Observation List
Get a list of Observation objects.
GET /api/v3/observations/?format=api&offset=8800
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See linked documentation for available information for each component." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2520, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 1, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/CMIP6_Terms_of_Use.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 39172, "uuid": "5a208384b3e0410992f4812347909ffc", "short_code": "proj", "title": "PRIMAVERA: National Centre for Atmospheric Science (NCAS) contribution", "abstract": "PRIMAVERA contribution by the National Centre for Atmospheric Science (NCAS) team." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 50418, 50419, 50426, 50427, 50429, 50431, 50468, 50475, 50496, 50498, 50554, 50555, 50557, 50559, 50561, 50566, 50575, 50579, 50583, 50584, 50586, 50587, 50588, 50589, 50590, 50591, 50595, 50596, 50597, 50598, 50599, 50600, 50603, 50605, 50608, 52746, 52747, 52755, 54228, 55097, 55103, 57241, 60438, 62525, 62736, 71572, 71574 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 39182, "uuid": "ae4bddbafe5640a38e457429a34cb8c4", "short_code": "coll", "title": "PRIMAVERA: National Centre for Atmospheric Science (NCAS) MetUM-GOML2-LR model output collection", "abstract": "PRIMAVERA Project : Collection of simulations from the National Centre for Atmospheric Science (NCAS) MetUM-GOML2-LR model." } ], "responsiblepartyinfo_set": [ 190766, 190767, 190768, 190769, 190770, 190771, 190772, 190774, 190775, 190773 ], "onlineresource_set": [ 80638 ] }, { "ob_id": 39184, "uuid": "5bd61c78606a4dd8839b0b89aa8ee303", "title": "PRIMAVERA: National Centre for Atmospheric Science (NCAS) MetUM-GOML2-LR model output for the \"primWP5-amv-pos\" experiment", "abstract": "PRIMAVERA Project data from the National Centre for Atmospheric Science (NCAS) MetUM-GOML2-LR model output for the \"primWP5-amv-pos\" experiment. These are available at the following frequencies: 6hrPlevPt, Amon, LImon, Lmon and day. The runs included the ensemble members: r10i1p1f1, r11i1p1f1, r12i1p1f1, r13i1p1f1, r14i1p1f1, r15i1p1f1, r1i1p1f1, r2i1p1f1, r3i1p1f1, r4i1p1f1, r5i1p1f1, r6i1p1f1, r7i1p1f1, r8i1p1f1 and r9i1p1f1.\n\nPRIMAVERA was a European Union Horizon2020 (grant agreement 641727) project.", "creationDate": "2022-11-15T09:28:36.649803", "lastUpdatedDate": "2022-11-15T09:28:36.649820", "latestDataUpdateTime": "2024-09-11T13:10:05", "updateFrequency": "asNeeded", "dataLineage": "Data were produced and verified by National Centre for Atmospheric Science (NCAS) scientists before upload to the Centre for Environmental Data Analysis (CEDA) and publication via the Earth Systems Grid Federation (ESGF).", "removedDataReason": "", "keywords": "PRIMAVERA, HighResMIP, climate change, NCAS, MetUM-GOML2-LR, primWP5-amv-pos, 6hrPlevPt, Amon, LImon, Lmon, day", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-11-15T09:28:36.756630", "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": 39185, "dataPath": "/badc/cmip6/data/PRIMAVERA/primWP5/NCAS/MetUM-GOML2-LR/primWP5-amv-pos", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 194091494082, "numberOfFiles": 692, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 10866, "startTime": "1981-09-01T06:00:00", "endTime": "1991-12-30T18:00:00" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39186, "uuid": "31cc85af3f8d472daeb14a7f050eea6c", "short_code": "comp", "title": "National Centre for Atmospheric Science (NCAS) running: experiment primWP5-amv-pos using the MetUM-GOML2-LR model.", "abstract": "National Centre for Atmospheric Science (NCAS) running the \"primWP5-amv-pos\" experiment using the MetUM-GOML2-LR model. See linked documentation for available information for each component." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2520, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 1, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/CMIP6_Terms_of_Use.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 39172, "uuid": "5a208384b3e0410992f4812347909ffc", "short_code": "proj", "title": "PRIMAVERA: National Centre for Atmospheric Science (NCAS) contribution", "abstract": "PRIMAVERA contribution by the National Centre for Atmospheric Science (NCAS) team." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 50418, 50419, 50426, 50427, 50429, 50431, 50468, 50475, 50496, 50498, 50554, 50555, 50557, 50559, 50561, 50566, 50575, 50579, 50583, 50584, 50586, 50587, 50588, 50589, 50590, 50591, 50595, 50596, 50597, 50598, 50599, 50600, 50603, 50605, 50608, 52746, 52747, 52755, 54228, 55097, 55103, 57241, 60438, 62525, 62736, 71572, 71574 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 39182, "uuid": "ae4bddbafe5640a38e457429a34cb8c4", "short_code": "coll", "title": "PRIMAVERA: National Centre for Atmospheric Science (NCAS) MetUM-GOML2-LR model output collection", "abstract": "PRIMAVERA Project : Collection of simulations from the National Centre for Atmospheric Science (NCAS) MetUM-GOML2-LR model." } ], "responsiblepartyinfo_set": [ 190790, 190791, 190792, 190793, 190794, 190795, 190796, 190798, 190799, 190797 ], "onlineresource_set": [ 80640 ] }, { "ob_id": 39189, "uuid": "7a7630213de543a9a19c7015dc198970", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.38 (v20221115)", "abstract": "Data for Figure 2.38 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 2.38 shows indices of multi-decadal climate variability from 1854-2019 based upon several sea surface temperature data products.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Gulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson- Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels with data provided for all panels.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n - AMV.nc contains Atlantic Multidecadal Variability (AMV) index from COBE, ERSST, HADI and KAPL.\r\n - PDV.nc contains PDV index from COBE, ERSST, HADI and KAPL.\r\n \r\n Data acronyms:\r\n COBE [Objective Analyses of Sea-Surface Temperature and Marine Meteorological Variables for the 20th Century using ICOADS and the Kobe Collection].\r\n ERSST [NOAA Extended Reconstructed Sea Surface Temperature].\r\n HADI [Hadley Centre Sea Ice and Sea Surface Temperature data set].\r\n KAPL [Kaplan Extended SST].\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Upper panel:\r\n - Data file: AMV.nc (10-year-filtered, yearly data, 1854-2019)\r\n \r\n Lower panel:\r\n - Data file: PDO.nc (10-year-filtered, yearly data, 1854-2019)\r\n \r\n In all the cases: - Blue line corresponds to COBE data set\r\n - Red correspinds to ERSST data set\r\n - Skyblue corresponds to HADI data set\r\n - Green corresponds to KAPL data set\r\n \r\n All sources in every nc file are arranged in columns 1 to 4, respectively.\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\r\n- Link to code for figure (archived on Zenodo)", "creationDate": "2022-11-15T12:04:32.538821", "lastUpdatedDate": "2022-11-15T12:04:32", "latestDataUpdateTime": "2024-03-09T03:19:19", "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 2.38, AMV, PDV", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-05-15T11:55:59", "doiPublishedTime": "2023-07-03T20:22:02.175212", "removedDataTime": null, "geographicExtent": { "ob_id": 3683, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 217, "highestLevelBound": 0.0, "lowestLevelBound": 0.0, "units": "" }, "result_field": { "ob_id": 39190, "dataPath": "/badc/ar6_wg1/data/ch_02/ch2_fig38/v20221115", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 10654, "numberOfFiles": 5, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 10870, "startTime": "1854-01-01T12:00:00", "endTime": "2009-12-31T12:00:00" }, "resultQuality": { "ob_id": 4127, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-11-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39191, "uuid": "c5efe20099744ea3847b70c1568b7f32", "short_code": "comp", "title": "Caption for Figure 2.38 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Indices of multi-decadal climate variability from 1854–2019 based upon several sea surface temperature data products. Shown are the indices of the AMV and PDV based on area averages for the regions indicated in Annex IV. 