data Quality Statement", "date": "2019-11-05" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 26870, "uuid": "b1b352825f5548a8bf0639afe335f5ae", "short_code": "comp", "title": "HadUK-Grid gridded climate observations methodology", "abstract": "The gridded data sets are based on the archive of UK weather observations held at the Met Office. The density of the station network used varies through time, and for different climate variables - for example, for the temperature variables the number of stations rises from about 270 in 1910s to 600 in the mid-1990s, before falling to 450 in 2006. Regression and interpolation are used to generate values on a regular grid from the irregular station network, taking into account factors such as latitude and longitude, altitude and terrain shape, coastal influence, and urban land use. This alleviates the impact of station openings and closures on homogeneity, but the impacts of a changing station network cannot be removed entirely, especially in areas of complex topography or sparse station coverage.\r\n\r\nThe methods used to generate the grids are described in more detail in a paper published by Hollis et al. (2019) https://doi.org/10.1002/gdj3.78 (see linked documentation on this record).\r\n\r\nTo help users combine the observational data sets with the UKCP18 climate projections, the 1km x 1km grid is averaged to grids at resolutions to match those of the climate projections. Each 5 x 5 km, 12 x 12 km, 25 x 25 km or 60 x 60 km grid box value is an average of the all the 1 × 1 km grid cell values that fall within it. A set of regional values for UK administrative regions, river basins and countries are calculated as the average of all 1 × 1 km grid cell values that fall within the defined geography." }, "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": 13164, "uuid": "ce252c81a7bd4717834055e31716b265", "short_code": "proj", "title": "Met Office Hadley Centre - Observations and Climate", "abstract": "The Met Office Hadley Centre is one of the UK's foremost climate change research centres.\r\n\r\nThe Hadley Centre produces world-class guidance on the science of climate change and provide a focus in the UK for the scientific issues associated with climate science.\r\n\r\nLargely co-funded by Department of Energy and Climate Change (DECC) and Defra (the Department for Environment, Food and Rural Affairs), the centre provides in-depth information to, and advise, the Government on climate science issues.\r\n\r\nAs one of the world's leading centres for climate science research, the Hadley Centre scientists make significant contributions to peer-reviewed literature and to a variety of climate science reports, including the Assessment Report of the IPCC. The Hadley Centre climate projections were the basis for the Stern Review on the Economics of Climate Change." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1633, 6023, 11520, 11521, 11522, 21577, 21581, 21632, 21634, 21635, 21637, 21638, 21642, 21662, 21663, 21664, 21665, 21666, 21667, 21669, 21671, 21674 ], "vocabularyKeywords": [], "identifier_set": [ 10930 ], "observationcollection_set": [ { "ob_id": 26862, "uuid": "4dc8450d889a491ebb20e724debe2dfb", "short_code": "coll", "title": "HadUK-Grid gridded and regional average climate observations for the UK", "abstract": "This Dataset Collection contains a number of different versions of the HadUK-Grid dataset, each of which present a set of gridded climate variables extending from the present back to the 19th Century. The primary purpose of these data are to facilitate monitoring of the UK climate and research into climate variability, climate change, impacts and adaptation. The Met Office uses these data for operational monitoring of the UK's climate.\r\n\r\nThe data have been interpolated from meteorological station data onto a uniform grid at 1km by 1km resolution to provide complete and consistent coverage across the UK. The 1km data set has been regridded to different resolutions and regional averages to create a collection allowing for comparison to data from UKCP18 climate projections.\r\n\r\nA new version of HadUK-Grid is released each year. The latest version is v1.3.1.ceda, released in June 2025 and containing data up to the end of 2024. A summary of previous releases can be found below. Provisional data for more recent months can be found on the Met Office web site https://www.metoffice.gov.uk/hadobs/hadukgrid/.\r\n\r\nEach version comprises eight Datasets - gridded data at 1, 5, 12, 25 and 60 km resolution, plus three sets of area averages (UK countries, admin regions and river basins).\r\n\r\nThe earliest year of data varies by variable and has changed as more data are digitised. Currently the start years are:\r\n1836 (monthly rainfall)\r\n1884 (monthly max/mean/min air temperature)\r\n1891 (daily rainfall)\r\n1910 (monthly sunshine)\r\n1931 (daily max/min air temperature)\r\n1961 (monthly days of ground frost, relative humidity, mean sea level pressure and vapour pressure)\r\n1969 (monthly mean wind speed)\r\n1971 (monthly days of lying snow)\r\n\r\nThe grids are provided at daily (max/min air temperature and rainfall only), monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods.\r\n\r\nThe latest release has been created by the Met Office funded by the UK Department for Science, Innovation and Technology (DSIT).\r\n\r\nPrevious versions were created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project.\r\n\r\nFor all versions, the data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\".\r\n\r\nThe data are provided under Open Government Licence v3 (see each dataset for links to licence and associated citations to use).\r\n\r\nList of dataset versions (latest first) and key differences (each release also extends the dataset by one year):\r\n\r\nv1.3.1.ceda (1836-2024) - Daily temperature extended back to 1931 (from 1960). Historical data recovery has improved daily rainfall over Scotland for 1922-1945.\r\nv1.3.0.ceda (1836-2023) - Historical data recovery has improved daily rainfall over Scotland for 1945-1960.\r\nv1.2.0.ceda (1836-2022) - Monthly sunshine extended back to 1910 (from 1919). Incorporation of Rainfall Rescue v2.\r\nv1.1.0.0 (1836-2021) - Addition of climate averages for 1991-2020. Rainfall Rescue v1 dataset incorporated into the monthly rainfall grids which are extended back to 1836 (from 1862).\r\nv1.0.3.0 (1862-2020)\r\nv1.0.2.1 (1862-2019) - Monthly sunshine extended back to 1919 (from 1929). Historical data recovery has also improved monthly rainfall 1862-1910, daily rainfall 1891-1910 and monthly temperature 1900-1909. Correction to the grid definition for 12 km grid product to match the UKCP18 climate model products.\r\nv1.0.1.0 (1862-2018) - Addition of 5km data.\r\nv1.0.0.0 (1862-2017) - Initial release.\r\n\r\nSee the change log file for each version for further details.\r\n\r\nNote: The introduction of the '.ceda' suffix was done to highlight that CEDA is the source of these data files compared to other potential sources (e.g. the UKCP User Interface https://ukclimateprojections-ui.metoffice.gov.uk/ui/home) The data values are the same - it is the way the data are packaged that may differ between sources.\r\n\r\nEach version following the initial release is accompanied by change log files. These list new files in the version compared with the previous version plus summary totals of the number of files that remained the same, modified and removed. Links to these change logs are available in the 'Details/Docs' section of each dataset. Additionally, a summary change log file is provided which gives an overview of all changes to the data sources and processing methods since the initial release. This summary can be found in the 'Details/Docs' section below or via the individual datasets.\r\n\r\nThis collection supersedes the UKCP09 Dataset Collection and contains all datasets within the major version 1 release (i.e. v1.#.#.#). See Hollis et al. (2019; linked documentation) for details on the version numbering utilised." } ], "responsiblepartyinfo_set": [ 147241, 147248, 147247, 147246, 147245, 147244, 147242, 147243, 147249, 147250, 147251, 147252, 147253, 147254, 147255 ], "onlineresource_set": [ 43222, 43220, 43223, 43221, 91095, 91096, 91097, 91098, 91099, 91100, 91101, 91102, 91103, 91104, 91105, 91106, 91107, 91108, 91109, 91110, 91111, 91112, 91113, 91114, 91115 ] }, { "ob_id": 32985, "uuid": "489e22fb2961482bb76711cedbeecedd", "title": "HadUK-Grid Climate Observations by UK countries, v1.0.3.0 (1862-2020)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK countries consistent with data from UKCP18 climate projections. The dataset spans the period from 1862 to 2020, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-08-02T10:52:45", "updateFrequency": "notPlanned", "dataLineage": "Data provided by the UK Met Office for archiving in the Centre for Environmental Data Analysis (CEDA) archives.", "removedDataReason": "", "keywords": "Met Office, UKCP18, BEIS, Defra, land surface, climate observations, hadobs", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2021-08-26T09:32:27", "doiPublishedTime": "2021-09-08T10:34:03", "removedDataTime": null, "geographicExtent": { "ob_id": 2309, "bboxName": "", "eastBoundLongitude": 1.76, "westBoundLongitude": -8.18, "southBoundLatitude": 49.16, "northBoundLatitude": 60.86 }, "verticalExtent": null, "result_field": { "ob_id": 32993, "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.0.3.0/country", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 12739647, "numberOfFiles": 133, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 9061, "startTime": "1862-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3344, "explanation": "Data quality control details for the HadUK-Grid version 1.0 datasets is available in section 2.2. of Hollis et al. (2018). See linked documentation for further details.", "passesTest": true, "resultTitle": "HadUK-Grid v1 Data Quality Statement", "date": "2019-11-05" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 26870, "uuid": "b1b352825f5548a8bf0639afe335f5ae", "short_code": "comp", "title": "HadUK-Grid gridded climate observations methodology", "abstract": "The gridded data sets are based on the archive of UK weather observations held at the Met Office. The density of the station network used varies through time, and for different climate variables - for example, for the temperature variables the number of stations rises from about 270 in 1910s to 600 in the mid-1990s, before falling to 450 in 2006. Regression and interpolation are used to generate values on a regular grid from the irregular station network, taking into account factors such as latitude and longitude, altitude and terrain shape, coastal influence, and urban land use. This alleviates the impact of station openings and closures on homogeneity, but the impacts of a changing station network cannot be removed entirely, especially in areas of complex topography or sparse station coverage.\r\n\r\nThe methods used to generate the grids are described in more detail in a paper published by Hollis et al. (2019) https://doi.org/10.1002/gdj3.78 (see linked documentation on this record).\r\n\r\nTo help users combine the observational data sets with the UKCP18 climate projections, the 1km x 1km grid is averaged to grids at resolutions to match those of the climate projections. Each 5 x 5 km, 12 x 12 km, 25 x 25 km or 60 x 60 km grid box value is an average of the all the 1 × 1 km grid cell values that fall within it. A set of regional values for UK administrative regions, river basins and countries are calculated as the average of all 1 × 1 km grid cell values that fall within the defined geography." }, "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": 13164, "uuid": "ce252c81a7bd4717834055e31716b265", "short_code": "proj", "title": "Met Office Hadley Centre - Observations and Climate", "abstract": "The Met Office Hadley Centre is one of the UK's foremost climate change research centres.\r\n\r\nThe Hadley Centre produces world-class guidance on the science of climate change and provide a focus in the UK for the scientific issues associated with climate science.\r\n\r\nLargely co-funded by Department of Energy and Climate Change (DECC) and Defra (the Department for Environment, Food and Rural Affairs), the centre provides in-depth information to, and advise, the Government on climate science issues.\r\n\r\nAs one of the world's leading centres for climate science research, the Hadley Centre scientists make significant contributions to peer-reviewed literature and to a variety of climate science reports, including the Assessment Report of the IPCC. The Hadley Centre climate projections were the basis for the Stern Review on the Economics of Climate Change." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1633, 6023, 11520, 11521, 11522, 21577, 21581, 21632, 21634, 21635, 21637, 21638, 21642, 21659, 21662, 21663, 21664, 21665, 21666, 21667, 21669, 21671 ], "vocabularyKeywords": [], "identifier_set": [ 10931 ], "observationcollection_set": [ { "ob_id": 26862, "uuid": "4dc8450d889a491ebb20e724debe2dfb", "short_code": "coll", "title": "HadUK-Grid gridded and regional average climate observations for the UK", "abstract": "This Dataset Collection contains a number of different versions of the HadUK-Grid dataset, each of which present a set of gridded climate variables extending from the present back to the 19th Century. The primary purpose of these data are to facilitate monitoring of the UK climate and research into climate variability, climate change, impacts and adaptation. The Met Office uses these data for operational monitoring of the UK's climate.\r\n\r\nThe data have been interpolated from meteorological station data onto a uniform grid at 1km by 1km resolution to provide complete and consistent coverage across the UK. The 1km data set has been regridded to different resolutions and regional averages to create a collection allowing for comparison to data from UKCP18 climate projections.\r\n\r\nA new version of HadUK-Grid is released each year. The latest version is v1.3.1.ceda, released in June 2025 and containing data up to the end of 2024. A summary of previous releases can be found below. Provisional data for more recent months can be found on the Met Office web site https://www.metoffice.gov.uk/hadobs/hadukgrid/.\r\n\r\nEach version comprises eight Datasets - gridded data at 1, 5, 12, 25 and 60 km resolution, plus three sets of area averages (UK countries, admin regions and river basins).\r\n\r\nThe earliest year of data varies by variable and has changed as more data are digitised. Currently the start years are:\r\n1836 (monthly rainfall)\r\n1884 (monthly max/mean/min air temperature)\r\n1891 (daily rainfall)\r\n1910 (monthly sunshine)\r\n1931 (daily max/min air temperature)\r\n1961 (monthly days of ground frost, relative humidity, mean sea level pressure and vapour pressure)\r\n1969 (monthly mean wind speed)\r\n1971 (monthly days of lying snow)\r\n\r\nThe grids are provided at daily (max/min air temperature and rainfall only), monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods.\r\n\r\nThe latest release has been created by the Met Office funded by the UK Department for Science, Innovation and Technology (DSIT).\r\n\r\nPrevious versions were created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project.\r\n\r\nFor all versions, the data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\".\r\n\r\nThe data are provided under Open Government Licence v3 (see each dataset for links to licence and associated citations to use).\r\n\r\nList of dataset versions (latest first) and key differences (each release also extends the dataset by one year):\r\n\r\nv1.3.1.ceda (1836-2024) - Daily temperature extended back to 1931 (from 1960). Historical data recovery has improved daily rainfall over Scotland for 1922-1945.\r\nv1.3.0.ceda (1836-2023) - Historical data recovery has improved daily rainfall over Scotland for 1945-1960.\r\nv1.2.0.ceda (1836-2022) - Monthly sunshine extended back to 1910 (from 1919). Incorporation of Rainfall Rescue v2.\r\nv1.1.0.0 (1836-2021) - Addition of climate averages for 1991-2020. Rainfall Rescue v1 dataset incorporated into the monthly rainfall grids which are extended back to 1836 (from 1862).\r\nv1.0.3.0 (1862-2020)\r\nv1.0.2.1 (1862-2019) - Monthly sunshine extended back to 1919 (from 1929). Historical data recovery has also improved monthly rainfall 1862-1910, daily rainfall 1891-1910 and monthly temperature 1900-1909. Correction to the grid definition for 12 km grid product to match the UKCP18 climate model products.\r\nv1.0.1.0 (1862-2018) - Addition of 5km data.\r\nv1.0.0.0 (1862-2017) - Initial release.\r\n\r\nSee the change log file for each version for further details.\r\n\r\nNote: The introduction of the '.ceda' suffix was done to highlight that CEDA is the source of these data files compared to other potential sources (e.g. the UKCP User Interface https://ukclimateprojections-ui.metoffice.gov.uk/ui/home) The data values are the same - it is the way the data are packaged that may differ between sources.\r\n\r\nEach version following the initial release is accompanied by change log files. These list new files in the version compared with the previous version plus summary totals of the number of files that remained the same, modified and removed. Links to these change logs are available in the 'Details/Docs' section of each dataset. Additionally, a summary change log file is provided which gives an overview of all changes to the data sources and processing methods since the initial release. This summary can be found in the 'Details/Docs' section below or via the individual datasets.\r\n\r\nThis collection supersedes the UKCP09 Dataset Collection and contains all datasets within the major version 1 release (i.e. v1.#.#.#). See Hollis et al. (2019; linked documentation) for details on the version numbering utilised." } ], "responsiblepartyinfo_set": [ 147263, 147262, 147261, 147260, 147259, 147258, 147256, 147257, 147264, 147265, 147266, 147267, 147268, 147269, 147270 ], "onlineresource_set": [ 43226, 43224, 43227, 43225, 91074, 91075, 91076, 91077, 91078, 91079, 91080, 91081, 91082, 91083, 91084, 91085, 91086, 91087, 91088, 91089, 91090, 91091, 91092, 91093, 91094 ] }, { "ob_id": 32986, "uuid": "bc774f1b83524437a8046d8b9a9e3c6d", "title": "HadUK-Grid Gridded Climate Observations on a 60km grid over the UK, v1.0.3.0 (1862-2020)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 60 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1862 to 2020, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-08-02T11:28:39", "updateFrequency": "notPlanned", "dataLineage": "Data provided by the UK Met Office for archiving in the Centre for Environmental Data Analysis (CEDA) archives.", "removedDataReason": "", "keywords": "Met Office, UKCP18, BEIS, Defra, land surface, climate observations, hadobs", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2021-08-26T09:32:19", "doiPublishedTime": "2021-09-08T10:34:38", "removedDataTime": null, "geographicExtent": { "ob_id": 2305, "bboxName": "HadUK-Grid area", "eastBoundLongitude": 4.59, "westBoundLongitude": -12.61, "southBoundLatitude": 48.83, "northBoundLatitude": 60.57 }, "verticalExtent": null, "result_field": { "ob_id": 32995, "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.0.3.0/60km", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 538615627, "numberOfFiles": 6163, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 9060, "startTime": "1862-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3344, "explanation": "Data quality control details for the HadUK-Grid version 1.0 datasets is available in section 2.2. of Hollis et al. (2018). See linked documentation for further details.", "passesTest": true, "resultTitle": "HadUK-Grid v1 Data Quality Statement", "date": "2019-11-05" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 26870, "uuid": "b1b352825f5548a8bf0639afe335f5ae", "short_code": "comp", "title": "HadUK-Grid gridded climate observations methodology", "abstract": "The gridded data sets are based on the archive of UK weather observations held at the Met Office. The density of the station network used varies through time, and for different climate variables - for example, for the temperature variables the number of stations rises from about 270 in 1910s to 600 in the mid-1990s, before falling to 450 in 2006. Regression and interpolation are used to generate values on a regular grid from the irregular station network, taking into account factors such as latitude and longitude, altitude and terrain shape, coastal influence, and urban land use. This alleviates the impact of station openings and closures on homogeneity, but the impacts of a changing station network cannot be removed entirely, especially in areas of complex topography or sparse station coverage.\r\n\r\nThe methods used to generate the grids are described in more detail in a paper published by Hollis et al. (2019) https://doi.org/10.1002/gdj3.78 (see linked documentation on this record).\r\n\r\nTo help users combine the observational data sets with the UKCP18 climate projections, the 1km x 1km grid is averaged to grids at resolutions to match those of the climate projections. Each 5 x 5 km, 12 x 12 km, 25 x 25 km or 60 x 60 km grid box value is an average of the all the 1 × 1 km grid cell values that fall within it. A set of regional values for UK administrative regions, river basins and countries are calculated as the average of all 1 × 1 km grid cell values that fall within the defined geography." }, "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": 13164, "uuid": "ce252c81a7bd4717834055e31716b265", "short_code": "proj", "title": "Met Office Hadley Centre - Observations and Climate", "abstract": "The Met Office Hadley Centre is one of the UK's foremost climate change research centres.\r\n\r\nThe Hadley Centre produces world-class guidance on the science of climate change and provide a focus in the UK for the scientific issues associated with climate science.\r\n\r\nLargely co-funded by Department of Energy and Climate Change (DECC) and Defra (the Department for Environment, Food and Rural Affairs), the centre provides in-depth information to, and advise, the Government on climate science issues.\r\n\r\nAs one of the world's leading centres for climate science research, the Hadley Centre scientists make significant contributions to peer-reviewed literature and to a variety of climate science reports, including the Assessment Report of the IPCC. The Hadley Centre climate projections were the basis for the Stern Review on the Economics of Climate Change." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1633, 6023, 7026, 7028, 11482, 11483, 11484, 11485, 11486, 11520, 11521, 11522, 21577, 21581, 21634, 21635, 21637, 21638, 21642, 21663, 21664, 21665, 21666, 21667, 21669, 21671 ], "vocabularyKeywords": [], "identifier_set": [ 10932 ], "observationcollection_set": [ { "ob_id": 26862, "uuid": "4dc8450d889a491ebb20e724debe2dfb", "short_code": "coll", "title": "HadUK-Grid gridded and regional average climate observations for the UK", "abstract": "This Dataset Collection contains a number of different versions of the HadUK-Grid dataset, each of which present a set of gridded climate variables extending from the present back to the 19th Century. The primary purpose of these data are to facilitate monitoring of the UK climate and research into climate variability, climate change, impacts and adaptation. The Met Office uses these data for operational monitoring of the UK's climate.\r\n\r\nThe data have been interpolated from meteorological station data onto a uniform grid at 1km by 1km resolution to provide complete and consistent coverage across the UK. The 1km data set has been regridded to different resolutions and regional averages to create a collection allowing for comparison to data from UKCP18 climate projections.\r\n\r\nA new version of HadUK-Grid is released each year. The latest version is v1.3.1.ceda, released in June 2025 and containing data up to the end of 2024. A summary of previous releases can be found below. Provisional data for more recent months can be found on the Met Office web site https://www.metoffice.gov.uk/hadobs/hadukgrid/.\r\n\r\nEach version comprises eight Datasets - gridded data at 1, 5, 12, 25 and 60 km resolution, plus three sets of area averages (UK countries, admin regions and river basins).\r\n\r\nThe earliest year of data varies by variable and has changed as more data are digitised. Currently the start years are:\r\n1836 (monthly rainfall)\r\n1884 (monthly max/mean/min air temperature)\r\n1891 (daily rainfall)\r\n1910 (monthly sunshine)\r\n1931 (daily max/min air temperature)\r\n1961 (monthly days of ground frost, relative humidity, mean sea level pressure and vapour pressure)\r\n1969 (monthly mean wind speed)\r\n1971 (monthly days of lying snow)\r\n\r\nThe grids are provided at daily (max/min air temperature and rainfall only), monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods.\r\n\r\nThe latest release has been created by the Met Office funded by the UK Department for Science, Innovation and Technology (DSIT).\r\n\r\nPrevious versions were created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project.\r\n\r\nFor all versions, the data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\".\r\n\r\nThe data are provided under Open Government Licence v3 (see each dataset for links to licence and associated citations to use).\r\n\r\nList of dataset versions (latest first) and key differences (each release also extends the dataset by one year):\r\n\r\nv1.3.1.ceda (1836-2024) - Daily temperature extended back to 1931 (from 1960). Historical data recovery has improved daily rainfall over Scotland for 1922-1945.\r\nv1.3.0.ceda (1836-2023) - Historical data recovery has improved daily rainfall over Scotland for 1945-1960.\r\nv1.2.0.ceda (1836-2022) - Monthly sunshine extended back to 1910 (from 1919). Incorporation of Rainfall Rescue v2.\r\nv1.1.0.0 (1836-2021) - Addition of climate averages for 1991-2020. Rainfall Rescue v1 dataset incorporated into the monthly rainfall grids which are extended back to 1836 (from 1862).\r\nv1.0.3.0 (1862-2020)\r\nv1.0.2.1 (1862-2019) - Monthly sunshine extended back to 1919 (from 1929). Historical data recovery has also improved monthly rainfall 1862-1910, daily rainfall 1891-1910 and monthly temperature 1900-1909. Correction to the grid definition for 12 km grid product to match the UKCP18 climate model products.\r\nv1.0.1.0 (1862-2018) - Addition of 5km data.\r\nv1.0.0.0 (1862-2017) - Initial release.\r\n\r\nSee the change log file for each version for further details.\r\n\r\nNote: The introduction of the '.ceda' suffix was done to highlight that CEDA is the source of these data files compared to other potential sources (e.g. the UKCP User Interface https://ukclimateprojections-ui.metoffice.gov.uk/ui/home) The data values are the same - it is the way the data are packaged that may differ between sources.\r\n\r\nEach version following the initial release is accompanied by change log files. These list new files in the version compared with the previous version plus summary totals of the number of files that remained the same, modified and removed. Links to these change logs are available in the 'Details/Docs' section of each dataset. Additionally, a summary change log file is provided which gives an overview of all changes to the data sources and processing methods since the initial release. This summary can be found in the 'Details/Docs' section below or via the individual datasets.\r\n\r\nThis collection supersedes the UKCP09 Dataset Collection and contains all datasets within the major version 1 release (i.e. v1.#.#.#). See Hollis et al. (2019; linked documentation) for details on the version numbering utilised." } ], "responsiblepartyinfo_set": [ 147271, 147274, 147272, 147273, 147278, 147277, 147276, 147275, 147279, 147280, 147281, 147282, 147283, 147284, 147285 ], "onlineresource_set": [ 43228, 43229, 43230, 43231, 91054, 91055, 91056, 91057, 91058, 91059, 91060, 91061, 91062, 91063, 91064, 91065, 91066, 91067, 91068, 91069, 91070, 91071, 91072, 91073 ] }, { "ob_id": 32987, "uuid": "616e6194a8c742d790f3b43bf66a534d", "title": "HadUK-Grid Gridded Climate Observations on a 25km grid over the UK, v1.0.3.0 (1862-2020)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 25 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1862 to 2020, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-08-02T11:38:59", "updateFrequency": "notPlanned", "dataLineage": "Data provided by the UK Met Office for archiving in the Centre for Environmental Data Analysis (CEDA) archives.", "removedDataReason": "", "keywords": "Met Office, UKCP18, BEIS, Defra, land surface, climate observations, hadobs", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2021-08-26T09:32:12", "doiPublishedTime": "2021-09-08T10:35:01", "removedDataTime": null, "geographicExtent": { "ob_id": 2305, "bboxName": "HadUK-Grid area", "eastBoundLongitude": 4.59, "westBoundLongitude": -12.61, "southBoundLatitude": 48.83, "northBoundLatitude": 60.57 }, "verticalExtent": null, "result_field": { "ob_id": 32996, "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.0.3.0/25km", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2142938841, "numberOfFiles": 6163, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 9059, "startTime": "1862-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3344, "explanation": "Data quality control details for the HadUK-Grid version 1.0 datasets is available in section 2.2. of Hollis et al. (2018). See linked documentation for further details.", "passesTest": true, "resultTitle": "HadUK-Grid v1 Data Quality Statement", "date": "2019-11-05" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 26870, "uuid": "b1b352825f5548a8bf0639afe335f5ae", "short_code": "comp", "title": "HadUK-Grid gridded climate observations methodology", "abstract": "The gridded data sets are based on the archive of UK weather observations held at the Met Office. The density of the station network used varies through time, and for different climate variables - for example, for the temperature variables the number of stations rises from about 270 in 1910s to 600 in the mid-1990s, before falling to 450 in 2006. Regression and interpolation are used to generate values on a regular grid from the irregular station network, taking into account factors such as latitude and longitude, altitude and terrain shape, coastal influence, and urban land use. This alleviates the impact of station openings and closures on homogeneity, but the impacts of a changing station network cannot be removed entirely, especially in areas of complex topography or sparse station coverage.\r\n\r\nThe methods used to generate the grids are described in more detail in a paper published by Hollis et al. (2019) https://doi.org/10.1002/gdj3.78 (see linked documentation on this record).\r\n\r\nTo help users combine the observational data sets with the UKCP18 climate projections, the 1km x 1km grid is averaged to grids at resolutions to match those of the climate projections. Each 5 x 5 km, 12 x 12 km, 25 x 25 km or 60 x 60 km grid box value is an average of the all the 1 × 1 km grid cell values that fall within it. A set of regional values for UK administrative regions, river basins and countries are calculated as the average of all 1 × 1 km grid cell values that fall within the defined geography." }, "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": 13164, "uuid": "ce252c81a7bd4717834055e31716b265", "short_code": "proj", "title": "Met Office Hadley Centre - Observations and Climate", "abstract": "The Met Office Hadley Centre is one of the UK's foremost climate change research centres.\r\n\r\nThe Hadley Centre produces world-class guidance on the science of climate change and provide a focus in the UK for the scientific issues associated with climate science.\r\n\r\nLargely co-funded by Department of Energy and Climate Change (DECC) and Defra (the Department for Environment, Food and Rural Affairs), the centre provides in-depth information to, and advise, the Government on climate science issues.\r\n\r\nAs one of the world's leading centres for climate science research, the Hadley Centre scientists make significant contributions to peer-reviewed literature and to a variety of climate science reports, including the Assessment Report of the IPCC. The Hadley Centre climate projections were the basis for the Stern Review on the Economics of Climate Change." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1633, 6023, 7026, 7028, 11482, 11483, 11484, 11485, 11486, 11520, 11521, 11522, 21577, 21581, 21634, 21635, 21637, 21638, 21642, 21663, 21664, 21665, 21666, 21667, 21669, 21671 ], "vocabularyKeywords": [], "identifier_set": [ 10933 ], "observationcollection_set": [ { "ob_id": 26862, "uuid": "4dc8450d889a491ebb20e724debe2dfb", "short_code": "coll", "title": "HadUK-Grid gridded and regional average climate observations for the UK", "abstract": "This Dataset Collection contains a number of different versions of the HadUK-Grid dataset, each of which present a set of gridded climate variables extending from the present back to the 19th Century. The primary purpose of these data are to facilitate monitoring of the UK climate and research into climate variability, climate change, impacts and adaptation. The Met Office uses these data for operational monitoring of the UK's climate.\r\n\r\nThe data have been interpolated from meteorological station data onto a uniform grid at 1km by 1km resolution to provide complete and consistent coverage across the UK. The 1km data set has been regridded to different resolutions and regional averages to create a collection allowing for comparison to data from UKCP18 climate projections.\r\n\r\nA new version of HadUK-Grid is released each year. The latest version is v1.3.1.ceda, released in June 2025 and containing data up to the end of 2024. A summary of previous releases can be found below. Provisional data for more recent months can be found on the Met Office web site https://www.metoffice.gov.uk/hadobs/hadukgrid/.\r\n\r\nEach version comprises eight Datasets - gridded data at 1, 5, 12, 25 and 60 km resolution, plus three sets of area averages (UK countries, admin regions and river basins).\r\n\r\nThe earliest year of data varies by variable and has changed as more data are digitised. Currently the start years are:\r\n1836 (monthly rainfall)\r\n1884 (monthly max/mean/min air temperature)\r\n1891 (daily rainfall)\r\n1910 (monthly sunshine)\r\n1931 (daily max/min air temperature)\r\n1961 (monthly days of ground frost, relative humidity, mean sea level pressure and vapour pressure)\r\n1969 (monthly mean wind speed)\r\n1971 (monthly days of lying snow)\r\n\r\nThe grids are provided at daily (max/min air temperature and rainfall only), monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods.\r\n\r\nThe latest release has been created by the Met Office funded by the UK Department for Science, Innovation and Technology (DSIT).\r\n\r\nPrevious versions were created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project.\r\n\r\nFor all versions, the data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\".\r\n\r\nThe data are provided under Open Government Licence v3 (see each dataset for links to licence and associated citations to use).\r\n\r\nList of dataset versions (latest first) and key differences (each release also extends the dataset by one year):\r\n\r\nv1.3.1.ceda (1836-2024) - Daily temperature extended back to 1931 (from 1960). Historical data recovery has improved daily rainfall over Scotland for 1922-1945.\r\nv1.3.0.ceda (1836-2023) - Historical data recovery has improved daily rainfall over Scotland for 1945-1960.\r\nv1.2.0.ceda (1836-2022) - Monthly sunshine extended back to 1910 (from 1919). Incorporation of Rainfall Rescue v2.\r\nv1.1.0.0 (1836-2021) - Addition of climate averages for 1991-2020. Rainfall Rescue v1 dataset incorporated into the monthly rainfall grids which are extended back to 1836 (from 1862).\r\nv1.0.3.0 (1862-2020)\r\nv1.0.2.1 (1862-2019) - Monthly sunshine extended back to 1919 (from 1929). Historical data recovery has also improved monthly rainfall 1862-1910, daily rainfall 1891-1910 and monthly temperature 1900-1909. Correction to the grid definition for 12 km grid product to match the UKCP18 climate model products.\r\nv1.0.1.0 (1862-2018) - Addition of 5km data.\r\nv1.0.0.0 (1862-2017) - Initial release.\r\n\r\nSee the change log file for each version for further details.\r\n\r\nNote: The introduction of the '.ceda' suffix was done to highlight that CEDA is the source of these data files compared to other potential sources (e.g. the UKCP User Interface https://ukclimateprojections-ui.metoffice.gov.uk/ui/home) The data values are the same - it is the way the data are packaged that may differ between sources.\r\n\r\nEach version following the initial release is accompanied by change log files. These list new files in the version compared with the previous version plus summary totals of the number of files that remained the same, modified and removed. Links to these change logs are available in the 'Details/Docs' section of each dataset. Additionally, a summary change log file is provided which gives an overview of all changes to the data sources and processing methods since the initial release. This summary can be found in the 'Details/Docs' section below or via the individual datasets.\r\n\r\nThis collection supersedes the UKCP09 Dataset Collection and contains all datasets within the major version 1 release (i.e. v1.#.#.#). See Hollis et al. (2019; linked documentation) for details on the version numbering utilised." } ], "responsiblepartyinfo_set": [ 147286, 147287, 147293, 147291, 147290, 147289, 147288, 147292, 147294, 147295, 147296, 147297, 147298, 147299, 147300 ], "onlineresource_set": [ 43234, 43235, 43232, 43233, 91034, 91035, 91036, 91037, 91038, 91039, 91040, 91041, 91042, 91043, 91044, 91045, 91046, 91047, 91048, 91049, 91050, 91051, 91052, 91053 ] }, { "ob_id": 32988, "uuid": "54a99222c1e741a4a70ef1caa8f10c7e", "title": "HadUK-Grid Gridded Climate Observations on a 12km grid over the UK, v1.0.3.0 (1862-2020)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 12 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from climate projections. The dataset spans the period from 1862 to 2020, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation). \r\n\r\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-08-02T11:54:38", "updateFrequency": "notPlanned", "dataLineage": "Data provided by the UK Met Office for archiving in the Centre for Environmental Data Analysis (CEDA) archives.", "removedDataReason": "", "keywords": "Met Office, UKCP18, BEIS, Defra, land surface, climate observations, hadobs", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2021-08-26T09:32:05", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2305, "bboxName": "HadUK-Grid area", "eastBoundLongitude": 4.59, "westBoundLongitude": -12.61, "southBoundLatitude": 48.83, "northBoundLatitude": 60.57 }, "verticalExtent": null, "result_field": { "ob_id": 32997, "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.0.3.0/12km", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 9151135715, "numberOfFiles": 6158, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 9058, "startTime": "1862-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3344, "explanation": "Data quality control details for the HadUK-Grid version 1.0 datasets is available in section 2.2. of Hollis et al. (2018). See linked documentation for further details.", "passesTest": true, "resultTitle": "HadUK-Grid v1 Data Quality Statement", "date": "2019-11-05" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 26870, "uuid": "b1b352825f5548a8bf0639afe335f5ae", "short_code": "comp", "title": "HadUK-Grid gridded climate observations methodology", "abstract": "The gridded data sets are based on the archive of UK weather observations held at the Met Office. The density of the station network used varies through time, and for different climate variables - for example, for the temperature variables the number of stations rises from about 270 in 1910s to 600 in the mid-1990s, before falling to 450 in 2006. Regression and interpolation are used to generate values on a regular grid from the irregular station network, taking into account factors such as latitude and longitude, altitude and terrain shape, coastal influence, and urban land use. This alleviates the impact of station openings and closures on homogeneity, but the impacts of a changing station network cannot be removed entirely, especially in areas of complex topography or sparse station coverage.\r\n\r\nThe methods used to generate the grids are described in more detail in a paper published by Hollis et al. (2019) https://doi.org/10.1002/gdj3.78 (see linked documentation on this record).\r\n\r\nTo help users combine the observational data sets with the UKCP18 climate projections, the 1km x 1km grid is averaged to grids at resolutions to match those of the climate projections. Each 5 x 5 km, 12 x 12 km, 25 x 25 km or 60 x 60 km grid box value is an average of the all the 1 × 1 km grid cell values that fall within it. A set of regional values for UK administrative regions, river basins and countries are calculated as the average of all 1 × 1 km grid cell values that fall within the defined geography." }, "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": 13164, "uuid": "ce252c81a7bd4717834055e31716b265", "short_code": "proj", "title": "Met Office Hadley Centre - Observations and Climate", "abstract": "The Met Office Hadley Centre is one of the UK's foremost climate change research centres.\r\n\r\nThe Hadley Centre produces world-class guidance on the science of climate change and provide a focus in the UK for the scientific issues associated with climate science.\r\n\r\nLargely co-funded by Department of Energy and Climate Change (DECC) and Defra (the Department for Environment, Food and Rural Affairs), the centre provides in-depth information to, and advise, the Government on climate science issues.\r\n\r\nAs one of the world's leading centres for climate science research, the Hadley Centre scientists make significant contributions to peer-reviewed literature and to a variety of climate science reports, including the Assessment Report of the IPCC. The Hadley Centre climate projections were the basis for the Stern Review on the Economics of Climate Change." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1633, 6023, 7026, 7028, 11482, 11483, 11484, 11485, 11486, 11520, 11521, 11522, 21577, 21581, 21634, 21635, 21637, 21638, 21642, 21663, 21664, 21665, 21666, 21667, 21669, 21671 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 26862, "uuid": "4dc8450d889a491ebb20e724debe2dfb", "short_code": "coll", "title": "HadUK-Grid gridded and regional average climate observations for the UK", "abstract": "This Dataset Collection contains a number of different versions of the HadUK-Grid dataset, each of which present a set of gridded climate variables extending from the present back to the 19th Century. The primary purpose of these data are to facilitate monitoring of the UK climate and research into climate variability, climate change, impacts and adaptation. The Met Office uses these data for operational monitoring of the UK's climate.\r\n\r\nThe data have been interpolated from meteorological station data onto a uniform grid at 1km by 1km resolution to provide complete and consistent coverage across the UK. The 1km data set has been regridded to different resolutions and regional averages to create a collection allowing for comparison to data from UKCP18 climate projections.\r\n\r\nA new version of HadUK-Grid is released each year. The latest version is v1.3.1.ceda, released in June 2025 and containing data up to the end of 2024. A summary of previous releases can be found below. Provisional data for more recent months can be found on the Met Office web site https://www.metoffice.gov.uk/hadobs/hadukgrid/.\r\n\r\nEach version comprises eight Datasets - gridded data at 1, 5, 12, 25 and 60 km resolution, plus three sets of area averages (UK countries, admin regions and river basins).\r\n\r\nThe earliest year of data varies by variable and has changed as more data are digitised. Currently the start years are:\r\n1836 (monthly rainfall)\r\n1884 (monthly max/mean/min air temperature)\r\n1891 (daily rainfall)\r\n1910 (monthly sunshine)\r\n1931 (daily max/min air temperature)\r\n1961 (monthly days of ground frost, relative humidity, mean sea level pressure and vapour pressure)\r\n1969 (monthly mean wind speed)\r\n1971 (monthly days of lying snow)\r\n\r\nThe grids are provided at daily (max/min air temperature and rainfall only), monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods.\r\n\r\nThe latest release has been created by the Met Office funded by the UK Department for Science, Innovation and Technology (DSIT).\r\n\r\nPrevious versions were created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project.\r\n\r\nFor all versions, the data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\".\r\n\r\nThe data are provided under Open Government Licence v3 (see each dataset for links to licence and associated citations to use).\r\n\r\nList of dataset versions (latest first) and key differences (each release also extends the dataset by one year):\r\n\r\nv1.3.1.ceda (1836-2024) - Daily temperature extended back to 1931 (from 1960). Historical data recovery has improved daily rainfall over Scotland for 1922-1945.\r\nv1.3.0.ceda (1836-2023) - Historical data recovery has improved daily rainfall over Scotland for 1945-1960.\r\nv1.2.0.ceda (1836-2022) - Monthly sunshine extended back to 1910 (from 1919). Incorporation of Rainfall Rescue v2.\r\nv1.1.0.0 (1836-2021) - Addition of climate averages for 1991-2020. Rainfall Rescue v1 dataset incorporated into the monthly rainfall grids which are extended back to 1836 (from 1862).\r\nv1.0.3.0 (1862-2020)\r\nv1.0.2.1 (1862-2019) - Monthly sunshine extended back to 1919 (from 1929). Historical data recovery has also improved monthly rainfall 1862-1910, daily rainfall 1891-1910 and monthly temperature 1900-1909. Correction to the grid definition for 12 km grid product to match the UKCP18 climate model products.\r\nv1.0.1.0 (1862-2018) - Addition of 5km data.\r\nv1.0.0.0 (1862-2017) - Initial release.\r\n\r\nSee the change log file for each version for further details.\r\n\r\nNote: The introduction of the '.ceda' suffix was done to highlight that CEDA is the source of these data files compared to other potential sources (e.g. the UKCP User Interface https://ukclimateprojections-ui.metoffice.gov.uk/ui/home) The data values are the same - it is the way the data are packaged that may differ between sources.\r\n\r\nEach version following the initial release is accompanied by change log files. These list new files in the version compared with the previous version plus summary totals of the number of files that remained the same, modified and removed. Links to these change logs are available in the 'Details/Docs' section of each dataset. Additionally, a summary change log file is provided which gives an overview of all changes to the data sources and processing methods since the initial release. This summary can be found in the 'Details/Docs' section below or via the individual datasets.\r\n\r\nThis collection supersedes the UKCP09 Dataset Collection and contains all datasets within the major version 1 release (i.e. v1.#.#.#). See Hollis et al. (2019; linked documentation) for details on the version numbering utilised." } ], "responsiblepartyinfo_set": [ 147301, 147308, 147307, 147306, 147305, 147302, 147303, 147304, 147309, 147310, 147311, 147312, 147313, 147314, 147315 ], "onlineresource_set": [ 43238, 43236, 43237, 43239 ] }, { "ob_id": 32989, "uuid": "786b3ce6be54468496a3e11ce2f2669c", "title": "HadUK-Grid Gridded Climate Observations on a 1km grid over the UK, v1.0.3.0 (1862-2020)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The datasets cover the UK at 1 km x 1 km resolution. These 1 km x 1 km data have been used to provide a range of other resolutions and across countries, administrative regions and river basins to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1862 to 2020, but the start time is dependent on climate variable and temporal resolution. \r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-08-19T03:20:31", "updateFrequency": "notPlanned", "dataLineage": "Data provided by the UK Met Office for archiving in the Centre for Environmental Data Analysis (CEDA) archives.", "removedDataReason": "", "keywords": "Met Office, UKCP18, BEIS, Defra, land surface, climate observations, hadobs", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2021-08-26T09:31:58", "doiPublishedTime": "2021-09-08T10:37:55", "removedDataTime": null, "geographicExtent": { "ob_id": 2305, "bboxName": "HadUK-Grid area", "eastBoundLongitude": 4.59, "westBoundLongitude": -12.61, "southBoundLatitude": 48.83, "northBoundLatitude": 60.57 }, "verticalExtent": null, "result_field": { "ob_id": 32998, "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.0.3.0/1km", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1275636193559, "numberOfFiles": 6163, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 9057, "startTime": "1862-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3344, "explanation": "Data quality control details for the HadUK-Grid version 1.0 datasets is available in section 2.2. of Hollis et al. (2018). See linked documentation for further details.", "passesTest": true, "resultTitle": "HadUK-Grid v1 Data Quality Statement", "date": "2019-11-05" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 26870, "uuid": "b1b352825f5548a8bf0639afe335f5ae", "short_code": "comp", "title": "HadUK-Grid gridded climate observations methodology", "abstract": "The gridded data sets are based on the archive of UK weather observations held at the Met Office. The density of the station network used varies through time, and for different climate variables - for example, for the temperature variables the number of stations rises from about 270 in 1910s to 600 in the mid-1990s, before falling to 450 in 2006. Regression and interpolation are used to generate values on a regular grid from the irregular station network, taking into account factors such as latitude and longitude, altitude and terrain shape, coastal influence, and urban land use. This alleviates the impact of station openings and closures on homogeneity, but the impacts of a changing station network cannot be removed entirely, especially in areas of complex topography or sparse station coverage.\r\n\r\nThe methods used to generate the grids are described in more detail in a paper published by Hollis et al. (2019) https://doi.org/10.1002/gdj3.78 (see linked documentation on this record).\r\n\r\nTo help users combine the observational data sets with the UKCP18 climate projections, the 1km x 1km grid is averaged to grids at resolutions to match those of the climate projections. Each 5 x 5 km, 12 x 12 km, 25 x 25 km or 60 x 60 km grid box value is an average of the all the 1 × 1 km grid cell values that fall within it. A set of regional values for UK administrative regions, river basins and countries are calculated as the average of all 1 × 1 km grid cell values that fall within the defined geography." }, "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": 13164, "uuid": "ce252c81a7bd4717834055e31716b265", "short_code": "proj", "title": "Met Office Hadley Centre - Observations and Climate", "abstract": "The Met Office Hadley Centre is one of the UK's foremost climate change research centres.\r\n\r\nThe Hadley Centre produces world-class guidance on the science of climate change and provide a focus in the UK for the scientific issues associated with climate science.