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": [ 21859, 63530, 63531 ], "vocabularyKeywords": [], "identifier_set": [ 12621 ], "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": [ 190819, 190820, 190821, 190822, 190823, 190824, 190825, 190826 ], "onlineresource_set": [ 80644, 82837, 80643, 80645 ] }, { "ob_id": 39195, "uuid": "94a315924ed54b08a79e331579fd8c2e", "title": "Surface velocity map of the Afar Rift Zone from 2014-19, geotiff version", "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.\r\nThese data have been provided in geotiff format instead of the original netcdf format.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:19:19", "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": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-11-24T16:17:24", "doiPublishedTime": null, "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": 39196, "dataPath": "/neodc/surface_velocities_afar_rift/geotiff_data/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2592221896, "numberOfFiles": 7, "fileFormat": "These files are provided in Geotiff format." }, "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": [], "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": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 190831, 190832, 190833, 190834, 190835, 190836, 190837, 190838, 190840, 190839, 190842, 190841 ], "onlineresource_set": [ 80647 ] }, { "ob_id": 39197, "uuid": "b839ae53abf94e23b0f61560349ccda1", "title": "DCMEX: cloud images from the NCAS Camera 11 from the New Mexico field campaign 2022", "abstract": "This dataset contains cloud images from the NCAS Camera 11, one of two identical cameras (designated as ncas-cam-11 and ncas-cam-12) captured at various sites around the Magdalena Mountains, New Mexico, USA, as part of the Deep Convective Microphysics Experiment (DCMEX). DCMEX examined the formation and development of clouds over mountains during July and August 2022.\r\n\r\nThese cameras were designed to take simultaneous images of the same object while placed a distance apart to create a stereo image, but this was not always possible; on some days only one camera was used or the two cameras were deployed in separate locations.\r\n\r\nThe images from this camera were taken during the duration of the DCMEX campaign of clouds from a range of sites. These are accompanied by similar images from a sibling camera (see connected dataset). Where the two cameras were operated at the same site they were synchronised in terms of camera settings (exposure, etc) and camera pointing directions to facilitate the onward use of images as stereoscopic imagery. For those latter instances files have been marked with stereo-a or stereo-b within the filename to denote where the images form the left of right image for such images. Other images do not contain these additional filename fields to denote when the cameras were used in stand-along mode. Note, due to the nature of coordinating images between the two cameras one was designated as the primary camera from which the settings were then conveyed to the secondary camera by the coordinating software. As a result exact image synchronisation wasn't possible and thus the secondary camera image may have a timestamp that is a second or so later.", "creationDate": "2022-11-16T09:59:58.958249", "lastUpdatedDate": "2022-11-16T10:41:02", "latestDataUpdateTime": "2024-03-09T03:21:16", "updateFrequency": "notPlanned", "dataLineage": "Data were collected by scientists on the DCMEX field campaign, with metadata added in accordance with the NCAS-IMAGE-1.0 metadata standard before archiving.", "removedDataReason": "", "keywords": "DCMEX, Camera, Images, AMOF, stereoscopic", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "final", "dataPublishedTime": "2023-12-15T14:20:20", "doiPublishedTime": "2023-12-15T14:30:29.474163", "removedDataTime": null, "geographicExtent": { "ob_id": 3686, "bboxName": "", "eastBoundLongitude": -106.89791, "westBoundLongitude": -106.89798, "southBoundLatitude": 34.022435, "northBoundLatitude": 34.022745 }, "verticalExtent": null, "result_field": { "ob_id": 42894, "dataPath": "/badc/ncas-mobile/data/ncas-cam-11/20220621_dcmex/v1.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 56752573539, "numberOfFiles": 10093, "fileFormat": "Data are JPG formatted." }, "timePeriod": { "ob_id": 10873, "startTime": "2022-07-15T00:00:00", "endTime": "2022-08-04T00:00:00" }, "resultQuality": { "ob_id": 4128, "explanation": "", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-11-16" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 41070, "uuid": "79feecea0baa4741a33fb02304bc16bf", "short_code": "acq", "title": "DCMEX NCAS Cam 11 deployment", "abstract": "DCMEX NCAS Cam 11 deployment" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38131, "uuid": "7231e85e8ec34e36a6d2815d7edf2330", "short_code": "proj", "title": "Deep Convective Microphysics Experiment (DCMEX)", "abstract": "The goal of the Deep Convective Microphysics Experiment (DCMEX) project is to ultimately reduce the uncertainty in equilibrium climate sensitivity by improving the representation of microphysical processes in global climate models (GCMs). It is the anvils produced by tropical systems in particular, that contribute significantly to cloud feedbacks. The anvil radiative properties, lifetimes and areal extent are the key parameters. DCMEX will determine the extent to which these are influenced, or even controlled by the cloud microphysics including the habits, concentrations and sizes of the ice particles that make up the anvils, which in turn depend on the microphysical processes in the mixed-phase region of the cloud as well as those occurring in the anvil itself.\r\n\r\nA measurement campaign took place in July-August 2022 over the Magdalena mountains, New Mexico. The FAAM BAe-146 aircraft, dual-polarisation doppler radar, aerosol instruments and stereo-camera observations collected data which was then combined with modelling activities to improve the representation of deep convective microphysics within climate models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12791 ], "observationcollection_set": [ { "ob_id": 38137, "uuid": "b1211ad185e24b488d41dd98f957506c", "short_code": "coll", "title": "DCMEX: Collection of in-situ airborne observations, ground-based meteorological and aerosol measurements and cloud imagery for the Deep Convective Microphysics Experiment", "abstract": "A collection of measurements made for the Deep Convective Microphysics Experiment (DCMEX) project. This includes in-situ airborne observations by the FAAM BAE-146 aircraft, cloud images from 2 NCAS cameras deployed at 3 sites in the area during the course of the field campaign and meteorological and aerosol measurements made at two groundbased stations.\r\n\r\nDCMEX examined the formation and development of clouds over mountains and was based in the Magdalena Mountains, New Mexico area, between July and August 2022.\r\n\r\nAssociated datasets are also available: \r\nTimelapse footage of deep convective clouds in New Mexico produced during the DCMEX field campaign https://doi.org/10.5281/zenodo.7756710 \r\nDCMEX ground based radar data https://doi.org/10.5281/zenodo.10472266 \r\nand, the aircraft ice nucleating particle filter analysis https://doi.org/10.5518/1476" } ], "responsiblepartyinfo_set": [ 190847, 190848, 190849, 190850, 190851, 190852, 190853, 200351, 198604, 198605, 198606, 198607, 198608, 198609, 198610, 198611, 198612, 198613 ], "onlineresource_set": [ 80648, 85018 ] }, { "ob_id": 39198, "uuid": "0b2759059ad6474098e40dad73e0a8ec", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.1 (v20221116)", "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.csv, 1-2000 time series, decadal smoothed, for years centred on 5-1996 CE [column 1 grey line, columns 2 and 3 grey shading]\r\n- panel_a/SPM1_1850-2020_obs.csv, 1850-2020 time series, decadal smoothed, for years centered on 1855-2016 CE [black line]\r\n- panel_a/SPM1_6500_recon.csv, 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- Link to the figure on the IPCC AR6 website\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\r\n- Link to the webpage of the WGI report", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:19:15", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\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": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-02-22T09:28:57", "doiPublishedTime": "2023-07-03T11:51:44.705627", "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": 39202, "dataPath": "/badc/ar6_wg1/data/spm/spm_01/v20221116", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 76519, "numberOfFiles": 7, "fileFormat": "csv" }, "timePeriod": { "ob_id": 9070, "startTime": "0001-01-01T00:00:00", "endTime": "2019-12-31T23:59:59" }, "resultQuality": { "ob_id": 4318, "explanation": "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).", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-11-16" }, "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": [], "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": [ 64246, 64247, 64248, 64249, 64250, 64251, 64252, 64253, 64254, 64255, 64256 ], "vocabularyKeywords": [], "identifier_set": [ 12561 ], "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": [ 190854, 190855, 190856, 190857, 190858, 190859, 190860, 190861, 190867, 190862, 190864, 190865 ], "onlineresource_set": [ 80650, 80651, 80649, 82811 ] }, { "ob_id": 39205, "uuid": "1b91153925dd474387bb696d59adbd15", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.5 (v20221116)", "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\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- Link to origin of figure (IPCC WG1 Summary for Policy Makers)\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).