\r\n\r\nLargely co-funded by Department of Energy and Climate Change (DECC) and Defra (the Department for Environment, Food and Rural Affairs), the centre provides in-depth information to, and advise, the Government on climate science issues.\r\n\r\nAs one of the world's leading centres for climate science research, the Hadley Centre scientists make significant contributions to peer-reviewed literature and to a variety of climate science reports, including the Assessment Report of the IPCC. The Hadley Centre climate projections were the basis for the Stern Review on the Economics of Climate Change." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1633, 6023, 7026, 7028, 11482, 11483, 11484, 11485, 11486, 11520, 11521, 11522, 21577, 21581, 21634, 21635, 21637, 21638, 21642, 21663, 21664, 21665, 21666, 21667, 21669, 21671 ], "vocabularyKeywords": [], "identifier_set": [ 10934 ], "observationcollection_set": [ { "ob_id": 26862, "uuid": "4dc8450d889a491ebb20e724debe2dfb", "short_code": "coll", "title": "HadUK-Grid gridded and regional average climate observations for the UK", "abstract": "This Dataset Collection contains a number of different versions of the HadUK-Grid dataset, each of which present a set of gridded climate variables extending from the present back to the 19th Century. The primary purpose of these data are to facilitate monitoring of the UK climate and research into climate variability, climate change, impacts and adaptation. The Met Office uses these data for operational monitoring of the UK's climate.\r\n\r\nThe data have been interpolated from meteorological station data onto a uniform grid at 1km by 1km resolution to provide complete and consistent coverage across the UK. The 1km data set has been regridded to different resolutions and regional averages to create a collection allowing for comparison to data from UKCP18 climate projections.\r\n\r\nA new version of HadUK-Grid is released each year. The latest version is v1.3.1.ceda, released in June 2025 and containing data up to the end of 2024. A summary of previous releases can be found below. Provisional data for more recent months can be found on the Met Office web site https://www.metoffice.gov.uk/hadobs/hadukgrid/.\r\n\r\nEach version comprises eight Datasets - gridded data at 1, 5, 12, 25 and 60 km resolution, plus three sets of area averages (UK countries, admin regions and river basins).\r\n\r\nThe earliest year of data varies by variable and has changed as more data are digitised. Currently the start years are:\r\n1836 (monthly rainfall)\r\n1884 (monthly max/mean/min air temperature)\r\n1891 (daily rainfall)\r\n1910 (monthly sunshine)\r\n1931 (daily max/min air temperature)\r\n1961 (monthly days of ground frost, relative humidity, mean sea level pressure and vapour pressure)\r\n1969 (monthly mean wind speed)\r\n1971 (monthly days of lying snow)\r\n\r\nThe grids are provided at daily (max/min air temperature and rainfall only), monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods.\r\n\r\nThe latest release has been created by the Met Office funded by the UK Department for Science, Innovation and Technology (DSIT).\r\n\r\nPrevious versions were created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project.\r\n\r\nFor all versions, the data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\".\r\n\r\nThe data are provided under Open Government Licence v3 (see each dataset for links to licence and associated citations to use).\r\n\r\nList of dataset versions (latest first) and key differences (each release also extends the dataset by one year):\r\n\r\nv1.3.1.ceda (1836-2024) - Daily temperature extended back to 1931 (from 1960). Historical data recovery has improved daily rainfall over Scotland for 1922-1945.\r\nv1.3.0.ceda (1836-2023) - Historical data recovery has improved daily rainfall over Scotland for 1945-1960.\r\nv1.2.0.ceda (1836-2022) - Monthly sunshine extended back to 1910 (from 1919). Incorporation of Rainfall Rescue v2.\r\nv1.1.0.0 (1836-2021) - Addition of climate averages for 1991-2020. Rainfall Rescue v1 dataset incorporated into the monthly rainfall grids which are extended back to 1836 (from 1862).\r\nv1.0.3.0 (1862-2020)\r\nv1.0.2.1 (1862-2019) - Monthly sunshine extended back to 1919 (from 1929). Historical data recovery has also improved monthly rainfall 1862-1910, daily rainfall 1891-1910 and monthly temperature 1900-1909. Correction to the grid definition for 12 km grid product to match the UKCP18 climate model products.\r\nv1.0.1.0 (1862-2018) - Addition of 5km data.\r\nv1.0.0.0 (1862-2017) - Initial release.\r\n\r\nSee the change log file for each version for further details.\r\n\r\nNote: The introduction of the '.ceda' suffix was done to highlight that CEDA is the source of these data files compared to other potential sources (e.g. the UKCP User Interface https://ukclimateprojections-ui.metoffice.gov.uk/ui/home) The data values are the same - it is the way the data are packaged that may differ between sources.\r\n\r\nEach version following the initial release is accompanied by change log files. These list new files in the version compared with the previous version plus summary totals of the number of files that remained the same, modified and removed. Links to these change logs are available in the 'Details/Docs' section of each dataset. Additionally, a summary change log file is provided which gives an overview of all changes to the data sources and processing methods since the initial release. This summary can be found in the 'Details/Docs' section below or via the individual datasets.\r\n\r\nThis collection supersedes the UKCP09 Dataset Collection and contains all datasets within the major version 1 release (i.e. v1.#.#.#). See Hollis et al. (2019; linked documentation) for details on the version numbering utilised." } ], "responsiblepartyinfo_set": [ 147316, 147322, 147319, 147318, 147317, 147320, 147321, 147323, 147324, 147325, 147326, 147327, 147328, 147329, 147330 ], "onlineresource_set": [ 43240, 43241, 43242, 43243, 87650, 87651, 90401, 90402, 90403, 90404, 90405, 90406, 90407, 90408, 90409, 90410, 90411, 90412, 90413, 90414, 90415, 90416, 90417, 90418, 90419, 90420, 90421, 90422 ] }, { "ob_id": 32990, "uuid": "f2da35c56afb4fa6aebf44094b65dff3", "title": "HadUK-Grid Gridded Climate Observations on a 5km grid over the UK, v1.0.3.0 (1862-2020)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 5 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1862 to 2020, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-03-09T02:37:28", "updateFrequency": "notPlanned", "dataLineage": "Data provided by the UK Met Office for archiving in the Centre for Environmental Data Analysis (CEDA) archives.", "removedDataReason": "", "keywords": "Met Office, UKCP18, BEIS, Defra, land surface, climate observations, hadobs", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2021-08-26T09:31:46", "doiPublishedTime": "2021-09-08T10:38:23", "removedDataTime": null, "geographicExtent": { "ob_id": 2305, "bboxName": "HadUK-Grid area", "eastBoundLongitude": 4.59, "westBoundLongitude": -12.61, "southBoundLatitude": 48.83, "northBoundLatitude": 60.57 }, "verticalExtent": null, "result_field": { "ob_id": 32999, "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.0.3.0/5km", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 51229517277, "numberOfFiles": 6163, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 9056, "startTime": "1862-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3344, "explanation": "Data quality control details for the HadUK-Grid version 1.0 datasets is available in section 2.2. of Hollis et al. (2018). See linked documentation for further details.", "passesTest": true, "resultTitle": "HadUK-Grid v1 Data Quality Statement", "date": "2019-11-05" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 26870, "uuid": "b1b352825f5548a8bf0639afe335f5ae", "short_code": "comp", "title": "HadUK-Grid gridded climate observations methodology", "abstract": "The gridded data sets are based on the archive of UK weather observations held at the Met Office. The density of the station network used varies through time, and for different climate variables - for example, for the temperature variables the number of stations rises from about 270 in 1910s to 600 in the mid-1990s, before falling to 450 in 2006. Regression and interpolation are used to generate values on a regular grid from the irregular station network, taking into account factors such as latitude and longitude, altitude and terrain shape, coastal influence, and urban land use. This alleviates the impact of station openings and closures on homogeneity, but the impacts of a changing station network cannot be removed entirely, especially in areas of complex topography or sparse station coverage.\r\n\r\nThe methods used to generate the grids are described in more detail in a paper published by Hollis et al. (2019) https://doi.org/10.1002/gdj3.78 (see linked documentation on this record).\r\n\r\nTo help users combine the observational data sets with the UKCP18 climate projections, the 1km x 1km grid is averaged to grids at resolutions to match those of the climate projections. Each 5 x 5 km, 12 x 12 km, 25 x 25 km or 60 x 60 km grid box value is an average of the all the 1 × 1 km grid cell values that fall within it. A set of regional values for UK administrative regions, river basins and countries are calculated as the average of all 1 × 1 km grid cell values that fall within the defined geography." }, "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": 13164, "uuid": "ce252c81a7bd4717834055e31716b265", "short_code": "proj", "title": "Met Office Hadley Centre - Observations and Climate", "abstract": "The Met Office Hadley Centre is one of the UK's foremost climate change research centres.\r\n\r\nThe Hadley Centre produces world-class guidance on the science of climate change and provide a focus in the UK for the scientific issues associated with climate science.\r\n\r\nLargely co-funded by Department of Energy and Climate Change (DECC) and Defra (the Department for Environment, Food and Rural Affairs), the centre provides in-depth information to, and advise, the Government on climate science issues.\r\n\r\nAs one of the world's leading centres for climate science research, the Hadley Centre scientists make significant contributions to peer-reviewed literature and to a variety of climate science reports, including the Assessment Report of the IPCC. The Hadley Centre climate projections were the basis for the Stern Review on the Economics of Climate Change." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 11484, 11485, 11486, 50512, 50516, 50517, 51186, 51187, 51188, 51189, 51193, 51195, 51196, 51197, 51200, 54988, 54989, 54990, 54991, 54992, 54993, 54994, 54995, 54996, 54997 ], "vocabularyKeywords": [], "identifier_set": [ 10935 ], "observationcollection_set": [ { "ob_id": 26862, "uuid": "4dc8450d889a491ebb20e724debe2dfb", "short_code": "coll", "title": "HadUK-Grid gridded and regional average climate observations for the UK", "abstract": "This Dataset Collection contains a number of different versions of the HadUK-Grid dataset, each of which present a set of gridded climate variables extending from the present back to the 19th Century. The primary purpose of these data are to facilitate monitoring of the UK climate and research into climate variability, climate change, impacts and adaptation. The Met Office uses these data for operational monitoring of the UK's climate.\r\n\r\nThe data have been interpolated from meteorological station data onto a uniform grid at 1km by 1km resolution to provide complete and consistent coverage across the UK. The 1km data set has been regridded to different resolutions and regional averages to create a collection allowing for comparison to data from UKCP18 climate projections.\r\n\r\nA new version of HadUK-Grid is released each year. The latest version is v1.3.1.ceda, released in June 2025 and containing data up to the end of 2024. A summary of previous releases can be found below. Provisional data for more recent months can be found on the Met Office web site https://www.metoffice.gov.uk/hadobs/hadukgrid/.\r\n\r\nEach version comprises eight Datasets - gridded data at 1, 5, 12, 25 and 60 km resolution, plus three sets of area averages (UK countries, admin regions and river basins).\r\n\r\nThe earliest year of data varies by variable and has changed as more data are digitised. Currently the start years are:\r\n1836 (monthly rainfall)\r\n1884 (monthly max/mean/min air temperature)\r\n1891 (daily rainfall)\r\n1910 (monthly sunshine)\r\n1931 (daily max/min air temperature)\r\n1961 (monthly days of ground frost, relative humidity, mean sea level pressure and vapour pressure)\r\n1969 (monthly mean wind speed)\r\n1971 (monthly days of lying snow)\r\n\r\nThe grids are provided at daily (max/min air temperature and rainfall only), monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods.\r\n\r\nThe latest release has been created by the Met Office funded by the UK Department for Science, Innovation and Technology (DSIT).\r\n\r\nPrevious versions were created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project.\r\n\r\nFor all versions, the data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\".\r\n\r\nThe data are provided under Open Government Licence v3 (see each dataset for links to licence and associated citations to use).\r\n\r\nList of dataset versions (latest first) and key differences (each release also extends the dataset by one year):\r\n\r\nv1.3.1.ceda (1836-2024) - Daily temperature extended back to 1931 (from 1960). Historical data recovery has improved daily rainfall over Scotland for 1922-1945.\r\nv1.3.0.ceda (1836-2023) - Historical data recovery has improved daily rainfall over Scotland for 1945-1960.\r\nv1.2.0.ceda (1836-2022) - Monthly sunshine extended back to 1910 (from 1919). Incorporation of Rainfall Rescue v2.\r\nv1.1.0.0 (1836-2021) - Addition of climate averages for 1991-2020. Rainfall Rescue v1 dataset incorporated into the monthly rainfall grids which are extended back to 1836 (from 1862).\r\nv1.0.3.0 (1862-2020)\r\nv1.0.2.1 (1862-2019) - Monthly sunshine extended back to 1919 (from 1929). Historical data recovery has also improved monthly rainfall 1862-1910, daily rainfall 1891-1910 and monthly temperature 1900-1909. Correction to the grid definition for 12 km grid product to match the UKCP18 climate model products.\r\nv1.0.1.0 (1862-2018) - Addition of 5km data.\r\nv1.0.0.0 (1862-2017) - Initial release.\r\n\r\nSee the change log file for each version for further details.\r\n\r\nNote: The introduction of the '.ceda' suffix was done to highlight that CEDA is the source of these data files compared to other potential sources (e.g. the UKCP User Interface https://ukclimateprojections-ui.metoffice.gov.uk/ui/home) The data values are the same - it is the way the data are packaged that may differ between sources.\r\n\r\nEach version following the initial release is accompanied by change log files. These list new files in the version compared with the previous version plus summary totals of the number of files that remained the same, modified and removed. Links to these change logs are available in the 'Details/Docs' section of each dataset. Additionally, a summary change log file is provided which gives an overview of all changes to the data sources and processing methods since the initial release. This summary can be found in the 'Details/Docs' section below or via the individual datasets.\r\n\r\nThis collection supersedes the UKCP09 Dataset Collection and contains all datasets within the major version 1 release (i.e. v1.#.#.#). See Hollis et al. (2019; linked documentation) for details on the version numbering utilised." } ], "responsiblepartyinfo_set": [ 147331, 147336, 147338, 147337, 147335, 147334, 147333, 147332, 147339, 147340, 147341, 147342, 147343, 147344, 147345 ], "onlineresource_set": [ 43246, 43247, 43244, 43245, 91013, 91014, 91015, 91016, 91017, 91018, 91019, 91020, 91021, 91022, 91023, 91024, 91025, 91026, 91027, 91028, 91029, 91030, 91031, 91032, 91033 ] }, { "ob_id": 32991, "uuid": "97bc0b64bc354898a242a42238e1b45c", "title": "HadUK-Grid Climate Observations by Administrative Regions over the UK, v1.0.3.0 (1862-2020)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK administrative regions consistent with data from UKCP18 climate projections. The dataset spans the period from 1862 to 2020, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-08-02T10:53:28", "updateFrequency": "notPlanned", "dataLineage": "Data provided by the UK Met Office for archiving in the Centre for Environmental Data Analysis (CEDA) archives.", "removedDataReason": "", "keywords": "Met Office, UKCP18, BEIS, Defra, land surface, climate observations, hadobs", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2021-08-26T09:31:38", "doiPublishedTime": "2021-09-08T10:39:05", "removedDataTime": null, "geographicExtent": { "ob_id": 2307, "bboxName": "", "eastBoundLongitude": 1.76, "westBoundLongitude": -8.18, "southBoundLatitude": 49.86, "northBoundLatitude": 60.86 }, "verticalExtent": null, "result_field": { "ob_id": 32994, "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.0.3.0/region", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 19805658, "numberOfFiles": 133, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 9055, "startTime": "1862-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3344, "explanation": "Data quality control details for the HadUK-Grid version 1.0 datasets is available in section 2.2. of Hollis et al. (2018). See linked documentation for further details.", "passesTest": true, "resultTitle": "HadUK-Grid v1 Data Quality Statement", "date": "2019-11-05" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 26870, "uuid": "b1b352825f5548a8bf0639afe335f5ae", "short_code": "comp", "title": "HadUK-Grid gridded climate observations methodology", "abstract": "The gridded data sets are based on the archive of UK weather observations held at the Met Office. The density of the station network used varies through time, and for different climate variables - for example, for the temperature variables the number of stations rises from about 270 in 1910s to 600 in the mid-1990s, before falling to 450 in 2006. Regression and interpolation are used to generate values on a regular grid from the irregular station network, taking into account factors such as latitude and longitude, altitude and terrain shape, coastal influence, and urban land use. This alleviates the impact of station openings and closures on homogeneity, but the impacts of a changing station network cannot be removed entirely, especially in areas of complex topography or sparse station coverage.\r\n\r\nThe methods used to generate the grids are described in more detail in a paper published by Hollis et al. (2019) https://doi.org/10.1002/gdj3.78 (see linked documentation on this record).\r\n\r\nTo help users combine the observational data sets with the UKCP18 climate projections, the 1km x 1km grid is averaged to grids at resolutions to match those of the climate projections. Each 5 x 5 km, 12 x 12 km, 25 x 25 km or 60 x 60 km grid box value is an average of the all the 1 × 1 km grid cell values that fall within it. A set of regional values for UK administrative regions, river basins and countries are calculated as the average of all 1 × 1 km grid cell values that fall within the defined geography." }, "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": 13164, "uuid": "ce252c81a7bd4717834055e31716b265", "short_code": "proj", "title": "Met Office Hadley Centre - Observations and Climate", "abstract": "The Met Office Hadley Centre is one of the UK's foremost climate change research centres.\r\n\r\nThe Hadley Centre produces world-class guidance on the science of climate change and provide a focus in the UK for the scientific issues associated with climate science.\r\n\r\nLargely co-funded by Department of Energy and Climate Change (DECC) and Defra (the Department for Environment, Food and Rural Affairs), the centre provides in-depth information to, and advise, the Government on climate science issues.\r\n\r\nAs one of the world's leading centres for climate science research, the Hadley Centre scientists make significant contributions to peer-reviewed literature and to a variety of climate science reports, including the Assessment Report of the IPCC. The Hadley Centre climate projections were the basis for the Stern Review on the Economics of Climate Change." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1633, 6023, 11520, 11521, 11522, 21577, 21581, 21632, 21634, 21635, 21637, 21638, 21642, 21662, 21663, 21664, 21665, 21666, 21667, 21669, 21671, 21673 ], "vocabularyKeywords": [], "identifier_set": [ 10936 ], "observationcollection_set": [ { "ob_id": 26862, "uuid": "4dc8450d889a491ebb20e724debe2dfb", "short_code": "coll", "title": "HadUK-Grid gridded and regional average climate observations for the UK", "abstract": "This Dataset Collection contains a number of different versions of the HadUK-Grid dataset, each of which present a set of gridded climate variables extending from the present back to the 19th Century. The primary purpose of these data are to facilitate monitoring of the UK climate and research into climate variability, climate change, impacts and adaptation. The Met Office uses these data for operational monitoring of the UK's climate.\r\n\r\nThe data have been interpolated from meteorological station data onto a uniform grid at 1km by 1km resolution to provide complete and consistent coverage across the UK. The 1km data set has been regridded to different resolutions and regional averages to create a collection allowing for comparison to data from UKCP18 climate projections.\r\n\r\nA new version of HadUK-Grid is released each year. The latest version is v1.3.1.ceda, released in June 2025 and containing data up to the end of 2024. A summary of previous releases can be found below. Provisional data for more recent months can be found on the Met Office web site https://www.metoffice.gov.uk/hadobs/hadukgrid/.\r\n\r\nEach version comprises eight Datasets - gridded data at 1, 5, 12, 25 and 60 km resolution, plus three sets of area averages (UK countries, admin regions and river basins).\r\n\r\nThe earliest year of data varies by variable and has changed as more data are digitised. Currently the start years are:\r\n1836 (monthly rainfall)\r\n1884 (monthly max/mean/min air temperature)\r\n1891 (daily rainfall)\r\n1910 (monthly sunshine)\r\n1931 (daily max/min air temperature)\r\n1961 (monthly days of ground frost, relative humidity, mean sea level pressure and vapour pressure)\r\n1969 (monthly mean wind speed)\r\n1971 (monthly days of lying snow)\r\n\r\nThe grids are provided at daily (max/min air temperature and rainfall only), monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods.\r\n\r\nThe latest release has been created by the Met Office funded by the UK Department for Science, Innovation and Technology (DSIT).\r\n\r\nPrevious versions were created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project.\r\n\r\nFor all versions, the data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\".\r\n\r\nThe data are provided under Open Government Licence v3 (see each dataset for links to licence and associated citations to use).\r\n\r\nList of dataset versions (latest first) and key differences (each release also extends the dataset by one year):\r\n\r\nv1.3.1.ceda (1836-2024) - Daily temperature extended back to 1931 (from 1960). Historical data recovery has improved daily rainfall over Scotland for 1922-1945.\r\nv1.3.0.ceda (1836-2023) - Historical data recovery has improved daily rainfall over Scotland for 1945-1960.\r\nv1.2.0.ceda (1836-2022) - Monthly sunshine extended back to 1910 (from 1919). Incorporation of Rainfall Rescue v2.\r\nv1.1.0.0 (1836-2021) - Addition of climate averages for 1991-2020. Rainfall Rescue v1 dataset incorporated into the monthly rainfall grids which are extended back to 1836 (from 1862).\r\nv1.0.3.0 (1862-2020)\r\nv1.0.2.1 (1862-2019) - Monthly sunshine extended back to 1919 (from 1929). Historical data recovery has also improved monthly rainfall 1862-1910, daily rainfall 1891-1910 and monthly temperature 1900-1909. Correction to the grid definition for 12 km grid product to match the UKCP18 climate model products.\r\nv1.0.1.0 (1862-2018) - Addition of 5km data.\r\nv1.0.0.0 (1862-2017) - Initial release.\r\n\r\nSee the change log file for each version for further details.\r\n\r\nNote: The introduction of the '.ceda' suffix was done to highlight that CEDA is the source of these data files compared to other potential sources (e.g. the UKCP User Interface https://ukclimateprojections-ui.metoffice.gov.uk/ui/home) The data values are the same - it is the way the data are packaged that may differ between sources.\r\n\r\nEach version following the initial release is accompanied by change log files. These list new files in the version compared with the previous version plus summary totals of the number of files that remained the same, modified and removed. Links to these change logs are available in the 'Details/Docs' section of each dataset. Additionally, a summary change log file is provided which gives an overview of all changes to the data sources and processing methods since the initial release. This summary can be found in the 'Details/Docs' section below or via the individual datasets.\r\n\r\nThis collection supersedes the UKCP09 Dataset Collection and contains all datasets within the major version 1 release (i.e. v1.#.#.#). See Hollis et al. (2019; linked documentation) for details on the version numbering utilised." } ], "responsiblepartyinfo_set": [ 147347, 147348, 147346, 147353, 147352, 147351, 147350, 147349, 147354, 147355, 147356, 147357, 147358, 147359, 147360 ], "onlineresource_set": [ 43248, 43249, 43250, 43251, 90992, 90993, 90994, 90995, 90996, 90997, 90998, 90999, 91000, 91001, 91002, 91003, 91004, 91005, 91006, 91007, 91008, 91009, 91010, 91011, 91012 ] }, { "ob_id": 33000, "uuid": "8220babd91534bb3b83b87141c4ccc14", "title": "ESA Ozone Climate Change Initiative (Ozone_cci): Merged SAGE II, Ozone_cci and OMPS-LP dataset of ozone profiles, v0002", "abstract": "The merged SAGE-CCI-OMPS+ dataset of ozone profiles is created using the data from several satellite instruments: SAGE II on ERBS; GOMOS, SCIAMACHY and MIPAS on Envisat; OSIRIS on Odin; ACE-FTS on SCISAT; OMPS on Suomi-NPP; POAM III on SPOT 4 and SAGE III on ISS. The merged dataset is created in the framework of European Space Agency Climate Change Initiative (Ozone_cci) with the aim of analyzing stratospheric ozone trends. For the merged dataset, we used the latest versions of the original ozone datasets. The long-term SAGE-CCI-OMPS+ dataset is created by computation and merging of deseasonalized anomalies from individual instruments. The detailed description of the dataset can be found in (Sofieva et al., 2017) and (Sofieva et al., 2023).\r\n\r\nThe merged SAGE-CCI-OMPS+ dataset consists of deseasonalized anomalies of ozone and ozone concentrations in 10 degree latitude bands from 90S to 90N and from 10 to 50 km in steps of 1 km covering the period from October 1984 to December 2021.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-09-11T13:11:59", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "Climate Change Initiative, CCI, Ozone, profiles", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-05-24T13:09:58", "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": 39832, "dataPath": "/neodc/esacci/ozone/data/limb_profiles/l3/merged_sage_cci_omps/monthly_zonal_mean/v0002", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 58123579, "numberOfFiles": 2, "fileFormat": "Data are in NetCDF format." }, "timePeriod": { "ob_id": 11043, "startTime": "1984-10-01T00:00:00", "endTime": "2021-12-01T23:59:59" }, "resultQuality": { "ob_id": 3726, "explanation": "For information on the data quality see the linked documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-07-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 33011, "uuid": "fb3a31dac5d04a10bc3333d6f13832c3", "short_code": "cmppr", "title": "Composite process for the ESA Ozone Climate Change Initiative: Merged SAGE II, Ozone_cci and OMPS-LP dataset of ozone profiles", "abstract": "The merged SAGE-CCI-OMPS+ dataset of ozone profiles is created using the data from several satellite instruments: SAGE II on ERBS; GOMOS, SCIAMACHY and MIPAS on Envisat; OSIRIS on Odin; ACE-FTS on SCISAT; OMPS on Suomi-NPP; POAM III on SPOT 4 and SAGE III on ISS. The SAGE-CCI-OMPS+ dataset is created by computation and merging of deseasonalized anomalies from individual instruments. The detailed description of the dataset can be found in (Sofieva et al., 2017) and (Sofieva et al., 2023)." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2531, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 11, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_ozone_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 14147, "uuid": "10b36f5715274b1d985c569501ceed68", "short_code": "proj", "title": "ESA Ozone Climate Change Initiative Project", "abstract": "The European Space Agency Ozone Climate Change Initiative (Ozone CCI) project is one of several projects of ESA's Climate Change Initiative (CCI), which will deliver various Essential Climate Variables (ECVs). \r\nOzone_cci aims at generating new high-quality satellite data sets that are essential to assess the fate of atmospheric ozone and better understand its link with anthropogenic activities." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 57120, 59246, 62501, 63805, 63807, 63809, 63810, 63813, 63815, 63816, 80057 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 14148, "uuid": "5806da02ef934a7c83d959bed50c2252", "short_code": "coll", "title": "ESA Ozone Climate Change Initiative (Ozone CCI) Dataset Collection", "abstract": "Datasets of ozone products produced by the ozone project within the ESA Climate Change Initiative (CCI). The project focuses on developing, delivering and characterizing a number of ozone ECV data products generated from European nadir, limb and occultation satellite sensors.\r\n\r\nThree main product lines have been developed:\r\n\r\nTotal ozone from nadir UV backscatter sensors:\r\nA time-series covering the period from 1995 until present generated by merging total ozone measurements from the GOME, SCIAMACHY, GOME-2 and OMI instruments. All European sensors are processed using the state-of-the-art direct-fitting algorithm developed for GOME (GDP5). \r\n\r\nOzone profiles from nadir UV backscatter sensors:\r\nA merged ozone profile data set from GOME, SCIAMACHY, OMI and GOME-2 instruments generated for a minimum of two contiguous years. Best elements from existing retrieval codes are combined in a single CCI algorithm applied to all sensors.\r\n\r\nOzone profiles from limb and occultation sensors:\r\nA merged ozone profile data set covering at least two contiguous years created from all limb/occultation sensors onboard of ENVISAT (GOMOS, MIPAS, SCIAMACHY) as well as from the Third Party Missions OSIRIS, SMR and ACE/FTS." } ], "responsiblepartyinfo_set": [ 147366, 147367, 147368, 147361, 147362, 147363, 147364, 147365 ], "onlineresource_set": [ 43260, 43261, 83222, 83323, 83324, 85402 ] }, { "ob_id": 33001, "uuid": "e0329fa69dfa49d0903e132dca1c4890", "title": "ESA Ozone Climate Change Initiative (Ozone_cci): MErged GRIdded Dataset of Ozone Profiles (MEGRIDOP), v0001", "abstract": "This dataset comprises the MErged GRIdded Dataset of Ozone Profiles (MEGRIDOP) in the stratosphere with a resolved longitudinal structure, which is derived from data by six limb and occultation satellite instruments: GOMOS, SCIAMACHY and MIPAS on Envisat, OSIRIS on Odin, OMPS on Suomi-NPP, and MLS on Aura. The merged dataset was generated as a contribution to the European Space Agency Climate Change Initiative Ozone project (Ozone_cci). The period of this merged time series of ozone profiles is from late 2001 until the end of 2022.\r\n\r\nThe monthly mean gridded ozone profiles and deseasonalised anomalies are provided in the altitude range from 10 to 50 km in bins of 10 degree latitude x 20 degree longitude. \r\n\r\nFor more details please see the associated readme file and Sofieva, V. F., Szeląg, M., Tamminen, J., Kyrölä, E., Degenstein, D., Roth, C., Zawada, D., Rozanov, A., Arosio, C., Burrows, J. P., Weber, M., Laeng, A., Stiller, G. P., von Clarmann, T., Froidevaux, L., Livesey, N., van Roozendael, M. and Retscher, C.: Measurement report: regional trends of stratospheric ozone evaluated using the MErged GRIdded Dataset of Ozone Profiles (MEGRIDOP), Atmos. Chem. Phys., 21(9), 6707–6720, doi:10.5194/acp-21-6707-2021, 2021", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-03-29T17:24:10", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "Climate Change Initiative, CCI, Ozone, profiles", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-05-24T13:10:27", "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": 39818, "dataPath": "/neodc/esacci/ozone/data/limb_profiles/l3/megridop/monthly_zonal_mean/v0001", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 755882923, "numberOfFiles": 2, "fileFormat": "Data are in NetCDF format." }, "timePeriod": { "ob_id": 11013, "startTime": "2001-11-01T00:00:00", "endTime": "2022-12-01T23:59:59" }, "resultQuality": { "ob_id": 3726, "explanation": "For information on the data quality see the linked documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-07-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 33002, "uuid": "696802f3a2c34d7f9e78492720a9ca16", "short_code": "cmppr", "title": "Composite process for the ESA Ozone Climate Change Initiative: MErged GRIdded Dataset of Ozone Profiles (MEGRIDOP), v0001", "abstract": "The MErged GRIdded Dataset of Ozone Profiles (MEGRIDOP) in the stratosphere with a resolved longitudinal structure, which is derived from data by six limb and occultation satellite instruments: GOMOS, SCIAMACHY and MIPAS on Envisat, OSIRIS on Odin, OMPS on Suomi-NPP, and MLS on Aura.\r\nThe merging is performed using deseasonalized anomalies.\r\n\r\nFor further information see the associated readme file and Sofieva, V. F., Szelag, M., Tamminen, J., Kyrölä, E., Degenstein, D., Roth, C., Zawada, D., Rozanov, A., Arosio, C., Burrows, J. P., Weber, M., Laeng, A., Stiller, G., von Clarmann, T., Froidevaux, L., Livesey, N., van Roozendael, M., and Retscher,\r\nC.: Measurement report: Regional trends of stratospheric ozone evaluated using the MErged GRIdded Dataset of Ozone Profiles (MEGRIDOP), Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2020-1117, in review, 2020." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2531, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 11, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_ozone_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 14147, "uuid": "10b36f5715274b1d985c569501ceed68", "short_code": "proj", "title": "ESA Ozone Climate Change Initiative Project", "abstract": "The European Space Agency Ozone Climate Change Initiative (Ozone CCI) project is one of several projects of ESA's Climate Change Initiative (CCI), which will deliver various Essential Climate Variables (ECVs). \r\nOzone_cci aims at generating new high-quality satellite data sets that are essential to assess the fate of atmospheric ozone and better understand its link with anthropogenic activities." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 54912, 57120, 59246, 62501, 63805, 63806, 63807, 63808, 63809, 63810, 63811, 63813, 63814, 83129 ], "vocabularyKeywords": [], "identifier_set": [ 12432 ], "observationcollection_set": [ { "ob_id": 14148, "uuid": "5806da02ef934a7c83d959bed50c2252", "short_code": "coll", "title": "ESA Ozone Climate Change Initiative (Ozone CCI) Dataset Collection", "abstract": "Datasets of ozone products produced by the ozone project within the ESA Climate Change Initiative (CCI). The project focuses on developing, delivering and characterizing a number of ozone ECV data products generated from European nadir, limb and occultation satellite sensors.\r\n\r\nThree main product lines have been developed:\r\n\r\nTotal ozone from nadir UV backscatter sensors:\r\nA time-series covering the period from 1995 until present generated by merging total ozone measurements from the GOME, SCIAMACHY, GOME-2 and OMI instruments. All European sensors are processed using the state-of-the-art direct-fitting algorithm developed for GOME (GDP5). \r\n\r\nOzone profiles from nadir UV backscatter sensors:\r\nA merged ozone profile data set from GOME, SCIAMACHY, OMI and GOME-2 instruments generated for a minimum of two contiguous years. Best elements from existing retrieval codes are combined in a single CCI algorithm applied to all sensors.\r\n\r\nOzone profiles from limb and occultation sensors:\r\nA merged ozone profile data set covering at least two contiguous years created from all limb/occultation sensors onboard of ENVISAT (GOMOS, MIPAS, SCIAMACHY) as well as from the Third Party Missions OSIRIS, SMR and ACE/FTS." } ], "responsiblepartyinfo_set": [ 147369, 147370, 147371, 147372, 147373, 147374, 147375, 147376 ], "onlineresource_set": [ 43255, 43256, 83223, 83340, 85403 ] }, { "ob_id": 33012, "uuid": "7b87da411ecc4ec5bb46165ff634806f", "title": "ESA Ozone Climate Change Initiative (Ozone_cci): Level 3 Total Ozone Merged Data Product, v0200", "abstract": "This dataset is a monthly mean gridded (level 3) total ozone data record produced by the ESA Ozone Climate Change Initiative project (Ozone_cci).\r\n\r\nThe data record covers the period between July 1995 and December 2020 and provides global coverage (except for the polar night) at a spatial resolution of 1°x1°. It is a merged data record combining harmonized data from the Global Ozone Monitoring Experiment (GOME) onboard the second European Remote Sensing satellite (ERS-2), the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) onboard the Environmental Satellite (ENVISAT), the Ozone Monitoring Instrument (OMI) onboard Aura, the GOME-2 instruments onboard the Meteorological Operational (MetOp) satellites A and B, and the Tropospheric Monitoring Instrument (TROPOMI) onboard the Seninel-5 Precursor (S5-P) satellite. The data record is suitable for applications such as the investigation of the long-term evolution of the atmospheric ozone layer or the evaluation of model simulations.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": null, "updateFrequency": "", "dataLineage": "Data were produced by the ESA Ozone CCI+ project team.", "removedDataReason": "", "keywords": "Climate Change Initiative, CCI, Ozone, profiles", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "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": 9063, "startTime": "1984-10-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3726, "explanation": "For information on the data quality see the linked documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-07-29" }, "validTimePeriod": { "ob_id": 9065, "startTime": "1995-07-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 33015, "uuid": "4304ad00744341cc9c8fa2ce53f6fe6a", "short_code": "cmppr", "title": "Composite process for the ESA Ozone Climate Change Initiative: Level 3 Total Ozone Merged Data Product", "abstract": "The L3 Total Ozone Merged Data Product from the ESA Ozone Climate Change intitative project is a merged data record combining harmonized data from the Global Ozone Monitoring Experiment (GOME) onboard the second European Remote Sensing satellite (ERS-2), the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) onboard the Environmental Satellite (ENVISAT), the Ozone Monitoring Instrument (OMI) onboard Aura, the GOME-2 instruments onboard the Meteorological Operational (MetOp) satellites A and B, and the Tropospheric Monitoring Instrument (TROPOMI) onboard the Seninel-5 Precursor (S5-P) satellite." }, "imageDetails": [ 111 ], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 14147, "uuid": "10b36f5715274b1d985c569501ceed68", "short_code": "proj", "title": "ESA Ozone Climate Change Initiative Project", "abstract": "The European Space Agency Ozone Climate Change Initiative (Ozone CCI) project is one of several projects of ESA's Climate Change Initiative (CCI), which will deliver various Essential Climate Variables (ECVs). \r\nOzone_cci aims at generating new high-quality satellite data sets that are essential to assess the fate of atmospheric ozone and better understand its link with anthropogenic activities." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 147394, 147395, 147396, 147398, 147399, 147391, 147392, 147393 ], "onlineresource_set": [ 43262, 43263, 43265, 43266 ] }, { "ob_id": 33019, "uuid": "9d93bed3b24648fcade5e427903c7da7", "title": "CCMI-2022: REF-D1 data produced by the NIWA-UKCA2 model at NIWA", "abstract": "This dataset contains model data for CCMI-2022 experiment refD1 produced by the NIWA-UKCA2 chemistry-climate model run by the modelling team at NIWA (National Institute of Water and Atmospheric Research) in New Zealand.\r\n\r\nThe refD1 experiment is a hindcast of the atmospheric state, using a prescribed evolution of sea surface temperature and sea ice from observations along with forcings for the extra-terrestrial solar flux, long-lived greenhouse gases and ozone depleting substances, stratospheric aerosols and an imposed quasi-biennial oscillation that approximate the observed variations over the historical period to the fullest extent possible.\r\n\r\nThe CCMI-2022 Chemistry-climate model initiative is a set of model experiments focused on the stratosphere, with the goals of providing updated projections towards the future evolution of the ozone layer and improving our understanding of chemistry-climate interactions from models.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Review of the global models used within phase 1 of the Chemistry-Climate Model Initiative (CCMI)\r\n- A new set of Chemistry-Climate Model Initiative (CCMI) Community Simulations to Update the Assessment of Models and Support Upcoming Ozone Assessment Activities, David Plummer and Tatsuya Nagashima and Simone Tilmes and Alex Archibald and Gabriel Chiodo and Suvarna Fadnavis and Hella Garny and Beatrice Josse and Joowan Kim and Jean-Francois Lamarque and Olaf Morgenstern and Lee Murray and Clara Orbe and Amos Tai and Martyn Chipperfield and Bernd Funke and Martin Juckes and Doug Kinnison and Markus Kunze and Beiping Luo and Katja Matthes and Paul A. Newman and Charlotte Pascoe and Thomas Peter (2021), SPARC Newsletter, volume 57, pp 22-30", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2021-08-05T15:58:48", "updateFrequency": "", "dataLineage": "These data are original data produced by NIWA-UKCA2, processed into CF-compliant NetCDF3 format. In a further step, the data has been processed using nccopy into compressed NetCDF4 format. Uploaded using rsync.", "removedDataReason": "", "keywords": "CCMI-2022, REF-D1, refD1, Hindcast, NIWA-UKCA, NIWA, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "250 km", "status": "ongoing", "dataPublishedTime": "2021-08-06T20:58:22", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 41, "highestLevelBound": 84.0, "lowestLevelBound": 0.0, "units": "km" }, "result_field": { "ob_id": 33020, "dataPath": "/badc/ccmi/data/post-cmip6/ccmi-2022/NIWA/NIWA-UKCA2/refD1", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 160155577528, "numberOfFiles": 409, "fileFormat": "CF-netCDF" }, "timePeriod": { "ob_id": 9072, "startTime": "1960-01-01T00:00:00", "endTime": "2018-12-31T23:59:59" }, "resultQuality": { "ob_id": 3727, "explanation": "Data are as given by the data provider, ceda-cc quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CCMI-2022 Data and Metadata Quality Statement", "date": "2021-08-05" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33021, "uuid": "f7d8ff75cbd04a85b5f7e36676248e23", "short_code": "comp", "title": "NIWA-UKCA2 deployed at NIWA", "abstract": "NIWA-UKCA2 deployed at NIWA" }, "procedureCompositeProcess": null, "imageDetails": [ 146 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2544, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "ccmi-2022", "label": "restricted: ccmi-2022 group", "licence": { "ob_id": 21, "licenceURL": "https://artefacts.ceda.ac.uk/licences/rugl_versions/rugl_v1-0.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32805, "uuid": "92dddf542adc44b5898f535be4179705", "short_code": "proj", "title": "CCMI-2022 Chemistry-climate model initiative, phase 2", "abstract": "CCMI-2022 Chemistry-climate model initiative, phase 2 is a World Climate Research Programme (WCRP) Stratosphere-Troposphere Processes and their Role in Climate (SPARC) project to study the evolution of the ozone layer using chemistry-climate model simulations. CCMI-2022 data will support the World Meteorologcial Organisation (WMO)/ United Nations Environment Programme (UNEP) Scientific Assessment of Ozone Depletion Report 2022." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 7795, 37947, 50415, 50416, 50417, 50418, 50427, 50428, 50430, 50432, 50433, 50434, 50435, 50436, 50438, 50441, 50442, 50444, 50446, 50448, 50450, 50451, 50460, 50473, 50475, 50478, 50479, 50481, 50485, 50486, 50496, 50498, 50501, 50502, 50566, 50598, 54836, 59918, 59919, 59920, 59921, 59922 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 33023, "uuid": "cc660aad3cb34f5cb1ff50db57f47653", "short_code": "coll", "title": "CCMI-2022 data produced by the NIWA-UKCA2 model at NIWA", "abstract": "The NIWA-UKCA2 model contribution to the CCMI-2022 set of experiments defined by the APARC- and IGAC-supported Chemistry-Climate Model Initiative.\r\n\r\nThe CCMI-2022 set of model experiments focus on the stratosphere, with the goals of providing updated projections of the future evolution of ozone and improving our understanding of chemistry-climate interactions and how they are represented in models.\r\n\r\nThe NIWA-UKCA2 chemistry-climate model is run by the modelling team at NIWA (National Institute of Water and Atmospheric Research) in New Zealand and configured to follow forcings as laid out in the CCMI2022 founding document (Plummer et al., 2021)\r\n\r\nAPARC (formerly SPARC) and IGAC projects coordinate international research in atmospheric chemistry. APARC (Atmospheric Processes And their Role in Climate) is a core project of the World Climate Research Programme (WCRP). IGAC is the International Global Atmospheric Chemistry which currently operates under the umbrella of Future Earth." } ], "responsiblepartyinfo_set": [ 147420, 147421, 147422, 147424, 147425, 147426, 147431, 178482, 147432 ], "onlineresource_set": [ 43315, 52163 ] }, { "ob_id": 33022, "uuid": "9ffc2afd029442a8bf4f1dfc5b688ca4", "title": "CCMI-2022: refD2 data produced by the NIWA-UKCA2 model at NIWA", "abstract": "This dataset contains model data for CCMI-2022 experiment refD2 produced by the NIWA-UKCA2 chemistry-climate model run by the modelling team at NIWA (National Institute of Water and Atmospheric Research) in New Zealand.\r\n\r\nThe refD2 experiment is the baseline projection for updated projections of ozone recovery. Specified forcings largely following the same specifications as for the SSP2-4.5 scenario of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), with the exception of the near-surface mixing ratio of Ozone Depleting Substances which follow the baseline projection from WMO (2018).\r\n\r\nThe CCMI-2022 Chemistry-climate model initiative is a set of model experiments focused on the stratosphere, with the goals of providing updated projections towards the future evolution of the ozone layer and improving our understanding of chemistry-climate interactions from models.\r\n\r\nWMO-2018 refers to the Scientific Assessment of Ozone Depletion: 2018.\r\n\r\nSSP2-4.5 is a Shared Socio-economic Pathway scenario that follows socio-economic storyline SSP2 with intermediate mitigation and adaptation challenges and climate forcing pathway RCP4.5 which leads to a radiative forcing of 4.5 Wm-2 by the year 2100.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Review of the global models used within phase 1 of the Chemistry-Climate Model Initiative (CCMI)\r\n- A new set of Chemistry-Climate Model Initiative (CCMI) Community Simulations to Update the Assessment of Models and Support Upcoming Ozone Assessment Activities, David Plummer and Tatsuya Nagashima and Simone Tilmes and Alex Archibald and Gabriel Chiodo and Suvarna Fadnavis and Hella Garny and Beatrice Josse and Joowan Kim and Jean-Francois Lamarque and Olaf Morgenstern and Lee Murray and Clara Orbe and Amos Tai and Martyn Chipperfield and Bernd Funke and Martin Juckes and Doug Kinnison and Markus Kunze and Beiping Luo and Katja Matthes and Paul A. Newman and Charlotte Pascoe and Thomas Peter (2021), SPARC Newsletter, volume 57, pp 22-30", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2021-08-05T23:54:58", "updateFrequency": "", "dataLineage": "These data are original data produced by NIWA-UKCA2, processed into CF-compliant NetCDF3 format. In a further step, the data has been processed using nccopy into compressed NetCDF4 format. Uploaded using rsync.", "removedDataReason": "", "keywords": "CCMI-2022, refD2, SSP245, Hindcast, Scenario, NIWA-UKCA, NIWA, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "250 km", "status": "completed", "dataPublishedTime": "2023-04-06T14:59:29", "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": 33038, "dataPath": "/badc/ccmi/data/post-cmip6/ccmi-2022/NIWA/NIWA-UKCA2/refD2", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 235652708245, "numberOfFiles": 322, "fileFormat": "Data are Net-CDF formatted" }, "timePeriod": { "ob_id": 9071, "startTime": "1960-01-01T00:00:00", "endTime": "2101-01-01T00:00:00" }, "resultQuality": { "ob_id": 3727, "explanation": "Data are as given by the data provider, ceda-cc quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CCMI-2022 Data and Metadata Quality Statement", "date": "2021-08-05" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33021, "uuid": "f7d8ff75cbd04a85b5f7e36676248e23", "short_code": "comp", "title": "NIWA-UKCA2 deployed at NIWA", "abstract": "NIWA-UKCA2 deployed at NIWA" }, "procedureCompositeProcess": null, "imageDetails": [ 146 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2544, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "ccmi-2022", "label": "restricted: ccmi-2022 group", "licence": { "ob_id": 21, "licenceURL": "https://artefacts.ceda.ac.uk/licences/rugl_versions/rugl_v1-0.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32805, "uuid": "92dddf542adc44b5898f535be4179705", "short_code": "proj", "title": "CCMI-2022 Chemistry-climate model initiative, phase 2", "abstract": "CCMI-2022 Chemistry-climate model initiative, phase 2 is a World Climate Research Programme (WCRP) Stratosphere-Troposphere Processes and their Role in Climate (SPARC) project to study the evolution of the ozone layer using chemistry-climate model simulations. CCMI-2022 data will support the World Meteorologcial Organisation (WMO)/ United Nations Environment Programme (UNEP) Scientific Assessment of Ozone Depletion Report 2022." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1050, 6021, 6022, 6023, 6024, 6087, 6092, 6093, 6506, 6814, 6816, 6818, 7767, 7768, 7769, 7770, 7778, 7795, 9616, 9617, 10410, 10413, 10414, 10885, 11082, 27607, 27654, 27657, 27681, 27682, 27723, 27743, 27745, 27747, 27748, 27749, 27758, 27766, 27778, 27781, 27782, 27783, 27784, 27790, 27794, 27795, 27807, 27809, 27817, 27819, 27821, 37946, 37947, 37948, 37949, 37950, 37951, 37952, 37953, 37954, 37955, 37956, 37957, 37958, 37959, 37960, 37961, 37962, 37963, 37964, 37965, 37966, 37967, 37968, 37969, 37970, 37971, 37972, 37973, 37974, 37975, 37976, 37977, 37978, 37979, 37980, 37981, 37982, 37983, 37984, 37985, 37986, 37987, 37988, 37989, 37990, 37991 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 33023, "uuid": "cc660aad3cb34f5cb1ff50db57f47653", "short_code": "coll", "title": "CCMI-2022 data produced by the NIWA-UKCA2 model at NIWA", "abstract": "The NIWA-UKCA2 model contribution to the CCMI-2022 set of experiments defined by the APARC- and IGAC-supported Chemistry-Climate Model Initiative.\r\n\r\nThe CCMI-2022 set of model experiments focus on the stratosphere, with the goals of providing updated projections of the future evolution of ozone and improving our understanding of chemistry-climate interactions and how they are represented in models.\r\n\r\nThe NIWA-UKCA2 chemistry-climate model is run by the modelling team at NIWA (National Institute of Water and Atmospheric Research) in New Zealand and configured to follow forcings as laid out in the CCMI2022 founding document (Plummer et al., 2021)\r\n\r\nAPARC (formerly SPARC) and IGAC projects coordinate international research in atmospheric chemistry. APARC (Atmospheric Processes And their Role in Climate) is a core project of the World Climate Research Programme (WCRP). IGAC is the International Global Atmospheric Chemistry which currently operates under the umbrella of Future Earth." } ], "responsiblepartyinfo_set": [ 147433, 147434, 147435, 147436, 147437, 147438, 147439, 178481, 147440 ], "onlineresource_set": [ 43318, 52162 ] }, { "ob_id": 33034, "uuid": "dae723e78f9f4a0b9712f352f0a0231d", "title": "weather@home 2010 global simulations and climatology (1986-2016)", "abstract": "This dataset contains atmospheric data, such as wind field and surface air temperature, obtained by running the weather@home general circulation model for the year 2010. The aim of this dataset is to study the 2010 weather extremes which have affected Western Russian and Pakistan. The dataset contains data for both the regional and global GCM models provided as NetCDF v3 files. The gridded global model output is at 1.25°x1.875° (N96) horizontal resolution. The regional model output is provided on a rotated longitude-latitude grid at a 50 km (0.44°x0.44°) horizontal resolution centred over South Asia. In order to compare 2010 data to the climatology of the model, the climatology is calculated for the years 1986 to 2016. This dataset has been produced by the University of Oxford, in cooperation the Potsdam Institute for Climate Impact Research and the VU University of Amsterdam in the context of the GOTHAM project, funded by the Belmont Forum through the Natural Environment Research Council (NE/P006779/1). The weather@home model runs on the climateprecictions.net platform, which provides a volunteer distributed computational system.\r\n\r\nThis dataset contains weather@home global and regional simulations for the year 2010 and for the climatology of both models for the period 1986-2016.\r\n\r\nThe data comprises of:\r\n~700 ensemble members for weather@home (global model) for 2010 (batch 778)\r\n~700 ensemble members for weather@home (regional model, South Asia) for 2010 (nested on batch 778)\r\n~170 ensemble members per year for the period (1987-2016) for weather@home (global model) (batch 845)\r\n~100 ensemble members per year for the period (1986-2015) for weather@home (regional model, South Asia) (batch 697, not nested on batch 845)\r\nextracted variables:\r\nAt daily resolution: geopotential height 300 hPa (item16202), meridional wind velocity at 300 hPa (item15202), surface air temperature at 1.5m (item 3236), precipitation (item5216)\r\nAt monthly resolution: soil moisture (item8208)", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-08-24T09:07:26", "updateFrequency": "notPlanned", "dataLineage": "GOTHAM Project weather@home platform (ClimatePrediction.Net) Data archived at Centre for Environmental Data Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "weather@home, climatology", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-09-08T07:55:42", "doiPublishedTime": "2021-09-08T10:45:27.457071", "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": 33035, "dataPath": "/badc/weather_at_home/data/b778-845_archive", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5355157184461, "numberOfFiles": 41213, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 9075, "startTime": "1986-01-01T00:00:00", "endTime": "2016-12-31T00:00:00" }, "resultQuality": { "ob_id": 3731, "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": "2021-08-10" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33037, "uuid": "f19d3640852647a2989b9ca6d91143c9", "short_code": "comp", "title": "weather@home model running on climateprediction.net", "abstract": "The weather@home model uses the Hadley Centre atmosphere only model version 3 with improved physics (HadAM3P) as a global driving model at N96 resolution, (1.25 x 1.875 degrees resolution, 19 levels) with 15 minutes timestep for dynamics. One-way nested within this is the Hadley Centre regional model (HadRM3P) run at high resolution. This is the regional model used in PRECIS experiments by the Met Office and in all weather@home experiments.\n\nIn this experiment it has 0.44 x 0.44 degrees resolution with a rotated pole to achieve approx. 50 km x 50 km resolution on 19 levels over South Asia. More info on https://www.climateprediction.net/education/climate-science/models-used/ and validation analysis of the weather@home system can be found in Massey et al 2015 https://doi.org/10.1002/qj.2455, and Guillod et al 2017 https://doi.org/10.5194/gmd-10-1849-2017." }, "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": [ { "ob_id": 33036, "uuid": "7d0499da1ae44223be8feaa7662f72ad", "short_code": "proj", "title": "GOTHAM - Global Teleconnections in the Earth’s Climate System – Processes, Modelling and Advanced Analysis Methods ", "abstract": "GOTHAM represents an ambitious research program to gain robust, relevant and transferable knowledge of past and present day patterns and trends of regional climate extremes and variability of vulnerable areas identified by the IPCC, including the tropics and high-latitudes. It will achieve this by identifying the influence of remote drivers, or teleconnections, on regional climate variability, and assessing their relative impact. It will also assess the potential for improved season- decadal prediction using a combination of contemporary climate models, citizen-science computing and advanced statistical analysis tools. GOTHAM has the direct backing of many international weather and climate research centres, and will lead to the improved development of seasonal-decadal forecasts at the regional level. The improved knowledge and understanding of dynamical factors that influence regional weather and climate in the tropics/sub-tropics, and polar regions, will directly feed through to weather and climate forecast services to assist in their decisions on which priority areas of their model development to target in order to improve forecast skills. For example, GOTHAM will advise whether a model is missing or misrepresenting important global teleconnections that significantly influence regional climate in identified vulnerable regions. These impacts will be achieved through regular meetings with GOTHAM investigator groups and their extended collaborative networks, and extensive involvement in wider science and science-policy programmes with co- aligned strategies, such as the core projects within the WCRP. Improved seasonal to decadal scale forecasts will improve predictions of extreme events and natural hazard risks such as flooding that can have devastating impact on society. There is real potential for project results feeding through to impacts-related research, such as those involved in hydrological and flood forecast modeling, and these will be explored in liaison with identified partners in Asia and Europe." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 4240, 60533, 60534, 60535, 60536, 60537, 60538, 60539, 60540, 60541, 60542, 60543, 60544, 60545, 60546 ], "vocabularyKeywords": [], "identifier_set": [ 10945 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 147499, 147500, 147501, 147502, 147503, 147504, 147509, 147510, 147511, 168965, 147512, 147513, 147514, 147515, 147516, 147517 ], "onlineresource_set": [ 43330, 43331 ] }, { "ob_id": 33045, "uuid": "2e3a3408af2f4e918458c09d4e5f7460", "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): Altimeter along-track high resolution sea level anomalies in some coastal regions from ENVISAT (2002-2010) and SARAL (2013-2016) satellite altimetry, v1.1", "abstract": "This dataset contains along-track sea level anomalies derived from satellite altimetry. Altimeter along-track sea level measurements from the RA2 instrument on ENVISAT and the Altika instrument on SARAL satellite missions have been processed to produce high resolution (20 Hz, corresponding to an along-track distance of ~300m) sea level anomalies, in order to provide long-term homogeneous sea level time series as close to the coast as possible in six different coastal regions (North-East Atlantic, Mediterranean Sea, Western Africa, North Indian Ocean, South-East Asia and Australia). \r\n\r\nThe product benefits from the spatial resolution provided by high-rate data, the Adaptive Leading Edge Subwaveform Retracker (ALES) and the post-processing strategy of the along-track (X-TRACK) algorithm, both developed for the processing of coastal altimetry data, as well as the best possible set of geophysical corrections. \r\n\r\nThe main objective of this product is to provide accurate altimeter Sea Level Anomalies (SLA) time series as close to the coast as possible in order to assess whether the coastal sea level trends experienced at the coast are similar to the observed sea level trends in the open ocean and to determine the causes of the potential discrepancies.\r\n\r\nThe Envisat and SARAL/AltiKa missions have the same ground track but the temporal gap between both missions prevents from computing reliable trends during the total period between both missions.\r\n\r\nThis dataset has been produced by the Climate Change Initiative Coastal Sea Level team, within the extension phase of the European Sapce Agency (ESA) Climate Change Initiative.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2022-01-19T15:24:31", "updateFrequency": "", "dataLineage": "This dataset has been produced by the Climate Change Initiative Coastal Sea Level team within the extension phase of the European Space Agency (ESA) Climate Change Initiative. A copy has been transferred to CEDA as part of the ESA CCI Open Data Portal project.", "removedDataReason": "", "keywords": "ESA CCI, SLA", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2021-12-09T13:53:57", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2716, "bboxName": "", "eastBoundLongitude": 160.0, "westBoundLongitude": -30.0, "southBoundLatitude": -45.0, "northBoundLatitude": 60.0 }, "verticalExtent": null, "result_field": { "ob_id": 33234, "dataPath": "/neodc/esacci/sea_level/data/XTRACK_ALES_SLA/ENVISAT_SARAL/SLA/v1.1/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 14592570173, "numberOfFiles": 1743, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 9077, "startTime": "2002-09-30T00:00:00", "endTime": "2016-06-16T23:59:59" }, "resultQuality": { "ob_id": 3559, "explanation": "The SL_cci+ XTRACK/ALES product (V1.1) has been produced based on the L2 altimeter algorithms recommended by the SL_cci", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-10-06" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 33047, "uuid": "d2d16e94cd4e4f5b95cebf99dc7fe2dd", "short_code": "cmppr", "title": "Composite process for the ESA Sea Level CCI Altimeter coastal sea level anomalies datasets based on XTRACK/ALES processing and ENVISAT and SARAL data.", "abstract": "The coastal sea level data products are based on a complete reprocessing of raw radar altimetry waveforms from the ENVISAT and SARAL missions to derive satellite-sea surface ranges as close as possible to the coast (a process called ‘retracking’) and optimization of the geophysical corrections applied to the range measurements to produce sea level time series at monthly interval, from 20 km offshore to the coast" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" }, { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2584, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 47, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_sealevel_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13331, "uuid": "a0a6fa39470a4a7baf847e3a1751f950", "short_code": "proj", "title": "ESA Sea Level Climate Change Initiative Project", "abstract": "The European Space Agency (ESA) Sea Level Climate Change Initiative (Sea_Level_cci) project is part of the ESA's Climate Change Initiative programme. \r\n\r\nIn the first phases of the CCI programme, the Sea Level project produced and validated global sea level Essential Climate Variable (ECV) products.\r\n\r\nIn the current phase, the objective is to produce a long-term and homogeneous sea level record as close to the coast as possible in order to assess whether the coastal sea level trends experienced at the coast are similar as the observed sea level trends in the open ocean and to determine the causes of the potential discrepancies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 2668, 2669, 30285, 30286, 30287, 30288, 30289, 30293, 30294, 30295, 30296 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 33235, "uuid": "eaec37f29d234843bfd50accee2de0d0", "short_code": "coll", "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): Collection of datasets of altimeter along-track high resolution sea level anomalies and associated trends in some coastal regions, v1.1", "abstract": "This dataset collection contains various along-track sea level anomaly products derived from satellite altimetry by the ESA Sea Level Climate Change Initiative project.\r\n\r\nTwo datasets containing along-track sea level anomalies derived from satellite altimetry have been derived; one containing data from the JASON satellites (JASON-1, JSON-2, and JSON-3), and the other from the RA2 instrument on ENVISAT and the Altika instrument on SARAL satellite missions.\r\n\r\nThese have been processed to produce high resolution (20 Hz, corresponding to an along-track distance of ~300m) sea level anomalies, in order to provide long-term homogeneous sea level time series as close to the coast as possible in six different coastal regions (North-East Atlantic, Mediterranean Sea, Western Africa, North Indian Ocean, South-East Asia and Australia). \r\n\r\nThe products benefits from the spatial resolution provided by high-rate data, the Adaptive Leading Edge Subwaveform Retracker (ALES) and the post-processing strategy of the along-track (X-TRACK) algorithm, both developed for the processing of coastal altimetry data, as well as the best possible set of geophysical corrections. \r\n\r\nAdditionally a database of coastal sea level anomalies and associated trends from Jason satellite altimetry, derived from the JASON sea level anomaly product is included." }, { "ob_id": 12684, "uuid": "56c94cb1410f4f2b8a41729c0e558617", "short_code": "coll", "title": "ESA Sea Level Climate Change Initiative (Sea Level CCI) dataset collection", "abstract": "As part of the European Space Agency's (ESA) Climate Change Initiative (CCI) programme, the Sea Level CCI project has produced a set of gridded multi-satellite merged products relating to the Sea Level Essential Climate Variable (ECV). These consist of a) a time series of monthly gridded Sea Level Anomalies (SLA) and b) Oceanic Indicators describing the evolution of the sea level anomalies.\r\n\r\nSea surface heights are measured above (or below) some reference level by altimeter satellites, surface height being the difference between a satellites position in orbit with respect to an arbitrary reference surface (the Earth's centre or a rough approximation of the Earth's surface: the reference ellipsoid) and the satellite-to-surface range (calculated by measuring the time taken for the signal to make the round trip). Through sending a microwave pulse to the ocean's surface, the satellites measured the surface heights through measuring the time taken for the pulse to return. \r\n\r\nThe current version is v1.1, and covers the period January 1993 - December 2014, and has been derived from the main altimeter missions: ERS-1, ERS-2, Envisat, TOPEX/Poseidon, Jason-1, Jason-2 and Geosat-Follow-On. A detailed description of the SL CCI project and the products can be found in Ablain et al., 2014, and further information is also provided in the Product User Guide. \r\n\r\nThe following DOI can be used to reference the product database (all products in the V1.1 release (as of December 2015)): DOI:10.5270/esa-sea_level_cci-1993_2014-v_1.1-201512. \r\n\r\n When using or referring to the SL_cci products, please mention the associated DOI (see above and the individual datasets) and also use the following citation where a detailed description of the SL_cci project and products can be found:\r\n\r\nAblain, M., Cazenave, A., Larnicol, G., Balmaseda, M., Cipollini, P., Faugère, Y., Fernandes, M. J., Henry, O., Johannessen, J. A., Knudsen, P., Andersen, O., Legeais, J., Meyssignac, B., Picot, N., Roca, M., Rudenko, S., Scharffenberg, M. G., Stammer, D., Timms, G., and Benveniste, J.: Improved sea level record over the satellite altimetry era (1993–2010) from the Climate Change Initiative project, Ocean Sci., 11, 67-82, doi:10.5194/os-11-67-2015, 2015.\r\n\r\nFor further information on the Sea Level CCI products, and to register your interest with the CCI team please email: info-sealevel@esa-sealevel-cci.org" } ], "responsiblepartyinfo_set": [ 147582, 147588, 147589, 147587, 147586, 147585, 147584, 147583 ], "onlineresource_set": [ 43341, 43340 ] }, { "ob_id": 33051, "uuid": "1a86e0326e1346febf121eca83bf1f08", "title": "ICOsahedral Nonhydrostatic global radiative-convective equilibrium model data, with additional diabatic heating perturbations", "abstract": "This dataset provides ICOsahedral Nonhydrostatic (ICON) Atmospheric Global Circulation Model (GCM) data for global radiative-convective equilibrium (RCE) 5-year simulations. Data files are provided in NetCDF format.\r\n\r\nThe RCE simulations are setup on an aquaplanet with no rotation and no diurnal cycle. To initialise the RCE state, homogenised boundary conditions are set where the solar insolation is 551.58 Wm-2 with a fixed zenith angle of 42.05. Together these values give a constant insolation of 409.6 Wm-2. This is equivalent to the annual mean tropical insolation (Wing et al., 2018). A 360-day calendar is used. The global ocean albedo is set to 0.07. Concentrations of tracers CO2, CH4, N2O and O2 are set to be constant in space and time and the O3 profile is the same as used in Popke et al. (2013). SSTs are kept globally constant throughout each simulation at either 290K or 305K. The model spin-up is done for 1 month, with horizontally homogenised radiative fluxes, where the longwave and shortwave cooling rates are averaged horizontally at each model level and timestep. Then the domain-averaged conditions over the final 5 days of these simulations are used to initialise the simulations, following Wing et al. (2018). Each simulation is then run for 5 years. Model output is given every 5 days, as a 5-day mean. Most simulations have been run with interactive radiation. However, in some, radiative feedbacks have been turned off by horizontally homogenising the radiative cooling rates at each model level and timestep, following Muller & Held (2012).\r\n\r\nSome simulations include a diabatic heating perturbation. This is achieved using the Max Planck Institute Aerosol Climatology version 2, Simple Plume (MACv2-SP) model. This model prescribes idealised aerosol plumes. Parameters that can be tailored include the aerosol optical depth (AOD), single scattering albedo (SSA), and plume spatial extent, and these are given as functions of time and wavelength. In this dataset, we remove any temporal dependence and use constant parameters for each variable. We also only use a single plume in our simulations. Experiments are performed with a single plume at latitude = 0, longitude = 0, AOD = 1.8 and SSA = 0.8. There are a couple of exceptions to this:\r\n- In file crh_aod_sensitivity.nc the AOD is varied from 0.3 to 1.8.\r\n- In files 290K_plume_homogLW_*.nc the AOD used is 1\r\n- In files 305K_plume_homogLW_*.nc the AOD used is 0.8.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-08-24T13:10:45", "updateFrequency": "", "dataLineage": "Data were generated using the ICOsahedral Nonhydrostatic (ICON) Atmospheric Global Circulation Model (GCM). ICON was run on the R02B04 grid, which has an approximate horizontal grid-spacing of 160km. The vertical resolution is set by assigning 47 stretched model levels between the surface and model top at 83km. The aerosol plume model used is the Max Planck Institute Aerosol Climatology version 2, Simple Plume (MACv2-SP) model. In this study we have removed any temporal dependence of the aerosols, and we vary only the aerosol optical depth. Data has been provided to Centre for Environmental Data Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "ICON, GCM, RCE, diabatic heating", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-08-25T11:06:01", "doiPublishedTime": "2021-08-26T14:09:27.775058", "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": 33049, "dataPath": "/badc/deposited2021/rce_diabatic-heating/data/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 212425332225, "numberOfFiles": 28, "fileFormat": "Data are netCDF formatted." }, "timePeriod": null, "resultQuality": { "ob_id": 3733, "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": "2021-08-24" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33050, "uuid": "377dd7df67b34831a66befd25a81d60e", "short_code": "comp", "title": "ICOsahedral Nonhydrostatic (ICON) Atmospheric Global Circulation Model (GCM) deployed on ARCHER computer", "abstract": "ICOsahedral Nonhydrostatic (ICON) Atmospheric Global Circulation Model (GCM) deployed on ARCHER computer" }, "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": [ { "ob_id": 877, "uuid": "16e1c51a9eeae188510f2308b18a14fa", "short_code": "proj", "title": "Natural Environment Research Council (NERC)", "abstract": "The Natural Environment Research Council (NERC) is the UK's main agency for funding and managing research, training and knowledge transfer in the environmental sciences." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 13295, 18405, 18408, 49475, 50559, 50561, 61147, 62687, 62708, 62714, 67738, 67739, 67740, 67741, 67742, 67743, 67744, 67745, 67746, 67747, 67748, 67749, 67750, 67751, 67752, 79183, 79184, 79185, 79186, 79187, 79188 ], "vocabularyKeywords": [], "identifier_set": [ 10921 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 147603, 147604, 147605, 147606, 147607, 147608, 147609, 147612, 147610, 168968, 147611 ], "onlineresource_set": [ 87782, 94781 ] }, { "ob_id": 33053, "uuid": "f24175ec280b4e1296bb80eb86f88f68", "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): High Latitude Sea Level Anomalies from satellite altimetry (by DTU/TUM)", "abstract": "This dataset contains high latitude sea level anomalies produced by DTU (Technical University of Denmark) and TUM (Technical University of Munich) as part of the ESA Sea Level CCI (Climate Change Initiative) project, covering both the Arctic and Antarctic regions.\r\n\r\nThe data comprises weekly means from August 1991 to April 2017 and has been obtained using satellite altimetry data from four satellite missions: ERS1 (weeks 0 - 217); ERS2 (weeks 218 - 573); Envisat (weeks 574 - 1020); CryoSat-2 (weeks 1021 - 1336).\r\n\r\nTwo datasets are available: dataset #1 is based on the ALES+ retracking without correction of the inverse barometer whereas dataset #2 has been corrected for this effect.\r\n\r\nDataset #1 is provided both 'masked' and 'unmasked', where the masked data have been masked using sea ice concentrations downloaded from osisaf.met.no/p/ice. Dataset #2 is provided both 'masked' and 'unmasked', where the masked data have had data points retrieved over land removed from the files.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-12-09T16:19:01", "updateFrequency": "", "dataLineage": "The product has been developed within the sea level project of the European Space Agency (ESA) Climate Change Initiative. A copy has been transferred to CEDA as part of the ESA CCI Open Data Portal project.", "removedDataReason": "", "keywords": "ESA CCI, SLA", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-12-16T11:13:44", "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": 33439, "dataPath": "/neodc/esacci/sea_level/data/DTU_TUM_Arctic_Antarctic_MSLA/20170720/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 12545439986, "numberOfFiles": 6593, "fileFormat": "Data are in ascii format.\r\n\r\nFor dataset 1 the columns are:\r\n\r\nLongitude,\r\nLatitude,\r\nHeight (always 0),\r\nSea level anomaly (SLA),\r\nSLA error,\r\nData origin (mission).\r\n\r\nThe time in the filename denotes the beginning of the data, and files contain weekly means.\r\n\r\n\r\nFor Dataset 2 the columns are: \r\n\r\nLongitude,\r\nLatitude,\r\nHeight (always 0),\r\nSea level anomaly (SLA).\r\n\r\nThe filename states the region and the month and year of the dataset (YYYYMM)." }, "timePeriod": { "ob_id": 9078, "startTime": "1991-08-01T00:00:00", "endTime": "2017-04-30T23:59:59" }, "resultQuality": { "ob_id": 3812, "explanation": "Data are as provided by the Sea Level CCI project team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-12-09" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 33445, "uuid": "0297f0c7495941b3bfdde3f74f9e9925", "short_code": "cmppr", "title": "Composite process for ESA Sea Level Climate Change Initiative (Sea_Level_cci): High Latitude Sea Level Anomalies from satellite altimetry (by DTU/TUM)", "abstract": "The data comprises weekly means from August 1991 to April 2017 and has been obtained using satellite altimetry data from four satellite missions: ERS1 (weeks 0 - 217); ERS2 (weeks 218 - 573); Envisat (weeks 574 - 1020); CryoSat-2 (weeks 1021 - 1336).\r\n\r\nTwo datasets are available: dataset #1 is based on the ALES+ retracking without correction of the inverse barometer whereas dataset #2 has been corrected for this effect.\r\n\r\nDataset #1 is provided both 'masked' and 'unmasked', where the masked data have been masked using sea ice concentrations downloaded from osisaf.met.no/p/ice. Dataset #2 is provided both 'masked' and 'unmasked', where the masked data have had data points retrieved over land removed from the files" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" }, { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2584, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 47, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_sealevel_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13331, "uuid": "a0a6fa39470a4a7baf847e3a1751f950", "short_code": "proj", "title": "ESA Sea Level Climate Change Initiative Project", "abstract": "The European Space Agency (ESA) Sea Level Climate Change Initiative (Sea_Level_cci) project is part of the ESA's Climate Change Initiative programme. \r\n\r\nIn the first phases of the CCI programme, the Sea Level project produced and validated global sea level Essential Climate Variable (ECV) products.\r\n\r\nIn the current phase, the objective is to produce a long-term and homogeneous sea level record as close to the coast as possible in order to assess whether the coastal sea level trends experienced at the coast are similar as the observed sea level trends in the open ocean and to determine the causes of the potential discrepancies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 12684, "uuid": "56c94cb1410f4f2b8a41729c0e558617", "short_code": "coll", "title": "ESA Sea Level Climate Change Initiative (Sea Level CCI) dataset collection", "abstract": "As part of the European Space Agency's (ESA) Climate Change Initiative (CCI) programme, the Sea Level CCI project has produced a set of gridded multi-satellite merged products relating to the Sea Level Essential Climate Variable (ECV). These consist of a) a time series of monthly gridded Sea Level Anomalies (SLA) and b) Oceanic Indicators describing the evolution of the sea level anomalies.\r\n\r\nSea surface heights are measured above (or below) some reference level by altimeter satellites, surface height being the difference between a satellites position in orbit with respect to an arbitrary reference surface (the Earth's centre or a rough approximation of the Earth's surface: the reference ellipsoid) and the satellite-to-surface range (calculated by measuring the time taken for the signal to make the round trip). Through sending a microwave pulse to the ocean's surface, the satellites measured the surface heights through measuring the time taken for the pulse to return. \r\n\r\nThe current version is v1.1, and covers the period January 1993 - December 2014, and has been derived from the main altimeter missions: ERS-1, ERS-2, Envisat, TOPEX/Poseidon, Jason-1, Jason-2 and Geosat-Follow-On. A detailed description of the SL CCI project and the products can be found in Ablain et al., 2014, and further information is also provided in the Product User Guide. \r\n\r\nThe following DOI can be used to reference the product database (all products in the V1.1 release (as of December 2015)): DOI:10.5270/esa-sea_level_cci-1993_2014-v_1.1-201512. \r\n\r\n When using or referring to the SL_cci products, please mention the associated DOI (see above and the individual datasets) and also use the following citation where a detailed description of the SL_cci project and products can be found:\r\n\r\nAblain, M., Cazenave, A., Larnicol, G., Balmaseda, M., Cipollini, P., Faugère, Y., Fernandes, M. J., Henry, O., Johannessen, J. A., Knudsen, P., Andersen, O., Legeais, J., Meyssignac, B., Picot, N., Roca, M., Rudenko, S., Scharffenberg, M. G., Stammer, D., Timms, G., and Benveniste, J.: Improved sea level record over the satellite altimetry era (1993–2010) from the Climate Change Initiative project, Ocean Sci., 11, 67-82, doi:10.5194/os-11-67-2015, 2015.\r\n\r\nFor further information on the Sea Level CCI products, and to register your interest with the CCI team please email: info-sealevel@esa-sealevel-cci.org" } ], "responsiblepartyinfo_set": [ 147621, 147622, 147623, 147624, 147625, 147626, 147627, 149302 ], "onlineresource_set": [ 43344, 43696, 43697, 43698, 43701, 43702, 43703, 43713 ] }, { "ob_id": 33059, "uuid": "ebe625b6f77945a68bda0ab7c78dd76b", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable snow (SCFV) from MODIS (2000 - 2020), version 2.0", "abstract": "This dataset contains Daily Snow Cover Fraction of viewable snow from the MODIS satellite instruments, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction viewable (SCFV) indicates the area of snow viewable from space over all land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. \r\n\r\nThe global SCFV product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included. \r\n\r\nThe SCFV time series provides daily products for the period 2000 – 2020. \r\n\r\nThe SCFV product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. \r\n\r\nThe retrieval method of the Snow_cci SCFV product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFV product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the Snow_cci SCFV retrieval method is applied. \r\n\r\nThe main differences of the Snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Metsämäki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the adaptation of the retrieval method using of a spatially variable ground reflectance instead of global constant values for snow free land, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data to assure in forested areas consistency of the SCFV and the SCFG CRDP v2.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/ebe625b6f77945a68bda0ab7c78dd76b) using the same retrieval approach.\r\n\r\nImprovements of the Snow_cci SCFV version 2.0 compared to the Snow_cci version 1.0 include (i) the utilisation of an updated ground reflectance map derived from statistical analyses of an extended MODIS time series, (ii) an update of the forest mask used for the transmissivity estimation, and (iii) an update of the constant reflectance value for wet snow based on the analysis of time series of the MODIS reflectance at 0.55 µm.\r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFV product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.\r\n\r\nThe SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nENVEO is responsible for the SCFV product development and generation from MODIS data, SYKE supported the development.\r\n\r\nThere are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2018. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFV products are available but have data gaps.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2022-01-07T16:49:49", "updateFrequency": "notPlanned", "dataLineage": "The snow_cci SCFV products from MODIS are based on the MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km (MOD021KM) and the MODIS/Terra Geolocation Fields 5-Min L1A Swath 1km (MOD03) Collection 6.1 data sets, provided by NASA.\r\n\r\nThe snow_cci SCF processing chain for MODIS includes the masking of clouds, the identification of certainly snow free areas, and the classification of snow cover fraction per pixel for all remaining observed pixels. Finally, permanent snow and ice areas as well as water bodies are masked in the SCFV products based on the Land Cover CCI map of the year 2000. All SCFV products are prepared according to the CCI data standards.\r\n\r\nAn automated and a manual quality check was performed on the full time series.\r\n\r\nWe acknowledge Norsk Regnesentral (Norwegian Computing Center, NR) for downloading the MODIS data from NASA, and UNINETT Sigma2 AS (Sigma2, The Norwegian e-infrastructure for Research & Education) for providing the processing infrastructure for the CRDP generation from MODIS.", "removedDataReason": "", "keywords": "ESA, CCI, Snow, Snow Cover Fraction", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-03-17T09:58:00", "doiPublishedTime": "2022-03-17T18:23:56", "removedDataTime": null, "geographicExtent": { "ob_id": 2614, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 35038, "dataPath": "/neodc/esacci/snow/data/scfv/MODIS/v2.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 206015589926, "numberOfFiles": 7559, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 9201, "startTime": "2000-02-24T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3646, "explanation": "The unbiased root mean square error of snow cover fraction adapted from the approach of Salminen et al. (2018) is added as an uncertainty layer in each product. The MODIS based SCFV products match the CCI data standards version 2.3, released in July 2021. For further information on the data quality, see the Snow_cci documentation..", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-04-26" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 33472, "uuid": "5996caf104734a74bdd627da97db10e5", "short_code": "cmppr", "title": "Composite process for the ESA Snow Climate Change Initiative SCFV MODIS v2.0 product", "abstract": "The snow_cci SCFV products from MODIS are based on the MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km (MOD021KM) and the MODIS/Terra Geolocation Fields 5-Min L1A Swath 1km (MOD03) Collection 6.1 data sets, provided by NASA.\r\n\r\nThe retrieval method of the snow_cci SCFV product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFV product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 550 nm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include (i) the utilisation of a background reflectance map derived from statistical analyses of MODIS time series replacing the constant values for snow free ground used in the GlobSnow approach, and (ii) the adaptation of the retrieval method for mapping in forested areas the SCFV. \r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFV product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" }, { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2571, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 38, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_snow_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30229, "uuid": "93cf539bc3004cc8b98006e69078d86b", "short_code": "proj", "title": "ESA Snow Climate Change Initiative (snow_cci)", "abstract": "The overarching goal of snow_cci is the generation of homogeneous, well calibrated, long-term time series of key snow cover parameters (snow area extent and snow mass) from multi-sensor satellite data for climate applications. A main motivation for this initiative are significant discrepancies in the climatologies, anomalies, and trends in global snow cover time series from different products, detected in the ESA QA4EO Satellite Snow Product Intercomparison and Evaluation project (SnowPEx).\r\n\r\nThe first phase of the project started in September 2018; the second phase started in February 2022. The third phase of the snow_cci project is planned to start in late 2025." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1050, 6021, 6022, 32070, 32071, 32072, 45042, 45043 ], "vocabularyKeywords": [], "identifier_set": [ 12064 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 147637, 147638, 147639, 147640, 147641, 147642, 147636, 147643, 147644, 170190, 147645, 147646, 147647 ], "onlineresource_set": [ 43354, 43350, 43351, 43352, 43355, 43346, 43347, 43349, 43348, 50462, 50463, 90894, 90895, 90896, 90897, 90898, 90899, 90900, 90901 ] }, { "ob_id": 33060, "uuid": "8847a05eeda646a29da58b42bdf2a87c", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from MODIS (2000-2020), version 2.0", "abstract": "This dataset contains Daily Snow Cover Fraction (snow on ground) from MODIS, produced by the Snow project of the ESA Climate Change Initiative programme.\r\n\r\nSnow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. \r\n\r\nThe global SCFG product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included. \r\n\r\nThe SCFG time series provides daily products for the period 2000 – 2020. \r\n\r\nThe SCFG product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. \r\n\r\nThe retrieval method of the Snow_cci SCFG product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFG product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The Snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. \r\n\r\nThe main differences of the Snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Metsämäki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the usage of spatially variable background reflectance and forest reflectance maps instead of global constant values for snow free land and forest, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data, and (v) the update of the global forest canopy transmissivity based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny, 2019) to assure in forested areas consistency of the SCFG and the SCFV CRDP v2.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/ebe625b6f77945a68bda0ab7c78dd76b) using the same retrieval approach.\r\n\r\nImprovements of the Snow_cci SCFG version 2.0 compared to the Snow_cci version 1.0 include (i) the utilisation of an updated background reflectance map derived from statistical analyses of an extended MODIS time series, (ii) an update of the forest canopy transmissivity map, and (iii) an update of the constant reflectance value for wet snow based on the analysis of time series of the MODIS reflectance at 0.55 µm.\r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFG product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.\r\n\r\nThe SCFG product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nENVEO is responsible for the SCFG product development and generation from MODIS data, SYKE supported the development.\r\n\r\nThere are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2018. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFG products are available but have data gaps.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2022-03-14T16:35:36", "updateFrequency": "notPlanned", "dataLineage": "The snow_cci SCFG products from MODIS are based on the MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km (MOD021KM) and the MODIS/Terra Geolocation Fields 5-Min L1A Swath 1km (MOD03) Collection 6.1 data sets, provided by NASA.\r\n\r\nThe snow_cci SCF processing chain for MODIS includes the masking of clouds, the identification of certainly snow free areas, and the classification of snow cover fraction per pixel for all remaining observed pixels. Finally, permanent snow and ice areas as well as water bodies are masked in the SCFG products using the corresponding classes from the Land Cover CCI map of the year 2000 as auxiliary layers. All SCFG products are prepared according to the CCI data standards.\r\n\r\nAn automated and a manual quality check was performed on the full time series.\r\n\r\nWe acknowledge Norsk Regnesentral (Norwegian Computing Center, NR) for downloading the MODIS data from NASA, and UNINETT Sigma2 AS (Sigma2, The Norwegian e-infrastructure for Research & Education) for providing the processing infrastructure for the CRDP generation from MODIS.", "removedDataReason": "", "keywords": "ESA, CCI, Snow, Snow Cover Fraction", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-03-17T09:58:00", "doiPublishedTime": "2022-03-23T14:39:43.105503", "removedDataTime": null, "geographicExtent": { "ob_id": 2614, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 34647, "dataPath": "/neodc/esacci/snow/data/scfg/MODIS/v2.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 208265531652, "numberOfFiles": 7561, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 9200, "startTime": "2000-02-24T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3837, "explanation": "The unbiased root mean square error of snow cover fraction adapted from the approach of Salminen et al. (2018) is added as uncertainty layer in each product. The MODIS based SCFG products are matching the CCI data standards version 2.3, released in July 2021. For more information on data quality, see the Snow_cci documentation.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-01-18" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 33465, "uuid": "3591ac5232f94aaa93dc142f6fe03e80", "short_code": "cmppr", "title": "Composite process for the ESA Snow Climate Change Initiative SCFG MODIS v2.0 product", "abstract": "The snow_cci SCFG products from MODIS are based on the MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km (MOD021KM) and the MODIS/Terra Geolocation Fields 5-Min L1A Swath 1km (MOD03) Collection 6.1 data sets, provided by NASA.\r\n\r\nThe retrieval method of the snow_cci SCFG product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFG product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. Improvements of the snow_cci SCFG version 2.0 compared to the snow_cci version 1.0 include (i) the utilisation of an updated background reflectance map derived from statistical analyses of an extended MODIS time series, (ii) an update of the forest canopy transmissivity map, and (iii) an update of the constant reflectance value for wet snow based on the analysis of time series of the MODIS reflectance at 0.55 µm. \r\n\r\nThe main differences compared to the GlobSnow approach, and the method described in Metsämäki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the usage of a spatially variable background reflectance map instead of a global constant value for snow free land, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data, and (v) the update of the forest canopy transmissivity to assure in forested areas consistency of the SCFG and the SCFV CRDP v2.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/ebe625b6f77945a68bda0ab7c78dd76b) using the same retrieval approach. \r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFG product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" }, { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2571, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 38, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_snow_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30229, "uuid": "93cf539bc3004cc8b98006e69078d86b", "short_code": "proj", "title": "ESA Snow Climate Change Initiative (snow_cci)", "abstract": "The overarching goal of snow_cci is the generation of homogeneous, well calibrated, long-term time series of key snow cover parameters (snow area extent and snow mass) from multi-sensor satellite data for climate applications. A main motivation for this initiative are significant discrepancies in the climatologies, anomalies, and trends in global snow cover time series from different products, detected in the ESA QA4EO Satellite Snow Product Intercomparison and Evaluation project (SnowPEx).\r\n\r\nThe first phase of the project started in September 2018; the second phase started in February 2022. The third phase of the snow_cci project is planned to start in late 2025." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1050, 6021, 6022, 32068, 32069, 32070, 32071, 32072 ], "vocabularyKeywords": [], "identifier_set": [ 12069 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 147648, 147649, 147650, 147651, 147652, 147653, 147654, 147655, 147656, 164406, 147657, 147658, 147659 ], "onlineresource_set": [ 43362, 43357, 43358, 43359, 43360, 43361, 43363, 43364, 43365, 47606, 47607, 43356, 90876, 90877, 90878, 90879, 90880, 90881, 90882, 90883, 90884 ] }, { "ob_id": 33061, "uuid": "763eb87e0682446cafa8c74488dd5fb8", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1982 - 2018), version 2.0", "abstract": "This dataset contains Daily Snow Cover Fraction of viewable snow from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction viewable (SCFV) indicates the area of snow viewable from space over land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. \r\n\r\nThe global SCFV product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.\r\n\r\nThe SCFV time series provides daily products for the period 1982-2018. \r\n\r\nThe product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the Cloud CCI cloud v3.0 mask product. \r\n\r\nThe retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre- and post-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.630 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. \r\n\r\nThe following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water; permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map.\r\n\r\nThe SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology and biology.\r\n\r\nThe Remote Sensing Research Group of the University of Bern is responsible for the SCFV product development and generation. ENVEO developed and prepared all auxiliary data sets used for the product generation. \r\n\r\nThe SCFV AVHRR product comprises one longer data gap of 92 between November 1994 and January 1995, and 16 individual daily gaps, resulting in a 99% data coverage over the entire study period of 37 years.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2022-01-07T13:27:26", "updateFrequency": "notPlanned", "dataLineage": "The snow_cci SCFV product based on AVHRR was developed and processed at the University of Bern in the frame of ESA CCI+ Snow project. The AVHRR baseline FCDR was pre-processed using pyGAC and pySTAT in the frame of the ESA CCI Cloud project (Devasthale et al. 2017, Stengel et al. 2020). \r\n\r\nThe final product is quality checked.", "removedDataReason": "", "keywords": "ESA, CCI, Snow, Snow Cover Fraction", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-03-17T16:44:03", "doiPublishedTime": "2022-03-17T16:51:03", "removedDataTime": null, "geographicExtent": { "ob_id": 2614, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 34673, "dataPath": "/neodc/esacci/snow/data/scfv/AVHRR_MERGED/v2.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 27651048076, "numberOfFiles": 13405, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 9202, "startTime": "1982-01-01T00:00:00", "endTime": "2018-12-30T23:59:59" }, "resultQuality": { "ob_id": 3843, "explanation": "The unbiased root mean square error per-pixel is added as an uncertainty layer in the product. The AVHRR based SCFV product matches the CCI data standards version 2.3, released in July 2021. For more information on data quality, see the Snow_cci documentation.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-01-25" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 33470, "uuid": "64b26970ed084e8bbdbd38d4e948c7cc", "short_code": "cmppr", "title": "Composite process for the ESA Snow Climate Change Initiative SCFV AVHRR v2.0 product", "abstract": "The ESA Snow Climate Change Initiative SCFG AVHRR v2.0 product is based on an AVHRR baseline FCDR pre-processed using pyGAC and pySTAT in the frame of the ESA CCI Cloud project.\r\n\r\nThe retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 630 nm and 1.61 µm (channel 3a or the reflective part of channel 3b), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. \r\n\r\nThe following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" }, { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2571, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 38, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_snow_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30229, "uuid": "93cf539bc3004cc8b98006e69078d86b", "short_code": "proj", "title": "ESA Snow Climate Change Initiative (snow_cci)", "abstract": "The overarching goal of snow_cci is the generation of homogeneous, well calibrated, long-term time series of key snow cover parameters (snow area extent and snow mass) from multi-sensor satellite data for climate applications. A main motivation for this initiative are significant discrepancies in the climatologies, anomalies, and trends in global snow cover time series from different products, detected in the ESA QA4EO Satellite Snow Product Intercomparison and Evaluation project (SnowPEx).\r\n\r\nThe first phase of the project started in September 2018; the second phase started in February 2022. The third phase of the snow_cci project is planned to start in late 2025." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 32072, 50416, 61130, 61131, 63201, 63202 ], "vocabularyKeywords": [], "identifier_set": [ 12061 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 147660, 147661, 147662, 147663, 147664, 147665, 147666, 147667, 147668, 147669, 147670, 168218, 147671, 147672, 147673 ], "onlineresource_set": [ 43371, 43372, 43373, 43374, 43375, 43367, 47643, 47644, 47645, 47646, 47647, 47642, 90902, 90903, 90904, 90905, 90906, 90907, 90908, 90909 ] }, { "ob_id": 33062, "uuid": "3f034f4a08854eb59d58e1fa92d207b6", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from AVHRR (1982 - 2018), version 2.0", "abstract": "This dataset contains Daily Snow Cover Fraction (snow on ground) from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction on ground (SCFG) indicates the area of snow observed from space over land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. \r\n\r\nThe global SCFG product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.\r\n\r\nThe SCFG time series provides daily products for the period 1982-2018. \r\n\r\nThe product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the Cloud CCI cloud v3.0 mask product. \r\n\r\nThe retrieval method of the snow_cci SCFG product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.63 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. \r\n\r\nThe following auxiliary data sets are used for product generation: i) ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map; ii) Forest canopy transmissivity map; this layer is based on the tree cover classes of the ESA CCI Land Cover 2000 data set and the tree cover density map from Landsat data for the year 2000 (Hansen et al., Science, 2013, DOI: 10.1126/science.1244693). This layer is used to apply a forest canopy correction and estimate in forested areas the fractional snow cover on ground.\r\n\r\nThe SCFG product is aimed to serve the needs of users working in cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nThe Remote Sensing Research Group of the University of Bern is responsible for the SCFG product development and generation. ENVEO developed and prepared all auxiliary data sets used for the product generation.