\r\n- Link to the figure on the IPCC AR6 website", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:19:16", "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, 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": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "2.5 Degrees", "status": "completed", "dataPublishedTime": "2023-02-22T12:09:25", "doiPublishedTime": "2023-07-03T12:14:03.061212", "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": 39206, "dataPath": "/badc/ar6_wg1/data/spm/spm_05/v20221116", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 35756500, "numberOfFiles": 15, "fileFormat": "net-CDF" }, "timePeriod": { "ob_id": 9027, "startTime": "1850-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": { "ob_id": 4320, "explanation": "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).", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-11-16" }, "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": [], "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": [ 30956, 46705, 46706, 46707, 46708, 46709, 46711, 50559, 50561, 63529 ], "vocabularyKeywords": [], "identifier_set": [ 12563 ], "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": [ 190886, 190880, 190887, 190881, 190882, 190883, 190884, 190885, 190888 ], "onlineresource_set": [ 80656, 80655, 82816 ] }, { "ob_id": 39223, "uuid": "4262e08336404147878b3adeb4e68950", "title": "CCMI-2022: refD1 data produced by the CCSR-NIES MIROC3.2 model at NIES", "abstract": "This dataset contains model data for CCMI-2022 experiment refD1 produced by the CCSR-NIES MIROC3.2 model run by the modelling team at NIES (National Institute for Environmental Studies) in Japan.\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": "2024-03-09T03:19:18", "updateFrequency": "", "dataLineage": "Data were produced by scientists at the National Institute for Environmental Studies (NIES) and published by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "CCMI-2022, refD1, Hindcast, CCSR-NIES MIROC3.2, NIES, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "250 km", "status": "ongoing", "dataPublishedTime": "2023-04-11T13:11:58", "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": 39224, "dataPath": "/badc/ccmi/data/post-cmip6/ccmi-2022/NIES/CCSRNIES-MIROC32/refD1", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 481094622619, "numberOfFiles": 838, "fileFormat": "net-CDF" }, "timePeriod": { "ob_id": 9009, "startTime": "1960-01-01T00:00:00", "endTime": "2018-12-31T23:59:59" }, "resultQuality": { "ob_id": 3704, "explanation": "Data checked by provider prior to archiving. Data passed CEDA quality control procedure, ceda-cc.", "passesTest": true, "resultTitle": "CCMI-2022 Data and Metadata Quality Statement", "date": "2021-06-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39225, "uuid": "ec11710ba1924a4e8f0a1793921633fa", "short_code": "comp", "title": "CCSR-NIES MIROC3.2 model deployed at NIES", "abstract": "CCSR-NIES MIROC3.2 model deployed at NIES" }, "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": [ 50415, 50417, 50419, 50426, 50427, 50428, 50429, 50431, 50432, 50434, 50435, 50436, 50437, 50438, 50439, 50440, 50441, 50442, 50443, 50444, 50445, 50446, 50447, 50448, 50449, 50450, 50451, 50452, 50453, 50454, 50455, 50456, 50457, 50458, 50459, 50460, 50461, 50462, 50463, 50464, 50465, 50466, 50467, 50468, 50469, 50470, 50471, 50472, 50473, 50475, 50476, 50477, 50478, 50479, 50481, 50482, 50483, 50484, 50485, 50486, 50489, 50490, 50491, 50492, 50493, 50494, 50495, 50496, 50497, 50498, 50499, 50501, 50502, 50503, 50504, 50505, 50506, 50507, 50508, 50509, 50555, 50566, 50591, 50596, 50598, 51210, 51211, 51662, 54088, 54366, 54378, 54635, 59920, 59921, 59922, 60439, 60440, 60441, 60442, 60443, 60444, 60445, 60446, 60448, 60449, 60451, 60452, 60453, 60454, 60456, 60457, 60458, 60459, 64080, 66395, 66396, 71613, 71614, 71619, 71634, 71783, 74528, 74530 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 39373, "uuid": "3a5c1bedafce46bfbde5c5d049d364d1", "short_code": "coll", "title": "CCMI-2022 data produced by the CCSRNIES-MIROC32 model at NIES", "abstract": "The CCSRNIES-MIROC32 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 CCSRNIES-MIROC32 chemistry-climate model is run by the modelling team at NIES (National Institute for Environmental Studies) in Japan 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." }, { "ob_id": 40190, "uuid": "a918c09740b345a089fd47c4c48526b4", "short_code": "coll", "title": "CCMI-2022 data produced by the MIROC-ES2H model from MIROC", "abstract": "The MIROC-ES2H model contribution to 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 MIROC-ES2H chemistry-climate model is based on a global climate model MIROC (Model for Interdisciplinary Research on Climate) which has been cooperatively developed by JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan) 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": [ 190947, 190948, 190949, 190950, 190951, 190952, 190953, 190961, 194242 ], "onlineresource_set": [ 80670, 82452 ] }, { "ob_id": 39226, "uuid": "25f1dfeed5224e26a132bba15bb91f38", "title": "MOSAiC: Wind profiles from HALO Doppler lidar -version 1.0", "abstract": "Aerosol backscatter and along-beam Doppler velocities from a HALO Doppler lidar on board the German icebreaker Polarstern ship for the Multidisciplinary drifting Observatory for Study of Arctic Climate (MOSAIC) project. \r\n\r\nThe University of Leeds participation in MOSAiC was funded by the Natural Environment Research Council (NERC, grant: NE/S002472/1) and involved instrumentation from the Atmospheric Measurement and Observations Facility of the UK's National Centre for Atmospheric Science (NCAS AMOF).", "creationDate": "2022-11-18T13:53:10.555256", "lastUpdatedDate": "2022-11-18T13:53:10", "latestDataUpdateTime": "2024-09-11T13:10:08", "updateFrequency": "notPlanned", "dataLineage": "Data were collected, quality controlled and prepared for archiving by the PI before upload to the Centre for Environmental Data Analysis (CEDA) for long term archiving.", "removedDataReason": "", "keywords": "MOSAIC, lidar, AMOF NE/S002472/1", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-11-18T14:37:15", "doiPublishedTime": "2022-11-18T16:08:45", "removedDataTime": null, "geographicExtent": { "ob_id": 3687, "bboxName": "", "eastBoundLongitude": 148.38, "westBoundLongitude": -165.07, "southBoundLatitude": 78.34, "northBoundLatitude": 89.99 }, "verticalExtent": null, "result_field": { "ob_id": 42835, "dataPath": "/badc/ncas-mobile/data/ncas-lidar-dop-1/20191006_mosaic/v1.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 349251084497, "numberOfFiles": 11051, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 10879, "startTime": "2019-10-06T00:00:00", "endTime": "2020-09-19T00:00:00" }, "resultQuality": { "ob_id": 4129, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-11-18" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 39228, "uuid": "251546888b054482b3fa16fd0c5db26a", "short_code": "acq", "title": "Acquisition for: Multidisciplinary drifting Observatory for Study of Arctic Climate (MOSAiC): Wind profiles from HALO Doppler lidar", "abstract": "Multidisciplinary drifting Observatory for Study of Arctic Climate (MOSAiC) project: Wind profiles from HALO Doppler lidar on board the German research vessel Polarstern." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "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": 37021, "uuid": "35a5a43ae2fa4289af0a3e5e2ca92a5a", "short_code": "proj", "title": "MOSAiC:The Multidisciplinary drifting Observatory for the Study of Arctic Climate", "abstract": "The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) initiative was a major international programme motivated by the rapid changes in Arctic climate observed over the last few decades. This is driven by an accelerated rise in the mean temperature of the Arctic; which is warming at 2-3 times the mean global rate. The most visible change is the dramatic reduction in sea ice extent, particularly of the summer minimum, which is decreasing at a rate of 13% per decade.\r\n\r\nThese rapid changes are the result of a combination of feedback processes - the best known is the ice albedo feedback, whereby the loss of ice exposes the land or sea surface beneath, lowering the area mean albedo and allowing more solar radiation to be absorbed, which warms the surface and enhances ice melt. Other feedbacks relate to the vertical profiles of atmospheric temperature and humidity, cloud properties, and large-scale atmospheric circulation.\r\n\r\nWhile climate models also show enhanced warming in the Arctic, they do not reproduce many of the observed details of the change; for example they do not reproduce the very rapid decline in the summer sea ice minimum observed over the last 10 years, and there are big differences between models. This has a significant impact on our ability to predict the future state of climate system. Poor model performance results from multiple leading-order deficiencies in their representation of physical processes in the Arctic system. MOSAiC aims to address these through a large-scale coordinated approach, making simultaneous measurements of the many interdependent processes relevant to climate over a full calendar year. This approach is necessary because of the strong linkages and feedbacks between different parts of the Arctic climate system and the strong seasonality in many processes. \r\nThe MOSAiC Boundary layer is a Natural Environment Research Council (NERC, grant: NE/S002472/1) funded contribution to this international project focused on measurements of atmospheric boundary layer dynamics and turbulent structure. This observational campaign took place on, and around, the icebreaker Polarstern, which was frozen in at the edge of the pack ice at the end of the summer melt. This provided ready access to both multi-year ice within the pack and to freshly forming ice just outside it. Measurements were made of all components of the surface energy budget on both the upper and lower sides of the ice, along with ice thickness, temperature, physical properties, topography, and deformation over time. The processes controlling the energy budget, including synoptic-scale forcing, cloud properties, turbulent mixing, and the interactions between them, will be studied in detail.\r\n\r\nGrantRef: NE/S002472/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 62716, 62717, 62718, 62719, 62720, 62721, 62722, 62723, 62724, 62725, 62726, 62727, 91927, 91928, 91929, 91930, 91931 ], "vocabularyKeywords": [], "identifier_set": [ 12295 ], "observationcollection_set": [ { "ob_id": 39477, "uuid": "a46fffe939cf4430b9ce812ffc5e03da", "short_code": "coll", "title": "Meteorological Observations for the Multidisciplinary drifting Observatory for Study of Arctic Climate (MOSAiC) project", "abstract": "This collection contains a range of meteorological observations made by instruments on board the German Icebreaker ship Polarstern for the Multidisciplinary drifting Observatory for Study of Arctic Climate (MOSAiC) project.\r\n\r\nThe University of Leeds participation in MOSAiC was funded by the Natural Environment Research Council (NERC, grant: NE/S002472/1) and involved instrumentation from the Atmospheric Measurement and Observations Facility of the UK's National Centre for Atmospheric Science (NCAS AMOF)." } ], "responsiblepartyinfo_set": [ 190962, 190963, 190964, 190965, 190966, 190967, 190968, 190969 ], "onlineresource_set": [] }, { "ob_id": 39234, "uuid": "a1166f1c71b4402a8948daa5c3bd8aa4", "title": "GloCAEM: Atmospheric electricity measurements at Syowa Station (1.4m pole), East Ongul Island, Antarctica", "abstract": "Global Coordination of Atmospheric Electricity Measurements (GloCAEM) project brought these experts together to make the first steps towards an effective global network for FW atmospheric electricity monitoring by holding workshops to discuss measurement practises and instrumentation, as well as establish recording and archiving procedures to archive electric field data in a standardised, easily accessible format, then by creating a central data repository. This project was funded in the UK under NERC grant NE/N013689/1.\r\n\r\nThis dataset contains measurements of atmospheric electricity and electric potential gradient made using a Boltek EFM 100 Instrument mounted at 1.4 m height on a metal pole and operated by the National Institute of Polar Research and Japan Meteorological Agency at Syowa Station, East Ongul Island, Antarctica.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:19:22", "updateFrequency": "unknown", "dataLineage": "Data were collected by the project team and sent to CEDA", "removedDataReason": "", "keywords": "GloCAEM, GEC, electric potential, electric field", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-01-16T16:52:18", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3688, "bboxName": "Syowa Station, Antarctica", "eastBoundLongitude": 39.59, "westBoundLongitude": 39.59, "southBoundLatitude": -69.00611, "northBoundLatitude": -69.00611 }, "verticalExtent": null, "result_field": { "ob_id": 39235, "dataPath": "/badc/glocaem/data/syowa-low", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 7292741653, "numberOfFiles": 5013, "fileFormat": "Data are BADC-CSV format" }, "timePeriod": { "ob_id": 10963, "startTime": "2015-01-01T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 3068, "explanation": "Data provided by project group", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2016-10-18" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 39231, "uuid": "5201abb5dd85431cb701e4740da4e127", "short_code": "acq", "title": "GLOCaeM potential gradient Kakioka Magnetic Observatory, Japan", "abstract": "Measurements of atmospheric electric potential gradient made at Kakioka Magnetic Observatory, Japan" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2546, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 20068, "uuid": "6ee6e0a3f57c4ca79e8cbc0daaafe76f", "short_code": "proj", "title": "Global Coordination of Atmospheric Electricity Measurements (GloCAEM)", "abstract": "It is well established that Earth has a \"Global atmospheric Electric Circuit\" (GEC), through which charge separation in thunderstorms sustains large scale current flow around the planet. The GEC generates an atmospheric electric field which is present globally, and is typically 100V/m near the surface in fair weather conditions. Measurements of electric field have been shown to include information about global thunderstorm activity, local aerosol concentrations and cloud cover, as well as changes in the space weather environment. Recent work has also suggested that atmospheric electrical changes may be effective as earthquake precursors, as well as being sensitive to release of radioactivity, as evidenced by the Fukushima disaster in 2011. \r\n\r\nThe global nature of the GEC means that in order that truly global signals are considered in understanding the processes within the circuit, many validating measurements must be made at different locations around the world. To date, no genuinely global network of FW atmospheric electricity measurements has ever existed, therefore, given the growing number of groups now involved in atmospheric electricity monitoring, such a proposal is timely. \r\n\r\nThis project brought these experts together to make the first steps towards an effective global network for FW atmospheric electricity monitoring by holding workshops to discuss measurement practises and instrumentation, as well as establish recording and archiving procedures to archive electric field data in a standardised, easily accessible format, then by creating a central data repository. This project was funded in the UK under NERC grant NE/N013689/1." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 64174, 64175, 64176, 64177, 64178, 64179, 64180, 64181, 64182, 64183, 64184, 64185, 64186, 64187, 64188, 64189, 64190, 64191, 64192, 64193, 64194, 64195, 64196, 64197, 64198, 64199, 64200, 64202, 64203, 64204, 64245 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 24981, "uuid": "bffd0262439a4ecb8fadf0134c4a4a41", "short_code": "coll", "title": "GloCAEM: Atmospheric electric potential gradient measurements", "abstract": "Global Coordination of Atmospheric Electricity Measurements (GloCAEM) project brought these experts together to make the first steps towards an effective global network for FW atmospheric electricity monitoring by holding workshops to discuss measurement practises and instrumentation, as well as establish recording and archiving procedures to archive electric field data in a standardised, easily accessible format, then by creating a central data repository. 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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. 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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": [ 191459, 191460, 191461, 191462, 191463, 191464, 191465, 191466, 191468, 194241 ], "onlineresource_set": [ 81008, 81009, 81010 ] }, { "ob_id": 39251, "uuid": "3fe603efac3646a2a2aa0a1513a3833a", "title": "WCRP CMIP6: Met Office Hadley Centre (MOHC) HadGEM3-GC31-MM model output for the \"dcppA-hindcast\" experiment", "abstract": "The World Climate Research Program (WCRP) Coupled Model Intercomparison Project, Phase 6 (CMIP6) data from the Met Office Hadley Centre (MOHC) HadGEM3-GC31-MM model output for the \"hindcast initialized based on observations and using historical forcing\" (dcppA-hindcast) experiment. These are available at the following frequencies: AERday, Amon, Eday, Omon, SImon and day. 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The runs included the ensemble members: r10i1p1f2, r11i1p1f2, r12i1p1f2, r16i1p1f2, r17i1p1f2, r18i1p1f2, r19i1p1f2, r1i1p1f2, r2i1p1f2, r3i1p1f2, r4i1p1f2, r5i1p1f2, r6i1p1f2, r7i1p1f2, r8i1p1f2 and r9i1p1f2.\n\nCMIP6 was a global climate model intercomparison project, coordinated by PCMDI (Program For Climate Model Diagnosis and Intercomparison) on behalf of the WCRP and provided input for the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report (AR6).\n\nThe official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record.", "creationDate": "2022-11-22T10:57:59.947188", "lastUpdatedDate": "2022-11-22T10:57:59.947208", "latestDataUpdateTime": "2024-09-11T13:10:16", "updateFrequency": "asNeeded", "dataLineage": "Data were produced and verified by Met Office Hadley Centre (MOHC) scientists before publication via the Earth Systems Grid Federation (ESGF) and a copy obtained by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "CMIP6, WCRP, climate change, MOHC, UKESM1-0-LL, ssp370, 3hr, 6hrLev, AERday, AERhr, AERmon, AERmonZ, Amon, CF3hr, CFday, CFmon, E3hr, Eday, EdayZ, Emon, EmonZ, LImon, Lmon, Oday, Omon, SIday, SImon, day", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-11-22T10:58:00.055361", "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": 39283, "dataPath": "/badc/cmip6/data/CMIP6/ScenarioMIP/MOHC/UKESM1-0-LL/ssp370", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 32098704934293, "numberOfFiles": 23259, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 10896, "startTime": "2015-01-01T00:29:59", "endTime": "2101-01-01T00:00:00" }, "resultQuality": { "ob_id": 3334, "explanation": "The CMIP6 data are checked by CEDA to ensure that they conform to the CMOR metadata standards (https://cmor.llnl.gov) Where any CMIP6 data are identified as having a quality issue this is recorded in the CMIP6 errata service: https://errata.es-doc.org/static/index.html", "passesTest": true, "resultTitle": "CMIP6 Quality Statement - primary data", "date": "2017-02-14" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39284, "uuid": "7c1623e287274decb2292ee9f7a9c6a6", "short_code": "comp", "title": "Met Office Hadley Centre (MOHC) running: experiment ssp370 using the UKESM1-0-LL model.", "abstract": "Met Office Hadley Centre (MOHC) running the \"gap-filling scenario reaching 7.0 based on SSP3\" (ssp370) experiment using the UKESM1-0-LL model. See linked documentation for available information for each component." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2520, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 1, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/CMIP6_Terms_of_Use.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 28398, "uuid": "04d56adf2c3247fc89af80c8f4bdb41b", "short_code": "proj", "title": "WCRP CMIP6: Met Office Hadley Centre (MOHC) contribution", "abstract": "World Climate Research Programme (WCRP) Coupled Model Intercomparison Project Phase 6 contribution to the project by the Met Office Hadley Centre (MOHC) team." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 6255, 7795, 9045, 10024, 11046, 27828, 27829, 50211, 50415, 50417, 50418, 50419, 50424, 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28401, "uuid": "ead9c313d7564425ae28ea3cd63b3c3f", "short_code": "coll", "title": "WCRP CMIP6: Met Office Hadley Centre (MOHC) UKESM1-0-LL model output collection", "abstract": "World Climate Research Programme (WCRP) Coupled Model Intercomparison Project Phase 6 (CMIP6): Collection of simulations from the Met Office Hadley Centre (MOHC) UKESM1-0-LL model.\n\nThe official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record." } ], "responsiblepartyinfo_set": [ 191656, 191657, 191658, 191659, 191660, 191661, 191662, 191664, 191665, 191663 ], "onlineresource_set": [ 82306, 82289, 82290, 82292, 82294, 82296, 82298, 82300, 82302, 82304, 82308, 82310, 82312, 82314, 82316, 82318, 82320, 82322, 82324, 82325, 82326 ] }, { "ob_id": 39307, "uuid": "26b89d8d76bd40bfbaf9fedfa383e9cf", "title": "UKESM1 ARISE-SAI climate simulations", "abstract": "The UKESM1 ARISE experiment explores the impacts of geoengineering via the injection of sulphur dioxide (SO2) into the stratosphere. The injections occur at four different latitudes: 15 degrees N & S and 30 degrees N & S, at an altitude of approximately 20 km.\r\n\r\nThe simulations are based on the medium-emissions CMIP6 scenario ssp245 and cover the years 2035 to 2070. They form a 5-member ensemble with initial conditions taken from the corresponding five members of the ssp245 simulations upon which they were based.", "creationDate": "2022-11-23T12:09:08.164717", "lastUpdatedDate": "2022-11-23T12:09:08", "latestDataUpdateTime": "2024-03-09T03:19:33", "updateFrequency": "notPlanned", "dataLineage": "Data were generated using the UKESM 1.0 climate model and output was processed using the same variable definitions and tools as for CMIP6, but with the MIP era and activity id both set to \"ARISE\" and the only experiment id used is \"arise-sai-1p5\".", "removedDataReason": "", "keywords": "", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-11-23T13:08:37", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3698, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 39308, "dataPath": "/badc/deposited2022/arise/data", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2630015458598, "numberOfFiles": 4974, "fileFormat": "Data are NetCDF formatted" }, "timePeriod": { "ob_id": 10904, "startTime": "2035-01-01T00:00:00", "endTime": "2071-01-01T00:00:00" }, "resultQuality": { "ob_id": 4138, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-11-23" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39310, "uuid": "bd62b6fb07c74d92acde45000b99dbbb", "short_code": "comp", "title": "UKESM 1.0 on Met Office Cray XC40 HPC", "abstract": "UKESM 1.0 on Met Office Cray XC40 HPC" }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 39309, "uuid": "960b613fc3c5476ebda9382ab12c8f3c", "short_code": "proj", "title": "UKESM1 ARISE-SAI climate simulations", "abstract": "The UKESM1 ARISE experiment explores the impacts of geoengineering via the injection of sulphur dioxide (SO2) into the stratosphere. The injections occur at four different latitudes: 15 degrees N & S and 30 degrees N & S, at an altitude of approximately 20 km.\n\nThe simulations are based on the medium-emissions CMIP6 scenario ssp245 and cover the years 2035 to 2070. They form a 5-member ensemble with initial conditions taken from the corresponding five members of the ssp245 simulations upon which they were based.