\r\n\r\nThe SCFG AVHRR product comprises one longer data gap of 92 between November 1994 and January 1995, and 16 individual daily gaps, resulting in a 99% data coverage over the entire study period of 37 years.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2022-01-04T18:18:42", "updateFrequency": "notPlanned", "dataLineage": "The snow_cci SCFG product based on AVHRR was developed and processed at the University of Bern in the frame of ESA CCI+ Snow project. The AVHRR baseline FCDR was pre-processed using pyGAC and pySTAT in the frame of the ESA CCI Cloud project (Devasthale et al. 2017, Stengel et al. 2020). \r\nThe final product is quality checked.\r\n\r\nData were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "ESA, CCI, Snow, Snow Cover Fraction", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-03-17T09:58:00", "doiPublishedTime": "2022-03-17T17:30:12", "removedDataTime": null, "geographicExtent": { "ob_id": 2614, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 35037, "dataPath": "/neodc/esacci/snow/data/scfg/AVHRR_MERGED/v2.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 26517990507, "numberOfFiles": 13405, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 9203, "startTime": "1982-01-01T00:00:00", "endTime": "2018-12-30T23:59:59" }, "resultQuality": { "ob_id": 3893, "explanation": "The unbiased root mean square error per-pixel is added as uncertainty layer in the product. The AVHRR based SCFG product matches the CCI data standards version 2.3, released in July 2021. For more information on data quality see the Snow_cci documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-02-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 33471, "uuid": "552e5a4245d24972902cb667ac881dca", "short_code": "cmppr", "title": "Composite process for the ESA Snow Climate Change Initiative SCFG AVHRR v2.0 product", "abstract": "The ESA Snow Climate Change Initiative SCFG AVHRR v2.0 product is based on an AVHRR baseline FCDR pre-processed using pyGAC and pySTAT in the frame of the ESA CCI Cloud project.\r\n\r\nThe retrieval method of the snow_cci SCFG product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 630 nm and 1.61 µm (channel 3a or the reflective part of channel 3b), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. \r\n\r\nThe following auxiliary data sets are used for product generation: i) ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map; ii) Forest canopy transmissivity map; this layer is based on the tree cover classes of the ESA CCI Land Cover 2000 data set and the tree cover density map from Landsat data for the year 2000 (Hansen et al., Science, 2013, DOI: 10.1126/science.1244693). This layer is used to apply a forest canopy correction and estimate in forested areas the fractional snow cover on ground." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" }, { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2571, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 38, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_snow_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30229, "uuid": "93cf539bc3004cc8b98006e69078d86b", "short_code": "proj", "title": "ESA Snow Climate Change Initiative (snow_cci)", "abstract": "The overarching goal of snow_cci is the generation of homogeneous, well calibrated, long-term time series of key snow cover parameters (snow area extent and snow mass) from multi-sensor satellite data for climate applications. A main motivation for this initiative are significant discrepancies in the climatologies, anomalies, and trends in global snow cover time series from different products, detected in the ESA QA4EO Satellite Snow Product Intercomparison and Evaluation project (SnowPEx).\r\n\r\nThe first phase of the project started in September 2018; the second phase started in February 2022. The third phase of the snow_cci project is planned to start in late 2025." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 32072, 50416, 61130, 61131, 61132, 62644 ], "vocabularyKeywords": [], "identifier_set": [ 12062 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 147674, 147675, 147676, 147677, 147678, 147679, 147680, 147681, 147682, 147683, 147684, 170189, 147685, 147686, 147687 ], "onlineresource_set": [ 43382, 43383, 43378, 43379, 43380, 43377, 43384, 43385, 43386, 50460, 43381, 50461, 89518, 89519, 89515, 89516, 89517, 89520, 89521, 89522, 89523, 89524, 87725, 87770 ] }, { "ob_id": 33067, "uuid": "d1c277836e754e279c9a964ad3d95828", "title": "UKESM1-CH4 output for methane emissions-driven and methane LBC-driven future climate projections under SSP3-7.0 and SSP1-2.6 scenarios", "abstract": "This dataset contains model output for experiments using the UKESM1-CH4 emissions-driven configuration of the UK Earth System model, based on the science version (Sellar et al.,2011, DOI:10.1029/2019MS001739) coupled with the United Kingdom Chemistry and Aerosol scheme (Archibald et al., DOI:10.5194/gmd-13-1223-2020). The simulation follows the experimental design of CMIP6-sponsored ScenarioMIP (O'Neill et al., DOI:10.5194/gmd-9-3461-2016) SSP3-7.0 and SSP1-2.6 Shared Socioeconomic Pathways. These simulation span 2015-2050 and use a coupled atmosphere-ocean climate model. To assess climate model variability, three ensemble members are archived which differ only in the initial conditions for the experiment. The data comprise annual mean output over the experimental period for temperature, ozone, methane, hydroxyl radical, precipitation and provided diagnostic air mass and diagnostics suitable for the calculation of the methane lifetime and budget. We archive data from this methane emissions-driven configuration as NZAME/ensemble experiment as by186(ensemble 1), bz146(ensemble 2) and bz473(ensemble 3). To improve reproducibility the UKESM1 jobids are included as identifiers. For comparison, emissions-driven experiments are included as {Scenario}/JOBID: SSP1-2.6 (bo812), and three ensemble members for SSP3-7.0: bo797(ens 1), ca723(ens 2) and cb039(ens3).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-09-06T10:52:35", "updateFrequency": "notPlanned", "dataLineage": "Data were generated using the UK Earth System Model 1 (UKESM1-CH4) at N96 horizontal resolution over global domain.", "removedDataReason": "", "keywords": "UKESM1-CH4, methane, emissions", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-09-09T17:04:17", "doiPublishedTime": "2022-01-20T09:52:51", "removedDataTime": null, "geographicExtent": { "ob_id": 2829, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 33068, "dataPath": "/badc/deposited2021/ukesm1_methane_emissions/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 12979666815, "numberOfFiles": 60, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 9080, "startTime": "2015-01-01T00:00:00", "endTime": "2050-01-01T23:59:59" }, "resultQuality": { "ob_id": 3735, "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": "2021-09-06" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33070, "uuid": "44353c09f9654eb1940b383ae70394d3", "short_code": "comp", "title": "UKESM1 deployed on MONSooN computer", "abstract": "UKESM1 deployed on MONSooN computer" }, "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": [ { "ob_id": 33069, "uuid": "b8d7a59f88834a4299a1786740591c19", "short_code": "proj", "title": "Doctoral Training programmes: Cambridge", "abstract": "Cambridge CDT training programme." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1633, 1635, 1636, 6023, 7781, 12567, 12582, 13817, 19039, 19043, 19067, 21576, 21990, 22005, 22142, 22172, 22196, 27599, 30340, 30362, 30363, 30372, 39012, 39013, 39014, 39015, 39016, 39017, 39018, 39019, 39020, 39021, 39022, 39023 ], "vocabularyKeywords": [], "identifier_set": [ 10979 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 147700, 147701, 147702, 147703, 147704, 147705, 147711, 147710, 147712, 168808, 147713, 147714, 147715 ], "onlineresource_set": [ 87701 ] }, { "ob_id": 33072, "uuid": "e7e62b725fb44373a36fa3624f9b059e", "title": "APPRAISE CLOUDS: aerosol chemistry from the University of Manchester Aerosol Mass Spectrometer deployed at the Chilbolton Observatory, Hampshire", "abstract": "Data were collected in December 2008 and January to March 2009 by the University of Manchester Aerosol Mass Spectrometer at the Chilbolton Observatory, Hampshire, in support of the FAAM campaign in the South-West of the UK. The dataset contains measurements of mass concentrations of organics, Sulphate, Nitrate, Ammonium and Chloride.\r\n\r\nThe data were collected for use in the CLOUDS project, which is one of multiple projects within the APPRAISE (Aerosol Properties, PRocesses And Influences on the Earth's climate) programme.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2011-08-08T11:48:16", "updateFrequency": "notPlanned", "dataLineage": "Data provided as is by the APPRAISE campaign participants for archiving at the BADC.", "removedDataReason": "", "keywords": "appraise, cloud, aerosol mass concentrations", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-11-18T14:35:18", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 92, "bboxName": "Chilbolton", "eastBoundLongitude": -1.427, "westBoundLongitude": -1.427, "southBoundLatitude": 51.145, "northBoundLatitude": 51.145 }, "verticalExtent": null, "result_field": { "ob_id": 33073, "dataPath": "/badc/appraise/data/clouds/man-ams", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1005834, "numberOfFiles": 3, "fileFormat": "NASA-Ames" }, "timePeriod": { "ob_id": 1927, "startTime": "2009-01-06T00:00:00", "endTime": "2009-01-06T23:59:59" }, "resultQuality": { "ob_id": 1734, "explanation": "Research data", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2013-11-06" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 33078, "uuid": "fa2470f761fd4c14aa294921eb5c0bfc", "short_code": "acq", "title": "APPRAISE Clouds - University of Manchester Aerosol Mass Spectrometer", "abstract": "APPRAISE Clouds - University of Manchester Aerosol Mass Spectrometer" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 18 ], "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": 6527, "uuid": "0419468bd7d7bb57f1ff652cee0a4026", "short_code": "proj", "title": "APPRAISE - Clouds (Aerosol cloud interactions and climate) Project", "abstract": "The aims of the Clouds (Aerosol Cloud Interactions and Climate) project are to assess the relative importance of the key processes by which aerosol control cloud microphysics in mixed phase clouds, to determine the properties and role of ice nuclei and their interaction with mixed phase clouds, and to assess the role of absorbing material above, below and within clouds. \r\n\r\nThe Clouds project data were mainly collected during FAAM (Facility for Airborne Atmospheric Measurements) flights. During these flights the FAAM aircraft was host to the University of Manchester Aerosol Mass Spectrometer, GRIMM Optical Particle Counter, Scanning Mobility Particle Sizer, and the Condensation Particle Counter. The Aerosol chamber at the University of Manchester was also used to collect data for the Clouds project. There were also ground measurements campaigns conducted at the Chilbolton Observatory site.\r\n\r\nThe APPRAISE-CLOUDS project was funded by NERC as part of the APPRAISE (Aerosol Properties, PRocesses And Influences on the Earth's climate) thematic programme and ran from September 2007 to June 2011." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 52353 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 147718, 147719, 147720, 147721, 147722, 147723, 147724, 147725, 147726, 147727, 147728 ], "onlineresource_set": [ 43389, 43390 ] }, { "ob_id": 33114, "uuid": "ac43cee2bf5e4942970492209ba95e49", "title": "Surface velocity map of the Afar Rift Zone from 2014-19", "abstract": "This dataset contains a map of ground movements covering the Afar Rift Zone in Ethiopia, Eritrea, and Djibouti for the time period between October 2014 and August 2019. The Afar region is located where three tectonic plates are pulling apart, creating rift segments which are 50-100 km long. Surface deformation on these segments is not constant in time, with episodes of rifting occurring periodically and magma intrusions causing sudden ground movements. We use frequent Sentinel-1 satellite Interferometric Synthetic Aperture Radar (InSAR) observations to measure surface displacements through time across the whole region. We relate these to ground based Global Navigation Satellite Systems (GNSS) observations and combine data from different satellite tracks to produce maps of the average surface velocity in three directions (perpendicular to the rift zone, parallel to the rift zone, and vertical). The continued observation of these time-varying ground movements is important for understanding how continents break up, with data here providing evidence of how tightly focussed extension is around the rift segments and of the subsurface magma movement at several volcanic centres.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2021-09-17T10:45:55", "updateFrequency": "", "dataLineage": "Sentinel-1 Single Look Complex (SLC) data were sourced from the European Space Agency (ESA) Copernicus Hub, and processed using LiCSAR software (Lazecky et al., 2020). Processing and analysis was then performed by the authors (University of Leeds, COMET) in MATLAB to account for atmospheric effects, estimate uncertainties, create deformation time series and average velocity maps, reference surface velocities to the regional GNSS network (King et al., 2019), and convert velocities from the satellite look direction into rift-perpendicular and rift-parallel horizontal and vertical components. Final velocity maps and variance estimates were exported to NetCDF format for archiving. Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "Sentinel-1, InSAR, GNSS, velocity, map", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-09-29T09:40:39", "doiPublishedTime": "2021-10-01T11:03:44", "removedDataTime": null, "geographicExtent": { "ob_id": 2838, "bboxName": "", "eastBoundLongitude": 45.0, "westBoundLongitude": 38.0, "southBoundLatitude": 8.0, "northBoundLatitude": 17.0 }, "verticalExtent": null, "result_field": { "ob_id": 33111, "dataPath": "/neodc/surface_velocities_afar_rift/data/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3635142350, "numberOfFiles": 2, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 9093, "startTime": "2014-10-11T00:00:00", "endTime": "2019-08-17T23:59:59" }, "resultQuality": { "ob_id": 3743, "explanation": "Data are as given by the data provider with quality control flags defined within each file. No quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-09-22" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 33168, "uuid": "9df00141e4ac4f83a209f0e9f737c232", "short_code": "cmppr", "title": "Surface velocity map of the Afar Rift Zone from 2014-19", "abstract": "Surface velocity map of the Afar Rift Zone from 2014-19" }, "imageDetails": [ 2 ], "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": 33113, "uuid": "e3d204fea8cf496a8b600ed61739d1a3", "short_code": "proj", "title": "RiftVolc", "abstract": "This NERC funded project aims to research past and current volcanism and volcanic hazards in the central Main Ethiopian Rift." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 46606, 46607, 46608, 46609, 46610, 46611 ], "vocabularyKeywords": [], "identifier_set": [ 10952 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 147872, 147873, 147874, 147875, 147876, 147877, 147878, 147881, 147879, 168974, 190843, 147880 ], "onlineresource_set": [ 43498 ] }, { "ob_id": 33115, "uuid": "aa4106a7a35246dfb84fb925a7d65650", "title": "1/12 degree Nucleus for European Modelling of the Ocean (NEMO) model of the Southern Ocean: JRA55-do with absolute wind stress experiment (1988 - 2007)", "abstract": "The dataset is a 20 year experiment using a NEMO-based 1/12 degree grid spacing model of the Southern Ocean as part of the ORCHESTRA (Ocean Regulation of Climate by Heat and Carbon Sequestration and Transports) LTS-M project. It uses the NEMO \"extended\" grid, although ice cavities are closed. The model was run on Archer, the national HPC platform. The dataset covers the full length of the model run and includes regular (5 day mean) output of the model state, as well as more frequent (1 day mean) output of surface variables and fluxes and 1 month mean of more extensive transport diagnostics. The experiment neglects the ocean surface current in the bulk formula calculations for surface fluxes, so-called absolute wind stress. It starts from the end of 1987 of JRA55IAF (Munday et al., 2021).\r\n\r\nForced by JRA55-do, an interannually-varying forcing set (Tsujino et al., 2018). With some additional forcing as supplied by the UK Met Office (freshwater runoff, tidal friction, geothermal heating) and additional freshwater runoff to suppress polynya formation.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-06-07T18:24:29", "updateFrequency": "notPlanned", "dataLineage": "Data were generated using a Nucleus for European Modelling of the Ocean (NEMO) based model run on Archer, the national HPC platform. Data were submitted to the British Oceanographic Data Centre (BODC, NOC) and then archived at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "oceans, climatologyMeteorologyAtmosphere", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "1/12 degree", "status": "completed", "dataPublishedTime": "2021-09-28T09:30:52", "doiPublishedTime": "2021-10-21T14:03:04", "removedDataTime": null, "geographicExtent": { "ob_id": 2807, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -86.0, "northBoundLatitude": -3.5 }, "verticalExtent": null, "result_field": { "ob_id": 33118, "dataPath": "/bodc/BAS210038/JRA55ABS-ORCH0083-LIM3", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 21581304390363, "numberOfFiles": 36030, "fileFormat": "Data are CF-compliant NetCDF formatted" }, "timePeriod": { "ob_id": 6782, "startTime": "1988-01-01T00:00:00", "endTime": "2007-12-31T23:59:59" }, "resultQuality": { "ob_id": 3732, "explanation": "The data are provided as-is with no quality control undertaken by the British Oceanographic Data Centre (BODC). The data suppliers have not indicated if any quality control has been undertaken on these data.", "passesTest": true, "resultTitle": "BODC Data Quality Statement", "date": "2021-07-02" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33039, "uuid": "90d3d4ea198c4f0c9cfeb1ee740cc566", "short_code": "comp", "title": "1/12deg Nucleus for European Modelling of the Ocean (NEMO) model of the Southern Ocean", "abstract": "This computation involved: NEMO-based 1/12 degree grid spacing model of the Southern Ocean as part of the ORCHESTRA LTS-M project. It uses the NEMO \"extended\" grid, although ice cavities are closed. The model was run on Archer, the national HPC platform." }, "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": [ { "ob_id": 32626, "uuid": "c21cf5fb28c84cb2820a4785c503ea8c", "short_code": "proj", "title": "ORCHESTRA - Ocean Regulation of Climate by Heat and Carbon Sequestration and Transports", "abstract": "The Ocean Regulation of Climate by Heat and Carbon Sequestration and Transports (ORCHESTRA) is a £8.4 million, five year (2016-2021) research programme funded by the Natural Environment Research Council (NERC). The aim of the research is to to advance the understanding of, and capability to predict, the Southern Ocean's impact on climate change via its uptake and storage of heat and carbon. The programme will significantly reduce uncertainties concerning how this uptake and storage by the ocean influences global climate, by conducting a series of unique fieldwork campaigns and innovative model developments.\r\nORCHESTRA involves scientists from many NERC Centres. This ambitious 5 year project began in spring 2016 and will use a combination of data collection, analyses and computer simulations to radically improve our ability to understand and predict the circulation of the Southern Ocean and its role in the global climate, with particular emphasis on the way that the Southern Ocean absorbs and stores heat and carbon.\r\n\r\nThe intensive 5-year observational program will involve ORCHESTRA scientists undertaking 12 expeditions on research ships in the Southern Ocean, with US collaborators performing a 13th. Ten of these expeditions will be annual north/south transects across the Antarctic Circumpolar Current, to evaluate how the ocean changes from one year to the next. The other three will form ‘boxes’ around the Atlantic sector of the Southern Ocean so that budgets can be performed. All these data, along with historical measurements, will be combined and analysed together. The observations will be used to inform and test a range of models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 10232, 10233, 11626, 11654, 11655, 22879, 36269, 36270, 36271, 36272, 36273, 36274, 36275, 36276, 36277, 36278, 36279, 36280, 36281, 36282, 36283, 36284, 36285, 36286, 36287, 36288, 36289, 36290, 36291, 36292, 36293, 36294, 36295, 36296, 36297, 36298, 36299, 36300, 36301, 36302, 36303, 36304, 36305, 36306, 36307, 36308, 36309, 36310, 36311, 36312, 36313, 36314, 36315, 36316, 36317, 36318, 36319, 36320, 36321, 36322, 36323, 36324, 36325, 36326, 36327, 36328, 36329, 36330, 36331, 36332, 36333, 36334, 36335, 36336, 36337, 36338, 36339, 36340, 36341, 36342, 36343, 36344, 36345, 36346, 36347, 36348, 36349, 36350 ], "vocabularyKeywords": [], "identifier_set": [ 10955 ], "observationcollection_set": [ { "ob_id": 33122, "uuid": "5297667993904b12b0f8cbd8400ab56b", "short_code": "coll", "title": "ORCHESTRA - Ocean Regulation of Climate by Heat and Carbon Sequestration and Transports: Innovative model run output", "abstract": "ORCHESTRA is a NERC-funded Long Term Science programme that involves scientists from many NERC Centres. This 5 year project began in spring 2016 and will use a combination of data collection, analyses and computer simulations to radically improve our ability to understand and predict the circulation of the Southern Ocean and its role in the global climate, with particular emphasis on the way that the Southern Ocean absorbs and stores heat and carbon.\r\nThis Collection provides the outputs of innovative model runs of the 1/12 degree Nucleus for European Modelling of the Ocean (NEMO) model of the Southern Ocean carried out within the project" } ], "responsiblepartyinfo_set": [ 147882, 147883, 147884, 147885, 147886, 147887, 147888, 147889, 147893 ], "onlineresource_set": [ 43401, 43407 ] }, { "ob_id": 33116, "uuid": "67ad0c489e2b4d18aa152e78f28ae0c0", "title": "1/12 degree Nucleus for European Modelling of the Ocean (NEMO) model of the Southern Ocean: JRA55-do interannually-varying forced control run (1978 - 2017)", "abstract": "The dataset is a 40 year control run of a NEMO-based 1/12 degree grid spacing model of the Southern Ocean as part of the ORCHESTRA (Ocean Regulation of Climate by Heat and Carbon Sequestration and Transports) LTS-M project. It uses the NEMO \"extended\" grid, although ice cavities are closed. The model was run on Archer, the national HPC platform. The dataset covers the full length of the model run and includes regular (5 day mean) output of the model state, as well as more frequent (1 day mean) output of surface variables and fluxes and 1 month mean of more extensive transport diagnostics. This is the second of two control runs and was initialised from the end of the 30th year (nominally 1978) of CORE2NYF (Munday et al., 2021), a 3+37 year control run forced with CORE2 (corrected normal year forcing version 2.0) normal year forcing.\r\n\r\nForced by JRA55-do, an interannually-varying forcing set (Tsujino et al., 2018). With some additional forcing as supplied by the UK Met Office (freshwater runoff, tidal friction, geothermal heating) and additional freshwater runoff to suppress polynya formation.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-06-09T11:06:12", "updateFrequency": "notPlanned", "dataLineage": "Data were generated using a Nucleus for European Modelling of the Ocean (NEMO) based model run on Archer, the national HPC platform. Data were submitted to the British Oceanographic Data Centre (BODC, NOC) and then archived at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "oceans, climatologyMeteorologyAtmosphere", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "1/12 degree", "status": "completed", "dataPublishedTime": "2021-09-28T09:24:14", "doiPublishedTime": "2021-10-21T14:02:54", "removedDataTime": null, "geographicExtent": { "ob_id": 2807, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -86.0, "northBoundLatitude": -3.5 }, "verticalExtent": null, "result_field": { "ob_id": 33117, "dataPath": "/bodc/BAS210037/JRA55IAF-ORCH0083-LIM3", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 42946975904824, "numberOfFiles": 72063, "fileFormat": "Data are CF-compliant NetCDF formatted" }, "timePeriod": { "ob_id": 9095, "startTime": "1978-01-01T00:00:00", "endTime": "2017-12-31T23:59:59" }, "resultQuality": { "ob_id": 3732, "explanation": "The data are provided as-is with no quality control undertaken by the British Oceanographic Data Centre (BODC). The data suppliers have not indicated if any quality control has been undertaken on these data.", "passesTest": true, "resultTitle": "BODC Data Quality Statement", "date": "2021-07-02" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33039, "uuid": "90d3d4ea198c4f0c9cfeb1ee740cc566", "short_code": "comp", "title": "1/12deg Nucleus for European Modelling of the Ocean (NEMO) model of the Southern Ocean", "abstract": "This computation involved: NEMO-based 1/12 degree grid spacing model of the Southern Ocean as part of the ORCHESTRA LTS-M project. It uses the NEMO \"extended\" grid, although ice cavities are closed. The model was run on Archer, the national HPC platform." }, "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": [ { "ob_id": 32626, "uuid": "c21cf5fb28c84cb2820a4785c503ea8c", "short_code": "proj", "title": "ORCHESTRA - Ocean Regulation of Climate by Heat and Carbon Sequestration and Transports", "abstract": "The Ocean Regulation of Climate by Heat and Carbon Sequestration and Transports (ORCHESTRA) is a £8.4 million, five year (2016-2021) research programme funded by the Natural Environment Research Council (NERC). The aim of the research is to to advance the understanding of, and capability to predict, the Southern Ocean's impact on climate change via its uptake and storage of heat and carbon. The programme will significantly reduce uncertainties concerning how this uptake and storage by the ocean influences global climate, by conducting a series of unique fieldwork campaigns and innovative model developments.\r\nORCHESTRA involves scientists from many NERC Centres. This ambitious 5 year project began in spring 2016 and will use a combination of data collection, analyses and computer simulations to radically improve our ability to understand and predict the circulation of the Southern Ocean and its role in the global climate, with particular emphasis on the way that the Southern Ocean absorbs and stores heat and carbon.\r\n\r\nThe intensive 5-year observational program will involve ORCHESTRA scientists undertaking 12 expeditions on research ships in the Southern Ocean, with US collaborators performing a 13th. Ten of these expeditions will be annual north/south transects across the Antarctic Circumpolar Current, to evaluate how the ocean changes from one year to the next. The other three will form ‘boxes’ around the Atlantic sector of the Southern Ocean so that budgets can be performed. All these data, along with historical measurements, will be combined and analysed together. The observations will be used to inform and test a range of models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 36269, 36270, 36324, 36331, 36334, 36337, 50623, 50624, 52949, 52960, 52977, 59647, 60131, 60132, 60135, 60136, 60143, 60148, 60151, 63050, 63109, 64089, 64090, 64091, 64092, 64093, 64094, 64095, 64096, 64097, 64098, 64099, 64100, 64101, 64102, 64103, 64104, 64105, 64106, 64107, 64108, 64109, 64110, 64111, 64112, 64113, 64114, 64115, 64116, 64117, 64118, 64119, 64120, 64121, 64122, 64123, 64124, 64125, 64126, 64127, 64128, 64129, 64130, 64131, 64132, 64133, 64134, 64135, 64136, 64137, 64138, 64139, 64140, 64141, 64142, 64143, 64144, 64145, 64146, 64147, 64148, 91409, 91410, 91411, 91412, 91413, 91414, 91415, 91416, 91417 ], "vocabularyKeywords": [], "identifier_set": [ 10954 ], "observationcollection_set": [ { "ob_id": 33122, "uuid": "5297667993904b12b0f8cbd8400ab56b", "short_code": "coll", "title": "ORCHESTRA - Ocean Regulation of Climate by Heat and Carbon Sequestration and Transports: Innovative model run output", "abstract": "ORCHESTRA is a NERC-funded Long Term Science programme that involves scientists from many NERC Centres. This 5 year project began in spring 2016 and will use a combination of data collection, analyses and computer simulations to radically improve our ability to understand and predict the circulation of the Southern Ocean and its role in the global climate, with particular emphasis on the way that the Southern Ocean absorbs and stores heat and carbon.\r\nThis Collection provides the outputs of innovative model runs of the 1/12 degree Nucleus for European Modelling of the Ocean (NEMO) model of the Southern Ocean carried out within the project" } ], "responsiblepartyinfo_set": [ 147894, 147895, 147896, 147897, 147898, 147899, 147900, 147901, 147906, 147903, 147907, 147904, 147905 ], "onlineresource_set": [ 43399, 43400 ] }, { "ob_id": 33121, "uuid": "f5a2bbcadec2428cab157653a7039919", "title": "BBUBL: airborne meteorological measurements from various avian sensor packages for flights in 2018-19 over the Birmingham conurbation", "abstract": "This dataset contains data from Avian-Meteorology-Instrument Packages (AvMIPs) from a series of flights over Birmingham as part of the Biotelemetry/Bio-aerial-platforms for the Urban Boundary Layer (BBUBL) project (NERC grant: NE/N003195/1), also known as City Flocks. The flights took place in 2018 and 2019.\r\n\r\nThe BBUBL project utilised Biotelemetry/bio-aerial-platforms as a novel and practicable solution to the data paucity above urban rooftops in the Urban Boundary Layer. The project developed a suite of low-cost Avian-Meteorology-Instrument Packages (AvMIPs) for ensemble deployment in Birmingham as a suitably large and heterogeneous test case.\r\n\r\nA range of different sensor packages were used which were subsequently further characterised through air-tunnel tests. See Thomas et al. (2018) citation (DOI:10.1175/BAMS-D-16-0181.1) listed in the online resources section of this record for further details, including a precursor system flown on a larger bird species. In summary, temperature sensors were compared against U.K. Accreditation Service (UKAS)-accredited sensors in a controlled temperature chamber (WKL 34/40; Weiss Technik, Belgium and Germany) and in ambient conditions at the University of Birmingham weather station.\r\n\r\nAdditional material by Thomas et al. (2018) included in the online resource section of this record provide additional material regarding the project and instrumentation usage.\r\n\r\nNote, within the data there are a range of sensor packet and bird IDs used to denote the different bird-sensor package combinations used. A range of sensor packets were used, with one lost and others found unusable, resulting in the sensor packet numbers shown in the data. Bird ID were taken from the bird ring numbers unless clashes existed, in which case an alternative two digit number was used, therefore are not consecutive and no other data from other bird and sensor package combinations are available.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-02-14T12:09:53", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "avian, meteorology, urban", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-03-03T15:32:44", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2806, "bboxName": "BBUBL WRF run domain", "eastBoundLongitude": -1.26129, "westBoundLongitude": -2.53084, "southBoundLatitude": 52.1007, "northBoundLatitude": 52.8473 }, "verticalExtent": null, "result_field": { "ob_id": 37154, "dataPath": "/badc/deposited2021/bbubl/data/bham-avian-sensor", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 102093452, "numberOfFiles": 102, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 10274, "startTime": "2018-08-16T12:08:00", "endTime": "2019-08-30T07:37:11" }, "resultQuality": { "ob_id": 4186, "explanation": "Sensor data characteristics and calibrations performed in wind tunnel prior to data preparation and delivery for long term archiving with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "BBUBL Avian Data Quality Statement", "date": "2023-02-14" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 37155, "uuid": "45d25f25b8884d8bbafa634af3082202", "short_code": "acq", "title": "BBUBL Avian data", "abstract": "BBUBL Avian data" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [], "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": 32757, "uuid": "9883bef6085849fea4db955a3f74ec5f", "short_code": "proj", "title": "BBUBL: Biotelemetry/Bio-aerial-platforms for the Urban Boundary Layer (also known as City Flocks) - NE/N003195/1", "abstract": "Attempts to improve the urban component in meteorology and numerical weather prediction models in recent years have been hampered by a paucity of meteorological data in the urban boundary layer (UBL), especially in the region above, but close to, building height. This region is precisely where local energy balances and drag combine with prevailing synoptic patterns to transmit fluid dynamical information up and down spatial scales, with implications for (i) urban weather prediction, (ii) event forecasting (e.g. heatwaves, climatic conditions during sporting events, releases of hazardous substances), and (iii) sustainable urban planning for high density liveable cities. However, capturing meteorological data in urban areas above the mean roof height is problematic using conventional techniques.\r\n\r\nThe BBUBL project proposed Biotelemetry/bio-aerial-platforms as a novel and practicable solution to the data paucity above urban rooftops in the UBL, and to circumvent the regulatory issues related to use of unmanned aerial systems. The project developed a suite of low-cost Avian-Meteorology-Instrument Packages (AvMIPs) for ensemble deployment in Birmingham as a suitably large and heterogeneous test case.\r\n\r\nThe AvMIPs were tested rigorously to determine: (i) data biases and reliability; (ii) sensor response to temperature variations; (iii) effect of radiation; and (iv) effect of bird's body temperature and other 'platform effects'. After quality assurance and control of the packages had been determined to be adequate, the primary targets of the AvMIP deployment were the thermal and moisture structures of the UBL at the city and neighbourhood scales. Favourable weather conditions for deployment will be identified via pre-deployment modelling using a mesoscale meteorological model (WRF, Weather Research and Forecasting). \r\n\r\nSubsequent analysis and interpretation of the AvMIP data and synthesis of the data together with Birmingham's canyon (3m) meteorological data were assisted by post-deployment modelling for the measurement periods.\r\n\r\nOverall, this project aimed to deliver a novel, and rigorously tested, technology for probing the UBL. A unique dataset for the UBL of a major European conurbation was obtained, elucidating climate mitigation issues such as the cooling (or heating) capability/capacity of a large park (or a city centre) to a city's UBL, and scientific issues such as the magnitude of the 'blending height' at which the effect of urban surface heterogeneity is no longer detectable.\r\n\r\nSuccess of the project was seen as a necessary step towards deployment of chemical sensors, and lead to generation of unprecedented datasets of the urban atmosphere for both research and city-planning purposes. Novel field deployments of the kind proposed by the project require strong partnerships with a wide variety of stakeholders. The Royal Pigeon Racing Association (RPRA) provide critical support in terms of birds that will behave in well determined ways. The RPRA have experience of mounting payloads on pigeons and so can ensure that payloads are appropriately in size, weight, etc., and that pigeon deployments delivered the data sought. Birmingham City Council supported the project in three ways: 1. As one of the principal end-users of the results (feeding into improved diagnosis and forecasting of urban climatology across the city through the joint city-university BUCCANEER project); 2. In order to facilitate use of birds in open urban spaces such as parks; and 3. In order to facilitate access to city buildings on which gulls are nesting. Dr Stefan Bodnar, an ecological consultant, supported the project by acting as principal bird handler and as a consultant for public dissemination of our work.\r\n \r\nObjectives\r\n\r\nTo directly address the data sparsity of meteorological measurements in the urban boundary layer (UBL), especially in the region above, but close to, building height, and to circumvent the regulatory issues related to use of Unmanned Aerial Systems, the project proposed developing and deploying instrument payloads on birds.\r\n\r\nThe project set out the following research questions, to be addressed by a series of objectives. RESEARCH QUESTIONS: \r\n(1) Can biotelemetry/bio-aerial-platforms be used to deliver observations of temperature, humidity and wind speed in the UBL with accuracy and precision sufficient for research?\r\n(2) Can the data derived from biotelemetry/bioaerial-platforms be used to inform the structure of the UBL at the city scale and the local IBLs at neighbourhood scales?\r\n(3) If so, what is the cooling (or heating) capability/capacity of a large park (or a city centre) to a city's UBL and what is the magnitude of the blending height? \r\n(4) How do such measurements compare to results derived from a numerical weather prediction (NWP) model?\r\n(5) What lessons can be learnt to assess the feasibility of other payloads (e.g. chemical sensors)? \r\n\r\nOBJECTIVES were: \r\na) To develop a suite of low-cost Avian-Meteorology-Instrument Packages (AvMIPs) (i) capable of sensing temperature (changes of at least 0.2 degree with a 1 sec response time) and humidity (changes of 5% with a 15 sec response time) and (ii) suitable for a range of bird taxa (raptors, pigeons, and gulls [Larus spp.]), with increasing deployment duration from mins to hours to days, respectively. \r\nb) To test AvMIPs rigorously in the following aspects: (i) data biases and reliability; (ii) responses to temperature variations; (iii) effect of radiation; and (iv) effect of bird's body temperature. \r\nc) To conduct pre-deployment modelling using a NWP model. This will be used to design a deployment plan. The robustness of this approach will ensure scales of interest (city and neighbourhood scales only), time of day (mainly daytime and early morning), and seasonality (both summer and winter) are all taken into account. \r\nd) To execute the deployment plan derived from c) in Birmingham for the favourable synoptic weather conditions for the ensemble AvMIP deployment. Post-deployment modelling will also be completed. \r\ne) To synthesise and analyse the AvMIP and BUCL data along with post-deployment modelling output, to identify the feasibility, reliability, applicability and benefits of the AvMIPs as an observational platform. Results from this analysis will be used to make recommendations and to scope the design of future biotelemetry/bio-aerial-platform payloads and deployment strategies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50542, 50543, 61654, 63203, 63204, 63205, 63206, 63207, 63208, 63209, 63210, 63211, 63212, 63213, 63214, 63215, 63216, 82839, 82840, 82841, 82842, 82843, 82844, 82845, 82846, 82847, 82848, 82849, 82850, 82851, 82852, 82853, 82854, 82855, 82856, 82857, 82858, 82859, 82860, 82861, 82862, 82863, 82864, 82865, 82866, 82867, 82868, 82869, 82870, 82871, 82872, 82873, 82874, 82875, 82876, 82877, 82878, 82879, 82880, 82881, 82882, 82883, 82884, 82885, 82886, 82887, 82888, 82889, 82890, 82891, 82892, 82893, 82894, 82895, 82896, 82897, 82898, 82899, 82900, 82901, 82902, 82903, 82904, 82905, 82906, 82907, 82908, 82909, 82910, 82911, 82912, 82913, 82914, 82915, 82916, 82917, 82918, 82919, 82920, 82921, 82922, 82923, 82924, 82925, 82926, 82927, 82928, 82929, 82930, 82931, 82932, 82933, 82934, 82935, 82936, 82937 ], "vocabularyKeywords": [], "identifier_set": [ 12524 ], "observationcollection_set": [ { "ob_id": 33079, "uuid": "1c3a4c29b0ea42f5b0e254b7571aa3fa", "short_code": "coll", "title": "BBUBL: avian-borne met sensor package data and WRF model run data", "abstract": "This dataset collection contains data from avian-borne meteorological sensors from a series of flights over Birmingham as part of the Biotelemetry/Bio-aerial-platforms for the Urban Boundary Layer (BBUBL)(NERC grant: NE/N003195/1) project (also known as City Flocks) along with Weather Research and Forecasting (WRF) model run output for the same period." } ], "responsiblepartyinfo_set": [ 147914, 147915, 147916, 147917, 147918, 147919, 177594, 177597, 177595, 177596, 193720, 193606, 193697, 193721, 193722, 193723, 193724 ], "onlineresource_set": [ 83053, 83206, 83207, 83208 ] }, { "ob_id": 33123, "uuid": "09ccee191a81402687c55bd74b4deeec", "title": "Eyjafjallajokull Volcanic Ash Cloud: Airborne Meteorology and Chemistry flight summaries from the EUFAR operators SAFIRE and DLR", "abstract": "The Icelandic Volcano, Eyjafjallajokull, started erupting on 14th April 2010. The volcanic ash cloud produced covered much of Northern Europe for several weeks causing extensive disruption to air travel. The UK and European atmospheric communities had many instruments - both airborne and ground-based, remote sensing and in-situ - taking measurements of the ash cloud throughout this period. This dataset contains summaries of meteorology and chemistry measurements from the EUFAR operators SAFIRE Falcon and ATR 42 aircraft 19th to 21st April 2010 and DLR Falcon Flight on 19 April 2010", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2010-04-23T11:51:20", "updateFrequency": "notPlanned", "dataLineage": "A copy of these data were made available to the British Atmospheric Data Centre (BADC) to assist the academic community during this volcanic ash event.", "removedDataReason": "", "keywords": "Eyjafjallajokull, aircraft, EUFAR, DLR, SAFIRE", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2010-04-23T18:00:00", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2840, "bboxName": "", "eastBoundLongitude": 13.0, "westBoundLongitude": -2.0, "southBoundLatitude": 43.0, "northBoundLatitude": 54.0 }, "verticalExtent": null, "result_field": { "ob_id": 33124, "dataPath": "/badc/eyjafjallajokull/data/eufar_operators", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2591500, "numberOfFiles": 7, "fileFormat": "PDF" }, "timePeriod": { "ob_id": 9098, "startTime": "2010-04-19T00:00:00", "endTime": "2010-04-21T18:00:00" }, "resultQuality": { "ob_id": 437, "explanation": "This is quick look unvetted data.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-09-19" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 33125, "uuid": "cb3fddd6107e4490a6594eb431314689", "short_code": "acq", "title": "Eyjafjallajokull Volcanic Ash Cloud SAFIRE and DLR flight summaries", "abstract": "Eyjafjallajokull Volcanic Ash Cloud SAFIRE and DLR flight summaries" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 18 ], "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": 13984, "uuid": "27f35145eb7b45b8829602a7e2cf544a", "short_code": "proj", "title": "Eyjafjallajokull Volcanic Ash Cloud Measurements and Imagery", "abstract": "The Icelandic Volcano, Eyjafjallajokull, started erupting on 14th April 2010. The volcanic ash cloud produced covered much of Northern Europe for several weeks causing extensive disruption to air travel. The UK and European atmospheric communities had many instruments - both airborne and ground-based, remote sensing and in-situ - taking measurements of the ash cloud throughout this period" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 147936, 147932, 147933, 147934, 147937, 147930, 147931, 147935, 147940 ], "onlineresource_set": [] }, { "ob_id": 33126, "uuid": "096690aba290452d9b7208133274b69b", "title": "Acoustic data collected from a glider deployment in the North Sea in June-July 2021 as part of the EU Marine Robots (EUMR) project.", "abstract": "This dataset consists of acoustic data collected using an icListen Smart Hydrophone (model SC35-ETH) integrated into an iRobot M1 Seaglider during a campaign in the North Sea, west of Ireland. The glider was deployed from 22nd June to 25th July 2021 as the sole glider in the campaign on behalf of the EU Marine Robots (EUMR) project. The acoustics data are provided as both raw acoustic waveform (.WAV format) files, and spectral analyses of the data (as ascii .txt files). Once the glider was recovered, the acoustics data were transferred directly from the glider and so no post processing has been completed.\r\nAn error in the instrument system led to an error in the timestamps on the data files. The first waveform was started at 2021-06-27 12:29:42, and this was recorded as 2010-01-01 00:02:30. The timestamps for all filenames and the timestamps of all records in the spectral files have been corrected for this time offset. All the filename name changes are provided in the timestamp_logs directory. In addition, some spectral files were not closed correctly and held multiple records. These files have been split into new files and timestamps corrected accordingly. The details of the new files generated are given in the SBF_recover.txt log file.\r\nThe environmental, positional and engineering parameters collected alongside the acoustic data are available through the British Oceanographic Data Centre (BODC) using the ERDDAP tool below.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-11-26T02:44:03", "updateFrequency": "notPlanned", "dataLineage": "Acoustics data were collected by an icListen Smart Hydrophone (SC35-ETH) mounted on an iRobot M1 Seaglider deployed in the North Sea. Data were transferred from the glider to a hard drive and then submitted to the British Oceanography Data Centre (BODC, NOC). timestamps included in the files and filenames were updating according to the ice offset from the data provider. Files containing multiple spectra were split into separate files, with appropriate file names. Data were subsequently archived at CEDA.", "removedDataReason": "", "keywords": "Ocean gliders, Acoustic waveforms, Ambient noise, Acoustic spectra", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2021-12-01T13:21:07", "doiPublishedTime": "2021-12-16T17:00:36", "removedDataTime": null, "geographicExtent": { "ob_id": 2844, "bboxName": "EUMR_glider_deployment", "eastBoundLongitude": -11.6464, "westBoundLongitude": -14.1919, "southBoundLatitude": 53.9645, "northBoundLatitude": 54.8844 }, "verticalExtent": null, "result_field": { "ob_id": 33127, "dataPath": "/bodc/CSC210177/EUMR_glider_acoustics/data", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 248012636166, "numberOfFiles": 39947, "fileFormat": "Raw acoustic data in WAV format; Spectral analyses of 10 minute segements of the acoustics files in ASCI text format" }, "timePeriod": { "ob_id": 9103, "startTime": "2021-06-22T06:53:40", "endTime": "2021-07-25T08:59:05" }, "resultQuality": { "ob_id": 3776, "explanation": "Data are as given by the data provider subject to time stamp updating, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-10-15" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 33250, "uuid": "d6dfab9e961646509a12e5216f1b998b", "short_code": "acq", "title": "EUMR Glider deployment", "abstract": "iRobot M1 Seaglider deployed in the North Sea as part of the EUMarineRobots (EUMR) project" }, "procedureComputation": null, "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": [], "vocabularyKeywords": [], "identifier_set": [ 10971 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 147944, 147946, 147974, 147943, 147942, 147941, 148430, 149466, 148431 ], "onlineresource_set": [ 43511, 43469, 43470, 43474 ] }, { "ob_id": 33132, "uuid": "ba3ac68281b94c7b9963278681ee8ee5", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.13 (v20211001)", "abstract": "Data for Figure 3.13 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 3.13 shows annual-mean precipitation rate (mm day-1) for the period 1995-2014.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has six panels, with data provided for four panels in subdirectories named panel_a, panel_b, panel_c and panel_d.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n \r\n - Global modelled annual-mean precipitation (mm/day) of CMIP6 for the period 1995–2014\r\n - Global bias of modelled annual-mean precipitation (mm/day) of CMIP6 for the period 1995–2014 to GPCP\r\n - Global root mean square bias of modelled precipitation (mm/day) of CMIP6 for the period 1995–2014 to GPCP\r\n - Global bias of modelled annual-mean precipitation (mm/day) of CMIP5 for the period 1985–2004 to GPCP\r\n\r\nGPCP is the Global Precipitation Climatology Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - panel_a/fig_3_13_a.nc\r\n - panel_b/fig_3_13_b.nc\r\n - panel_c/fig_3_13_c.nc\r\n - panel_d/fig_3_13_d.nc\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:44:20", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 3, Human influence, large-scale indicators, Natural variability, anthropogenically-forced change, observed changes, Figure 3.13, global mean precipitation", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "1x1 Degrees", "status": "completed", "dataPublishedTime": "2021-10-29T12:00:13", "doiPublishedTime": "2023-02-08T17:47:18.345920", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 45, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "1x1" }, "result_field": { "ob_id": 33241, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig13/v20211001/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1085827, "numberOfFiles": 7, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 9101, "startTime": "1985-01-01T12:00:00", "endTime": "2014-12-31T12:00:00" }, "resultQuality": { "ob_id": 3747, "explanation": "Data are as given by the data provider with quality control flags defined within each file. No quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-09-27" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33134, "uuid": "d54a48cbbd834e78a6c574772a12cc38", "short_code": "comp", "title": "Caption for Figure 3.13 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Annual-mean precipitation rate (mm day–1) for the period 1995–2014. (a) Multi-model (ensemble) mean constructed with one realization of the CMIP6 historical experiments from each model. (b) Multi-model mean bias, defined as the difference between the CMIP6 multi-model mean and precipitation analyses from the Global Precipitation Climatology Project (GPCP) version 2.3 (Adler et al., 2003). (c) Multi-model mean of the root mean square error calculated over all months separately and averaged with respect to the precipitation analyses from GPCP version 2.3. (d) Multi-model-mean bias, calculated as the difference between the CMIP6 multi-model mean and the precipitation analyses from GPCP version 2.3. Also shown is the multi-model mean bias as the difference between the multi-model mean of (e) high resolution and (f) low-resolution simulations of four HighResMIP models and the precipitation analyses from GPCP version 2.3. Uncertainty is represented using the advanced approach. No overlay indicates regions with robust signal, where ≥66% of models show change greater than variability threshold and ≥80% of all models agree on sign of change; diagonal lines indicate regions with no change or no robust signal, where <66% of models show a change greater than the variability threshold; crossed lines indicate regions with conflicting signal, where ≥66% of models show change greater than variability threshold and <80% of all models agree on the sign of change. For more information on the advanced approach, please refer to the Cross-Chapter Box Atlas.1. Dots in panel (e) mark areas where the bias in high resolution versions of the HighResMIP models is lower in at least three out of four models than in the corresponding low-resolution versions. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 60415 ], "vocabularyKeywords": [], "identifier_set": [ 12359 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 147959, 147960, 147961, 147962, 147963, 147964, 147966, 147967 ], "onlineresource_set": [ 43458, 52370, 43409, 82865, 88562, 90115, 90116, 90117, 90118, 90119, 90120, 90121, 90122, 90123, 90124, 90125, 90126, 90127, 90128, 90129, 90130, 90131, 90132, 90133, 90134, 90135, 90136, 90137, 90138, 90139, 94607 ] }, { "ob_id": 33141, "uuid": "bfa7f9b9bde5414c96a281fa0a566e0c", "title": "HiTemp: High density temperature and meteorological measurements within the Urban Birmingham conurbation. CSV version as supplied by project team.", "abstract": "This dataset contains temperature and meteorological data from a high density network of meteorological sensors installed within the Birmingham conurbation: This includes low-cost, battery-powered WiFi Aginova Sentinel Micro air temperature sensors, operated at 73 stations and 25 Vaisala WXT520 weather transmitters measuring temperature, precipitation, relative humidity, wind speed and direction, pressure, solar radiation and quality flags. \r\n\r\nThis data is the basic Comma Separated Variable (CSV) format version as supplied by the project team. Reformatted versions with inclusive metadata have superceded this data.\r\n\r\nThese measurements have been made by the Birmingham Urban Climate Laboratory (BUCL) for the HiTemp (High Density Measurements within the Urban Environment) project in order to study the Birmingham Urban Heat Island (UHI)", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-09-24T13:46:57", "updateFrequency": "notPlanned", "dataLineage": "Data received from BUCL team and stored as supplied at CEDA.", "removedDataReason": "", "keywords": "HiTemp, BUCL, UHI, network of sensors, urban heat island", "publicationState": "working", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 775, "bboxName": "", "eastBoundLongitude": -1.76169, "westBoundLongitude": -2.0031, "southBoundLatitude": 52.39653, "northBoundLatitude": 52.59617 }, "verticalExtent": { "ob_id": 14, "highestLevelBound": 182.0, "lowestLevelBound": 93.0, "units": "metres above sea level" }, "result_field": { "ob_id": 33142, "dataPath": "/badc/hitemp/data/raw", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2597407524, "numberOfFiles": 28661, "fileFormat": "csv" }, "timePeriod": { "ob_id": 3781, "startTime": "2012-05-31T23:00:00", "endTime": "2014-08-21T23:00:00" }, "resultQuality": { "ob_id": 3752, "explanation": "Data in CSV format", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-09-27" }, "validTimePeriod": { "ob_id": 3780, "startTime": "2012-05-31T23:00:00", "endTime": "2014-08-21T23:00:00" }, "procedureAcquisition": { "ob_id": 33143, "uuid": "76940e44b2b54a9b9b354fa5ce762020", "short_code": "acq", "title": "BUCL network of temperature and Meteorological sensors", "abstract": "HiTemp networks of temperature and meteorological sensors installed within the Birmingham conurbation" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 18 ], "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": 13923, "uuid": "09a956ffbc6e40f5ba8078caf67eb991", "short_code": "proj", "title": "HiTemp: High Density Measurements within the Urban Environment", "abstract": "The NERC-funded HiTemp project was conducted by the Birmingham Urban Climate Laboratory (BUCL) research team to examine Birmingham's Urban Heat Island (UHI). The project operated a high density air temperature-sensor network and will lead to a number of research projects examining Birmingham's UHI in more detail than ever-before possible.\r\n\r\nThe long-term aim of the HiTemp project was to identify, model and promote adaptation to the impacts of urban heat and climate change on the people and infrastructure of major conurbations (i.e. health, society, infrastructure and energy).\r\n\r\nHiTemp installed networks of meteorological sensors within the Birmingham conurbation: Approximately 250 low-cost, battery-powered WiFi air temperature sensors (Figure 2) and 30 full automatic weather stations (AWS), measuring temperature, precipitation, relative humidity, wind speed and direction, pressure, solar radiation. The design was based around a nested network of sensors:\r\n\r\n Network 1 (coarse array): 30 AWS sited in primary electricity sub-stations (average spacing of 3km)\r\n Network 2 (wide array): 150 air temperature sensors located on schools (one in every medium super output area (MSOA), or areas containing a population of 7,200)\r\n Network 3 (fine array): 100 air temperature sensors located on lampposts in the CBD (50/km2)\r\n\r\nBoth of the air temperature sensor networks utilise existing WiFi networks (i.e. school and public WiFi networks), whilst the AWS network will utilise either GPRS or wired internet connections. The project will ultimately provide a series of demonstration sensor networks for measuring air temperature and other meteorological parameters.\r\n\r\nIndustrial project partners include Birmingham City Council, who are interested in assessing heat health risk across Birmingham; E.ON/Western Power, who are interested in examining the impacts of heat and climate change on transformers; Campbell Scientific, who are supplying some of the meteorological equipment; and Aginova, who are developing and supplying the bespoke WiFi air temperature sensor." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 147988, 147989, 147990, 147987, 147991, 147992, 147993, 147994, 147995, 147996, 147997, 147998, 147999 ], "onlineresource_set": [ 43410, 43411, 43412, 43413 ] }, { "ob_id": 33152, "uuid": "84fd20b6e5a04271a007e2150eb17c7e", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.18 (v20211001)", "abstract": "Data for Figure 3.18 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 3.18 shows instantaneous Northern-Hemisphere blocking frequency (% of days) in the extended northern winter season (DJFM) for the years 1979-2000. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains blocking freuency (1979-2000) of\r\n \r\n - ERA5 reanalysis\r\n - CMIP5 and CMIP6 multi-model mean\r\n\r\nERA5 is the fifth generation ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric reanalysis of the global climate.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n AR6_WG1_Chap3_Figure3_18_blocking.csv (lines and shading)\r\n Corresponding line and shading colours are described in the metadata associated with the datafile\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:44:12", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 3, Human influence, large-scale indicators, Natural variability, anthropogenically-forced change, observed changes, Figure 3.18, blocking frequency", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-10-31T12:18:00", "doiPublishedTime": "2023-02-08T18:02:23.623896", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 52, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 33244, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig18/v20211001/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 22667, "numberOfFiles": 4, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 9109, "startTime": "1979-01-01T12:00:00", "endTime": "2000-12-31T12:00:00" }, "resultQuality": { "ob_id": 3756, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-09-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33154, "uuid": "f31047a90ad9439593a34b95b0bff03b", "short_code": "comp", "title": "Caption for Figure 3.18 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Instantaneous Northern-Hemisphere blocking frequency (% of days) in the extended northern winter season (December–January–February–March – DJFM) for the years 1979–2000. Results are shown for ERA5 reanalysis (black), CMIP5 (blue) and CMIP6 (red) models. Coloured lines show multi-model means and shaded ranges show corresponding 5–95% ranges constructed with one realization from each model. Figure is adapted from Davini and D’Andrea (2020), their Figure 12 and following the D’Andrea et al. (1998) definition of blocking. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 85603, 85604, 85605, 85606, 85607, 85608, 85609, 85610 ], "vocabularyKeywords": [], "identifier_set": [ 12364 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 148031, 148032, 148033, 148034, 148035, 148036, 148037, 148038, 148039 ], "onlineresource_set": [ 43460, 43459, 52375, 82870, 88567, 90015, 90016, 90017, 90018, 90019, 90020, 90021, 90022, 90023, 90024, 90025, 90026, 90027, 90028, 90029, 90030, 90031, 90032, 90033, 90034, 90035, 90036, 90037, 90038 ] }, { "ob_id": 33155, "uuid": "03cc44f98b0e4a4b97df37662e62be79", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.3 (v20211001)", "abstract": "Data for Figure 3.3 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 3.3 shows the global annual-mean surface (2 m) air temperature (°C) and the model bias to ERA5.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has six panels, with data provided for four panels in subdirectories named panel_a, panel_b, panel_c and panel_d.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n - Global modelled annual-mean surface (2 m) air temperature (°C) of CMIP6 for the period 1995–2014\r\n - Global bias of modelled annual-mean surface (2 m) air temperature (°C) of CMIP6 for the period 1995–2014 to reanalysis ERA5\r\n - Global root mean square bias of modelled annual-mean surface (2 m) air temperature (°C) of CMIP6 for the period 1995–2014 to reanalysis ERA5\r\n - Global bias of modelled annual-mean surface (2 m) air temperature (°C) of CMIP5 for the period 1985–2004 to reanalysis ERA5\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project. \r\nERA5 is the fifth generation ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric reanalysis of the global climate.\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - panel_a/tas_mean_cmip6.nc; global map\r\n - panel_b/tas_bias_cmip6.nc; global map\r\n - panel_c/tas_rms_bias_cmip6.nc; global map\r\n - panel_d/tas_bias_cmip5.nc; global map\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2021-12-03T12:03:07", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 3, Human influence, large-scale indicators, Natural variability, anthropogenically-forced change, observed changes, Figure 3.3, near-surface air temperature, global temperature, bias", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "1x1 Degrees", "status": "completed", "dataPublishedTime": "2021-10-29T12:00:00", "doiPublishedTime": "2023-02-08T17:01:29.914897", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 53, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "1x1" }, "result_field": { "ob_id": 33281, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig03/v20211001/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1064117, "numberOfFiles": 7, "fileFormat": "netCDF" }, "timePeriod": { "ob_id": 9110, "startTime": "1985-01-01T12:00:00", "endTime": "2014-12-31T12:00:00" }, "resultQuality": { "ob_id": 3757, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-09-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33157, "uuid": "43505750b2364ce0b581d96a975b2e91", "short_code": "comp", "title": "Caption for Figure 3.3 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Annual mean surface (2 m) air temperature (°C) for the period 1995–2014. (a) Multi-model (ensemble) mean constructed with one realization of the CMIP6 historical experiment from each model. (b) Multi-model mean bias, defined as the difference between the CMIP6 multi-model mean and the climatology of the fifth generation of European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses of the global climate (ERA5). (c) Multi-model mean of the root mean square error calculated over all months separately and averaged with respect to the climatology from ERA5. (d) Multi-model-mean bias as the difference between the CMIP6 multi-model mean and the climatology from ERA5. Also shown is the multi-model mean bias as the difference between the multi-model mean of (e) high-resolution and (f) low-resolution simulations of four HighResMIP models and the climatology from ERA5. Uncertainty is represented using the advanced approach: No overlay indicates regions with robust signal, where ≥66% of models show change greater than variability threshold and ≥80% of all models agree on sign of change; diagonal lines indicate regions with no change or no robust signal, where <66% of models show a change greater than the variability threshold; crossed lines indicate regions with conflicting signal, where ≥66% of models show change greater than variability threshold and <80% of all models agree on sign of change. For more information on the advanced approach, please refer to Cross-Chapter Box Atlas.1. Dots in panel (e) mark areas where the bias in high resolution versions of the HighResMIP models is lower in at least three out of four models than in the corresponding low-resolution versions. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 67753 ], "vocabularyKeywords": [], "identifier_set": [ 12350 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" }, { "ob_id": 43269, "uuid": "9177e927f0664da9bf1e25f57561d0d8", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Synthesis Report (SYR)", "abstract": "This dataset collection contains datasets relating to the figures found in the Synthesis Report (SYR) of the IPCC Sixth Assessment Report (AR6).\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 2.4\r\n- data for Figure 2.5\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6" } ], "responsiblepartyinfo_set": [ 148042, 148043, 148044, 148045, 148046, 148047, 148048, 148049 ], "onlineresource_set": [ 43462, 43461, 52361, 82855, 88580, 90233, 90234, 90235, 90236, 90237, 90238, 90239, 90240, 90241, 90242, 90243, 90244, 90245, 90246, 90247, 90248, 90249, 90250, 90251, 90252, 90253, 90254, 90255, 90256, 90257, 94612 ] }, { "ob_id": 33158, "uuid": "cf006675070548359e22e36d354d0f92", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.10 (v20211001)", "abstract": "Data for Figure 3.10 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 3.10 shows observed and simulated tropical mean temperature trends through the atmosphere.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has three panels, with data provided for all panels in subdirectories named panel_a, panel_b and panel_c.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n Temperature trend from radiosonde, reanalysis and CMIP6 data, including their uncertainty where available.\r\n\r\n CMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - panel_a/ipcc_ar6_chap3_fig_3_10_panel_a_with_metadata.csv \r\n - panel_b/ipcc_ar6_chap3_fig_3_10_panel_b_with_metadata.csv\r\n - panel_c/ipcc_ar6_chap3_fig_3_10_panel_c_with_metadata.csv\r\n\r\nDetails of data provided in relation to each figure panel and its elements are described in the metadata associated with each file.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:44:17", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 3, Human influence, large-scale indicators, Natural variability, anthropogenically-forced change, observed changes, Figure 3.10, temperature trends", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-10-29T12:00:10", "doiPublishedTime": "2023-02-08T17:36:32.037282", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 54, "highestLevelBound": 20.0, "lowestLevelBound": 850.0, "units": "" }, "result_field": { "ob_id": 33239, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig10/v20211001/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 16931, "numberOfFiles": 6, "fileFormat": "Data are BADC-CSV formatted." }, "timePeriod": { "ob_id": 9111, "startTime": "1979-01-01T12:00:00", "endTime": "2014-12-31T12:00:00" }, "resultQuality": { "ob_id": 3758, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-09-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33160, "uuid": "ef8b84ebbd8241038031c269a97e5514", "short_code": "comp", "title": "Caption for Figure 3.10 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Observed and simulated tropical mean temperature trends through the atmosphere. Vertical profiles of temperature trends in the tropics (20°S–20°N) for three periods: (a) 1979–2014, (b) 1979–1997 (ozone depletion era) and (c) 1998–2014 (ozone stabilisation era). The black lines show trends in the RICH 1.7 (long dashed) and Radiosonde Observation Correction using Reanalysis (RAOBCORE) 1.7 (dashed) radiosonde datasets (Haimberger et al., 2012), and in the ERA5/5.1 reanalysis (solid). Grey envelopes are centred on the RICH 1.7 trends, but show the uncertainty based on 32 RICH-obs members of version 1.5.1 of the dataset, which used version 1.7.3 of the RICH software but with the parameters of version 1.5.1. ERA5 was used as reference for calculating the adjustments between 2010 and 2019, and ERA-Interim was used for the years before that. Red lines show trends in CMIP6 historical simulations from one realization of 60 models. Blue lines show trends in 46 CMIP6 models that used prescribed, rather than simulated, sea surface temperatures (SSTs). Figure is adapted from Mitchell et al. (2020), their Figure 1. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 46781, 46782, 46783, 46784, 46785, 46786, 46787, 46788, 46789, 46790, 46791, 46792 ], "vocabularyKeywords": [], "identifier_set": [ 12356 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 148052, 148053, 148054, 148055, 148056, 148057, 148059, 148058 ], "onlineresource_set": [ 43464, 52368, 43463, 82862, 88559, 90167, 90168, 90169, 90170, 90171, 90172, 90173, 90174, 90175, 90176, 90177, 90178, 90179, 90180, 90181, 90182, 90183, 90184, 90185, 90186, 90187, 90188, 90189, 90190, 94605 ] }, { "ob_id": 33161, "uuid": "06a4ad1656774087a5b6db3c7a57211e", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.4 (v20211028)", "abstract": "Data for Figure 3.4 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 3.4 shows observed and simulated time series of the anomalies in annual and global mean near-surface air temperature (GSAT). \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels, with data provided for all panels in subdirectories named panel_a and panel_b.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n Observed and simulated global near-surface air temperature change (1850-2014) with uncertainty range for simulated time series.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\npanel_a/fig_3_4_panel_a.nc\r\n- black line: model = 60\r\n- red line: model = 59\r\n- colored lines: model = 0, 1, ..., 58\r\npanel_b/tsline_collect_tasa.nc:\r\n- red line: experiment = 0, stat = 0\r\n- blue line: experiment = 1, stat = 0\r\n- red shaded region: experiment = 0, stat = 1 and stat = 2\r\n- blue shaded region: experiment = 1, stat = 1 and stat = 2\r\npanel_b/tsline_collect_tasa_ref.nc\r\n- HadCRUT5: dataset = 0\r\n- BerkleyEarth: dataset = 1\r\n- NOAAGlobalTemp-Interim: dataset = 2\r\n- Kadow: dataset =3\r\n\r\nWhere HadCRUT5, BerkleyEarth, NOAAGlobalTemp-Interim, and Kadow are gridded datasets of global historical surface temperature.\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-01-28T18:12:50", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 3, Human influence, large-scale indicators, Natural variability, anthropogenically-forced change, observed changes, Figure 3.4, global near-surface air temperature anomaly", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-01-31T12:04:00", "doiPublishedTime": "2023-02-08T17:08:32.278656", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 55, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 34676, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig04/v20211028", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 107476, "numberOfFiles": 6, "fileFormat": "netCDF" }, "timePeriod": { "ob_id": 9112, "startTime": "1850-01-01T12:00:00", "endTime": "2014-12-31T12:00:00" }, "resultQuality": { "ob_id": 3759, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-09-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33163, "uuid": "dec11389f39b47f2b64068ad22909741", "short_code": "comp", "title": "Caption for Figure 3.4 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Observed and simulated time series of the anomalies in annual and global mean near-surface air temperature (GSAT). All anomalies are differences from the 1850–1900 time-mean of each individual time series. The reference period 1850–1900 is indicated by grey shading. (a) Single simulations from CMIP6 models (thin lines) and the multi-model mean (thick red line). Observational data (thick black lines) are from the Met Office Hadley Centre HadCRUT5, and are blended surface temperature (2 m air temperature over land and sea surface temperature over the ocean). All models have been subsampled using the HadCRUT5 observational data mask. Vertical lines indicate large historical volcanic eruptions. CMIP6 models which are marked with an asterisk are either tuned to reproduce observed warming directly, or indirectly by tuning equilibrium climate sensitivity. Inset: GSAT for each model over the reference period, not masked to any observations. (b) Multi-model means of CMIP5 (blue line) and CMIP6 (red line) ensembles and associated 5th to 95th percentile ranges (shaded regions). Observational data are HadCRUT5, Berkeley Earth, National Oceanic and Atmospheric Administration NOAAGlobalTemp-Interim and Kadow et al. (2020). Masking was done as in (a). CMIP6 historical simulations are extended with SSP2-4.5 simulations for the period 2015–2020 and CMIP5 simulations are extended with RCP4.5 simulations for the period 2006–2020. All available ensemble members were used (see Section 3.2). The multi-model means and percentiles were calculated solely from simulations available for the whole time span (1850–2020). Figure is updated from Bock et al. (2020), their Figures 1 and 2. / CC BY 4.0 https://creativecommons.org/licenses/by/4.0/. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 4385, 46697, 46966, 60412, 60414, 63623 ], "vocabularyKeywords": [], "identifier_set": [ 12351 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" }, { "ob_id": 43269, "uuid": "9177e927f0664da9bf1e25f57561d0d8", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Synthesis Report (SYR)", "abstract": "This dataset collection contains datasets relating to the figures found in the Synthesis Report (SYR) of the IPCC Sixth Assessment Report (AR6).\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 2.4\r\n- data for Figure 2.5\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6" } ], "responsiblepartyinfo_set": [ 148062, 148063, 148064, 148065, 148066, 148067, 148068, 148069 ], "onlineresource_set": [ 43465, 43466, 52362, 82856, 88590, 90223, 90224, 90225, 90226, 90227, 90228, 90229, 90230, 90231, 90232 ] }, { "ob_id": 33164, "uuid": "b6ca8b2d797348dd86823cfc2da7ba83", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.5 (v20211001)", "abstract": "Data for Figure 3.5 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 3.5 shows the standard deviation of annually averaged zonal-mean near-surface air temperature.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\nWhen citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n - Simulated (CMIP6) standard deviation of near-surface air temperature\r\n - Observed standard deviation of near-surface air temperature\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nDatafile: fig_3_5.nc, black lines:\r\n - HadCRUT5: model = 62\r\n - BerkleyEarth: model = 61\r\n - NOAAGlobalTemp-Interim: model = 60\r\n - Kadow: model = 59\r\n - colored lines: model = 0, 1, ..., 58\r\n\r\nWhere HadCRUT5, BerkleyEarth, NOAAGlobalTemp-Interim, and Kadow are gridded datasets of global historical surface temperature.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:44:00", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 3, Human influence, large-scale indicators, Natural variability, anthropogenically-forced change, observed changes, Figure 3.5, standard deviation, near-surface air temperature", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-10-29T12:00:05", "doiPublishedTime": "2023-02-08T17:12:47.856551", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 56, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 33282, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig05/v20211001/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3756898, "numberOfFiles": 4, "fileFormat": "netCDF" }, "timePeriod": { "ob_id": 9113, "startTime": "1995-01-01T12:00:00", "endTime": "2014-12-31T12:00:00" }, "resultQuality": { "ob_id": 3760, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-09-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33166, "uuid": "99adb852b6a442f9a91c484b09789de2", "short_code": "comp", "title": "Caption for Figure 3.5 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "The standard deviation of annually averaged zonal-mean near-surface air temperature. This is shown for four detrended observed temperature datasets (HadCRUT5, Berkeley Earth, NOAAGlobalTemp-Interim and Kadow et al. (2020), for the years 1995-2014) and 59 CMIP6 pre-industrial control simulations (one ensemble member per model, 65 years) (after Jones et al., 2013). For line colours see the legend of Figure 3.4. Additionally, the multi-model mean (red) and standard deviation (grey shading) are shown. Observational and model datasets were detrended by removing the least-squares quadratic trend. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 52664, 63623, 67753 ], "vocabularyKeywords": [], "identifier_set": [ 12352 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" }, { "ob_id": 43269, "uuid": "9177e927f0664da9bf1e25f57561d0d8", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Synthesis Report (SYR)", "abstract": "This dataset collection contains datasets relating to the figures found in the Synthesis Report (SYR) of the IPCC Sixth Assessment Report (AR6).\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 2.4\r\n- data for Figure 2.5\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6" } ], "responsiblepartyinfo_set": [ 148073, 148072, 148074, 148075, 148076, 148079, 148078, 148077 ], "onlineresource_set": [ 43468, 43467, 52363, 82857, 88596, 90214, 90215, 90216, 90217, 90218, 90219, 90220, 90221, 90222, 94632 ] }, { "ob_id": 33169, "uuid": "e7e31b86b2644e0da69090bc37360c97", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction – snow on ground (SCFG) from AATSR (2002 – 2012), version 1.0", "abstract": "This dataset contains Daily Snow Cover Fraction (snow on ground) from AATSR, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the transparency (“transmissivity”) of the forest canopy. The SCFG is given in percentage (%) per grid cell. \r\n\r\nThe global SCFG product is available at 0.01° grid size (about 1 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included. \r\n\r\nThe SCFG time series provides daily products for the period 2002 – 2012. \r\n\r\nThe SCFG product is based on Advanced Along-Track Scanning Radiometer (AATSR) data aboard the Envisat satellite. \r\n\r\nThe retrieval method of the snow_cci SCFG product from AATSR data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. \r\n\r\nImprovements to the GlobSnow algorithm implemented for snow_cci version 1 include the usage of a global forest transmissivity map developed and created within snow_cci based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny 2019). The forest transmissivity map provides the local transparency of the forest canopy and is applied or estimating the fractional snow cover on the ground.\r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the SCFG product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.\r\n\r\nThe SCFG product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nThe Norwegian Computing Center (Norsk Regnesentral, NR) is responsible for the SCFG product development and generation from AATSR data. The Remote Sensing Research Group of the University of Bern supported the development. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation.\r\n\r\nThere are a few days without any AATSR acquisitions in the years 2002, 2003, 2004, 2006, 2008, 2010 and 2012.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-03-20T12:51:34", "updateFrequency": "notPlanned", "dataLineage": "The snow_cci SCFG products from AATSR are based on the AATSR version 4.0 (2021) reprocessed dataset (which is conformant with SLSTR), provided by ESA.\r\n\r\nThe snow_cci SCF processing chain for AATSR includes the masking of clouds, the identification of clearly snow-free areas, and the retrieval of snow cover fraction per grid cell for all remaining observed grid cells. Finally, permanent snow and ice areas as well as water bodies are masked in the SCFG products using the corresponding classes from the Land Cover CCI map of the year 2000 as auxiliary layers. All SCFG products are prepared according to the CCI data standards.\r\n\r\nThe processing chain was developed by Norsk Regnesentral (Norwegian Computing Center, NR), and the processing took place on the Fram supercomputer operated by UNINETT Sigma2 AS (Sigma2, The Norwegian e-infrastructure for Research & Education). \r\n\r\nData were supplied for archiving at the Centre for Environmental Data Analysis (CEDA) as part of the ESA CCI Open Data Portal.", "removedDataReason": "", "keywords": "ESA, CCI, Snow, Snow Cover Fraction", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-08-08T13:20:26", "doiPublishedTime": "2023-08-08T13:20:17.884745", "removedDataTime": null, "geographicExtent": { "ob_id": 2614, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 39795, "dataPath": "/neodc/esacci/snow/data/scfg/AATSR/v1.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 59896343883, "numberOfFiles": 3558, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 11078, "startTime": "2002-05-20T00:00:00", "endTime": "2012-04-08T23:59:59" }, "resultQuality": { "ob_id": 4164, "explanation": "The unbiased estimate of the root mean square error of the snow cover fraction is adapted from the approach of Salberg et al. (2021) and is added as an uncertainty layer in each product. The AATSR-based SCFG products are matching the CCI data standards version 2.3, released in July 2021. Salberg, A.-B., K. Luojus, C. Derksen, C. Marin, R. Solberg, L. Keuris, G. Schwaizer, T. Nagler, (2021) ESA CCI+ Snow ECV: End-to-End ECV Uncertainty Budget, version 3.0, November 2021.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-02" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 39625, "uuid": "c291c9a8c8774e83a1ae295effd4b211", "short_code": "cmppr", "title": "Composite process for the ESA Snow Climate Change Initiative SCFG AATSR v1.0", "abstract": "The SCFG product is based on Advanced Along-Track Scanning Radiometer (AATSR) data aboard the Envisat satellite. \r\n\r\nThe retrieval method of the snow_cci SCFG product from AATSR data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2571, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 38, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_snow_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30229, "uuid": "93cf539bc3004cc8b98006e69078d86b", "short_code": "proj", "title": "ESA Snow Climate Change Initiative (snow_cci)", "abstract": "The overarching goal of snow_cci is the generation of homogeneous, well calibrated, long-term time series of key snow cover parameters (snow area extent and snow mass) from multi-sensor satellite data for climate applications. A main motivation for this initiative are significant discrepancies in the climatologies, anomalies, and trends in global snow cover time series from different products, detected in the ESA QA4EO Satellite Snow Product Intercomparison and Evaluation project (SnowPEx).\r\n\r\nThe first phase of the project started in September 2018; the second phase started in February 2022. The third phase of the snow_cci project is planned to start in late 2025." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 32072, 61132, 62644, 62645, 63200 ], "vocabularyKeywords": [], "identifier_set": [ 12672 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 148085, 148086, 148087, 148088, 148089, 148090, 148091, 148084, 148092, 148093, 148094, 148095, 148096, 148097 ], "onlineresource_set": [ 43415, 43416, 43417, 43419, 82894, 82896, 83402, 83403, 82895 ] }, { "ob_id": 33170, "uuid": "0aeba0c203c2447b9553a78f99d3a276", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction – snow on ground (SCFG) from ATSR-2 (1995 – 2003), version 1.0", "abstract": "This dataset contains Daily Snow Cover Fraction (snow on ground) from ATSR-2, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the transparency (“transmissivity”) of the forest canopy. The SCFG is given in percentage (%) per grid cell. \r\n\r\nThe global SCFG product is available at 0.01° grid size (about 1 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included.\r\n \r\nThe SCFG time series provides daily products for the period 1995 – 2003. \r\n\r\nThe SCFG product is based on Along-Track Scanning Radiometer 2 (ATSR-2) data aboard the ERS-2 satellite. \r\n\r\nThe retrieval method of the snow_cci SCFG product from ATSR-2 data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. \r\n\r\nImprovements to the GlobSnow algorithm implemented for snow_cci version 1 include the usage of a global forest transmissivity map developed and created within snow_cci based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny 2019). The forest transmissivity map provides the local transparency of the forest canopy and is applied or estimating the fractional snow cover on the ground.\r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the SCFG product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.\r\n\r\nThe SCFG product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nThe Norwegian Computing Center (Norsk Regnesentral, NR) is responsible for the SCFG product development and generation from ATSR-2 data. The Remote Sensing Research Group of the University of Bern supported the development. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation.\r\n\r\nThere are a few days without any ATSR-2 acquisitions in the years 1995, 1996, 1999, 2000, 2001, 2002 and 2003.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-03-20T11:29:26", "updateFrequency": "notPlanned", "dataLineage": "The snow_cci SCFG products from ATSR-2 are based on the ATSR-2 version 3.0 dataset provided by ESA.\r\n\r\nThe snow_cci SCF processing chain for ATSR-2 includes the masking of clouds, the identification of clearly snow-free areas, and the retrieval of snow cover fraction per grid cell for all remaining observed grid cells. Finally, permanent snow and ice areas as well as water bodies are masked in the SCFG products using the corresponding classes from the Land Cover CCI map of the year 2000 as auxiliary layers. All SCFG products are prepared according to the CCI data standards.\r\n\r\nThe processing chain was developed by Norsk Regnesentral (Norwegian Computing Center, NR), and the processing took place on the Fram supercomputer operated by UNINETT Sigma2 AS (Sigma2, The Norwegian e-infrastructure for Research & Education). \r\n\r\nData were supplied for archiving at the Centre for Environmental Data Analysis (CEDA) as part of the ESA CCI Open Data Portal.", "removedDataReason": "", "keywords": "ESA, CCI, Snow, Snow Cover Fraction", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-08-08T13:19:55", "doiPublishedTime": "2023-08-08T13:19:45.160827", "removedDataTime": null, "geographicExtent": { "ob_id": 2614, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 39796, "dataPath": "/neodc/esacci/snow/data/scfg/ATSR-2/v1.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 38195200395, "numberOfFiles": 2601, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 8857, "startTime": "1995-08-01T00:00:00", "endTime": "2003-06-22T23:59:59" }, "resultQuality": { "ob_id": 4165, "explanation": "The unbiased estimate of the root mean square error of the snow cover fraction is adapted from the approach of Salberg et al. (2021) and is added as an uncertainty layer in each product. The ATSR-2-based SCFG products are matching the CCI data standards version 2.3, released in July 2021. Salberg, A.-B., K. Luojus, C. Derksen, C. Marin, R. Solberg, L. Keuris, G. Schwaizer, T. Nagler, (2021) ESA CCI+ Snow ECV: End-to-End ECV Uncertainty Budget, version 3.0, November 2021.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-02" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 39621, "uuid": "6d6fe28261814202a5fe937d10372d76", "short_code": "cmppr", "title": "Composite process for the ESA Snow Climate Change Initiative SCFG ATSR-2 v1.0", "abstract": "The SCFG product is based on Along-Track Scanning Radiometer 2 (ATSR-2) data aboard the ESR-2 satellite. \r\n\r\nThe retrieval method of the snow_cci SCFG product from ATSR-2 data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. \r\n\r\nImprovements to the GlobSnow algorithm implemented for snow_cci version 1 include the usage of a global forest transmissivity map developed and created within snow_cci based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny 2019). The forest transmissivity map provides the local transparency of the forest canopy and is applied or estimating the fractional snow cover on the ground.\r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the SCFG product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2571, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 38, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_snow_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30229, "uuid": "93cf539bc3004cc8b98006e69078d86b", "short_code": "proj", "title": "ESA Snow Climate Change Initiative (snow_cci)", "abstract": "The overarching goal of snow_cci is the generation of homogeneous, well calibrated, long-term time series of key snow cover parameters (snow area extent and snow mass) from multi-sensor satellite data for climate applications. A main motivation for this initiative are significant discrepancies in the climatologies, anomalies, and trends in global snow cover time series from different products, detected in the ESA QA4EO Satellite Snow Product Intercomparison and Evaluation project (SnowPEx).\r\n\r\nThe first phase of the project started in September 2018; the second phase started in February 2022. The third phase of the snow_cci project is planned to start in late 2025." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 32072, 61132, 62644, 62645, 63200 ], "vocabularyKeywords": [], "identifier_set": [ 12671 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 148099, 148100, 148101, 148102, 148103, 148104, 148105, 148098, 148106, 148107, 148108, 148109, 148110, 148111 ], "onlineresource_set": [ 43426, 43427, 43428, 43430, 43431, 43433, 43432, 83404, 83405 ] }, { "ob_id": 33171, "uuid": "70061acca284432ca31fd8a5cbd604d0", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction – viewable snow (SCFV) from ATSR-2 (1995 – 2003), version 1.0", "abstract": "This dataset contains Daily Snow Cover Fraction of viewable snow from ATSR-2, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction viewable (SCFV) indicates the area of snow viewable from space over all land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel.\r\n\r\nThe global SCFV product is available at 0.01° grid size (about 1 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included. \r\n\r\nThe SCFV time series provides daily products for the period 1995 – 2003. \r\n\r\nThe SCFV product is based on Along-Track Scanning Radiometer 2 (ATSR-2) data aboard the ERS-2 satellite. \r\n\r\nThe retrieval method of the snow_cci SCFV product from ATSR-2 data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include adaptation of the retrieval method for mapping in forested areas the SCFV. \r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the SCFV product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.\r\n\r\nThe SCFV product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nThe Norwegian Computing Center (Norsk Regnesentral, NR) is responsible for the SCFV product development and generation from ATSR-2 data. The Remote Sensing Research Group of the University of Bern supported the development. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation.\r\n\r\nThere are a few days without any ATSR-2 acquisitions in the years 1995, 1996, 1999, 2000, 2001, 2002 and 2003.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-03-20T12:11:54", "updateFrequency": "notPlanned", "dataLineage": "The snow_cci SCFV products from ATSR-2 are based on the ATSR-2 version 3.0 dataset provided by ESA.\r\n\r\nThe snow_cci SCF processing chain for ATSR-2 includes the masking of clouds, the identification of clearly snow-free areas, and the retrieval of snow cover fraction per grid cell for all remaining observed grid cells. Finally, permanent snow and ice areas as well as water bodies are masked in the SCFV products using the corresponding classes from the Land Cover CCI map of the year 2000 as auxiliary layers. All SCFV products are prepared according to the CCI data standards.\r\n\r\nThe processing chain was developed by Norsk Regnesentral (Norwegian Computing Center, NR), and the processing took place on the Fram supercomputer operated by UNINETT Sigma2 AS (Sigma2, The Norwegian e-infrastructure for Research & Education).\r\n\r\nData were supplied for archiving at the Centre for Environmental Data Analysis (CEDA) as part of the ESA CCI Open Data Portal.", "removedDataReason": "", "keywords": "ESA, CCI, Snow, Snow Cover Fraction", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-08-08T13:19:00", "doiPublishedTime": "2023-08-08T13:18:52.243127", "removedDataTime": null, "geographicExtent": { "ob_id": 2614, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 39798, "dataPath": "/neodc/esacci/snow/data/scfv/ATSR-2/v1.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 37880078293, "numberOfFiles": 2601, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 11079, "startTime": "1995-08-01T00:00:00", "endTime": "2003-06-22T23:59:59" }, "resultQuality": { "ob_id": 4163, "explanation": "The unbiased estimate of the root mean square error of the snow cover fraction is adapted from the approach of Salberg et al. (2021) and is added as an uncertainty layer in each product. The ATSR-2-based SCFV products are matching the CCI data standards version 2.3, released in July 2021. Salberg, A.-B., K. Luojus, C. Derksen, C. Marin, R. Solberg, L. Keuris, G. Schwaizer, T. Nagler, (2021) ESA CCI+ Snow ECV: End-to-End ECV Uncertainty Budget, version 3.0, November 2021.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-02" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 39627, "uuid": "3c84376df2d44981ae15a6d9c87e7369", "short_code": "cmppr", "title": "Composite process for the ESA Snow Climate Change Initiative SCFV ATSR-2 v1.0", "abstract": "The SCFV product is based on Along-Track Scanning Radiometer 2 (ATSR-2) data aboard the ESR-2 satellite. \r\n\r\nThe retrieval method of the snow_cci SCFV product from ATSR-2 data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include adaptation of the retrieval method for mapping in forested areas the SCFV." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2571, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 38, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_snow_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30229, "uuid": "93cf539bc3004cc8b98006e69078d86b", "short_code": "proj", "title": "ESA Snow Climate Change Initiative (snow_cci)", "abstract": "The overarching goal of snow_cci is the generation of homogeneous, well calibrated, long-term time series of key snow cover parameters (snow area extent and snow mass) from multi-sensor satellite data for climate applications. A main motivation for this initiative are significant discrepancies in the climatologies, anomalies, and trends in global snow cover time series from different products, detected in the ESA QA4EO Satellite Snow Product Intercomparison and Evaluation project (SnowPEx).\r\n\r\nThe first phase of the project started in September 2018; the second phase started in February 2022. The third phase of the snow_cci project is planned to start in late 2025." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 32072, 62645, 63200, 63201, 63202 ], "vocabularyKeywords": [], "identifier_set": [ 12670 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 148112, 148114, 148115, 148116, 148117, 148118, 148119, 148113, 148120, 148121, 148122, 148123, 148124, 148125 ], "onlineresource_set": [ 43437, 43438, 43439, 82898, 43441, 82897, 82899, 83406, 83407 ] }, { "ob_id": 33172, "uuid": "d7773cb976d64b1c900a518773428df6", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction – viewable snow (SCFV) from AATSR (2002 – 2012), version 1.0", "abstract": "This dataset contains Daily Snow Cover Fraction of viewable snow from AATSR, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction viewable (SCFV) indicates the area of snow viewable from space over all land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. \r\n\r\nThe global SCFV product is available at 0.01° grid size (about 1 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included. \r\nThe SCFV time series provides daily products for the period 2002 – 2012. \r\n\r\nThe SCFV product is based on Advanced Along-Track Scanning Radiometer (AATSR) data aboard the Envisat satellite. \r\n\r\nThe retrieval method of the snow_cci SCFV product from AATSR data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include adaptation of the retrieval method for mapping in forested areas the SCFV.\r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the SCFV product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.\r\n\r\nThe SCFV product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nThe Norwegian Computing Center (Norsk Regnesentral, NR) is responsible for the SCFV product development and generation from AATSR data. The Remote Sensing Research Group of the University of Bern supported the development. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation.\r\n\r\nThere are a few days without any AATSR acquisitions in the years 2002, 2003, 2004, 2006, 2008, 2010 and 2012.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-03-20T12:53:17", "updateFrequency": "notPlanned", "dataLineage": "The snow_cci SCFV products from AATSR are based on the AATSR version 4.0 (2021) reprocessed dataset (which is conformant with SLSTR), provided by ESA.\r\n\r\nThe snow_cci SCF processing chain for AATSR includes the masking of clouds, the identification of clearly snow-free areas, and the retrieval of snow cover fraction per grid cell for all remaining observed grid cells. Finally, permanent snow and ice areas as well as water bodies are masked in the SCFV products using the corresponding classes from the Land Cover CCI map of the year 2000 as auxiliary layers. All SCFV products are prepared according to the CCI data standards.\r\n\r\nThe processing chain was developed by Norsk Regnesentral (Norwegian Computing Center, NR), and the processing took place on the Fram supercomputer operated by UNINETT Sigma2 AS (Sigma2, The Norwegian e-infrastructure for Research & Education). \r\n\r\nData were supplied for archiving at the Centre for Environmental Data Analysis (CEDA) as part of the CCI Open Data Portal project.", "removedDataReason": "", "keywords": "ESA, CCI, Snow, Snow Cover Fraction", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-08-08T13:18:27", "doiPublishedTime": "2023-08-08T13:18:16.922994", "removedDataTime": null, "geographicExtent": { "ob_id": 2614, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 39797, "dataPath": "/neodc/esacci/snow/data/scfv/AATSR/v1.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 63476170290, "numberOfFiles": 3558, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 11080, "startTime": "2002-05-20T00:00:00", "endTime": "2012-04-08T23:59:59" }, "resultQuality": { "ob_id": 4166, "explanation": "The unbiased estimate of the root mean square error of the snow cover fraction is adapted from the approach of Salberg et al. (2021) and is added as an uncertainty layer in each product. The AATSR-based SCFV products are matching the CCI data standards version 2.3, released in July 2021. Salberg, A.-B., K. Luojus, C. Derksen, C. Marin, R. Solberg, L. Keuris, G. Schwaizer, T. Nagler, (2021) ESA CCI+ Snow ECV: End-to-End ECV Uncertainty Budget, version 3.0, November 2021.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-02-02" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 39619, "uuid": "88c72629c2504d97bcaa4b7f90966c8b", "short_code": "cmppr", "title": "Composite process for the ESA Snow Climate Change Initiative SCFV AATSR v1.0", "abstract": "The SCFV product is based on Advanced Along-Track Scanning Radiometer (AATSR) data aboard the Envisat satellite.\r\n\r\nThe retrieval method of the snow_cci SCFV product from AATSR data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include adaptation of the retrieval method for mapping in forested areas the SCFV.\r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the SCFV product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2571, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 38, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_snow_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30229, "uuid": "93cf539bc3004cc8b98006e69078d86b", "short_code": "proj", "title": "ESA Snow Climate Change Initiative (snow_cci)", "abstract": "The overarching goal of snow_cci is the generation of homogeneous, well calibrated, long-term time series of key snow cover parameters (snow area extent and snow mass) from multi-sensor satellite data for climate applications. A main motivation for this initiative are significant discrepancies in the climatologies, anomalies, and trends in global snow cover time series from different products, detected in the ESA QA4EO Satellite Snow Product Intercomparison and Evaluation project (SnowPEx).\r\n\r\nThe first phase of the project started in September 2018; the second phase started in February 2022. The third phase of the snow_cci project is planned to start in late 2025." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 32072, 62645, 63200, 63201, 63202 ], "vocabularyKeywords": [], "identifier_set": [ 12669 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 148126, 148127, 148128, 148129, 148130, 148131, 148133, 148132, 148134, 148135, 148136, 148137, 148138, 148139 ], "onlineresource_set": [ 43454, 43447, 43448, 43449, 43451, 43452, 43453, 83408, 83409 ] }, { "ob_id": 33174, "uuid": "bbb759da50fe4cd5a1387c7462655908", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.9 (v20211028)", "abstract": "Data for Figure 3.9 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 3.9 shows global, land, ocean and continental annual mean near-surface air temperatures anomalies in CMIP6 models and observations. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has ten panels, with data provided for all panels in subdirectories named panel_a, panel_b, panel_c, panel_d, panel_e, panel_f, panel_g, panel_h, panel_i and panel_j.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n - Observed global near-surface air temperature change (1850-2020). \r\n - CMIP6 historical anthropogenic and natural global warming (1850-2020).\r\n - CMIP6 historical natural-only global warming (1850-2020).\r\n - CMIP6 historical greenhouse gas only global warming (1850-2020).\r\n - CMIP6 historical aerosol only global warming (1850-2020).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - panel_a/fig_3_9_a.