\n\nThere are three targets for the ARISE simulations:\n\n 1. Annual, global-mean near-surface air temperature to be cooled down to, and then maintained at, 1.5 degC above the pre-industrial mean (the latter determined from the long-term mean of UKESM1's CMIP6 piControl simulation).\n 2. The hemispheric temperature balance to be returned to and maintained at the same level as when the global-mean temperature was 1.5 degC above pre-industrial.\n 3. Same as target (2) but for the average poles-tropics temperature difference.\n\nUsing a 5-member ensemble mean, a period in UKESM1's CMIP6 historical/ssp245 simulations was identified when the 20-year mean global temperature was 1.5 degC above pre-industrial. The temperature pattern during this period (2014-2033) was used to define targets (2) and (3) above.\n\nThe amount of SO2 injected at each location is adjusted annually by an algorithm which examines the temperature distribution in the previous year, compares it with the three targets descibed above, and adjusts the injection rate for the next year accordingly" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 6255, 27828, 27829, 50415, 50417, 50418, 50423, 50424, 50429, 50475, 50496, 50498, 50542, 50543, 50568, 50575, 50577, 50588, 50591, 50599, 50602, 52744, 52745, 52755, 54207, 54211, 54240, 54256, 54279, 54415, 54830, 59480, 59481, 60438, 63527, 63528 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 191755, 191756, 191757, 191758, 191759, 191760, 191765, 191766, 191767, 191768 ], "onlineresource_set": [ 82332 ] }, { "ob_id": 39320, "uuid": "f39f0aa295304d55beeb0a850760b061", "title": "Mesoscale convective system tracks, diagnosed from European 2.2km climate model simulations", "abstract": "This dataset contains mesoscale convective systems (MCS) tracks and their summary statistics, diagnosed from the UK Met Office (UKMO) Unified Model and ETH-Zurich (ETHZ) COSMO 2.2km Europe-wide convection permitting model simulations. The simulations are described in Berthou et al (2020) and Brogli et al (2022); they include two ECMWF Interim hindcast simulations (one from each model), two GCM-driven simulations (UM-only, present-climate and end-of-century RCP8.5), and one pseudo-global warming simulation (COSMO-only, end-of-century RCP8.5). The tracks are diagnosed using the DYMECS tracking scheme (Stein et al 2014 and Crook et al 2019). The tracking scheme detects all precipitation objects, but only mesoscale convection systems (MCSs) are sub-selected for analysis; MCSs are defined to be tracks with lifetime peak precipitation exceeding 20 mm/h and lifetime peak area exceeding 1000 squared kilometres. Data are in csv files, and are Python and R readable. These data were produced for the NERC-supported FUTURE-STORMS project (grant number: NE/R01079X/1); the underlying climate simulations were produced for the EU Horizon 2020 project European Climate Prediction System (EUCP) project (grant: 776613).", "creationDate": "2022-11-24T14:14:50.053064", "lastUpdatedDate": "2022-11-24T14:44:06", "latestDataUpdateTime": "2024-09-11T13:10:15", "updateFrequency": "notPlanned", "dataLineage": "Data is derived from various of 2.2km Europe-wide convection permitting model simulations, which are part of the European Climate Prediction project. A subset of the raw model data will be made publicly available in the futurre through the Earth System Grid Federation nodes. Data were prepared by the project team before archiving at CEDA.", "removedDataReason": "", "keywords": "extreme precipitation,convection-permitting models,object tracking", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-12-02T14:50:45", "doiPublishedTime": "2022-12-06T09:56:46.035257", "removedDataTime": null, "geographicExtent": { "ob_id": 3699, "bboxName": "", "eastBoundLongitude": 25.0, "westBoundLongitude": -11.0, "southBoundLatitude": 35.5, "northBoundLatitude": 59.5 }, "verticalExtent": null, "result_field": { "ob_id": 39344, "dataPath": "/badc/deposited2022/EUCP_UKMO_ETH_MCSs/data", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 143008084, "numberOfFiles": 6, "fileFormat": "BADC-CSV" }, "timePeriod": { "ob_id": 10908, "startTime": "1998-01-01T00:00:00", "endTime": "2105-12-31T00:00:00" }, "resultQuality": { "ob_id": 4139, "explanation": "Derived directly from climate simulations without any bias corrections.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-11-24" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39322, "uuid": "b82bf09141a641029c94bba72f25b54a", "short_code": "comp", "title": "Computation for Mesoscale convective system tracks", "abstract": "Mesoscale convective systems tracks and their summary statistics were produced using the UK Met Office (UKMO) Unified Model and ETH-Zurich (ETHZ) COSMO 2.2km Europe-wide convection permitting model simulations. The simulations are described in Berthou et al (2020) and Brogli et al (2022); they include two ECMWF Interim hindcast simulations (one from each model), two GCM-driven simulations (UM-only, present-climate and end-of-century RCP8.5), and one pseudo-global warming simulation (COSMO-only, end-of-century RCP8.5)." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 39321, "uuid": "a0ef4de5c121435dbb602e1f941b1268", "short_code": "proj", "title": "FUTURE-STORMS: Quantifying uncertainties and identifying drivers of future changes in weather extremes from convection-permitting model ensembles", "abstract": "In FUTURE-STORMS, convection-permitting model simulations were carried out over a Europe-wide domain by the UK Met Office and ETH Zurich, as part of the European Prediction System (EUCP) project. These EUCP simulations were exploited under FUTURE STORMS. The simulations are then inter-compared, exploring how heavy rainfall and extreme weather are represented in the present-day, and how are they projected to change under global warming. The project is funded by NERC (grant number: NE/R01079X/1)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 64205, 64206, 64207, 64208, 64209, 64210, 64211, 64212, 64213, 64214, 64215, 64216, 64217, 64218, 64219, 64220, 64221, 64222, 64223, 64224, 64225, 64226, 64227, 64228, 64229, 64230, 64231, 64232, 64233, 64234, 64235, 64236, 64237, 64238, 64239, 64240, 64241, 64242, 64243, 64244 ], "vocabularyKeywords": [], "identifier_set": [ 12301 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 191786, 191787, 191788, 191789, 191790, 191791, 191799, 192483, 191800, 191801, 191802, 191803, 191804, 191805 ], "onlineresource_set": [ 82349, 82350, 82351, 82352, 87572 ] }, { "ob_id": 39323, "uuid": "edd6991e06024f7d9ee06460ec7cd7f6", "title": "Solar-induced Chlorophyll Fluorescence from GOSAT's TANSO-FTS by the University of Leicester (v3.0)", "abstract": "Solar-induced Chlorophyll Fluorescence (SIF) data created from the Greenhouse Gas Observing Satellite (GOSAT) Level 1B data using an adapted version of the University of Leicester Full-Physics retrieval scheme (UoL-FP). These dataset contains both Level 2 and Level 3 S-polarised SIF. The Level 2 data are in daily files and are not averaged, whilst the Level 3 data are averaged spatially and temporally on both a monthly and weekly timescale.\r\n\r\nThe SIF data was derived from L1B data from the TANSO-FTS ( Thermal and Near Infrared Sensor for carbon Observation - Fourier Transform Spectrometer) instrument on the GOSAT satellite. For each GOSAT sounding, the S-polarised spectra have been extracted from two narrow micro-windows outside the Oxygen A-band, at around 755 nm and 772 nm. 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Newman and Charlotte Pascoe and Thomas Peter (2021), SPARC Newsletter, volume 57, pp 22-30\r\n\r\n- Chemical mechanisms and their applications in the Goddard Earth Observing System (GEOS) earth system model. Nielsen, J. E., Pawson, S., Molod, A., Auer, B., da Silva, A. M., Douglass, A. R., ... Wargan, K. (2017). Journal of Advances in Modeling Earth Systems, 9, 3019–3044. https://doi.org/10.1002/2017MS001011\r\n- Change in tropospheric ozone in the recent decades and its contribution to global total ozone. Liu, J., Strode, S. A., Liang, Q., Oman, L.D., Colarco, P. R., Fleming, E. L., et al. (2022). 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Sea ice and sea surface temperatures (SSTs) are specified to follow a repeating annual cycle taken from those used by the same model for their refD2 experiment over 2020 - 2030, the period when SAI is assumed to have been initiated.