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas)\r\n - panel_b/fig_3_9_b.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas)\r\n - panel_c/fig_3_9_c.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas)\r\n - panel_d/fig_3_9_d.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas)\r\n - panel_e/fig_3_9_e.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas)\r\n - panel_f/fig_3_9_f.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas)\r\n - panel_g/fig_3_9_g.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas)\r\n - panel_h/fig_3_9_h.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas)\r\n - panel_i/fig_3_9_i.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas)\r\n - panel_j/fig_3_9_j.nc (yearly data, 1850-2020); observed and CMIP6 data (also shaded areas)\r\n \r\nPlotted data corresponds to the following \"exp\" and \"stat\" indices:\r\n brown line: exp = 0, stat = 0\r\n green line: exp = 1, stat = 0\r\n grey line: exp = 2, stat = 0\r\n blue line: exp =3, stat = 0\r\n black line: exp = 4, stat = 0\r\n shaded regions: stat = 1 and 2, exp = 0, 1, 2 and 3\r\nThe ensemble spread (shaded regions) of CMIP6 data shown in figure 3.9 are the mean, 5th and 95th percentiles. The in-file metadata labels the same ensemble spread as the mean, min and max.\r\n\r\npanel_a: Global Ocean\r\npanel_b: Global\r\npanel_c: Global Land\r\npanel_d: North America\r\npanel_e: Central and South America\r\npanel_f: Europe and North Africa\r\npanel_g: Africa\r\npanel_h: Asia\r\npanel_i: Australasia\r\npanel_j: Antarctica\r\n\r\nAcronyms - CMIP6 - The sixth phase of the Coupled Model Intercomparison Project.\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:06:46", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 3, Human influence, large-scale indicators, Natural variability, anthropogenically-forced change, observed changes, Figure 3.9, global near-surface air temperature", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-03-09T10:47:43", "doiPublishedTime": "2023-02-08T17:35:27.821381", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 57, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 33249, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig09/v20211028", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 201466827, "numberOfFiles": 13, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 9114, "startTime": "1850-01-01T12:00:00", "endTime": "2020-12-31T12:00:00" }, "resultQuality": { "ob_id": 3761, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-09-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33176, "uuid": "b445009a1b3d49ceb64985b7e577ee01", "short_code": "comp", "title": "Caption for Figure 3.9 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Global, land, ocean and continental annual mean near-surface air temperatures anomalies in CMIP6 models and observations. Time series are shown for CMIP6 historical anthropogenic and natural (brown), natural-only (green), greenhouse gas only (grey) and aerosol only (blue) simulations (multi-model means shown as thick lines, and shaded ranges between the 5th and 95th percentiles) and for HadCRUT5 (black). All models have been subsampled using the HadCRUT5 observational data mask. Temperature anomalies are shown relative to 1950–2010 for Antarctica and relative to 1850–1900 for other continents. CMIP6 historical simulations are expand by the SSP2-4.5 scenario simulations. All available ensemble members were used (see Section 3.2). Regions are defined by Iturbide et al. (2020). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 4385, 46696, 46697, 60412 ], "vocabularyKeywords": [], "identifier_set": [ 12355 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 148153, 148154, 148155, 148156, 148157, 148158, 148160, 148159 ], "onlineresource_set": [ 43477, 43476, 52367, 82861, 88600, 94630 ] }, { "ob_id": 33177, "uuid": "7273023a04d24da58ec5d83343cd861d", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.12 (v20211001)", "abstract": "Data for Figure 3.12 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 3.12 shows column water vapor path trends (%/decade) for the period 1998-2019 averaged over the near-global oceans (50°S-50°N) \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The dataset contains water vapor path trends for the period 1998-2019 for:\r\n \r\n - observed average (RSS and ERA5)\r\n - simulated bins (CMIP5 and CMIP6)\r\n - simulated fit (CMIP5 and CMIP6)\r\n\r\nRSS (Remote Sensing Systems) refers to geophysical data collected by satellite microwave sensors.\r\nERA5 is the fifth generation ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric reanalysis of the global climate.\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - atmosphere_mass_content_of_water_vapor_trend_era5.nc (var = 'prw_trend', purple line)\r\n - atmosphere_mass_content_of_water_vapor_trend_rss.nc (var = 'prw_trend', orange line)\r\n - atmosphere_mass_content_of_water_vapor_trends_kde_fit_cmip5.nc (var = 'trend_bins', blue line)\r\n - atmosphere_mass_content_of_water_vapor_trends_kde_fit_cmip6.nc (var = 'trend_bins', red line)\r\n - atmosphere_mass_content_of_water_vapor_trends_pdf_cmip5.nc (var = 'trend_bins', blue bars)\r\n - atmosphere_mass_content_of_water_vapor_trends_pdf_cmip6.nc (var = 'trend_bins', red bars)\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:44:16", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 3, Human influence, large-scale indicators, Natural variability, anthropogenically-forced change, observed changes, Figure 3.12, water vapor path trend", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-10-29T12:00:12", "doiPublishedTime": "2023-02-08T17:44:06.056698", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 58, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 33240, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig12/v20211001/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 48305, "numberOfFiles": 9, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 9115, "startTime": "1998-01-01T12:00:00", "endTime": "2014-12-31T12:00:00" }, "resultQuality": { "ob_id": 3762, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-09-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33179, "uuid": "6f270d895ab04ebab7c15d98d26fa3a8", "short_code": "comp", "title": "Caption for Figure 3.12 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Column water vapour path trends (%/decade) for the period 1998–2019 averaged over the near-global oceans (50°S–50°N). The figure shows satellite data (RSS) and ERA5.1 reanalysis, as well as CMIP5 (sky blue) and CMIP6 (brown) historical simulations. All available ensemble members were used (see Section 3.2). Fits to the model trend probability distributions were performed with kernel density estimation. Figure is updated from Santer et al. (2007). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 40721, 40722, 40723, 40724, 40725, 40726, 40727, 40728, 40729 ], "vocabularyKeywords": [], "identifier_set": [ 12358 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 148165, 148166, 148167, 148168, 148169, 148170, 148163, 148164, 148171, 148172, 148173 ], "onlineresource_set": [ 43478, 52369, 43479, 82864, 88561, 90140, 90141, 90142, 90143, 90144, 90145, 90146, 90147, 90148, 90149, 90150, 90151, 90152, 90153, 90154, 90155, 90156, 90157, 90158, 90159, 90160, 90161, 90162, 90163, 90164, 90165, 90166, 94598 ] }, { "ob_id": 33180, "uuid": "e3d21f98cc764d1185b0d6e662532831", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.42 (v20211001)", "abstract": "Data for Figure 3.42 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 3.42 shows relative space-time root-mean-square deviation (RMSD) calculated from the climatological seasonal cycle of the CMIP simulations (1980-1999) compared to observational datasets. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels, with data provided for all panels in subdirectories named panel_a and panel_b.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains the grade value of the performance metrics for all diagnostics (variables) and models.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n panel_a/fig_3_42_a.nc and panel_b/fig_3_42_b.nc:\r\n \r\n - variable 'grade' with three dimensions:\r\n * 'diagnostics': variables on the y-axis of the metrics plot\r\n * 'models': models on the x-axis of the metrics plot\r\n * 'reference': 0: upper triangle; 1: lower triangle\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to the figure on the IPCC AR6 website", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2021-12-03T12:01:58", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 3, Human influence, large-scale indicators, Natural variability, anthropogenically-forced change, observed changes, Figure 3.42, performance metrics", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-10-31T12:42:00", "doiPublishedTime": "2023-02-08T19:34:34.315760", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 59, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 33247, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig42/v20211001/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 52371, "numberOfFiles": 5, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 9116, "startTime": "1980-01-01T12:00:00", "endTime": "1999-12-31T12:00:00" }, "resultQuality": { "ob_id": 3763, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-09-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33182, "uuid": "6d55a717cdc044c1b39d34fbd7eb2519", "short_code": "comp", "title": "Caption for Figure 3.42 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Relative space–time root-mean-square deviation (RMSD) calculated from the climatological seasonal cycle of the CMIP simulations (1980–1999) compared to observational datasets. (a) CMIP3, CMIP5, and CMIP6 for 16 atmospheric variables (b) CMIP5 and CMIP6 for 10 land variables and four ocean/sea-ice variables. A relative performance measure is displayed, with blue shading indicating better and red shading indicating worse performance than the median of all model results. A diagonal split of a grid square shows the relative error with respect to the reference data set (lower right triangle) and an additional data set (upper left triangle). Reference/additional datasets are from top to bottom in (a): ERA5/NCEP, GPCP-SG/GHCN, CERES-EBAF/-, CERES-EBAF/-, CERES-EBAF/-, CERES-EBAF/-, JRA-55/ERA5, ESACCI-SST/HadISST, ERA5/NCEP, ERA5/NCEP, ERA5/NCEP, ERA5/NCEP, ERA5/NCEP, ERA5/NCEP, AIRS/ERA5, ERA5/NCEP and in (b): CERES-EBAF/-, CERES-EBAF/-, CERES-EBAF/-, CERES-EBAF/-, LandFlux-EVAL/-, Landschuetzer2016/ JMA-TRANSCOM; MTE/FLUXCOM, LAI3g/-, JMA-TRANSCOM, ESACCI-SOILMOISTURE/-, HadISST/ATSR, HadISST/-, HadISST/-, ERA-Interim/-. White boxes are used when data are not available for a given model and variable. Figure is updated and expanded from Bock et al. (2020), their Figure 5 CC BY 4.0 https://creativecommons.org/licenses/by/4.0/. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 46723, 46724, 46725, 70811 ], "vocabularyKeywords": [], "identifier_set": [ 12388 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 148176, 148177, 148178, 148179, 148180, 148181, 148182, 148183 ], "onlineresource_set": [ 43481, 43480, 52397, 82622, 88593, 89754, 89773, 89774, 89755, 89756, 89757, 89758, 89759, 89760, 89761, 89762, 89763, 89764, 89765, 89766, 89767, 89768, 89769, 89770, 89771, 89772, 89775, 89776, 89777, 89778, 89779, 94628 ] }, { "ob_id": 33186, "uuid": "ea70491805b14edf957d69cbc27a8d81", "title": "EUMETNET Sandbox: Met Office WOW data v1", "abstract": "The Met Office WOW V1 dataset contains observations from all Public Weather Stations (PWS) contributing to WOW (Weather Observations Website) within Europe. The dataset is for a single year (2020), made available for use within the EUMETNET Sandbox project. EUMETNET (a grouping of 31 European National Meteorological Services) instigated the EUMETNET Sandbox project to bring novel observations and observations from technology trials and field campaigns to the research community to enable R&D activities.\r\n\r\nThe data are not quality controlled and are presented in the format provided by the Met Office. The data are provided in a single file per month.\r\n\r\nThe data were extracted from the WOW database using a latitude/longitude bounding box (North West corner 90N 70W to South East corner 10N 40E) so there are a small number of stations outside of Europe present.\r\n\r\nThe meteorological values are unchanged from those extracted from the WoW archive. For example, there is no Quality Control of the data, no calibration of the instruments and no unit conversions have been applied.\r\n\r\nThe data were extracted from the WoW archive by the Met Office operators of the WoW system. The data have not been manipulated to meet any international data format standards. The file names have been modified, from those provided by the Met Office, to contain location, data and status information.\r\n\r\nThe records for each station are comma separated. Not all sites report all variables, nor at all times. In most cases only the principle meteorological variables, e.g., Air temperature, Wet bulb, etc. are present. For a full list of available parameters within the files see the 'variables' information on this record.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-10-05T10:15:10", "updateFrequency": "notPlanned", "dataLineage": "Data were extracted from the Met Office WOW (Weather Observers Website) database and prepared by the data provider before upload for archiving at the Centre for Environmental Data Analysis (CEDA). Data were originally provided to WOW by Public Weather Stations (PWS) from a range of sites, mainly in Europe but some further afield. Data were provided with MD5s to allow checking of data during upload and ingest to ensure no corruptions.", "removedDataReason": "", "keywords": "EUMETNET, WOW, meteorology, Europe, land, daily, hourly", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-10-18T14:35:32", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2867, "bboxName": "Met Office WOW area", "eastBoundLongitude": 40.0, "westBoundLongitude": -70.0, "southBoundLatitude": 10.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 33183, "dataPath": "/badc/eumetnet-sandbox/data/metoffice-wow/v1", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 49724282402, "numberOfFiles": 25, "fileFormat": "Data are ASCII formatted." }, "timePeriod": { "ob_id": 9124, "startTime": "2020-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 33230, "uuid": "a07497435c034d15b66e82cfd73b5aec", "short_code": "acq", "title": "Met Office WOW instrument network", "abstract": "Met Office WOW instrument network" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 220 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2654, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "eumetnet-ukmo-wow", "label": "restricted: eumetnet-ukmo-wow group", "licence": { "ob_id": 97, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/ukmo-wow_runcgl_licence.pdf", "licenceClassifications": [ { "ob_id": 7, "classification": "specific" } ] } } ], "projects": [ { "ob_id": 33185, "uuid": "dbea2ea135f149acba3868e657e7d994", "short_code": "proj", "title": "EUMETNET Sandbox", "abstract": "EUMETNET Sandbox project seeks to develop use of novel data sources (e.g. crowd sourced data) to improve operational weather forecasting capabilities." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 12731, 13195, 13197, 13200, 13201, 20927, 20939, 20951, 21911, 31969, 31970, 40198, 40199, 40200, 40201, 40202, 40203, 40204, 40205, 40206, 40207, 40208, 40209, 40210, 40211, 40212, 40213, 40214, 40215, 40216, 40217, 40218, 40219, 40220, 40221, 40222, 40223, 40224, 40225, 40226, 40227, 40228, 40229, 40230, 40231, 40232, 40233, 40234, 40235, 40236, 40237, 40238, 40239, 40240, 40241, 40242, 40243, 40244, 40245, 40246 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 33184, "uuid": "37d6ea7956a74af0bef827b94e0fb602", "short_code": "coll", "title": "EUMETNET Sandbox: surface observations from Met Office WOW and Netatmo networks.", "abstract": "The aim of the EUMETNET Sandbox is to bring novel observations and observations from technology trials and field campaigns to the research community to enable R&D activities.\r\n\r\nThe first datasets to be made available on the Sandbox are two datasets from Personal Weather Stations (PWS) provided by Netatmo and WOW. These datasets are historical archives for a single year (2020) to support development of quality control tools and other activities carried out by EUMETNET Members and ECMWF.\r\n\r\nThe Netatmo and WOW Datasets contain land surface observations, usually from weather stations located in the gardens of the public. The data are not quality controlled prior to being made available in the CEDA Archive. The datasets are stored in the format and resolution provided by the Met Office (for WOW data) and Netatmo. The WOW data are stored in a csv format with a single file per month. The files contain the data from all WOW stations within Europe. The Netatmo data are stored in tar/zip files, one file per country per month." } ], "responsiblepartyinfo_set": [ 148194, 148195, 148196, 148197, 148198, 148199, 148200, 148201 ], "onlineresource_set": [ 43506, 43499 ] }, { "ob_id": 33187, "uuid": "e8793d74a651426692faa100e3b2acd3", "title": "EUMETNET Sandbox: Netatmo observing network data v1", "abstract": "The Netatmo V1 dataset contains observations from all Public Weather Stations (PWS) contributing to the Netatmo database within Europe. Netatmo is a company that designs and manufactures a range of smart weather station instruments for the home. The dataset is for a single year (2020), made available for use within the EUMETNET Sandbox project. EUMETNET (a grouping of 31 European National Meteorological Services) instigated the EUMETNET Sandbox project to bring novel observations and observations from technology trials and field campaigns to the research community to enable R&D activities.\r\n\r\nThe data are not quality controlled and are presented in the format provided by Netatmo. The data are provided in a single file per month per country*.\r\n\r\nThe data were extracted from the Netatmo database country by country. \r\n\r\nThe meteorological values are unchanged from those extracted from the Netatmo archive. For example, there is no Quality Control of the data, no calibration of the instruments and no unit conversions have been applied. \r\n\r\nThe data were extracted from the Netatmo database by Netatmo operators of the Netatmo system. The data have not been manipulated to meet any international data format standards. \r\n\r\nFor each station there is always a metadata file 'n'.metadata.json. There are up to 4 data files associated with each station represented by a metadata file. In some cases, all 4 data files are present for the station. In other cases, only one data file is present. The 'n' in the file name allows the metadata file to be associated with the meteorological data files\r\n\r\n1. n.pressure.historic.csv - surface pressure for station n\r\n2. n.outdoor.historic.csv - Contains air temperature and humidity for station n\r\n3. n.wind.historic.csv - Contains wind and gust data for station n\r\n4. n.rain.historic.csv - rainfall data for station n\r\n\r\nThe data files are semi-colon separated and use UNIX epoch time \r\n*Countries present in the Netatmo dataset\r\n\r\nAustria, Spain, Iceland, Norway, Belgium, Finland, Italy, Poland, Switzerland, France, Luxembourg, Portugal, Cyprus, United Kingdom, Latvia, Serbia, Czech Republic, Greece, Montenegro, Sweden, Germany, Croatia, North Macedonia, Slovenia, Denmark, Hungary, Malta, Slovakia, Estonia, Ireland and the Netherlands", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-06-28T16:31:48", "updateFrequency": "notPlanned", "dataLineage": "The data were extracted from the Netatmo database country by country and supplied to the project partners. These were then prepared and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) alongside MD5 values for each file, used to ensure data transfer and ingestion were without corruption.", "removedDataReason": "", "keywords": "EUMETNET, Netatmo, meteorology, Europe, land, daily, hourly", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-10-18T14:39:31", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 929, "bboxName": "Europe", "eastBoundLongitude": 45.0, "westBoundLongitude": -30.0, "southBoundLatitude": 30.0, "northBoundLatitude": 75.0 }, "verticalExtent": null, "result_field": { "ob_id": 33188, "dataPath": "/badc/eumetnet-sandbox/data/netatmo/v1", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 170777431781, "numberOfFiles": 745, "fileFormat": "Data are provided in ASCII files within compressed TAR ball. See documentation for file format information." }, "timePeriod": { "ob_id": 9123, "startTime": "2020-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 33229, "uuid": "0026b8fe5d8b42a78d0050327b862c84", "short_code": "acq", "title": "Netatmo instrument network", "abstract": "Netatmo instrument network" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 220 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2653, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "eumetnet-netatmo", "label": "restricted: eumetnet-netatmo group", "licence": { "ob_id": 96, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/netatmo_licence.pdf", "licenceClassifications": [ { "ob_id": 8, "classification": "unclear" }, { "ob_id": 7, "classification": "specific" } ] } } ], "projects": [ { "ob_id": 33185, "uuid": "dbea2ea135f149acba3868e657e7d994", "short_code": "proj", "title": "EUMETNET Sandbox", "abstract": "EUMETNET Sandbox project seeks to develop use of novel data sources (e.g. crowd sourced data) to improve operational weather forecasting capabilities." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 40285, 40286, 40287, 40288, 40289, 40290, 40291, 40292 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 33184, "uuid": "37d6ea7956a74af0bef827b94e0fb602", "short_code": "coll", "title": "EUMETNET Sandbox: surface observations from Met Office WOW and Netatmo networks.", "abstract": "The aim of the EUMETNET Sandbox is to bring novel observations and observations from technology trials and field campaigns to the research community to enable R&D activities.\r\n\r\nThe first datasets to be made available on the Sandbox are two datasets from Personal Weather Stations (PWS) provided by Netatmo and WOW. These datasets are historical archives for a single year (2020) to support development of quality control tools and other activities carried out by EUMETNET Members and ECMWF.\r\n\r\nThe Netatmo and WOW Datasets contain land surface observations, usually from weather stations located in the gardens of the public. The data are not quality controlled prior to being made available in the CEDA Archive. The datasets are stored in the format and resolution provided by the Met Office (for WOW data) and Netatmo. The WOW data are stored in a csv format with a single file per month. The files contain the data from all WOW stations within Europe. The Netatmo data are stored in tar/zip files, one file per country per month." } ], "responsiblepartyinfo_set": [ 148203, 148204, 148205, 148206, 148207, 148208, 148209, 148202 ], "onlineresource_set": [ 43507, 43500 ] }, { "ob_id": 33189, "uuid": "44adfc4f92834bd9950341dd24d6d2e0", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.23 (v20211001)", "abstract": "Data for Figure 3.23 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 3.23 shows multi-model-mean bias of (a) sea surface temperature and (b) near-surface salinity, defined as the difference between the CMIP6 multi-model mean and the climatology from the World Ocean Atlas 2018. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels, with data provided for all panels in subdirectories named panel_a and panel_b.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n \r\n - Global bias of modelled annual-mean sea surface temperature (°C) of CMIP6 for the period 1995–2014 to WOA 2018\r\n - Global bias of modelled annual-mean sea surface salinity (PSS-76) of CMIP6 for the period 1995–2014 to WOA 2018\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nWOA 2018 is the World Ocean Atlas 2018.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - panel_a/fig_3_23_a.nc (sst, CMIP6 Sea Surface Temperature Bias)\r\n - panel_b/fig_3_23_b.nc (sss, CMIP6 Sea Surface Salinity Bias)\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2021-12-03T12:01:41", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 3, Human influence, large-scale indicators, Natural variability, anthropogenically-forced change, observed changes, Figure 3.23, global sea surface temperature, global sea surface salinity", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "1x1 Degrees", "status": "completed", "dataPublishedTime": "2021-10-31T12:23:00", "doiPublishedTime": "2023-02-08T18:07:30.757494", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 60, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "1x1" }, "result_field": { "ob_id": 33246, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig23/v20211001/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 544281, "numberOfFiles": 5, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 9117, "startTime": "1995-01-01T12:00:00", "endTime": "2014-12-31T12:00:00" }, "resultQuality": { "ob_id": 3764, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-10-01" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33191, "uuid": "676dcc478d454cd385b7e85d03150881", "short_code": "comp", "title": "Caption for Figure 3.23 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Multi-model-mean bias of (a) sea surface temperature and (b) near-surface salinity, defined as the difference between the CMIP6 multi-model mean and the climatology from the World Ocean Atlas 2018. The CMIP6 multi-model mean is constructed with one realization of 46 CMIP6 historical experiments for the period 1995–2014 and the climatology from the World Ocean Atlas 2018 is an average over all available years (1955-2017). Uncertainty is represented using the advanced approach: No overlay indicates regions with robust signal, where ≥66% of models show change greater than variability threshold and ≥80% of all models agree on sign of change; diagonal lines indicate regions with no change or no robust signal, where <66% of models show a change greater than the variability threshold; crossed lines indicate regions with conflicting signal, where ≥66% of models show change greater than the variability threshold and <80% of all models agree on sign of change. For more information on the advanced approach, please refer to the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 67754, 67755 ], "vocabularyKeywords": [], "identifier_set": [ 12369 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 148212, 148213, 148214, 148215, 148216, 148217, 148218, 148219 ], "onlineresource_set": [ 43482, 43483, 52380, 82875, 88573, 89912, 89913, 89914, 89915, 89916, 89917, 89918, 89919, 89920, 89921, 89922, 89923, 89924, 89925, 89926, 89927, 89928, 89929, 89930, 89931, 89932, 89933, 89934, 89935, 89936, 89937 ] }, { "ob_id": 33192, "uuid": "8c9c35e4c877440abcaa10b9aa173c33", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.14 (v20211001)", "abstract": "Data for Figure 3.14 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.14 shows wet and dry region tropical mean (30°S-30°N) annual precipitation anomalies.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with data provided for all panels in subdirectories named panel_a, panel_b, panel_c and panel_d.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The dataset contains timeseries (1988-2020) of annual precipitation anomalies from\r\n\r\n - observation (GPCP)\r\n - reanalysis (ERA5)\r\n - multi-model mean (CMIP6)\r\n\r\nGPCP is the Global Precipitation Climatology Project.\r\nERA5 is the fifth generation ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric reanalysis of the global climate.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - panel_a/AR6_WG1_Chap3_Figure3_14_panel_a_wetdry.csv (timeseries for wet regions)\r\n - panel_b/AR6_WG1_Chap3_Figure3_14_panel_b_wetdry.csv (timeseries for dry regions)\r\n - panel_c/AR6_WG1_Chap3_Figure3_14_panel_c_wetdry.csv (scaling factors for wet regions)\r\n - panel_d/AR6_WG1_Chap3_Figure3_14_panel_d_wetdry.csv (scaling factors for dry regions)\r\n Details on data provided in relation to each figure panel and its elements in the metadata associated to the corresponding files.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:06:38", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 3, Human influence, large-scale indicators, Natural variability, anthropogenically-forced change, observed changes, Figure 3.14, Tropical precipitation, detection and attribution", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-10-31T12:14:00", "doiPublishedTime": "2023-02-08T17:57:21.363075", "removedDataTime": null, "geographicExtent": { "ob_id": 2877, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -30.0, "northBoundLatitude": 30.0 }, "verticalExtent": { "ob_id": 61, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 33242, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig14/v20211001/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 111783, "numberOfFiles": 7, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 9118, "startTime": "1988-01-01T12:00:00", "endTime": "2020-12-31T12:00:00" }, "resultQuality": { "ob_id": 3765, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-10-01" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33194, "uuid": "2499b863c7604b4eb318916a4c0cd492", "short_code": "comp", "title": "Caption for Figure 3.14 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Wet (a) and dry (b) region tropical mean (30°S–30°N) annual precipitation anomalies. Observed data are shown with black lines (GPCP), ERA5 reanalysis in grey, single model simulations results are shown with light blue/red lines (CMIP6), and multi-model-mean results are shown with dark blue/red lines (CMIP6). Wet and dry region annual anomalies are calculated as the running mean over 12 months relative to a 1988–2020 based period. The regions are defined as the wettest third and driest third of the surface area, calculated for the observations and for each model separately for each season (following Polson and Hegerl, 2017). Scaling factors (c, d) are calculated for the combination of the wet and dry region mean, where the observations, reanalysis and all the model simulations are first standardized using the mean standard deviation of the pre-industrial control simulations. Two total least squares regression methods are used: noise in variables (following Polson and Hegerl, 2017) which estimates a best estimate and a 5–95% confidence interval using the pre-industrial controls (circle and thick green line) and the pre-industrial controls with double the variance (thin green line); and a bootstrap method (DelSole et al., 2019) (5–95% confidence interval shown with a purple line and best estimate with a purple circle). Panel (c) shows results for GPCP and panel (d) for ERA5. Figure is adapted from Schurer et al. (2020). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 86222, 86223, 86224, 86225, 86226, 86227, 86228, 86229, 86230, 86231, 86232, 86233, 86234, 86235, 86236, 86237, 86238, 86239, 86240, 86241, 86242, 86243, 86244 ], "vocabularyKeywords": [], "identifier_set": [ 12360 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 148222, 148223, 148224, 148225, 148226, 148227, 148228, 148229, 148230, 148231 ], "onlineresource_set": [ 43488, 43489, 52371, 82866, 88563, 90093, 90094, 90095, 90096, 90097, 90098, 90099, 90100, 90101, 90102, 90103, 90104, 90105, 90106, 90107, 90108, 90109, 90110, 90111, 90112, 90113, 90114 ] }, { "ob_id": 33195, "uuid": "a6b79b1abac64d72a1a3f2fcf62ee81e", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.15 (v20211001)", "abstract": "Data for Figure 3.15 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 3.15 shows observed and simulated time series of anomalies in zonal average annual mean precipitation.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has six panels, with data provided for five panels in subdirectories named panel_a, panel_c, panel_d, panel_e and panel_f.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n \r\n - Observed precipitation change (1950-2014). \r\n - Historical anthropogenic and natural precipitation change (1950-2014).\r\n - Historical natural-only precipitation change (1950-2014).\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - panel_a/fig_3_15_a.nc (yearly data, 1950-2014); observed and CMIP6 data (also shaded areas)\r\n - panel_c/fig_3_15_c.nc (yearly data, 1950-2014); observed and CMIP6 data (also shaded areas)\r\n - panel_d/fig_3_15_d.nc (yearly data, 1950-2014); observed and CMIP6 data (also shaded areas)\r\n - panel_e/fig_3_15_e.nc (yearly data, 1950-2014); observed and CMIP6 data (also shaded areas)\r\n - panel_f/fig_3_15_f.nc (yearly data, 1950-2014); observed and CMIP6 data (also shaded areas)\r\n * brown line: exp = 0, stat = 0\r\n * green line: exp = 1, stat = 0\r\n * shaded regions: exp = 1 or 2, stat = 1 and 2\r\n * black lines: exp = 2, stat = 0 (GHCN), stat = 1 (GPCP-SG), stat = 2 (CRU)\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nGHCN is the Global Historical Climatology Network.\r\nGPCP-SG refers to the Global Precipitation Climatology Project and Satellite-Guage combined precipitation dataset.\r\nCRU refers to the Climatic Research Unit gridded Time Series climate dataset.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:06:39", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 3, Human influence, large-scale indicators, Natural variability, anthropogenically-forced change, observed changes, Figure 3.15, precipitation anomaly", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-10-31T12:15:00", "doiPublishedTime": "2023-02-08T17:58:47.516390", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 62, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 33243, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig15/v20211001/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 33224, "numberOfFiles": 8, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 9119, "startTime": "1950-01-01T12:00:00", "endTime": "2014-12-31T12:00:00" }, "resultQuality": { "ob_id": 3766, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-10-01" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33197, "uuid": "d2bc88dbe8c84a48bcc1daeb0349270a", "short_code": "comp", "title": "Caption for Figure 3.15 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Observed and simulated time series of anomalies in zonal average annual mean precipitation. (a), (c–f) Evolution of global and zonal average annual mean precipitation (mm day–1) over areas of land where there are observations, expressed relative to the base-line period of 1961–1990, simulated by CMIP6 models (one ensemble member per model) forced with both anthropogenic and natural forcings (brown) and natural forcings only (green). Multi-model means are shown in thick solid lines and shading shows the 5–95% confidence interval of the individual model simulations. The data is smoothed using a low pass filter. Observations from three different datasets are included: gridded values derived from Global Historical Climatology Network (GHCN version 2) station data, updated from Zhang et al. (2007), data from the Global Precipitation Climatology Product (GPCP L3 version 2.3, Huffman and Bolvin, 2013) and from the Climate Research Unit (CRU TS4.02, Harris et al. (2014)). Also plotted are boxplots showing interquartile and 5–95% ranges of simulated trends over the period for simulations forced with both anthropogenic and natural forcings (brown) and natural forcings only (blue). Observed trends for each observational product are shown as horizontal lines. Panel (b) shows annual mean precipitation rate (mm day–1) of GHCN version 2 for the years 1950–2014 over land areas used to compute the plots. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 4385, 46696, 46697, 60415 ], "vocabularyKeywords": [], "identifier_set": [ 12361 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 148234, 148235, 148236, 148237, 148238, 148239, 148240, 148241 ], "onlineresource_set": [ 43490, 43491, 52372, 82867, 88564, 90068, 90069, 90070, 90071, 90072, 90073, 90074, 90075, 90076, 90077, 90078, 90079, 90080, 90081, 90082, 90083, 90084, 90085, 90086, 90087, 90088, 90089, 90090, 90091, 90092 ] }, { "ob_id": 33198, "uuid": "7493e7dd46854227beb4f891a80a1016", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.19 (v20211001)", "abstract": "Data for Figure 3.19 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 3.19 shows long-term mean (thin black contour) and linear trend (colour) of zonal mean DJF zonal winds over 1985-2014 in the SH. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels, with data provided for all panels in subdirectories named panel_a and panel_b.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n \r\n - ERA5 zonal-mean wind (1985-2014)\r\n - ERA5 zonal-mean wind trend (1985-2014)\r\n - CMIP6 zonal-mean wind (1985-2014)\r\n - CMIP6 zonal-mean wind trend (1985-2014)\r\n\r\nERA5 is the fifth generation ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric reanalysis of the global climate.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - panel_a/fig_3_19_a_mean.nc (contour, ERA5 mean)\r\n - panel_a/fig_3_19_a_trend.nc (color, ERA5 trend)\r\n - panel_b/fig_3_19_b_mean.nc (contour, CMIP6 mean)\r\n - panel_b/fig_3_19_b_trend.nc (color, CMIP6 trend)\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:06:43", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 3, Human influence, large-scale indicators, Natural variability, anthropogenically-forced change, observed changes, Figure 3.19, trend of zonal-mean wind", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "1 Degrees", "status": "completed", "dataPublishedTime": "2021-10-31T12:19:00", "doiPublishedTime": "2023-02-08T18:03:14.199801", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 63, "highestLevelBound": 3.0, "lowestLevelBound": 1000.0, "units": "1" }, "result_field": { "ob_id": 33245, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig19/v20211001/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 229043, "numberOfFiles": 7, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 9120, "startTime": "1985-01-01T12:00:00", "endTime": "2014-12-31T12:00:00" }, "resultQuality": { "ob_id": 3767, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-10-01" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33200, "uuid": "d8f6b26e718945f598541716bfee0328", "short_code": "comp", "title": "Caption for Figure 3.19 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Long-term mean (thin black contour) and linear trend (colour) of zonal mean DJF zonal winds over 1985–2014 in the SH. Displayed are (a) ERA5 and (b) CMIP6 multi-model mean (58 CMIP6 models). The solid contours show positive (westerly) and zero long-term mean zonal wind, and the dashed contours show negative (easterly) long-term mean zonal wind. Only one ensemble member per model is included. Figure is modified from Eyring et al. (2013), their Figure 12. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 19854, 46697, 52664, 60450, 67756 ], "vocabularyKeywords": [], "identifier_set": [ 12365 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 148244, 148245, 148246, 148247, 148248, 148249, 148250, 148251 ], "onlineresource_set": [ 43492, 43493, 52376, 82871, 88568, 89989, 89990, 89991, 89992, 89993, 89994, 89995, 89996, 89997, 89998, 89999, 90000, 90001, 90002, 90003, 90004, 90005, 90006, 90007, 90008, 90009, 90010, 90011, 90012, 90013, 90014, 94611 ] }, { "ob_id": 33201, "uuid": "80c10f8aeb7049778c5a15ede4917128", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.43 (v20211001)", "abstract": "Data for Figure 3.43 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 3.43 shows centred pattern correlations between models and observations for the annual mean climatology over the period 1980-1999. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains all correlation pattern values displayed in the figure.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n fig_3_43.nc:\r\n \r\n - variable: 'cor' with two dimensions:\r\n * 'vars': variables on the x-axis (same order as in the figure)\r\n * 'models': name of each models (the attribute 'project' contains mapping to 'CMIP3', 'CMIP5' or 'CMIP6')\r\n\r\n'cor' refers to the pattern correlation between models and observations.\r\nCMIP3 is the third phase of the Coupled Model Intercomparison Project.\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n \r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to the figure on the IPCC AR6 website.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:06:41", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 3, Human influence, large-scale indicators, Natural variability, anthropogenically-forced change, observed changes, Figure 3.43,pattern correlation", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-10-31T12:43:00", "doiPublishedTime": "2023-02-08T19:35:20.234889", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 64, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 33248, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig43/v20211001/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 41061, "numberOfFiles": 4, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 9121, "startTime": "1980-01-01T12:00:00", "endTime": "1999-12-31T12:00:00" }, "resultQuality": { "ob_id": 3768, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-10-01" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33203, "uuid": "41f3a263da774cf9a88ee7661df6ab72", "short_code": "comp", "title": "Caption for Figure 3.43 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Centred pattern correlations between models and observations for the annual mean climatology over the period 1980–1999. Results are shown for individual CMIP3 (cyan), CMIP5 (blue) and CMIP6 (red) models (one ensemble member from each model is used) as short lines, along with the corresponding ensemble averages (long lines). Correlations are shown between the models and the primary reference observational data set (from left to right: ERA5, GPCP-SG, CERES-EBAF, CERES-EBAF, CERES-EBAF, CERES-EBAF, JRA-55, ESACCI-SST, ERA5, ERA5, ERA5, ERA5, ERA5, ERA5, AIRS, ERA5). In addition, the correlation between the primary reference and additional observational datasets (from left to right: NCEP, GHCN, -, -, -, -, ERA5, HadISST, NCEP, NCEP, NCEP, NCEP, NCEP, NCEP, ERA5, NCEP) are shown (solid grey circles) if available. To ensure a fair comparison across a range of model resolutions, the pattern correlations are computed after regridding all datasets to a resolution of 4º in longitude and 5º in latitude. Figure is updated and expanded from Bock et al. (2020), their Figure 7 CC BY4.0 https://creativecommons.org/licenses/by/4.0/.. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 46724, 63640, 63641 ], "vocabularyKeywords": [], "identifier_set": [ 12389 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 148254, 148255, 148256, 148257, 148258, 148259, 148260, 148261 ], "onlineresource_set": [ 43494, 43495, 52398, 82624, 88594, 89729, 89730, 89731, 89732, 89733, 89734, 89735, 89736, 89737, 89738, 89739, 89740, 89741, 89742, 89743, 89744, 89745, 89746, 89747, 89748, 89749, 89750, 89751, 89752, 89753 ] }, { "ob_id": 33207, "uuid": "0e80e12edb7e4dc9b9219df77c0fe9d6", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.25 (v20211005)", "abstract": "Data for Figure 2.25 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 2.25 shows changes in permafrost temperature for 4 Arctic regions over the period 1974-2019 shown as average departures from the International Polar Year (2007-2009) baseline.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The dataset contains regional average departures (anomalies) of observed permafrost temperature relative to International Polar Year (2007-20909) baseline from 1974-2019 for 4 regions as defined in Romanovsky et al. (2017):\r\n \r\n - Permafrost temperature Barents region (1974-2019)\r\n - Permafrost temperature Baffin Bay Davis Strait region (1979-2019)\r\n - Permafrost temperature anomaly Beaufort-Chukchi Sea region (1978-2019)\r\n - Permafrost temperature Interior Alaska and Central Mackenzie Valley NWT (1983-2019)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 2.25\r\n Data file: Arctic_permafrost_temperature_anomaly.csv; (annual data, average regional anomalies) relates to green line (Barents), purple line (Baffin), blue line (Beaufort-Chukchi) and red line (Interior Alaska and Central Mackenzie Valley)\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to input data figure 2.25.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-05-15T11:01:58", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, Intergovernmental Panel on Climate Change, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 2, Changing state, Multi-millennial context, pre-industrial, Natural forcing, anthropogenic forcing, Radiative forcing, Large-scale indicators, observed changes, Modes of variability, Figure 2.25, Permafrost, Arctic, Ground Temperature", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "Based on point data Other", "status": "ongoing", "dataPublishedTime": "2023-05-15T10:29:52", "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": { "ob_id": 66, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "Based on point data" }, "result_field": { "ob_id": 39220, "dataPath": "/badc/ar6_wg1/data/ch_02/ch2_fig25/v20211005", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5874, "numberOfFiles": 4, "fileFormat": "BADC-CSV" }, "timePeriod": { "ob_id": 9125, "startTime": "1974-01-01T12:00:00", "endTime": "2019-12-31T12:00:00" }, "resultQuality": { "ob_id": 3770, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-10-05" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33209, "uuid": "0a5eb3c4c7564750875e4e1310b509e5", "short_code": "comp", "title": "Caption for Figure 2.25 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Changes in permafrost temperature. Average departures of permafrost temperature (measured in the upper 20–30 m) from a baseline established during International Polar Year (2007–2009) for Arctic regions. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 64247, 65394, 65395, 65396, 65397 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 32717, "uuid": "3da412ad9912427d9bb808b57faa21a7", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 2.2\r\n- data for Figure 2.4\r\n- data for Figure 2.5\r\n- data for Figure 2.6\r\n- data for Figure 2.9\r\n- data for Figure 2.11\r\n- input data for Figure 2.11\r\n- data for Figure 2.12\r\n- input data for Figure 2.12\r\n- data for Figure 2.13\r\n- input data for Figure 2.13\r\n- data for Figure 2.14\r\n- data for Figure 2.15\r\n- input data for Figure 2.15\r\n- input data for Figure 2.16\r\n- data for Figure 2.17\r\n- data for Figure 2.22\r\n- input data for Figure 2.23\r\n- data for Figure 2.25\r\n- input data for Figure 2.25\r\n- data for Figure 2.26\r\n- input data for Figure 2.27\r\n- data for Figure 2.28\r\n- input data for Figure 2.29\r\n- data for Figure 2.36\r\n- data for Figure 2.37\r\n- data for Figure 2.38\r\n- data for Cross-Chapter Box 2.1.1\r\n- data for Cross-Chapter Box 2.3.1" } ], "responsiblepartyinfo_set": [ 148277, 148278, 148279, 148280, 148281, 148282, 148283, 148284 ], "onlineresource_set": [ 43509, 43676, 52432, 82834 ] }, { "ob_id": 33218, "uuid": "5f331c418e9f4935b8eb1b836f8a91b8", "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017 and 2018, v3", "abstract": "This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017 and 2018. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. \r\n\r\nThis release of the data is version 3. Compared to version 2, this is a consolidated version of the Above Ground Biomass (AGB) maps. This version also includes a preliminary estimate of AGB changes for two epochs.\r\n\r\nThe data products consist of two (2) global layers that include estimates of:\r\n1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots\r\n2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)\r\n\r\nIn addition, files describing the AGB change between 2018 and the other two years are provided (labelled as 2018_2010 and 2018_2017). These consist of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.\r\n\r\nData are provided in both netcdf and geotiff format.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-10-11T15:47:10", "updateFrequency": "notPlanned", "dataLineage": "Data was created by the Biomass CCI team.", "removedDataReason": "", "keywords": "ESA, CCI, Biomass", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-11-25T12:12:01", "doiPublishedTime": "2021-11-26T13:38:42", "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": 33219, "dataPath": "/neodc/esacci/biomass/data/agb/maps/v3.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 187315069883, "numberOfFiles": 2916, "fileFormat": "Data are netCDF and geotiff formatted." }, "timePeriod": { "ob_id": 8795, "startTime": "2010-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3355, "explanation": "For information on the quality of the data see the documentation and website for the Biomass CCI", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2019-11-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 33268, "uuid": "c57edbf3a959498f9a7d7bcbbb951d59", "short_code": "cmppr", "title": "CCI Biomass v3.0", "abstract": "CCI Biomass" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" }, { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2542, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 20, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_biomass_terms_and_conditions_v2.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 29954, "uuid": "748552f369494506bc790c8a51854c38", "short_code": "proj", "title": "ESA CCI Biomass Climate Change Initiative (CCI)", "abstract": "The primary science objective ESA’s Climate Change Initiative + Biomass project is to provide global maps of above-ground biomass (Mg ha-1) with these being capable of supporting quantification of biomass change. The mapping aims to achieve 500 m to 1 km spatial resolution with a relative error of less than 20 % where AGB exceeds 50 Mg ha-1. Although this resolution is finer than required for current climate modelling, it will allow more refined information to be inferred (e.g. forest age structure and the disturbance regime) that is relevant for climate and has the potential to be exploited by carbon cycle and climate models as they develop." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 27612, 27613, 27614, 27615, 27616, 27617, 41232, 41233 ], "vocabularyKeywords": [], "identifier_set": [ 10964 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 148317, 148318, 148316, 148324, 148323, 148322, 148321, 148320, 148319, 148325 ], "onlineresource_set": [ 43513, 43516, 43517, 43514, 43515, 88933, 88934, 88924, 88925, 88926, 88927, 88928, 88929, 88930, 88931, 88932, 87643, 87644, 87787, 87948, 87954, 87955, 88113, 95062 ] }, { "ob_id": 33222, "uuid": "119a5d4795c94d2e94f610647640edc0", "title": "Monthly ocean and sea-ice output from 1/4° NEMO GO6 integration forced by CORE2 data", "abstract": "Monthly output from an integration of the GO6 configuration of the NEMO (Nucleus for European Modelling of the Ocean) ocean and sea-ice model, forced by the CORE2 (Coordinated Ocean-ice Reference Experiments version 2.0) corrected inter-annual forcing (CIAF) surface field dataset. UK Global Ocean GO6 consists of version 3.6 of NEMO and version 5.2.1 of the CICE (Community Ice CodE) sea-ice model, and the present simulation is on the global eORCA025 1/4° grid. The ocean is initialised from a climatology based on the EN3 monthly objective analysis (Ingleby and Huddleston, 2007) averaged over years 2004–2008, and is integrated from 1958 to 2007. The model was run on the Archer supercomputing platform through the Rose/Cylc interface on Puma, and the run ID on the Puma system is u-ap795.