\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\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\nWMO-2018 refers to the Scientific Assessment of Ozone Depletion: 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\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": "2022-11-30T12:42:49", "latestDataUpdateTime": "2025-04-10T01:56:55", "updateFrequency": "", "dataLineage": "Data were produced by scientists at the CNRM (Centre National de Recherches Meteorologiques), CERFACS (Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique) and published by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "CCMI-2022, senD2-sai, refD2, SSP245, Scenario, CNRM-MOCAGE, CNRM-CERFACS, CNRM, CERFACS, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "250 km", "status": "completed", "dataPublishedTime": "2024-11-11T15:57:21", "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": 39372, "dataPath": "/badc/ccmi/data/post-cmip6/ccmi-2022/CNRM-CERFACS/CNRM-MOCAGE/senD2-sai", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1665244732760, "numberOfFiles": 41280, "fileFormat": "Files are Net-CDF formatted" }, "timePeriod": { "ob_id": 10918, "startTime": "2020-01-01T00:00:00", "endTime": "2100-01-01T00:00:00" }, "resultQuality": { "ob_id": 3954, "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": "2022-05-19" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39368, "uuid": "f13d79567f5f427b800a474bd9b34619", "short_code": "comp", "title": "CNRM-MOCAGE model deployed at CNRM-CERFACS", "abstract": "CNRM-MOCAGE model deployed at CNRM-CERFACS" }, "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": [ 6021, 6022, 6023, 50415, 50416, 50417, 50418, 50419, 50420, 50421, 50422, 50423, 50424, 50425, 50426, 50427, 50428, 50429, 50430, 50431, 50432, 50433, 50434, 50435, 50436, 50437, 50438, 50439, 50440, 50441, 50442, 50443, 50444, 50445, 50446, 50447, 50448, 50449, 50450, 50451, 50452, 50453, 50454, 50455, 50456, 50457, 50458, 50459, 50460, 50461, 50462, 50463, 50464, 50465, 50466, 50467, 50468, 50469, 50470, 50471, 50472, 50473, 50474, 50475, 50476, 50477, 50478, 50479, 50480, 50481, 50482, 50483, 50484, 50485, 50486, 50487, 50488, 50489, 50490, 50491, 50492, 50493, 50494, 50495, 50496, 50497, 50498, 50499, 50500, 50501, 50502, 50503, 50504, 50505, 50506, 50507, 50508, 66396, 71691 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 39387, "uuid": "2bcef71c38f4426c997d956ea79a7aaa", "short_code": "coll", "title": "CCMI-2022 data produced by the CNRM-MOCAGE model at CNRM-CERFACS", "abstract": "The CNRM-MOCAGE 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 CNRM-MOCAGE chemistry-climate model is run by the modelling team at the CNRM (Centre National de Recherches Meteorologiques), CERFACS (Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique) 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. 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The data provided are ICOsahedral Nonhydrostatic (ICON) Atmospheric Global Circulation Model (GCM) data for global radiative-convective equilibrium (RCE) 2-year simulations with a circulation idealised island. 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 305K. Model output is given every 10 days (for the 10dayouput files), and every 4 hours (for the 4houroutput files). 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. These files are indicated by homogLW and homogSW in the file names respectively. \r\n\r\nSimulations also have a circular, idealised island, centred at lat/lon=0 degrees. Island is modelled using the Jena Scheme for Biosphere-Atmosphere Coupling in Hamburg 4 (JSBACH4) model, coupled to the ICON GCM. This land is initialised with tropical rainforest vegetation. The island has a radius of 40 degrees, except for data in the different_islandsize file where the island radius varies from 10 to 80 degrees (as indicated in the variable names).", "creationDate": "2022-11-30T16:31:30.781537", "lastUpdatedDate": "2022-11-30T16:31:30", "latestDataUpdateTime": "2022-11-30T17:49:47", "updateFrequency": "notPlanned", "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. 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It focused on understanding dynamical aspects of convection to provide observational data to develop next generation km scale and urban-scale models to improve prediction of convective storms in km scale Numerical Weather Prediction (NWP) and climate models.\r\n\r\nThe emphasis of WesCon was understanding of dynamical processes (particularly updrafts and turbulence) and their interaction with other processes of importance. \r\n\r\nThe project involved the use of the FAAM Bae-146 aircraft, the Jade-Dimona aircraft and groundbased instruments at Cardington, Netheravon, and nearby ground sites. This project ran in conjunction with WOEST: WesCon - Observing the Evolving Structures of Turbulence project involving groundbased, sonde and radar observations. Both WesCon and WOEST were part of the wider ParaChute programme.\r\n\r\nThe Wessex Convection experiment (WesCon) took place from during summer 2023" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50842, 50851, 50853, 50856, 50857, 50865, 50880, 50888, 50889, 50905, 50907, 50911, 50932, 50933, 50934, 50936, 50937, 50942, 50947, 50948, 50949, 50950, 50952, 50953, 50965, 50967, 50969, 50970, 50971, 50973, 50978, 50979, 50982, 50983, 50984, 50985, 50986, 50987, 50988, 50989, 50990, 50991, 50992, 50993, 50994, 50995, 50996, 50997, 50998, 50999, 51000, 51001, 51002, 51003, 51004, 51005, 51006, 51007, 51008, 51009, 51010, 51011, 51012, 51013, 51014, 51015, 51016, 51017, 51018, 51019, 51020, 51021, 51022, 51023, 51024, 51025, 51026, 51027, 51028, 51029, 51030, 51031, 51032, 51033, 51034, 51035, 51036, 51037, 51038, 51039, 51040, 51041, 51042, 51044, 51045, 51054, 51055, 51056, 51057, 51058, 51059, 51060, 51061, 51062, 51063, 51064, 51065, 51066, 51067, 51068, 51069, 51070, 51071, 51072, 51073, 51074, 51075, 51076, 51077, 51078, 51079, 51080, 51081, 51082, 51083, 51084, 51087, 51098, 51222, 51223, 51225, 51227, 51228, 51229, 51230, 51271, 51272, 51279, 51280, 53949, 53951, 53954, 54967, 54971, 54975, 54976, 59964, 59965, 59966, 59967, 59968, 59969, 59970, 59971, 59972, 59973, 59974, 59975, 59976, 60050, 60051, 60052, 60053, 60054, 60055, 60056, 60057, 60058, 60059, 60060, 60061, 60062, 60063, 60064, 60065, 60066, 60067, 60068, 60069, 60070, 60071, 60072, 60073, 60074, 60075, 60077, 60078, 60079, 60080, 60081, 60087, 60089, 60090, 60091, 60092, 60093, 60094, 60095, 60096, 60097, 60098, 60099, 60100, 60101, 60102, 60103, 60104, 60105, 60106, 60107, 60108, 60109, 60110, 60111, 60112, 60113, 60114, 60115, 60116, 60117, 60118, 60119, 60172, 60173, 60202, 60203, 60204, 60205, 60206, 60207, 60208, 60209, 60210, 60211, 60212, 60213, 60214, 60215, 60216, 60217, 60218, 60219, 60220, 60221, 60222, 60223, 60224, 60225, 60226, 60227, 60228, 60229, 60230, 60231, 60232, 60233, 60234, 60235, 60236, 60237, 60238, 60239, 60240, 60241, 60242, 60243, 60244, 60245, 60246, 60247, 60248, 60249, 60250, 60251, 60252, 60253, 60254, 60255, 60256, 60257, 60258, 60259, 60260, 60261, 60262, 60263, 60264, 60265, 60266, 60267, 60268, 60269, 60270, 60271, 60272, 60273, 60274, 60275, 60276, 60277, 60278, 60279, 60280, 60281, 60282, 60283, 60284, 60287, 60308, 60309, 62646, 62647, 62648, 62649, 62650, 62651, 62652, 62653, 62654, 62655, 62656, 62657, 62658, 62659, 62660, 62661, 62662, 62663, 62664, 62665, 62666, 62669, 62671, 62678, 62679, 65806, 71784, 72012, 73930, 73931, 73932, 73933, 73934, 73935, 73936, 73937, 73938, 73939, 73940, 73941, 73942, 73943, 73944, 73945, 73946, 73947, 73948, 73949, 73950, 73951, 73952, 73953, 73954, 73955, 73956, 73957, 73958, 73959, 73960, 73961, 73962, 73963, 73964, 73965, 73966, 73967, 74055, 74056, 74057, 74058, 74059, 74060, 74061, 74063, 74064, 74065, 74066, 74067, 74068, 74069, 74070, 74071, 74072, 74073, 74074, 74075, 74076, 74077, 74078, 74079, 74080, 74081, 74082, 74083, 74084, 74085, 74086, 74087, 74088, 74089, 74090, 74092, 74093, 74095, 74096, 74097, 74099, 74100, 74101, 74102, 74103, 74104, 74105, 74504, 74505, 74506, 74507, 74508, 74509, 74510, 74511, 74512, 74513, 74514, 74515, 74516, 76041, 76042, 76043, 76044, 76045, 76046, 76047, 76048, 76049, 76050, 76051, 76052, 76053, 76054 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. 