\r\nThe integrations were funded by the Natural Environment Research Council (NERC) under the Atlantic Climate System Integrated Study (ACSIS) project (NE/N018044/1).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-10-13T14:11:42", "updateFrequency": "notPlanned", "dataLineage": "Data were generated using a Nucleus for European Modelling of the Ocean (NEMO) based model run on Archer, the national HPC platform. Data were submitted to the British Oceanographic Data Centre (BODC, NOC) and then archived at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "Oceans, sea-ice, temperature, salinity", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "0.25°", "status": "completed", "dataPublishedTime": "2021-10-22T15:03:11", "doiPublishedTime": "2021-10-28T12:36:16", "removedDataTime": null, "geographicExtent": { "ob_id": 2868, "bboxName": "NEMO Global Grid", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 33220, "dataPath": "/bodc/SOC210128/CORE2-AP795", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2365963039496, "numberOfFiles": 3003, "fileFormat": "Data are CF-compliant NetCDF formatted" }, "timePeriod": { "ob_id": 9129, "startTime": "1958-01-01T00:00:00", "endTime": "2007-12-31T23:59:59" }, "resultQuality": { "ob_id": 3775, "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": "2021-10-12" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33221, "uuid": "5178b8c9b5a949a5875cc625ac17987b", "short_code": "comp", "title": "GO6 configuration of the NEMO (Nucleus for European Modelling of the Ocean) ocean and sea-ice model", "abstract": "This computation used the GO6 configuration of the NEMO model, which consists of version 3.6 of NEMO and version 5.2.1 of the CICE sea-ice model, on the global eORCA025 1/4° grid\r\nThe sea ice configuration is GSI8.." }, "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": [ 2638, 2639, 36269, 36270, 36324, 36331, 36334, 36337, 40492, 50623, 50624, 53098, 53099, 53100, 53101, 53102, 53103, 53104, 54847, 54897, 59484, 59485, 59486, 59487, 59488, 59489, 59490, 59491, 59492, 59493, 59494, 59495, 59496, 59497, 59498, 59499, 59500, 59501, 59503, 59507, 59510, 59512, 59513, 59514, 59515, 59516, 59517, 59518, 59519, 59520, 59522, 59523, 59525, 59526, 59527, 59528, 59529, 59530, 59531, 59532, 59534, 59535, 59536, 59538, 59539, 59540, 59545, 59547, 59549, 59550, 59551, 59552, 59553, 59554, 59555, 59556, 59557, 59558, 59559, 59563, 59564, 59565, 59566, 59569, 59570, 59572, 59573, 59575, 59596, 59597, 59599, 59600, 59605, 59610, 59611, 59612, 59613, 59614, 59615, 59617, 59618, 59619, 59620, 59622, 59624, 59625, 59626, 59637, 59641, 59646, 59647, 59670, 59698, 59780, 59791, 59792, 60131, 60135, 60139, 60146, 60148, 60149, 61861, 62270, 62271, 62272, 62273, 62274, 62275, 62277, 62278, 62279, 62280, 62281, 62282, 62283, 62284, 62285, 62286, 62287, 62288, 62289, 62290, 62291, 62292, 62293, 62294, 62295, 62296, 62297, 62298, 62299, 75401, 75410, 83907, 83908, 83909, 83910, 83911, 83912 ], "vocabularyKeywords": [], "identifier_set": [ 10958 ], "observationcollection_set": [ { "ob_id": 33256, "uuid": "770a885a8bc34d51ad71e87ef346d6a8", "short_code": "coll", "title": "The North Atlantic Climate System Integrated Study: model run output", "abstract": "ACSIS (the North Atlantic Climate System Integrated Study) was an integrated programme of sustained observations, synthesis, and numerical modelling designed to address the overarching objective of enhancing the UK's capability to detect, attribute and predict changes in the North Atlantic (NA) Climate System, comprising: the North Atlantic Ocean, the atmosphere above it including its composition, and interactions with Arctic Sea Ice and the Greenland Ice Sheet. ACSIS was a partnership between six NERC centres (NCAS, NOC, BAS, NCEO, CPOM, PML) and the UK Met Office, exploiting the partners' unique capabilities in observing and simulating the atmosphere including its composition, the ocean, the cryosphere, and the fully coupled climate system.\r\nThis collection includes global ocean simulations generated within the project.\r\nACSIS was funded by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) grant NE/N018028/1" } ], "responsiblepartyinfo_set": [ 148335, 148336, 148337, 148338, 148339, 148340, 148341, 148342, 148343, 148344 ], "onlineresource_set": [ 43519, 43520, 43526, 43536, 43527, 43537, 43518, 43549, 43640 ] }, { "ob_id": 33223, "uuid": "14fc02a823c142a49adb3194a0f0d1f6", "title": "NCEO Particulate Organic Carbon estimates from quality-controlled BGC-Argo data (2010-06-01 to 2021-09-22), V1.0", "abstract": "This is an in-situ dataset of estimates of particulate organic carbon (POC) based on all the current (2021-09-22) profiles of optical backscattering (BBP) collected by (BGC)Biogeochemical-Argo floats. The dataset spans from 2010-06-01 to 2021-09-22 and covers the upper 2000 dbars of the water column (continuous profiles have been binned in 41 vertical bins). The dataset was produced by first devising a new set of automatic tests to quality control the large BBP dataset available (>31M records over >130k profiles). The QCed BBP was then converted into POC using an empirical algorithm (average of the POC: BBP slopes of Cetinic et al., 2012)", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2021-10-12T09:48:37", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "NCEO, Particulate Organic Carbon, BGC-Argo", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-10-25T16:35:49", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2869, "bboxName": "POC BGC Argo", "eastBoundLongitude": 179.991, "westBoundLongitude": 179.985, "southBoundLatitude": 75.647, "northBoundLatitude": 78.759 }, "verticalExtent": null, "result_field": { "ob_id": 33224, "dataPath": "/neodc/oceanic_poc/data/bgc_argo/20100601_20210922_v1.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 60013770, "numberOfFiles": 2, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 9131, "startTime": "2010-06-01T00:00:00", "endTime": "2021-09-22T00:00:00" }, "resultQuality": { "ob_id": 3774, "explanation": "BBP data have been quality controlled to the best of PML's current capabilities. Conversion to POC is based on an empirical algorithm derived in the North Atlantic and therefore is expected to be affected by uncertainties of (100%)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-10-12" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33227, "uuid": "5a0dba8901934f60b6993a3d6eda4f1e", "short_code": "comp", "title": "Computation for Particulate Organic Carbon estimates from quality-controlled BGC-Argo data", "abstract": "The POC estimates were produces using an empirical algorithm (average of the POC:BBP slopes of described Cetinic et al., 2012. Details of algorithm and input BGC Argo data can be found in the documentation section." }, "procedureCompositeProcess": null, "imageDetails": [ 130 ], "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": 5002, "uuid": "60e718d3f2957f742c89b2b4fc159718", "short_code": "proj", "title": "National Centre for Earth Observation (NCEO)", "abstract": "The National Centre for Earth Observation is a partnership of scientists and institutions, from a range of disciplines, who are using data from Earth observation satellites to monitor global and regional changes in the environment and to improve understanding of the Earth system so that we can predict future environmental conditions.\r\n\r\nNCEO's Vision is to unlock the full potential of Earth observation to monitor, diagnose and predict climate and environmental changes, ensuring that these scientific advances are delivered to the wider community embedded in world class science." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 4385, 46846, 46847, 46848, 46849, 46850 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "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." } ], "responsiblepartyinfo_set": [ 148345, 148346, 148347, 148348, 148349, 148350, 148369, 148370 ], "onlineresource_set": [ 43524, 43525 ] }, { "ob_id": 33226, "uuid": "4c545155dfd145a1b02a5d0e577ae37d", "title": "Monthly ocean and sea-ice output from 1/4° NEMO GO6 integration forced by JRA55 data", "abstract": "Monthly output from an integration of the UK Global Ocean, GO6, configuration of the NEMO (Nucleus for European Modelling of the Ocean) ocean and sea-ice model, forced by the JRA-55 (Japanese 55-year atmospheric reanalysis: Tsujino, 2018) surface field dataset. GO6 consists of version 3.6 of NEMO and version 5.2.1 of the CICE (Community Ice CodE) sea-ice model, and the present simulation is on the global eORCA025 1/4° grid. The ocean is initialised from a climatology based on the EN3 monthly objective analysis (Ingleby and Huddleston, 2007) averaged over years 2004–2008, and is integrated from 1958 to 2020. The sea-ice fields are only available for the period 1989 to 2001. The model was run on the Archer supercomputing platform through the Rose/Cylc interface on Puma, and the run ID on the Puma system is u-ba494.\t\t\r\nThe integrations were funded by the Natural Environment Research Council (NERC) under the Atlantic Climate System Integrated Study (ACSIS) project (NE/N018044/1).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-08-06T22:52:14", "updateFrequency": "notPlanned", "dataLineage": "Data were generated using a Nucleus for European Modelling of the Ocean (NEMO) based model run on Archer, the national HPC platform. Data were submitted to the British Oceanographic Data Centre (BODC, NOC) and then archived at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "Oceans, sea-ice, temperature, salinity", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "0.25°", "status": "completed", "dataPublishedTime": "2021-10-22T15:03:30", "doiPublishedTime": "2021-10-28T12:36:09", "removedDataTime": null, "geographicExtent": { "ob_id": 2868, "bboxName": "NEMO Global Grid", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 33252, "dataPath": "/bodc/SOC210130/JRA55-BA494", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3215771465031, "numberOfFiles": 3176, "fileFormat": "CF Compliant NetcDF" }, "timePeriod": { "ob_id": 9134, "startTime": "1958-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3775, "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": "2021-10-12" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33221, "uuid": "5178b8c9b5a949a5875cc625ac17987b", "short_code": "comp", "title": "GO6 configuration of the NEMO (Nucleus for European Modelling of the Ocean) ocean and sea-ice model", "abstract": "This computation used the GO6 configuration of the NEMO model, which consists of version 3.6 of NEMO and version 5.2.1 of the CICE sea-ice model, on the global eORCA025 1/4° grid\r\nThe sea ice configuration is GSI8.." }, "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": [ 2638, 2639, 18124, 36269, 36270, 36324, 36331, 36334, 36337, 40492, 41071, 50571, 50612, 50616, 50623, 50624, 53098, 53099, 53100, 53101, 53102, 53103, 53104, 54483, 54847, 54897, 59484, 59485, 59486, 59487, 59488, 59489, 59490, 59491, 59492, 59493, 59494, 59495, 59496, 59497, 59498, 59499, 59500, 59501, 59503, 59507, 59510, 59512, 59513, 59514, 59515, 59516, 59517, 59518, 59519, 59520, 59522, 59523, 59525, 59526, 59527, 59528, 59529, 59530, 59531, 59532, 59534, 59535, 59536, 59538, 59539, 59540, 59545, 59547, 59549, 59550, 59551, 59552, 59553, 59554, 59555, 59556, 59557, 59558, 59559, 59563, 59564, 59565, 59566, 59569, 59570, 59572, 59573, 59575, 59596, 59597, 59599, 59600, 59605, 59610, 59611, 59612, 59613, 59614, 59615, 59617, 59618, 59619, 59620, 59622, 59624, 59625, 59626, 59637, 59638, 59639, 59640, 59641, 59642, 59643, 59644, 59645, 59646, 59648, 59649, 59651, 59652, 59653, 59654, 59655, 59656, 59657, 59658, 59659, 59660, 59661, 59662, 59663, 59664, 59665, 59667, 59668, 59669, 59670, 59671, 59673, 59674, 59675, 59676, 59677, 59678, 59679, 59680, 59681, 59682, 59683, 59684, 59685, 59686, 59687, 59688, 59689, 59690, 59691, 59692, 59693, 59694, 59695, 59696, 59697, 59698, 59771, 59772, 59773, 59774, 59775, 59777, 59778, 59779, 59780, 59781, 59782, 59785, 59786, 59787, 59788, 59789, 59790, 59791, 59792, 60131, 60135, 60146, 60148, 61861, 62270, 62271, 62273, 62274, 62275, 62277, 62278, 62279, 62280, 62281, 62282, 62283, 62285, 62286, 62287, 62290, 62291, 62292, 62293, 62294, 62295, 62296, 62297, 62298, 62299, 62307, 62308, 62310, 62311, 62312, 75373, 75399, 75401, 75404, 75410, 75414, 75427, 80924, 80932, 80935, 80937, 80943, 83907, 83908, 83909, 83911, 83912, 88551 ], "vocabularyKeywords": [], "identifier_set": [ 10957 ], "observationcollection_set": [ { "ob_id": 33256, "uuid": "770a885a8bc34d51ad71e87ef346d6a8", "short_code": "coll", "title": "The North Atlantic Climate System Integrated Study: model run output", "abstract": "ACSIS (the North Atlantic Climate System Integrated Study) was an integrated programme of sustained observations, synthesis, and numerical modelling designed to address the overarching objective of enhancing the UK's capability to detect, attribute and predict changes in the North Atlantic (NA) Climate System, comprising: the North Atlantic Ocean, the atmosphere above it including its composition, and interactions with Arctic Sea Ice and the Greenland Ice Sheet. ACSIS was a partnership between six NERC centres (NCAS, NOC, BAS, NCEO, CPOM, PML) and the UK Met Office, exploiting the partners' unique capabilities in observing and simulating the atmosphere including its composition, the ocean, the cryosphere, and the fully coupled climate system.\r\nThis collection includes global ocean simulations generated within the project.\r\nACSIS was funded by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) grant NE/N018028/1" } ], "responsiblepartyinfo_set": [ 148355, 148356, 148357, 148358, 148359, 148360, 148361, 148362, 148366, 148367 ], "onlineresource_set": [ 43530, 43531, 43521, 43522, 43538, 43539, 43557, 43641 ] }, { "ob_id": 33236, "uuid": "118104a74a5e460b8cea189c67558e0b", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.3 (v20210809)", "abstract": "Data for Figure SPM.3 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure SPM.3 shows the synthesis of assessed observed and attributable regional changes in hot extremes, heavy precipitation and agricultural and ecological droughts and confidence in human contribution to the observed changes in the world’s regions.\r\n---------------------------------------------------\r\nHow to cite this dataset\r\n---------------------------------------------------\r\nIPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001.\r\n\r\n---------------------------------------------------\r\nFigure subpanels\r\n---------------------------------------------------\r\nThe figure has three panels, with data provided for all panels in subdirectories named panel_a, panel_b and panel_c.\r\n---------------------------------------------------\r\nList of data provided\r\n---------------------------------------------------\r\nPanel a: Synthesis of assessment of observed change in hot extremes and confidence in human contribution to the observed changes in the AR6 land-regions, excluding Antarctica.\r\n\r\nPanel b: Synthesis of assessment of observed change in heavy precipitation and confidence in human contribution to the observed changes in the AR6 land-regions, excluding Antarctica.\r\n\r\nPanel c: Synthesis of assessment of observed change in agricultural and ecological drought and confidence in human contribution to the observed changes in the AR6 land-regions, excluding Antarctica.\r\n---------------------------------------------------\r\nData provided in relation to figure\r\n---------------------------------------------------\r\n\r\n·\tData file: panel_a/SPM3_panel_a.csv (AR6 world regions, observed change in hot extremes, confidence in human contribution); middle entry relates to the colour of the map, showing [increase] (red), [decrease](blue),[low agreement in type of change](white/grey),[limited data and/or literature](grey) .\r\n·\tData file: panel_b/SPM3_panel_b.csv (AR6 world regions, observed change in heavy precipitation, confidence in human contribution); middle entry relates to the colour of the map, showing [increase] (green), [decrease](yellow),[low agreement in type of change](white/grey),[limited data and/or literature](grey) .\r\n\r\n·\tData file: panel_c/SPM3_panel_c.csv (AR6 world regions, observed change in agricultural and ecological drought, confidence in human contribution); middle entry relates to the colour of the map, showing [increase] (yellow), [decrease](green),[low agreement in type of change](white/grey),[limited data and/or literature](grey) \r\n\r\n---------------------------------------------------\r\nSources of additional information\r\n---------------------------------------------------\r\nThe data in the files is an assessment of section 11.9 in chapter 11 that is provided in the second first two columns of the tables in that section.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-10-14T10:54:26", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, SPM, Sixth Assessment Report, Working Group I, Physical Science Basis, Summary for Policymakers, Figure SPM.3, Extremes, Chapter 11.", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-10-22T09:40:48", "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": 33237, "dataPath": "/badc/ar6_wg1/data/spm/spm_03/v20210809", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 17260, "numberOfFiles": 6, "fileFormat": "Data are BADC-CSV formatted" }, "timePeriod": { "ob_id": 9132, "startTime": "1950-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3722, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-07-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33238, "uuid": "4aebe150972142f1adc2932a40abb295", "short_code": "comp", "title": "Caption for Figure SPM.3 from the Summary for Policymakers of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "The IPCC AR6 WGI inhabited regions are displayed as hexagons with identical size in their approximate geographical location (see legend for regional acronyms). All assessments are made for each region as a whole and for the 1950s to the present. Assessments made on different time scales or more local spatial scales might differ from what is shown in the figure. The colours in each panel represent the four outcomes of the assessment on observed changes. White and light grey striped hexagons are used where there is low agreement in the type of change for the region as a whole and grey hexagons are used when there is limited data and/or literature that prevents an assessment of the region as a whole. Other colours indicate at least medium confidence in the observed change. The confidence level for the human influence on these observed changes is based on assessing trend detection and attribution and event attribution literature, and it is indicated by the number of dots: three dots for high confidence, two dots for medium confidence and one dot for low confidence (filled: limited agreement; empty: limited evidence). \r\n\r\nPanel a) For hot extremes, the evidence is mostly drawn from changes in metrics based on daily maximum temperatures; regional studies using other indices (heatwave duration, frequency and intensity) are used in addition. Red hexagons indicate regions where there is at least medium confidence in an observed increase in hot extremes. \r\n\r\nPanel b) For heavy precipitation, the evidence is mostly drawn from changes in indices based on one-day or five-day precipitation amounts using global and regional studies. Green hexagons indicate regions where there is at least medium confidence in an observed increase in heavy precipitation. \r\n\r\nPanel c) Agricultural and ecological droughts are assessed based on observed and simulated changes in total column soil moisture, complemented by evidence on changes in surface soil moisture, water balance (precipitation minus evapotranspiration) and indices driven by precipitation and atmospheric evaporative demand. Yellow hexagons indicate regions where there is at least medium confidence in an observed increase in this type of drought and green hexagons indicate regions where there is at least medium confidence in an observed decrease in agricultural and ecological drought. \r\n\r\nFor all regions, table TS.5 shows a broader range of observed changes besides the ones shown in this figure. Note that SSA is the only region that does not display observed changes in the metrics shown in this figure, but is affected by observed increases in mean temperature, decreases in frost, and increases in marine heatwaves. {11.9, Table TS.5, Box TS.10, Figure 1, Atlas 1.3.3, Figure Atlas.2}" }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 65386, 65387, 65388, 86919 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 148397, 148391, 148392, 148393, 148394, 148395, 148396, 148406, 148407, 168221, 148408 ], "onlineresource_set": [ 43534, 43535, 47469 ] }, { "ob_id": 33251, "uuid": "a0708d25b4fc44c5ab1b06e12fef2f2e", "title": "Monthly ocean and sea-ice output from 1/4° NEMO GO6 integration forced by DFS5.2 data", "abstract": "Monthly output from an integration of the UK Global Ocean, GO6, configuration of the NEMO (Nucleus for European Modelling of the Ocean) ocean and sea-ice model, forced by the DFS5.2 (Drakkar Forcing Set: Dussin et al, 2016) surface field dataset. GO6 consists of version 3.6 of NEMO and version 5.2.1 of the CICE (Community Ice CodE) sea-ice model, and the present simulation is on the global eORCA025 1/4° grid. The ocean is initialised from a climatology based on the EN3 monthly objective analysis (Ingleby and Huddleston, 2007) averaged over years 2004–2008, and is integrated from 1958 to 2015. The sea-ice fields are only available for the period 1958 to 2008. The model was run on the Archer supercomputing platform through the Rose/Cylc interface on Puma, and the run ID on the Puma system is u-ao882.\t\t\r\nThe integrations were funded by the Natural Environment Research Council (NERC) under the Atlantic Climate System Integrated Study (ACSIS) project (NE/N018044/1).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-08-18T16:59:23", "updateFrequency": "notPlanned", "dataLineage": "Data were generated using a Nucleus for European Modelling of the Ocean (NEMO) based model run on Archer, the national HPC platform. Data were submitted to the British Oceanographic Data Centre (BODC, NOC) and then archived at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "Oceans, sea-ice, temperature, salinity", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "0.25°", "status": "completed", "dataPublishedTime": "2021-10-22T15:03:45", "doiPublishedTime": "2021-10-28T12:36:03", "removedDataTime": null, "geographicExtent": { "ob_id": 2868, "bboxName": "NEMO Global Grid", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 33228, "dataPath": "/bodc/SOC210129/DFS5.2-AO882", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2755554548758, "numberOfFiles": 3397, "fileFormat": "CF-compliant NetCDF" }, "timePeriod": { "ob_id": 9133, "startTime": "1958-01-01T00:00:00", "endTime": "2015-12-31T23:59:59" }, "resultQuality": { "ob_id": 3775, "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": "2021-10-12" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33221, "uuid": "5178b8c9b5a949a5875cc625ac17987b", "short_code": "comp", "title": "GO6 configuration of the NEMO (Nucleus for European Modelling of the Ocean) ocean and sea-ice model", "abstract": "This computation used the GO6 configuration of the NEMO model, which consists of version 3.6 of NEMO and version 5.2.1 of the CICE sea-ice model, on the global eORCA025 1/4° grid\r\nThe sea ice configuration is GSI8.." }, "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": [ 2638, 2639, 36269, 36270, 36324, 36331, 36334, 36337, 40492, 50623, 50624, 53098, 53099, 53100, 53101, 53102, 53103, 53104, 54847, 54897, 59484, 59485, 59486, 59487, 59488, 59489, 59490, 59491, 59492, 59493, 59494, 59495, 59496, 59497, 59498, 59499, 59500, 59501, 59503, 59507, 59510, 59512, 59513, 59514, 59515, 59516, 59517, 59518, 59519, 59520, 59522, 59523, 59525, 59526, 59527, 59528, 59529, 59530, 59531, 59532, 59534, 59535, 59536, 59538, 59539, 59540, 59545, 59547, 59549, 59550, 59551, 59552, 59553, 59554, 59555, 59556, 59557, 59558, 59559, 59563, 59564, 59565, 59566, 59569, 59570, 59572, 59573, 59575, 59596, 59597, 59599, 59600, 59605, 59610, 59611, 59612, 59613, 59614, 59615, 59617, 59618, 59619, 59620, 59622, 59624, 59625, 59626, 59637, 59641, 59646, 59647, 59670, 59698, 59780, 59791, 59792, 60131, 60135, 60139, 60142, 60146, 60148, 60149, 61861, 62270, 62271, 62272, 62273, 62274, 62275, 62276, 62277, 62278, 62279, 62280, 62281, 62282, 62283, 62284, 62285, 62286, 62287, 62288, 62289, 62290, 62291, 62292, 62293, 62294, 62295, 62296, 62297, 62298, 62299 ], "vocabularyKeywords": [], "identifier_set": [ 10956 ], "observationcollection_set": [ { "ob_id": 33256, "uuid": "770a885a8bc34d51ad71e87ef346d6a8", "short_code": "coll", "title": "The North Atlantic Climate System Integrated Study: model run output", "abstract": "ACSIS (the North Atlantic Climate System Integrated Study) was an integrated programme of sustained observations, synthesis, and numerical modelling designed to address the overarching objective of enhancing the UK's capability to detect, attribute and predict changes in the North Atlantic (NA) Climate System, comprising: the North Atlantic Ocean, the atmosphere above it including its composition, and interactions with Arctic Sea Ice and the Greenland Ice Sheet. ACSIS was a partnership between six NERC centres (NCAS, NOC, BAS, NCEO, CPOM, PML) and the UK Met Office, exploiting the partners' unique capabilities in observing and simulating the atmosphere including its composition, the ocean, the cryosphere, and the fully coupled climate system.\r\nThis collection includes global ocean simulations generated within the project.\r\nACSIS was funded by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) grant NE/N018028/1" } ], "responsiblepartyinfo_set": [ 148435, 148436, 148437, 148438, 148439, 148440, 148441, 148445, 148446, 148447 ], "onlineresource_set": [ 43550, 43551, 43552, 43553, 43554, 43555, 43556, 43642 ] }, { "ob_id": 33257, "uuid": "02fd1d886bad40f3bb2eef3271900823", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 2.13 (v20211022)", "abstract": "Input Data for Figure 2.13 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 2.13 shows the global trends in surface specific humidity and surface relative humidity over 1973-2019\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with input data provided for panels (a) HadISDH_blendq_1_0_0_2019f.nc and (c) HadISDH_blendRH_1_0_0_2019f.nc \r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains global maps of specific and relative humidity trends over 1973-2019\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n This dataset is the input data used in the code that generates panel (a) and panel (c) for figure 2.13 \r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The following changes to filenames were made to archive the data (due to filenaming restrictions). To use the data with any associated figure code, the filenames should be reverted.\r\n\r\n HadISDH_blendq_1_0_0_2019f.nc -> HadISDH.blendq.1.0.0.2019f.nc \r\n HadISDH_blendRH_1_0_0_2019f.nc -> HadISDH.blendRH.1.0.0.2019f.nc \r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to the figure on the IPCC AR6 website", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:05:28", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, Intergovernmental Panel on Climate Change, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 2, Changing state, Multi-millennial context, pre-industrial, Natural forcing, anthropogenic forcing, Radiative forcing, Large-scale indicators, observed changes, Modes of variability, Figure 2.13, global specific humidity, global relative humidity", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "5° Degrees", "status": "completed", "dataPublishedTime": "2022-07-15T08:31:55", "doiPublishedTime": "2023-07-04T14:25:11", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 68, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "5°" }, "result_field": { "ob_id": 37674, "dataPath": "/badc/ar6_wg1/data/ch_02/inputdata_ch2_fig13/v20211022", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 81050071, "numberOfFiles": 5, "fileFormat": "txt, netCDF" }, "timePeriod": { "ob_id": 9135, "startTime": "1973-01-01T12:00:00", "endTime": "2019-12-31T12:00:00" }, "resultQuality": { "ob_id": 3777, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-10-22" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33259, "uuid": "847e841295024995b6d3be4ce9268c58", "short_code": "comp", "title": "Caption for Figure 2.13 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Changes in surface humidity. Changes in surface humidity. (a) Trends in surface specific humidity over 1973–2019. Trends are calculated using OLS regression with significance assessed following AR(1) adjustment after Santer et al. (2008) ; ‘x’ marks denote non-significant trends). (b) Global average surface specific humidity annual anomalies (1981–2010 base period). (c) as (a) but for the relative humidity. (d) as (b) but for the global average surface relative humidity annual anomalies. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50542, 50543, 60438, 63772, 63773, 63774, 63775, 63776, 63777, 63778, 63779, 63780, 63781, 63782, 63783, 63784, 63785, 63786, 63787, 63788, 63789, 63790, 63791, 63792, 63793, 63794, 63795, 63796, 63797, 63798, 63799, 63800, 63801, 63802, 63803, 63804 ], "vocabularyKeywords": [], "identifier_set": [ 12625 ], "observationcollection_set": [ { "ob_id": 32717, "uuid": "3da412ad9912427d9bb808b57faa21a7", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 2.2\r\n- data for Figure 2.4\r\n- data for Figure 2.5\r\n- data for Figure 2.6\r\n- data for Figure 2.9\r\n- data for Figure 2.11\r\n- input data for Figure 2.11\r\n- data for Figure 2.12\r\n- input data for Figure 2.12\r\n- data for Figure 2.13\r\n- input data for Figure 2.13\r\n- data for Figure 2.14\r\n- data for Figure 2.15\r\n- input data for Figure 2.15\r\n- input data for Figure 2.16\r\n- data for Figure 2.17\r\n- data for Figure 2.22\r\n- input data for Figure 2.23\r\n- data for Figure 2.25\r\n- input data for Figure 2.25\r\n- data for Figure 2.26\r\n- input data for Figure 2.27\r\n- data for Figure 2.28\r\n- input data for Figure 2.29\r\n- data for Figure 2.36\r\n- data for Figure 2.37\r\n- data for Figure 2.38\r\n- data for Cross-Chapter Box 2.1.1\r\n- data for Cross-Chapter Box 2.3.1" } ], "responsiblepartyinfo_set": [ 148460, 148461, 148462, 148463, 148464, 148465, 148466, 148467, 148468, 196292 ], "onlineresource_set": [ 43564, 43563, 82843 ] }, { "ob_id": 33260, "uuid": "8ec2d4b94f8e4756ad31858ff8256464", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 2.15 (v20211022)", "abstract": "Input data for Figure 2.15 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 2.15 provides global precipitation trend maps and time series for a variety of data sources\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has six panels, with input data provided for panel (a) (cru_masked_2019_2), panels (b) and (e) (gpcc_v2020_msk2.nc), panel (d) (cru_masked_2019_2.nc), and panel (f) (gpcp2019.nc)\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains observed global precipitation data from a variety of sources covering the period 1891-2019\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n This dataset is the input data used in the code that generates panels (a), (b), (d), (e) and (f) for figure 2.15. \r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to the figure on the IPCC AR6 website", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-06-30T15:25:41", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, Intergovernmental Panel on Climate Change, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 2, Changing state, Multi-millennial context, pre-industrial, Natural forcing, anthropogenic forcing, Radiative forcing, Large-scale indicators, observed changes, Modes of variability, Figure 2.15, global precipitation", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "2.5 Degrees", "status": "completed", "dataPublishedTime": "2022-07-15T08:32:09", "doiPublishedTime": "2023-07-04T14:37:17", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 69, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "2.5°" }, "result_field": { "ob_id": 37675, "dataPath": "/badc/ar6_wg1/data/ch_02/inputdata_ch2_fig15/v20211022", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 267591347, "numberOfFiles": 6, "fileFormat": "txt, netCDF" }, "timePeriod": { "ob_id": 9136, "startTime": "1891-01-01T12:00:00", "endTime": "2019-12-31T12:00:00" }, "resultQuality": { "ob_id": 3778, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-10-22" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33262, "uuid": "3396acfa8103486eb7a86024cfe9c1f3", "short_code": "comp", "title": "Caption for Figure 2.15 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Changes in observed precipitation. (a, b) Spatial variability of observed precipitation trends over land for 1901–2019 for two global in-situ products. Trends are calculated using OLS regression with significance assessed following AR(1) adjustment after Santer et al. (2008) (‘x’ marks denote non-significant trends). (c) Annual time series and decadal means from 1891 to date relative to a 1981–2010 climatology (note that different products commence at distinct times). (d, e) as (a, b), but for the periods starting in 1980. (f) is for the same period for the globally complete merged GPCP v2.3 product. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1633, 6019, 6020, 6645, 6646, 9042, 9043, 11960, 19309, 49480, 49481, 49482 ], "vocabularyKeywords": [], "identifier_set": [ 12626 ], "observationcollection_set": [ { "ob_id": 32717, "uuid": "3da412ad9912427d9bb808b57faa21a7", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 2.2\r\n- data for Figure 2.4\r\n- data for Figure 2.5\r\n- data for Figure 2.6\r\n- data for Figure 2.9\r\n- data for Figure 2.11\r\n- input data for Figure 2.11\r\n- data for Figure 2.12\r\n- input data for Figure 2.12\r\n- data for Figure 2.13\r\n- input data for Figure 2.13\r\n- data for Figure 2.14\r\n- data for Figure 2.15\r\n- input data for Figure 2.15\r\n- input data for Figure 2.16\r\n- data for Figure 2.17\r\n- data for Figure 2.22\r\n- input data for Figure 2.23\r\n- data for Figure 2.25\r\n- input data for Figure 2.25\r\n- data for Figure 2.26\r\n- input data for Figure 2.27\r\n- data for Figure 2.28\r\n- input data for Figure 2.29\r\n- data for Figure 2.36\r\n- data for Figure 2.37\r\n- data for Figure 2.38\r\n- data for Cross-Chapter Box 2.1.1\r\n- data for Cross-Chapter Box 2.3.1" } ], "responsiblepartyinfo_set": [ 148473, 148474, 148475, 148476, 148477, 148470, 148471, 148472, 148779, 196293 ], "onlineresource_set": [ 43565, 43566, 82844 ] }, { "ob_id": 33264, "uuid": "b735718d66c1403fbf6b93ba3bd3b1a9", "title": "A-CURE: Monthly mean perturbed parameter ensemble data", "abstract": "A-CURE was a NERC funded project that tackled one of the most challenging and persistent problems in atmospheric science – understanding and quantifying how changes in aerosol particles caused by anthropogenic activities affect climate. The data here are monthly mean variable data from a large perturbed parameter ensemble of UKESM1 simulations, nudged to horizontal winds above around 2km. Each variable has 220 or 221 members, as indicated in file names. Some months have one fewer member because a model variant repeatedly did not run to completion due to combined model parameter values. The 221 members are model variants that combine the effects of 54 aerosol and physical atmosphere parameters. Variable data in this ensemble span the uncertainty in UKESM1 from these parametric sources.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-09-11T13:15:09", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).\r\n\r\nData were generated using the Met Office UK Earth System Model 1 (UKESM1) at N96 horizontal resolution. Data from field files were converted to postprocessed (pp) files on the ARCHER HPC. Variable data from each model variant was extracted from pp files on the JASMIN HPC and collated into individual monthly mean files for each variable.", "removedDataReason": "", "keywords": "aerosol, climate, atmosphere, emissions, ppe", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-11-18T14:36:58", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2872, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 33269, "dataPath": "/badc/a-cure/data/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 89335134563, "numberOfFiles": 110, "fileFormat": "The data are in NetCDF format." }, "timePeriod": { "ob_id": 9137, "startTime": "2016-12-01T00:00:00", "endTime": "2017-11-01T23:59:59" }, "resultQuality": { "ob_id": 3779, "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": "2021-10-25" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33265, "uuid": "648ba0d02e03494192d9b2b0cd10707f", "short_code": "comp", "title": "Met Office United Kingdom Earth System Model 1 (UKESM1)", "abstract": "Met Office United Kingdom Earth System Model 1 (UKESM1) on the ARCHER HPC platform, submitted via pumatest." }, "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": [ { "ob_id": 33263, "uuid": "28ade693804346e69ecb071ef2517ba8", "short_code": "proj", "title": "A-CURE: The Aerosol-Climate Uncertainty REduction Project", "abstract": "A-CURE tackles one of the most challenging and persistent problems in atmospheric science - to understand and quantify how changes in aerosol particles caused by human activities affect climate. Emissions of aerosol particles to the atmosphere through industrial activity, transport and combustion of waste have increased the amount of solar radiation reflected by the Earth, which has caused a cooling effect that partly counteracts the warming effect of greenhouse gases. The magnitude of the so-called aerosol radiative forcing is highly uncertain over the industrial period. According to the latest intergovernmental panel (IPCC) assessment, the global mean radiative forcing of climate caused by aerosol emissions over the industrial period lies between 0 and -2 W m-2 compared to a much better understood and tighter constrained forcing of 1.4 W m-2 to 2.2 W m-2 due to CO2 emissions. This large uncertainty has persisted through all IPCC assessments since 1996 and significantly limits our confidence in global climate change projections. The aerosol uncertainty therefore limits our ability to define strategies for reaching a 1.5 or 2oC target for global mean temperature increase.\r\n\r\nA-CURE aims to reduce the uncertainty in aerosol radiative forcing through the most comprehensive ever synthesis of aerosol, cloud and atmospheric radiation measurements combined with innovative ways to analyse global model uncertainty. The overall approach will be to produce a large set of model simulations that spans the uncertainty range of the model input parameters. Advanced statistical methods will then be used to generate essentially millions of model simulations that enable the full uncertainty of the model to be explored. The spread of these simulations will then be narrowed by comparing the simulated aerosols and clouds against extensive atmospheric measurements. \r\n\r\nFollowing A-CURE, improved estimates of aerosol forcing on regional and global scales will enable substantial improvements in our understanding of historical climate, climate sensitivity and climate projections. We will use the improved climate model with narrowed uncertainty to determine the implications for reaching either a 1.5 or 2oC target for global mean temperature increase." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 19043, 21990, 22005, 51186, 51187, 54875, 62353, 67757, 67758, 67759, 67760, 67761, 67762, 67763, 67764, 67765, 67766, 67767, 67768, 67769, 67770, 67771, 67772, 67773, 67774, 67775, 67776, 67777, 67778, 67779, 67780, 67781, 67782, 67783, 67784, 67785, 67786, 67787, 67788, 67789, 67790, 67791, 67792, 67793, 67794, 67795, 67796, 67797, 67798, 67799, 67800, 67801, 67802, 67803, 67804, 67805, 67806, 67807, 67808, 67809, 67810, 67811, 67812 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 148486, 148495, 148496, 148497, 148498, 148499, 148501, 148494, 148487, 148500, 148558, 148488, 148489, 148490, 148491, 148492, 148493 ], "onlineresource_set": [] }, { "ob_id": 33272, "uuid": "b0cf5a39647e4036bd5351274c5daa5f", "title": "Improved high-resolution ocean reanalysis of Met Office GloSea5 data using the simple smoother algorithm, v1.0, June 2016", "abstract": "The dataset is a Global ¼° 0-2000m ocean reanalysis for June 2016, providing temperature and salinity data. The product is based on Met Office GloSea5 reanalysis (Blockley et al. 2014), with the simple smoother algorithm applied to improve the original reanalysis. The simple smoother is designed for smooth data adjustments in sequentially generated reanalysis products by utilizing knowledge of future assimilation increments. A decay time parameter is applied to the smoother increments to account for memory decay timescales in the ocean. Further information is available in the documentation section.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-10-25T16:51:23", "updateFrequency": "", "dataLineage": "Data were prepared by the NCEO DA team at the University of Reading and provided to CEDA for archival.", "removedDataReason": "", "keywords": "ocean reanalysis, smnoother, temperature, salinity", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-10-29T12:25:59", "doiPublishedTime": "2021-10-29T12:49:19", "removedDataTime": null, "geographicExtent": { "ob_id": 2873, "bboxName": "Global - smoother reanalysis", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 33270, "dataPath": "/neodc/ocean_reanalysis/smoother/data/v1.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 31328941517, "numberOfFiles": 32, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 9138, "startTime": "2016-06-01T00:00:00", "endTime": "2016-06-30T00:00:00" }, "resultQuality": { "ob_id": 3780, "explanation": "Data were validated by the NCEO DA project team at the University of Reading", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-10-25" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33271, "uuid": "9079ba8ed02144779cf343dc0b3f6d16", "short_code": "comp", "title": "Improved Global ocean reanalysis using the smoother algorithm v1.0 June 2016", "abstract": "Met Office GloSea5 reanalysis data were processed on the Met Office Monsoon supercomputer using the smoother algorithm. Please see the documentation section for further information" }, "procedureCompositeProcess": null, "imageDetails": [ 130 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2521, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: 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": 5002, "uuid": "60e718d3f2957f742c89b2b4fc159718", "short_code": "proj", "title": "National Centre for Earth Observation (NCEO)", "abstract": "The National Centre for Earth Observation is a partnership of scientists and institutions, from a range of disciplines, who are using data from Earth observation satellites to monitor global and regional changes in the environment and to improve understanding of the Earth system so that we can predict future environmental conditions.\r\n\r\nNCEO's Vision is to unlock the full potential of Earth observation to monitor, diagnose and predict climate and environmental changes, ensuring that these scientific advances are delivered to the wider community embedded in world class science." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 52949, 70762, 70763 ], "vocabularyKeywords": [], "identifier_set": [ 10959 ], "observationcollection_set": [ { "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." } ], "responsiblepartyinfo_set": [ 148509, 148510, 148511, 148512, 148513, 148514, 148515, 148516, 148517, 148518 ], "onlineresource_set": [ 43567, 43568, 43635 ] }, { "ob_id": 33275, "uuid": "6a6ccbb8ef2645308a60dc47e9b8b5fb", "title": "BICEP / NCEO: Monthly global Phytoplankton Carbon, between 1998-2020 at 9 km resolution (derived from the Ocean Colour Climate Change Initiative v5.0 dataset)", "abstract": "This dataset contains monthly global carbon products for pico-, nano- and microphytoplankton (C_picophyto, C_nanophyto and C_microphyto, respectively, in mg C m-3) and the total phytoplankton community (C_phyto in mg C m-3) for the period of 1998 to 2020 at 9 km spatial resolution.\r\n\r\nA spectrally-resolved photoacclimation model was unified with a primary production model that simulated photosynthesis as a function of irradiance using a two-parameter photosynthesis versus irradiance (P-I) function to estimate the carbon content of marine phytoplankton based on ocean-colour remote sensing products (Sathyendranath et al. 2020 and references therein for details). The photoacclimation model contains a maximum chlorophyll-to-carbon ratio for three different phytoplankton size classes (pico-, nano- and microphytoplankton) that was inferred from field data, as in Sathyendranath et al. (2020). Chlorophyll-a products were obtained from the European Space Agency (ESA) Ocean Colour Climate Change Initiative (OC-CCI v5.0 dataset). Photosynthetic Active Radiation (PAR) products were obtained from the National Aeronautics and Space Administration (NASA) and were corrected for inter-sensor bias in products. Mixed Layer Depth (MLD) was obtained from the French Research Institute for Exploration of the Sea (Ifremer). In situ datasets P-I parameters were incorporated as described in Kulk et al. (2020). \r\n\r\nThe phytoplankton carbon products were generated as part of the ESA Biological Pump and Carbon Exchange Processes (BICEP) project. Support from the Simons Foundation grant ‘Computational Biogeochemical Modeling of Marine Ecosystems’ (CBIOMES, number 549947) and from the National Centre of Earth Observation (NCEO) is acknowledged.\r\n\r\nData are provided as netCDF files containing carbon products for pico-, nano- and microphytoplankton (C_picophyto, C_nanophyto and C_microphyto, respectively, in mg C m-3) and the total phytoplankton community (C_phyto in mg C m-3) for the period of 1998 to 2020 at 9 km spatial resolution. Additional variables that were used for the calculation of the phytoplankton carbon products are also provided, including chlorophyll-a (chl_a in mg m-3), photosynthetically activate radiation (par, in µmol photons m-2 d-1), mixed layer depth (mld in m) and the mean spectral nondimensional irradiance (mean_spectral_i_star).\r\n\r\nReferences:\r\n\r\nSathyendranath, S.; Platt, T.; Kovač, Ž.; Dingle, J.; Jackson, T.; Brewin, R.J.W.; Franks, P.; Marañón, E.; Kulk, G.; Bouman, H.A. Reconciling models of primary production and photoacclimation. Applies Optics, 2020, 59, C100. doi.org/10.1364/AO.386252\r\n\r\nKulk, G.; Platt, T.; Dingle, J.; Jackson, T.; Jönsson, B.F.; Bouman, H.A., Babin, M.; Doblin, M.; Estrada, M.; Figueiras, F.G.; Furuya, K.; González, N.; Gudfinnsson, H.G.; Gudmundsson, K.; Huang, B.; Isada, T.; Kovač, Ž.; Lutz, V.A.; Marañón, E.; Raman, M.; Richardson, K.; Rozema, P.D.; Van de Poll, W.H.; Segura, V.; Tilstone, G.H.; Uitz, J.; van Dongen-Vogels, V.; Yoshikawa, T.; Sathyendranath S. Primary production, an index of climate change in the ocean: Satellite-based estimates over two decades. Remote Sens. 2020, 12,826. doi:10.3390/rs12050826", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-11-18T18:10:50", "updateFrequency": "", "dataLineage": "Data were produced by the Plymouth Marine Laboratory and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). The research underpinning the work was supported by the European Space Agency (ESA) Biological Pump and Carbon Export Processes (BICEP) project and the product generation was supported by the National Centre for Earth Observation (NCEO). \r\n\r\nFurther details on the used methods can be found in Sathyendranath et al. (2020).\r\n\r\nSathyendranath, S.; Platt, T.; Kovač, Ž.; Dingle, J.; Jackson, T.; Brewin, R.J.W.; Franks, P.; Marañón, E.; Kulk, G.; Bouman, H.A. Reconciling models of primary production and photoacclimation. Applies Optics, 2020, 59, C100. doi.org/10.1364/AO.386252", "removedDataReason": "", "keywords": "phytoplankton, carbon, ocean colour, remote sensing", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-12-21T14:14:59", "doiPublishedTime": "2021-12-22T15:42:03", "removedDataTime": null, "geographicExtent": { "ob_id": 2875, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 33321, "dataPath": "/neodc/bicep/data/phytoplankton_carbon/v5.0/monthly/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 164841495511, "numberOfFiles": 277, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 9140, "startTime": "1998-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3784, "explanation": "The satellite-based estimates of phytoplankton carbon are consistent with in situ data (Figure 7, Sathyendranath et al. 2020) and the theoretical model used to generate the products compared well with results from earlier published empirical models (Figure 8, Sathyendranath et al. 2020). Further details are provided in Sathyendranath et al. (2020).", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-10-26" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33276, "uuid": "47f37aea9c174706a348b692669e8b02", "short_code": "comp", "title": "Marine phytoplankton carbon", "abstract": "A spectrally-resolved photoacclimation model was unified with a primary production model that simulated photosynthesis as a function of irradiance using a two-parameter photosynthesis versus irradiance (P-I) function to estimate the carbon content of marine phytoplankton based on ocean-colour remote sensing products (Sathyendranath et al. 2020 and references therein for details). The photoacclimation model contains a maximum chlorophyll-to-carbon ratio for three different phytoplankton size classes (pico-, nano- and microphytoplankton) that was inferred from field data. Chlorophyll-a products were obtained from the European Space Agency (ESA) Ocean Colour Climate Change Initiative (OC-CCI v5). Photosynthetic Active Radiation (PAR) products were obtained from the National Aeronautics and Space Administration (NASA) and were corrected for inter-sensor bias in products. Mixed Layer Depth (MLD) was obtained from the French Research Institute for Exploration of the Sea (Ifremer). In situ datasets of chlorophyll-a profile parameters and P-I parameters were incorporated as described in Kulk et al. (2020)\r\n\r\n\r\nSathyendranath, S.; Platt, T.; Kovač, Ž.; Dingle, J.; Jackson, T.; Brewin, R.J.W.; Franks, P.; Marañón, E.; Kulk, G.; Bouman, H.A. Reconciling models of primary production and photoacclimation. Applies Optics, 2020, 59, C100. doi.org/10.1364/AO.386252\r\n\r\nKulk, G.; Platt, T.; Dingle, J.; Jackson, T.; Jönsson, B.F.; Bouman, H.A., Babin, M.; Doblin, M.; Estrada, M.; Figueiras, F.G.; Furuya, K.; González, N.; Gudfinnsson, H.G.; Gudmundsson, K.; Huang, B.; Isada, T.; Kovac, Z.; Lutz, V.A.; Marañón,\r\nE.; Raman, M.; Richardson, K.; Rozema, P.D.; Van de Poll, W.H.; Segura, V.; Tilstone, G.H.; Uitz, J.; van Dongen-Vogels, V.; Yoshikawa, T.; Sathyendranath S. Primary production, an index of climate change in the ocean: Satellite-based estimates over two decades. Remote Sens. 2020, 12, 826. doi:10.3390/rs12050826" }, "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": [ { "ob_id": 5002, "uuid": "60e718d3f2957f742c89b2b4fc159718", "short_code": "proj", "title": "National Centre for Earth Observation (NCEO)", "abstract": "The National Centre for Earth Observation is a partnership of scientists and institutions, from a range of disciplines, who are using data from Earth observation satellites to monitor global and regional changes in the environment and to improve understanding of the Earth system so that we can predict future environmental conditions.\r\n\r\nNCEO's Vision is to unlock the full potential of Earth observation to monitor, diagnose and predict climate and environmental changes, ensuring that these scientific advances are delivered to the wider community embedded in world class science." }, { "ob_id": 31968, "uuid": "cd161a8305384eb38109036a74d7e2b9", "short_code": "proj", "title": "ESA Biological Pump and Carbon Export Processes (BICEP) Project", "abstract": "The ESA Biological Pump and Carbon Export Processes (BICEP) project is an ESA project led by the Plymouth Marine Laboratory. The objective of the BICEP project is to further advance our capacity to better characterise the different components of the ocean biological carbon pump, its pools and fluxes, its variability in space and time and the understanding of its processes and interactions with the earth system, from a synergetic use of space data, in-situ measurements and model outputs.\r\n\r\nThe development of the BICEP datasets was also supported the Simons Foundation grant 'Computational Biogeochemical Modeling of Marine Ecosystems' (CBIOMES, number 549947)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50416, 69036, 69037, 70809, 70810, 90463, 90464, 90465, 90466, 90467, 90468 ], "vocabularyKeywords": [], "identifier_set": [ 10975 ], "observationcollection_set": [ { "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": [ 148530, 148531, 148532, 148533, 148534, 148535, 148536, 149467, 148537, 148538, 148830, 148539, 148540, 148541, 148542, 148543 ], "onlineresource_set": [ 43572, 94777, 94778 ] }, { "ob_id": 33277, "uuid": "d6a2c152a6d144099cd7710298554942", "title": "MRF A257 flight: Airborne atmospheric and chemistry measurements taken on board the Met Office C-130 Hercules aircraft", "abstract": "The Meteorological Research Flight (MRF) was a Met Office facility, which flew a well-instrumented C-130 Hercules aircraft for atmospheric research purposes.\r\n\r\nThis dataset contains airborne atmospheric and chemistry measurements taken on board the Met Office C-130 Hercules aircraft flight A257 for research purposes.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-10-29T12:02:07", "updateFrequency": "notPlanned", "dataLineage": "Data collected by instruments on-board the MRF C-130 during flight A257. Data acquired from the Met Office by BADC for wider academic use.", "removedDataReason": "", "keywords": "Met Office, atmosphere, chemistry, airborne, C-130", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-10-29T12:17:21", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2876, "bboxName": "a257 flight", "eastBoundLongitude": -0.7777, "westBoundLongitude": -3.08724, "southBoundLatitude": 51.28335, "northBoundLatitude": 58.45334 }, "verticalExtent": null, "result_field": { "ob_id": 33278, "dataPath": "/badc/ukmo-mrf/data/a257", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 163070431, "numberOfFiles": 7, "fileFormat": "Data are binary formatted." }, "timePeriod": { "ob_id": 9141, "startTime": "1993-05-27T10:05:05", "endTime": "1993-05-27T16:17:03" }, "resultQuality": { "ob_id": 108, "explanation": "Data quality controlled by the Met Office.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2012-11-19" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 33279, "uuid": "2130bca5ec58478ea3fbc530f76fd9a6", "short_code": "acq", "title": "MRF Flight A257", "abstract": "MRF Flight A257" }, "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": 19356, "uuid": "640ebc68a38a4c44915fe4807b8dd00e", "short_code": "proj", "title": "Met Office Met. Research Flight (MRF) C-130 Sorties", "abstract": "The Met. Research Flight (MRF) was a Met Office facility, which operated a well instrumented C-130 Hercules (also referred to as Mk.2 Hercules) aircraft for research purposes. The C-130 was in service from 1972 to 2001 and flew over 1800 research sorties. The large capacity and long endurance of this platform made it ideal for atmospheric research in the areas of cloud physics, atmospheric radiation, atmospheric chemistry, satellite activities, mesoscale meteorology and boundary layer studies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 431, "uuid": "605b99a139b1162de6a1096a11b0a8b4", "short_code": "coll", "title": "Met Office Met. Research Flight C-130: Airborne atmospheric measurements", "abstract": "The Met. Research Flight (MRF) was a Met Office facility, which operated a well instrumented C-130 Hercules (also referred to as Mk.2 Hercules) aircraft for research purposes. The C-130 was in service from 1972 to 2001 and flew over 1800 research sorties. The large capacity and long endurance of this platform made it ideal for atmospheric research in the areas of cloud physics, atmospheric radiation, atmospheric chemistry, satellite activities, mesoscale meteorology and boundary layer studies.\r\n\r\nThe BADC holds data collected by the C-130 during NERC (Natural Environment Research Council) funded flights, such as those made during ACSOE (Atmospheric Chemistry Studies in the Oceanic Environment) and UTLS (Upper Troposphere - Lower Stratosphere) projects. The basic set of measurements include ozone, nitrogen oxides, water vapour, aerosols, wind, position and temperature. These are often supplemented by project specific measurements.\r\n\r\nThe aircraft was able to operate scientifically throughout the troposphere from a minimum altitude of 15 m (50 ft) where permitted, up to a maximum of 10 km. The aircraft had a maximum working flight time of 12 hours.\r\n\r\nThe C-130 was taken out of service in March 2001 and a new joint NERC-Met Office Facility for Airborne Aircraft Measurements (FAAM) was established operating a BAe-146-301 aircraft." } ], "responsiblepartyinfo_set": [ 148589, 148590, 148591, 148592, 148593, 148594, 148595, 148596, 148597 ], "onlineresource_set": [ 43573, 43575 ] }, { "ob_id": 33291, "uuid": "43b0c376ad184543a1bbceeceec0e85d", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.41 (v20211028)", "abstract": "Data for Figure 3.41 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 3.41 is a summary figure showing simulated and observed changes in key large-scale indicators of climate change across the climate system, for continental, ocean basin and larger scales. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The data of each panel is provided in a single file.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This datasets contains global and regional anomaly time-series for:\r\n \r\n - near-surface air temperature (1850-2020)\r\n - precipitation (1950-2014)\r\n - sea ice extent (1979-2014)\r\n - ocean heat content (1850-2014)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nnear-surface air temperature (tas)\r\n-fig_3_41_tas_global.nc, fig_3_41_tas_land.nc, fig_3_41_tas_north_america.nc, fig_3_41_tas_central_south_america.nc, fig_3_41_tas_europe_north_africa.nc, fig_3_41_tas_africa.nc, fig_3_41_tas_asia.nc, fig_3_41_tas_australasia.nc, fig_3_41_tas_antarctic.nc:\r\nbrown line: exp = 0, stat = 0 (mean); shaded region: stat = 1 (5th percentile) and 2 (95th percentile)\r\ngreen line: exp = 1, stat = 0 (mean); shaded region: stat = 1 (5th percentile) and 2 (95th percentile)\r\nblack line: exp = 4, stat = 0 (mean)\r\n\r\nocean heat content (ohc)\r\n-fig_3_41_ohc_global.nc:\r\nbrown line: ncl5 = 0, ncl6 = 0 (mean); shaded region: ncl6 = 1 (5th percentile) and 2 (95th percentile)\r\ngreen line: ncl5 = 1, ncl6 = 0 (mean); shaded region: ncl6 = 1 (5th percentile) and 2 (95th percentile)\r\nblack line: ncl5 = 2, ncl6 = 0 (mean)\r\n\r\nprecipitation (pr)\r\n-fig_3_41_pr_60N_90N.nc:\r\nbrown line: exp = 0, stat = 0 (mean); shaded region: stat = 1 (5th percentile) and 2 (95th percentile)\r\ngreen line: exp = 1, stat = 0 (mean); shaded region: stat = 1 (5th percentile) and 2 (95th percentile)\r\nblack line: exp = 2, stat = 0 (mean)\r\n\r\nsea ice extent (siconc)\r\n-fig_3_41_siconc_nh.nc, fig_3_41_siconc_sh.nc:\r\nbrown line: exp = 0, stat = 0 (mean); shaded region: stat = 1 (5th percentile) and 2 (95th percentile)\r\ngreen line: exp = 1, stat = 0 (mean); shaded region: stat = 1 (5th percentile) and 2 (95th percentile)\r\nblack line: exp = 2, stat = 0 (mean)\r\n\r\nThe ensemble spread (shaded regions) of CMIP6 data shown in figure 3.41 are the mean, 5th and 95th percentiles. \r\nThe in-file metadata labels the same ensemble spread with mean, min and max.\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to the figure on the IPCC AR6 website", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:44:51", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 3, Human influence, large-scale indicators, Natural variability, anthropogenically-forced change, observed changes, Figure 3.41, attribution, near-surface air temperature change, precipitation change, change of sea ice extent, change in ocean heat content", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-03-09T11:29:29", "doiPublishedTime": "2023-02-08T19:32:36.594368", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 70, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 36807, "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_fig41/v20211028", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 201475223, "numberOfFiles": 16, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 9142, "startTime": "1850-01-01T12:00:00", "endTime": "2020-12-31T12:00:00" }, "resultQuality": { "ob_id": 3786, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-11-03" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33293, "uuid": "786a126f87794677b1804a0742e651bc", "short_code": "comp", "title": "Caption for Figure 3.41 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Summary figure showing simulated and observed changes in key large-scale indicators of climate change across the climate system, for continental, ocean basin and larger scales. Black lines show observations, brown lines and shading show the multi-model mean and 5th–95th percentile ranges for CMIP6 historical simulations including anthropogenic and natural forcing, and blue lines and shading show corresponding ensemble means and 5th–95th percentile ranges for CMIP6 natural-only simulations. Temperature time series are as in Figure 3.9, but with smoothing using a low pass filter. Precipitation time series are as in Figure 3.15 and ocean heat content as in Figure 3.26. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 4385, 46696, 46697, 60412, 60413, 60414, 60415 ], "vocabularyKeywords": [], "identifier_set": [ 12387 ], "observationcollection_set": [ { "ob_id": 32718, "uuid": "932c79b1f7cd4f34b7c542f316f39c17", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 3: Human influence on the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- data for Figure 3.2\r\n- data for Figure 3.3\r\n- data for Figure 3.4\r\n- data for Figure 3.5\r\n- data for Figure 3.6\r\n- data for Figure 3.7\r\n- data for Figure 3.8\r\n- data for Figure 3.9\r\n- data for Figure 3.10\r\n- data for Figure 3.11\r\n- data for Figure 3.12\r\n- data for Figure 3.13\r\n- data for Figure 3.14\r\n- data for Figure 3.15\r\n- data for Figure 3.16\r\n- data for Figure 3.17\r\n- data for Figure 3.18\r\n- data for Figure 3.19\r\n- data for Figure 3.20\r\n- data for Figure 3.21\r\n- data for Figure 3.22\r\n- data for Figure 3.23\r\n- data for Figure 3.24\r\n- data for Figure 3.25\r\n- data for Figure 3.26\r\n- data for Figure 3.27\r\n- input data for Figure 3.27\r\n- data for Figure 3.28\r\n- input data for Figure 3.28\r\n- data for Figure 3.29\r\n- data for Figure 3.30\r\n- data for Figure 3.31\r\n- data for Figure 3.32\r\n- data for Figure 3.33\r\n- data for Figure 3.34\r\n- data for Figure 3.35\r\n- data for Figure 3.36\r\n- data for Figure 3.37\r\n- data for Figure 3.38\r\n- data for Figure 3.39\r\n- data for Figure 3.40\r\n- data for Figure 3.41\r\n- data for Figure 3.42\r\n- data for Figure 3.43\r\n- data for Figure 3.44\r\n- data for Cross-Chapter Box 3.1.1\r\n- data for Cross-Chapter Box 3.2.1\r\n- data for FAQ 3.1, Figure 1\r\n- data for FAQ 3.2., Figure 1\r\n- data for FAQ 3.3, Figure 1" } ], "responsiblepartyinfo_set": [ 148653, 148654, 148655, 148656, 148657, 148658, 148660, 168269, 148659 ], "onlineresource_set": [ 43593, 43594, 52396, 82621, 88592 ] }, { "ob_id": 33296, "uuid": "8bbebd486eed4b7392dd306462e3f724", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.22 (v20211109)", "abstract": "Data for Figure 2.22 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 2.22 shows the April snow cover extent (SCE) for the Northern Hemisphere over the 1922-2018 period.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains Northern Hemisphere April snow cover extent (SCE) time series from 1922-2018 for:\r\n \r\n - Observed SCE (1922-1991) from Brown (2000)\r\n - Blended Multi-observation SCE dataset (1967-2018) from Mudryk et al. (2020)\r\n - Upper and lower boundary for uncertainty range for each time series\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 2.22\r\n Data file: Apr_snow_cover_extent_SCE.csv; (yearly data) relates to data for blue line (Brown 2000) and red line (Multi-obs Mudryk et al. 2020) and the upper and lower boundaries for uncertainty range (shaded area) for each time series\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n- Link to the figure on the IPCC AR6 website\r\n- Link to the report component containing the figure (Chapter 2)\r\n- Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:44:53", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, Intergovernmental Panel on Climate Change, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 2, Changing state, Multi-millennial context, pre-industrial, Natural forcing, anthropogenic forcing, Radiative forcing, Large-scale indicators, observed changes, Modes of variability, Figure 2.22, Snow cover extent, Northern Hemisphere", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-16T15:49:46", "doiPublishedTime": "2023-07-03T20:05:02", "removedDataTime": null, "geographicExtent": { "ob_id": 3819, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": 0.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 72, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "degrees" }, "result_field": { "ob_id": 39219, "dataPath": "/badc/ar6_wg1/data/ch_02/ch2_fig22/v20211109", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 9927, "numberOfFiles": 4, "fileFormat": "CSV" }, "timePeriod": { "ob_id": 9143, "startTime": "1922-04-01T12:00:00", "endTime": "2018-04-30T12:00:00" }, "resultQuality": { "ob_id": 3787, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-11-09" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33216, "uuid": "8cddcb183c324fac86e14d62eed44cd5", "short_code": "comp", "title": "Caption for Figure 2.22 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "April snow cover extent (SCE) for the Northern Hemisphere (1922–2018). Shading shows very likely range. The trend over the entire 1922–2018 period (black line) is –0.29 (± 0.07) million km2 per decade. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1)" }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 64247, 65390, 65391, 88546, 88547 ], "vocabularyKeywords": [], "identifier_set": [ 12619 ], "observationcollection_set": [ { "ob_id": 32717, "uuid": "3da412ad9912427d9bb808b57faa21a7", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 2.2\r\n- data for Figure 2.4\r\n- data for Figure 2.5\r\n- data for Figure 2.6\r\n- data for Figure 2.9\r\n- data for Figure 2.11\r\n- input data for Figure 2.11\r\n- data for Figure 2.12\r\n- input data for Figure 2.12\r\n- data for Figure 2.13\r\n- input data for Figure 2.13\r\n- data for Figure 2.14\r\n- data for Figure 2.15\r\n- input data for Figure 2.15\r\n- input data for Figure 2.16\r\n- data for Figure 2.17\r\n- data for Figure 2.22\r\n- input data for Figure 2.23\r\n- data for Figure 2.25\r\n- input data for Figure 2.25\r\n- data for Figure 2.26\r\n- input data for Figure 2.27\r\n- data for Figure 2.28\r\n- input data for Figure 2.29\r\n- data for Figure 2.36\r\n- data for Figure 2.37\r\n- data for Figure 2.38\r\n- data for Cross-Chapter Box 2.1.1\r\n- data for Cross-Chapter Box 2.3.1" } ], "responsiblepartyinfo_set": [ 148668, 148669, 148670, 148671, 148672, 148673, 148674, 148675, 148676, 196263 ], "onlineresource_set": [ 43596, 43675, 82833 ] }, { "ob_id": 33298, "uuid": "9c350d4ff7ee438f9f1fc7252cbb2282", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci) : Global remote sensing merged multi-mission monthly gridded significant wave height from altimetry, L4 product, version 3", "abstract": "The ESA Sea State Climate Change Initiative (CCI) project has produced global multi-sensor time-series of along-track satellite altimeter significant wave height data (referred to as Level 4 (L4) data) with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 3 Remote Sensing Significant Wave Height product, gridded over a global regular cylindrical projection (1°x1° resolution), averaging valid and good measurements from all available altimeters on a monthly basis (using the L2P products also available). These L4 products are meant for statistics and visualization.\r\n\r\nThe altimeter data used in the Sea State CCI dataset v3 come from multiple satellite missions spanning from 2002 to 2021 ( Envisat, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL, Sentinel-3A), therefore spanning over a shorter time range than version 1.1. Unlike version 1.1, this version 3 involved a complete and consistent retracking of all the included altimeters. Many altimeters are bi-frequency (Ku-C or Ku-S) and only measurements in Ku band were used, for consistency reasons, being available on each altimeter but SARAL (Ka band).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-09-30T17:40:59", "updateFrequency": "notPlanned", "dataLineage": "Data was produced by the ESA Sea State CCI team at Ifremer and was transferred to CEDA as part of the ESA CCI Open Data Portal project.", "removedDataReason": "", "keywords": "Sea State, CCI, Significant Wave Height", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-10-10T09:23:54", "doiPublishedTime": "2022-10-11T15:07:23", "removedDataTime": null, "geographicExtent": { "ob_id": 2578, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -80.0, "northBoundLatitude": 80.0 }, "verticalExtent": null, "result_field": { "ob_id": 38280, "dataPath": "/neodc/esacci/sea_state/data/v3_release/altimeter/l4/v3.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 414414459, "numberOfFiles": 225, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 10551, "startTime": "2002-06-01T00:00:00", "endTime": "2021-01-31T23:59:59" }, "resultQuality": { "ob_id": 3368, "explanation": "This product provides an average and other statistical moments of sea wave height computed over the L2P products whose data were quality checked and compared to in situ reference data. For more details on the validation process, refer to the product user guide.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-01-20" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 38290, "uuid": "df96995ca0ce43158f8b2f00a6d9de51", "short_code": "cmppr", "title": "Composite process for the ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track significant wave height from altimetery, version 3 datasets", "abstract": "The altimeter data used in the ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track significant wave height from altimetery, version 3 datasets come from multiple satellite missions (Envisat, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL, Sentinel-3A)." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2604, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 63, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_sea_state_terms_and_conditions.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 30004, "uuid": "7cfcd20428c3454fafa4e1afec2cf923", "short_code": "proj", "title": "ESA Sea State Climate Change Initiative Project", "abstract": "The European Space Agency (ESA) Sea State CCI+ project is part of ESA's Climate Change Initiative (CCI) programme. The CCI programme was launched by ESA in 2010, to produce long term datasets of Essential Climate Variables (ECV's) derived from global satellite data. In this context, the Sea State CCI+ project was kicked off in 2018 in order to produce a CDR for the new ECV \"Sea State\"." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 12066, 60940, 60941, 66538, 67813, 67814, 67815, 67816, 67817, 67818, 67819, 67820, 67821, 67822, 67823, 67824, 67825, 67826, 67827, 67828, 67829, 67830, 67831, 67832, 67833 ], "vocabularyKeywords": [], "identifier_set": [ 12211 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 148683, 148684, 148677, 148680, 148679, 148678, 148682, 148681, 148685, 148686, 148687 ], "onlineresource_set": [ 43597, 43599, 43598 ] }, { "ob_id": 33299, "uuid": "e6af67fca81c40b7bb3eddaadde06909", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci) : Global remote sensing daily merged multi-mission along-track significant wave height from altimetry, L3 product, version 3", "abstract": "The ESA Sea State Climate Change Initiative (CCI) project has produced global daily merged multi-sensor time-series of along-track satellite altimeter significant wave height data (referred to as Level 3 (L3) data) with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 3 Remote Sensing Significant Wave Height product, which provides along-track data at approximately 6 km spatial resolution. It has been generated from upstream Sea State CCI L2P products, edited and merged into daily products, retaining only valid and good quality measurements from all altimeters over one day, with simplified content (only a few key parameters). This is close to what is delivered in Near-Real Time by the CMEMS (Copernicus - Marine Environment Monitoring Service) project. It covers the date range from 2002-2021.\r\n\r\nThe altimeter data used in the Sea State CCI dataset v3 come from multiple satellite missions (Envisat, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL, Sentinel-3A), therefore spanning over a shorter time range than version 1.1. Unlike version 1.1, this version 3 involved a complete and consistent retracking of all the included altimeters. Many altimeters are bi-frequency (Ku-C or Ku-S) and only measurements in Ku band were used, for consistency reasons, being available on each altimeter but SARAL (Ka band).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-09-30T17:55:40", "updateFrequency": "notPlanned", "dataLineage": "Data was produced by the ESA Sea State CCI team at Ifremer and was transferred to CEDA as part of the ESA CCI Open Data Portal project.", "removedDataReason": "", "keywords": "Sea State, CCI, Significant Wave Height", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-10-10T09:33:24", "doiPublishedTime": "2022-10-11T15:07:07", "removedDataTime": null, "geographicExtent": { "ob_id": 2577, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -80.0, "northBoundLatitude": 80.0 }, "verticalExtent": null, "result_field": { "ob_id": 38281, "dataPath": "/neodc/esacci/sea_state/data/v3_release/altimeter/l3/v3.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 61289669218, "numberOfFiles": 6928, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 10552, "startTime": "2002-01-15T00:00:00", "endTime": "2021-01-01T23:59:59" }, "resultQuality": { "ob_id": 3374, "explanation": "This product is a subset of the L2P products whose data were quality checked and compared to in situ reference data. For more details on the validation process, refer to the product user guide.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-01-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 38290, "uuid": "df96995ca0ce43158f8b2f00a6d9de51", "short_code": "cmppr", "title": "Composite process for the ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track significant wave height from altimetery, version 3 datasets", "abstract": "The altimeter data used in the ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track significant wave height from altimetery, version 3 datasets come from multiple satellite missions (Envisat, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL, Sentinel-3A)." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2604, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 63, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_sea_state_terms_and_conditions.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 30004, "uuid": "7cfcd20428c3454fafa4e1afec2cf923", "short_code": "proj", "title": "ESA Sea State Climate Change Initiative Project", "abstract": "The European Space Agency (ESA) Sea State CCI+ project is part of ESA's Climate Change Initiative (CCI) programme. The CCI programme was launched by ESA in 2010, to produce long term datasets of Essential Climate Variables (ECV's) derived from global satellite data. In this context, the Sea State CCI+ project was kicked off in 2018 in order to produce a CDR for the new ECV \"Sea State\"." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 60940, 60941, 66527, 66529, 66539, 67834, 67835, 67836, 67837, 67838, 67839, 67840 ], "vocabularyKeywords": [], "identifier_set": [ 12209 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 148688, 148689, 148694, 148695, 148691, 148690, 148693, 148692, 148696, 148697, 148698 ], "onlineresource_set": [ 43601, 43600, 43602, 87891, 87786 ] }, { "ob_id": 33300, "uuid": "8cb46a5efaa74032bf1833438f499cc3", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track significant wave height from altimetry, L2P product, version 3", "abstract": "The ESA Sea State Climate Change Initiative (CCI) project has produced global multi-sensor time-series of along-track satellite altimeter significant wave height data (referred to as Level 2P (L2P) data) with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 3 Remote Sensing Significant Wave Height product, which provides along-track data at approximately 6 km spatial resolution, separated per satellite and pass, including all measurements with flags, corrections and extra parameters from other sources. These are expert products with rich content and no data loss. \r\n\r\nThe altimeter data used in the Sea State CCI dataset v3 come from multiple satellite missions spanning from 2002 to 2022021 (Envisat, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL, Sentinel-3A), therefore spanning over a shorter time range than version 1.1. Unlike version 1.1, this version 3 involved a complete and consistent retracking of all the included altimeters. Many altimeters are bi-frequency (Ku-C or Ku-S) and only measurements in Ku band were used, for consistency reasons, being available on each altimeter but SARAL (Ka band).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-10-05T12:54:38", "updateFrequency": "notPlanned", "dataLineage": "Data was produced by the ESA Sea State CCI team at Ifremer and was transferred to CEDA as part of the ESA CCI Open Data Portal project.", "removedDataReason": "", "keywords": "CCI, Sea State, Significant Wave Height", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-10-10T09:36:01", "doiPublishedTime": "2022-10-11T15:07:13", "removedDataTime": null, "geographicExtent": { "ob_id": 2576, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -80.0, "northBoundLatitude": 80.0 }, "verticalExtent": null, "result_field": { "ob_id": 38282, "dataPath": "/neodc/esacci/sea_state/data/v3_release/altimeter/l2p/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 203068747647, "numberOfFiles": 709369, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 9145, "startTime": "2001-01-15T00:00:00", "endTime": "2020-07-08T23:59:59" }, "resultQuality": { "ob_id": 3367, "explanation": "The data were quality checked and compared to in situ reference data. For more details on the validation process, refer to the product user guide.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-01-20" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 38290, "uuid": "df96995ca0ce43158f8b2f00a6d9de51", "short_code": "cmppr", "title": "Composite process for the ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track significant wave height from altimetery, version 3 datasets", "abstract": "The altimeter data used in the ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track significant wave height from altimetery, version 3 datasets come from multiple satellite missions (Envisat, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL, Sentinel-3A)." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2604, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 63, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_sea_state_terms_and_conditions.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 30004, "uuid": "7cfcd20428c3454fafa4e1afec2cf923", "short_code": "proj", "title": "ESA Sea State Climate Change Initiative Project", "abstract": "The European Space Agency (ESA) Sea State CCI+ project is part of ESA's Climate Change Initiative (CCI) programme. The CCI programme was launched by ESA in 2010, to produce long term datasets of Essential Climate Variables (ECV's) derived from global satellite data. In this context, the Sea State CCI+ project was kicked off in 2018 in order to produce a CDR for the new ECV \"Sea State\"." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 60940, 60941, 66521, 66522, 66523, 66526, 66527, 66529, 66531, 66538, 66539, 67837, 67838, 67839, 67841, 67842, 67843, 67844, 67845, 67846, 67847, 67848, 67849, 67850, 67851, 67852, 67853, 67854, 67855, 67856, 67857, 67858 ], "vocabularyKeywords": [], "identifier_set": [ 12210 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 148700, 148704, 148705, 148706, 148699, 148702, 148701, 148703, 148707, 148708, 148709, 182352, 182353, 182354, 182355 ], "onlineresource_set": [ 43605, 43603, 43604, 92690 ] }, { "ob_id": 33301, "uuid": "81bab8b378db443db4482a5961a58ec1", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track significant wave height (SWH) from SAR WV onboard Sentinel-1A & 1B, L2P product, release version 3.", "abstract": "The ESA Sea State Climate Change Initiative (CCI) project has produced global multi-sensor time-series of along-track satellite synthetic aperture radar (SAR) significant wave height (SWH) data (referred to as SAR WV onboard Sentinel-1 Level 2P (L2P) SWH data) with a particular focus for use in climate studies.\r\n \r\nThis dataset contains the Sentinel-1 SAR Remote Sensing Significant Wave Height product (version 1.0), which is part of the ESA Sea State CCI Version 3.0 release. This product provides along-track SWH measurements at 20km resolution every 100km, processed using the Quach et al statistical model , separated per satellite and pass, including all measurements with flags, corrections and extra parameters from other sources. These are expert products with rich content and no data loss. The SAR Wave Mode data used in the Sea State CCI dataset v3 come from Sentinel-1 satellite missions spanning from 2015 to 2021 (Sentinel-1 A, Sentinel-1 B)", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-10-07T10:18:37", "updateFrequency": "notPlanned", "dataLineage": "Data was produced by the ESA Sea State CCI team at Ifremer and was transferred to CEDA as part of the ESA CCI Open Data Portal project.", "removedDataReason": "", "keywords": "CCI, Sea State, Significant Wave Height, SAR", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-10-12T14:26:27", "doiPublishedTime": "2022-10-13T06:51:07", "removedDataTime": null, "geographicExtent": { "ob_id": 2576, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -80.0, "northBoundLatitude": 80.0 }, "verticalExtent": null, "result_field": { "ob_id": 38301, "dataPath": "/neodc/esacci/sea_state/data/v3_release/sar/l2p/sentinel1/swh/v1.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 9147079125, "numberOfFiles": 127451, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 10559, "startTime": "2015-03-19T00:00:00", "endTime": "2021-07-08T23:59:59" }, "resultQuality": { "ob_id": 3367, "explanation": "The data were quality checked and compared to in situ reference data. For more details on the validation process, refer to the product user guide.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-01-20" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 38296, "uuid": "61335301ac8649ec9f24912e7c85cb8d", "short_code": "cmppr", "title": "Composite process for the ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track significant wave height (SWH) from SAR WV onboard Sentinel-1A & 1B, L2P product, version 3", "abstract": "The SAR Wave Mode data used in this dataset comes from Sentinel-1 satellite missions spanning from 2015 to 2021 (Sentinel-1 A, Sentinel-1 B).\r\n\r\nFor more information on the dataset see the product user guide." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2604, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 63, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_sea_state_terms_and_conditions.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 30004, "uuid": "7cfcd20428c3454fafa4e1afec2cf923", "short_code": "proj", "title": "ESA Sea State Climate Change Initiative Project", "abstract": "The European Space Agency (ESA) Sea State CCI+ project is part of ESA's Climate Change Initiative (CCI) programme. The CCI programme was launched by ESA in 2010, to produce long term datasets of Essential Climate Variables (ECV's) derived from global satellite data. In this context, the Sea State CCI+ project was kicked off in 2018 in order to produce a CDR for the new ECV \"Sea State\"." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 66538, 66539, 67859, 67860, 67861, 67862, 67863, 67864, 67865, 67866, 67867, 67868, 67869, 67870, 67871, 67872, 67873, 67874 ], "vocabularyKeywords": [], "identifier_set": [ 12216 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 148710, 148711, 148712, 148713, 148714, 148715, 148716, 182272, 148719, 182273, 182274, 182275, 182276 ], "onlineresource_set": [ 43606, 43607, 43608 ] }, { "ob_id": 33302, "uuid": "17c2ce31784048de93996275ee976fff", "title": "ESA Sea Level Budget Closure Climate Change Initiative (SLBC_cci): Time series of global mean sea level budget and ocean mass budget elements (1993-2016, at monthly resolution), version 2.2", "abstract": "This dataset is a compilation of time series, together with uncertainties, of the following elements of the global mean sea level budget and ocean mass budget:\r\n(a) global mean sea level\r\n(b) the steric contribution to global mean sea level, that is, the effect of ocean water density change, which is dominated, on a global average, by thermal expansion\r\n(c) the mass contribution to global mean sea level\r\n(d) the global glaciers contribution (excluding Greenland and Antarctica)\r\n(e) the Greenland Ice Sheet and Greenland peripheral glaciers contribution\r\n(f) the Antarctic Ice Sheet contribution\r\n(g) the contribution from changes in land water storage (including snow cover).\r\n\r\nThe compilation is a result from the Sea-level Budget Closure (SLBC_cci) project conducted in the framework of ESA’s Climate Change Initiative (CCI). It provides assessments of the global mean sea level and ocean mass budgets. Assessment of the global mean sea level budget means to assess how well (a) agrees, within uncertainties, to the sum of (b) and (c) or to the sum of (b), (d), (e), (f) and (g). Assessment of the ocean mass budget means to assess how well (c) agrees to the sum (d), (e), (f) and (g).\r\n\r\nAll time series are expressed in terms of anomalies (in millimetres of equivalent global mean sea level) with respect to the mean value over the 10-year reference period 2006-2015. \r\nThe temporal resolution is monthly. The temporal range is from January 1993 to December 2016. Some time series do not cover this full temporal range. All time series are complete over the temporal range from January 2003 to August 2016.\r\n\r\nFor some elements, more than one time series are given, as a result of different assessments from different data sources and methods.\r\n\r\nData and methods underlying the time series are as follows:\r\n(a) satellite altimetry analysis by the Sea Level CCI project.\r\n(b) a new analysis of Argo drifter data with incorporation of sea surface temperature data; an alternative time series consists in an ensemble mean over previous global mean steric sea level anomaly time series.\r\n(c) analysis of monthly global gravity field solutions from the Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry mission.\r\n(d) results from a global glacier model.\r\n(e) analysis of satellite radar altimetry over the Greenland Ice Sheet, amended by results from the global glacier model for the Greenland peripheral glaciers; an alternative time series consists of results from GRACE satellite gravimetry.\r\n(f) analysis of satellite radar altimetry over the Antarctic Ice Sheet; an alternative time series consists of results from GRACE satellite gravimetry.\r\n(g) results from the WaterGAP global hydrological model.\r\n\r\nVersion 2.2 is an update of the previous Version 2.1. The update concerns the estimates of ocean mass change from GRACE.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-11-17T18:10:48", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "sea level budget closure, ESA, CCI", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2021-11-17T18:13:27", "doiPublishedTime": "2021-11-18T15:07:25", "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": 33303, "dataPath": "/neodc/esacci/sea_level_budget_closure/data/timeseries_slb_elements/v2.2/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 114114, "numberOfFiles": 2, "fileFormat": "Data are BADC-CSV formatted." }, "timePeriod": { "ob_id": 8835, "startTime": "1993-01-01T00:00:00", "endTime": "2016-12-31T23:59:59" }, "resultQuality": { "ob_id": 3588, "explanation": "See documentation from the Sea Level Budget Closure project", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-03-23" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 32244, "uuid": "b712474c4a8e415b9fa6877652acb93a", "short_code": "comp", "title": "Compilation of the ESA Climate Change Initiative Sea Level Budget Closure", "abstract": "The compilation is a result from the Sea-level Budget Closure (SLBC_cci) project conducted in the framework of ESA’s Climate Change Initiative (CCI). \r\nData and methods underlying the time series are as follows:\r\n(a) satellite altimetry analysis by the Sea Level CCI project.\r\n(b) a new analysis of Argo drifter data with incorporation of sea surface temperature data; an alternative time series consists in an ensemble mean over previous global mean steric sea level anomaly time series.\r\n(c) analysis of monthly global gravity field solutions from the Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry mission.\r\n(d) results from a global glacier model.\r\n(e) analysis of satellite radar altimetry over the Greenland Ice Sheet, amended by results from the global glacier model for the Greenland peripheral glaciers; an alternative time series consists of results from GRACE satellite gravimetry.\r\n(f) analysis of satellite radar altimetry over the Antarctic Ice Sheet; an alternative time series consists of results from GRACE satellite gravimetry.\r\n(g) results from the WaterGAP global hydrological model." }, "procedureCompositeProcess": null, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" }, { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2585, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 48, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_slbc_terms_and_conditions.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 32240, "uuid": "a549c26e68634b12893dab827b392e66", "short_code": "proj", "title": "ESA Sea-Level Budget Closure Climate Change Initiative project (SLBC_cci)", "abstract": "To assess the accuracy and reliability of our knowledge about sea-level change and its causes, assessments of the sea-level budget (SLB) are indispensable. Closure of the sea-level budget implies that the observed changes of GMSL equal the sum of observed (or otherwise assessed) contributions, namely changes in ocean mass and the steric component. Closure of the ocean mass budget (OMB) implies that the observed ocean-mass change (e.g., from the Gravity Recovery and Climate Experiment, GRACE) is equal to assessed changes of water mass (in solid, liquid or gaseous state) outside the ocean, which are dominated by mass changes of land ice (glaciers and ice sheets) and water stored on the continents as liquid water or snow (land water). Misclosure of these budgets indicates errors in the assessment of some of the components (including effects of undersampling) or contributions from unassessed elements in the budget.\r\n\r\nSince 2010, ESA has developed the Climate Change Initiative (CCI) programme in order to produce consistent and continuous space-based records for Essential Climate Variables (ECVs). The SLBC_cci project was conducted from 2017 to 2019 as the first cross-ECV project within CCI. The project aimed at taking advantage of the improved quality of sea-level-related earth observation datasets produced within the CCI programme. The project also developed new data products based on existing CCI products and on other data sources. SLBC_cci concentrated on datasets generated within CCI or by the consortium members as they have thorough insights into the genesis and uncertainty characteristics of the datasets. This facilitated progress towards working in a consistent framework of product specification, uncertainty characterization, and sea level budget analysis, and enabled the identification of unresolved inconsistencies as a prerequisite for future improvements." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 41999, 42000, 42001, 42002, 42003, 42004, 42005, 42006, 42007, 42008, 42009, 42010, 42011, 42012, 42013, 42014, 42015, 42016, 42017, 42018, 42019, 42020, 42021 ], "vocabularyKeywords": [], "identifier_set": [ 10961 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 148721, 148722, 148723, 148724, 148725, 148726, 148727, 148728, 148729, 148730, 148731, 148732, 148733, 148734, 148735, 148736, 148737, 148738, 148739, 148740, 148741, 148742, 148743, 148744, 148745, 148746, 148747, 148748, 148749, 148750, 148751, 148752, 148753, 148754, 148755, 148756, 148757, 148758, 148759 ], "onlineresource_set": [ 43610, 43611, 43612, 43609, 87678, 89309, 89310, 89311, 89312, 89313, 89314, 88117, 88137 ] }, { "ob_id": 33304, "uuid": "8dcf91b3ebb44458b67124896b131ac5", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.36 (v20211112)", "abstract": "Data for Figure 2.36 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 2.36 shows changes in ENSO variability over time. The upper panel shows multi-centennial changes while the lower panel shows changes during the instrumental period.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n \r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n There are two subpanels. Data are provided for lower panel. Links to the data used for the upper panel are provided as part of the Supplementary Material for Chapter 2\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains 30-year standard deviations in instrumental Nino 3.4, the proxy mean, and the Southern Oscillation Index (SOI), beginning from the first SOI data (1876-1905).\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel b:\r\n \r\n - Data file: Figure_2_36-instrumental_period_data.csv, second column blue line\r\n - Data file: Figure_2_36-instrumental_period_data.csv, third column cyan line\r\n - Data file: Figure_2_36-instrumental_period_data.csv, fourth column orange line", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2021-11-12T11:57:37", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, Intergovernmental Panel on Climate Change, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 2, Changing state, Multi-millennial context, pre-industrial, Natural forcing, anthropogenic forcing, Radiative forcing, Large-scale indicators, observed changes, Modes of variability, Figure 2.36, ENSO, ENSO variance", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "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": { "ob_id": 73, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": null, "timePeriod": { "ob_id": 9149, "startTime": "1905-01-01T12:00:00", "endTime": "2018-12-31T12:00:00" }, "resultQuality": { "ob_id": 3788, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-11-12" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33306, "uuid": "3834577c0ecf490896c2b849b727e9c6", "short_code": "comp", "title": "Caption for Figure 2.36 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Reconstructed and historical variance ratio of El Niño–Southern Oscillation (ENSO). (a) 30-year running variance of the reconstructed annual mean Niño 3.4 or related indicators from various published reconstructions. (b) Variance of June–November Southern Oscillation Index (SOI) and April–March mean Niño 3.4 (1981–2010 base period) along with the mean reconstruction from (a). Further details on data sources and processing are available in the chapter data table (Table 2.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 32717, "uuid": "3da412ad9912427d9bb808b57faa21a7", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 2.2\r\n- data for Figure 2.4\r\n- data for Figure 2.5\r\n- data for Figure 2.6\r\n- data for Figure 2.9\r\n- data for Figure 2.11\r\n- input data for Figure 2.11\r\n- data for Figure 2.12\r\n- input data for Figure 2.12\r\n- data for Figure 2.13\r\n- input data for Figure 2.13\r\n- data for Figure 2.14\r\n- data for Figure 2.15\r\n- input data for Figure 2.15\r\n- input data for Figure 2.16\r\n- data for Figure 2.17\r\n- data for Figure 2.22\r\n- input data for Figure 2.23\r\n- data for Figure 2.25\r\n- input data for Figure 2.25\r\n- data for Figure 2.26\r\n- input data for Figure 2.27\r\n- data for Figure 2.28\r\n- input data for Figure 2.29\r\n- data for Figure 2.36\r\n- data for Figure 2.37\r\n- data for Figure 2.38\r\n- data for Cross-Chapter Box 2.1.1\r\n- data for Cross-Chapter Box 2.3.1" } ], "responsiblepartyinfo_set": [ 148760, 148761, 148762, 148763, 148764, 148765, 148766, 148767, 168267, 148768 ], "onlineresource_set": [ 43613, 43614, 52419, 82835 ] }, { "ob_id": 33307, "uuid": "78ad6999f2d743d2a7db16757c27b549", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 2.27 (v20211112)", "abstract": "Input data for Figure 2.27 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 2.27 presents the ocean salinity trends during historical period (1950-2019) for the near surface (global map, panel a) and zonal mean sub-surface (panel b), with regions of non-significant changes masked by 'x' marks. \r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n \r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels, with data provided for all panels (DurackandWijffels_GlobalOceanChanges_19500101-20191231__210122-205355_beta.nc) and processed via the MATLAB script (Figure_2_27.m) linked in the Related Documens section.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains the global ocean salinity estimates from Durack & Wijffels (2010) based on observations from 01-01-1950 to 12-31-2019:\r\n \r\n - Mean salinity (for the Jan/1950 to Dec/2019 period, units in PSS).\r\n - Salinity change (for the same period, PSS/70-years).\r\n - Salinity change error (same period, PSS/70-years).\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n The processing of the salinity estimates from Durack & Wejffels (2010), is done in the MATLAB script (Figure_2_27.m).\r\n\r\n Panel a: \r\n - Ocean surface salinity change (1950-2019) and time mean (for isohalines).\r\n\r\n Panel b:\r\n - Zonal mean ocean subsurface salinity (0-2000m) change (1950-2019) and time mean (for isohalines).\r\n\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data.\r\n ---------------------------------------------------\r\n The salinity change from the dataset has unit PSS/70-years. Units for salinity change and salinity change error have been converted to PSS/decade.\r\n\r\n Salinity change error from the dataset must be multiplied by 1.09 (to account for the resolved error when a bootstrap resampling was undertaken) x 1.64485 (i.e., z-value for 90% confidence interval) in order to get the uncertainty for the stippling.\r\n\r\n The stippling in both panels is done for regions (either surface salinity, panel a, or zonal mean salinity, panel b) where the salinity uncertainties are larger than the salinity trend.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to the code for the figure, archived on Zenodo.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:44:53", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 2, Changing state, Multi-millennial context, pre-industrial, Natural forcing, anthropogenic forcing, Radiative forcing, Large-scale indicators, observed changes, Modes of variability, Figure 2.27 Input, ocean salinity", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "1 Degrees", "status": "ongoing", "dataPublishedTime": "2023-05-15T15:03:24", "doiPublishedTime": "2023-07-04T15:15:02.764110", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 74, "highestLevelBound": 0.0, "lowestLevelBound": 2000.0, "units": "1" }, "result_field": { "ob_id": 37672, "dataPath": "/badc/ar6_wg1/data/ch_02/inputdata_ch2_fig27/v20211112", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 40250893, "numberOfFiles": 4, "fileFormat": "txt, netCDF" }, "timePeriod": { "ob_id": 9150, "startTime": "1950-01-01T12:00:00", "endTime": "2019-12-31T12:00:00" }, "resultQuality": { "ob_id": 3789, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-11-12" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 33309, "uuid": "55dade268be04e85a4f0760bcb0cac64", "short_code": "comp", "title": "Caption for Figure 2.27 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Changes in ocean salinity. Estimates of salinity trends using a total least absolute differences fitting method for (a) global near-surface salinity (SSS) changes and (b) global zonal mean subsurface salinity changes. Black contours show the associated climatological mean salinity (either near-surface (a) or subsurface (b)) for the analysis period (1950–2019). Both panels represent changes of Practical Salinity Scale 1978 [PSS-78], per decade. In both panels green denotes freshening regions and orange/brown denotes regions with enhanced salinities (‘x’ marks denote non-significant changes). Further details on data sources and processing are available in the chapter data table (Table 2.SM.1)." }, "procedureCompositeProcess": null, "imageDetails": [ 218 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 32705, "uuid": "3234e9111d4f4354af00c3aaecd879b7", "short_code": "proj", "title": "Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 9042, 9043, 9045, 49490, 50542, 50543, 60438, 63765, 63766, 63767, 63768, 63769, 63770, 63771 ], "vocabularyKeywords": [], "identifier_set": [ 12629 ], "observationcollection_set": [ { "ob_id": 32717, "uuid": "3da412ad9912427d9bb808b57faa21a7", "short_code": "coll", "title": "IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system", "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 2.2\r\n- data for Figure 2.4\r\n- data for Figure 2.5\r\n- data for Figure 2.6\r\n- data for Figure 2.9\r\n- data for Figure 2.11\r\n- input data for Figure 2.11\r\n- data for Figure 2.12\r\n- input data for Figure 2.12\r\n- data for Figure 2.13\r\n- input data for Figure 2.13\r\n- data for Figure 2.14\r\n- data for Figure 2.15\r\n- input data for Figure 2.15\r\n- input data for Figure 2.16\r\n- data for Figure 2.17\r\n- data for Figure 2.22\r\n- input data for Figure 2.23\r\n- data for Figure 2.25\r\n- input data for Figure 2.25\r\n- data for Figure 2.26\r\n- input data for Figure 2.27\r\n- data for Figure 2.28\r\n- input data for Figure 2.29\r\n- data for Figure 2.36\r\n- data for Figure 2.37\r\n- data for Figure 2.38\r\n- data for Cross-Chapter Box 2.1.1\r\n- data for Cross-Chapter Box 2.3.1" } ], "responsiblepartyinfo_set": [ 148770, 148771, 148772, 148773, 148774, 148775, 148776, 148777, 179516 ], "onlineresource_set": [ 43674, 82848, 43673, 83480 ] } ] }