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Since sea ice is a sensitive climate indicator with large seasonal and regional variability, the climate research community require long-term and regular observations of the key ice parameters in both Arctic and Antarctic." } ], "responsiblepartyinfo_set": [ 192484, 192485, 192486, 192487, 192488, 192489, 192490, 192607, 192491, 192492, 192493, 192494, 192495 ], "onlineresource_set": [ 82583, 82585, 82581, 82579, 82580, 82584 ] }, { "ob_id": 39468, "uuid": "93f2cf31702d4cf3a35f35f899fc0c6b", "title": "MOSAiC: Wind profiles and acoustic backscatter from a Scintec MFAS Sodar on Icebreaker Polarstern -version 1.0", "abstract": "Wind profiles and acoustic backscatter from a Scintec Flat Array Sodar (MFAS) Sodar deployed on the sea ice during for the international Multidisciplinary drifting Observatory for the Study of Arctic Climate\r\n(MOSAiC). Variables include vertical backscatter, intensity, mean wind speed and direction, wind components, and standard deviations of the wind variables - in the instrument reference frame - along with wind speed and direction in the earth frame, and quality control variables at 10 minute intervals. Also vertical backscatter only at 5 minute intervals.\r\n\r\nThe University of Leeds participation in the project- MOSAiC Boundary Layer -was funded by the Natural Environment Research Council (NERC, grant: NE/S002472/1) and involved instrumentation from the Atmospheric Measurement and Observations Facility of the UK's National Centre for Atmospheric Science (NCAS AMOF). This was a year-long project on the German icebreaker Polarstern to study Arctic climate focused on measurements of atmospheric boundary layer dynamics and turbulent structure.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-09-29T14:29:43", "latestDataUpdateTime": "2022-12-08T10:48:14", "updateFrequency": "notPlanned", "dataLineage": "Data were collected, quality controlled and prepared for archiving by the instrument scientists before upload to the Centre for Environmental Data Analysis (CEDA) for long term archiving.", "removedDataReason": "", "keywords": "MOSAiC, NE/S002472/1, AMOF, Arctic Ocean", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2022-12-12T13:58:12", "doiPublishedTime": "2022-12-13T10:06:21", "removedDataTime": null, "geographicExtent": { "ob_id": 3725, "bboxName": "Sodar on Polarstern for Mosaic", "eastBoundLongitude": 148.38, "westBoundLongitude": -165.07, "southBoundLatitude": 78.34, "northBoundLatitude": 89.99 }, "verticalExtent": null, "result_field": { "ob_id": 42758, "dataPath": "/badc/ncas-mobile/data/ncas-sodar-1/20191016_mosaic/v1.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 898221322, "numberOfFiles": 477, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 10948, "startTime": "2019-10-16T00:00:00", "endTime": "2020-09-19T00:00:00" }, "resultQuality": { "ob_id": 4147, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-12-08" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 39475, "uuid": "64341dbbe1094b87b2e928feb04b0481", "short_code": "acq", "title": "Acquisition for: MOSAiC: Wind speed, wind profile, wind direction and acoustic backscatter from Scintec MFAS sodar on Icebreaker Polarstern", "abstract": "Acquisition for: MOSAiC: Wind speed, wind profile, wind direction and acoustic backscatter from Scintec MFAS sodar on Icebreaker Polarstern" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "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": 37021, "uuid": "35a5a43ae2fa4289af0a3e5e2ca92a5a", "short_code": "proj", "title": "MOSAiC:The Multidisciplinary drifting Observatory for the Study of Arctic Climate", "abstract": "The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) initiative was a major international programme motivated by the rapid changes in Arctic climate observed over the last few decades. This is driven by an accelerated rise in the mean temperature of the Arctic; which is warming at 2-3 times the mean global rate. The most visible change is the dramatic reduction in sea ice extent, particularly of the summer minimum, which is decreasing at a rate of 13% per decade.\r\n\r\nThese rapid changes are the result of a combination of feedback processes - the best known is the ice albedo feedback, whereby the loss of ice exposes the land or sea surface beneath, lowering the area mean albedo and allowing more solar radiation to be absorbed, which warms the surface and enhances ice melt. Other feedbacks relate to the vertical profiles of atmospheric temperature and humidity, cloud properties, and large-scale atmospheric circulation.\r\n\r\nWhile climate models also show enhanced warming in the Arctic, they do not reproduce many of the observed details of the change; for example they do not reproduce the very rapid decline in the summer sea ice minimum observed over the last 10 years, and there are big differences between models. This has a significant impact on our ability to predict the future state of climate system. Poor model performance results from multiple leading-order deficiencies in their representation of physical processes in the Arctic system. MOSAiC aims to address these through a large-scale coordinated approach, making simultaneous measurements of the many interdependent processes relevant to climate over a full calendar year. This approach is necessary because of the strong linkages and feedbacks between different parts of the Arctic climate system and the strong seasonality in many processes. \r\nThe MOSAiC Boundary layer is a Natural Environment Research Council (NERC, grant: NE/S002472/1) funded contribution to this international project focused on measurements of atmospheric boundary layer dynamics and turbulent structure. This observational campaign took place on, and around, the icebreaker Polarstern, which was frozen in at the edge of the pack ice at the end of the summer melt. This provided ready access to both multi-year ice within the pack and to freshly forming ice just outside it. Measurements were made of all components of the surface energy budget on both the upper and lower sides of the ice, along with ice thickness, temperature, physical properties, topography, and deformation over time. The processes controlling the energy budget, including synoptic-scale forcing, cloud properties, turbulent mixing, and the interactions between them, will be studied in detail.\r\n\r\nGrantRef: NE/S002472/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 62767, 66315, 66316, 66317, 66318, 66319, 66320, 66321, 66322, 66323, 66324, 66325, 66326, 66327, 66328, 66329, 66330, 66331, 66332, 66333, 66334, 66335, 66336, 66337, 66338, 66339, 66340, 66341, 66342, 66343, 66344, 66345, 66346, 66347, 66348, 66349, 66350 ], "vocabularyKeywords": [], "identifier_set": [ 12311 ], "observationcollection_set": [ { "ob_id": 39477, "uuid": "a46fffe939cf4430b9ce812ffc5e03da", "short_code": "coll", "title": "Meteorological Observations for the Multidisciplinary drifting Observatory for Study of Arctic Climate (MOSAiC) project", "abstract": "This collection contains a range of meteorological observations made by instruments on board the German Icebreaker ship Polarstern for the Multidisciplinary drifting Observatory for Study of Arctic Climate (MOSAiC) project.\r\n\r\nThe University of Leeds participation in MOSAiC was funded by the Natural Environment Research Council (NERC, grant: NE/S002472/1) and involved instrumentation from the Atmospheric Measurement and Observations Facility of the UK's National Centre for Atmospheric Science (NCAS AMOF)." } ], "responsiblepartyinfo_set": [ 192513, 192514, 192515, 192516, 192517, 192518, 192519, 192520 ], "onlineresource_set": [] }, { "ob_id": 39470, "uuid": "f86d9c7823f44a178b0a95c7e22fd1f6", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from Laboratoire Inter-Universitaire Des Systèmes Atmosphériques (LISA/IPSL)'s vaisala-cl31 instrument deployed at the Lisa-P7 site, France", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Laboratoire Inter-Universitaire Des Systèmes Atmosphériques (LISA/IPSL)'s vaisala-cl31 deployed at the Lisa-P7 site, France.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-250-1001-75113006.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool.\r\n \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.", "creationDate": "2022-12-08T16:12:39.153307", "lastUpdatedDate": "2022-12-08T16:12:39", "latestDataUpdateTime": "2025-07-18T00:36:14", "updateFrequency": "daily", "dataLineage": "Data were collected by instrument and transmitted to the central E-PROFILE processing hub at the UK's Met Office before preparation and delivery to the Centre for Environmental Data Analysis (CEDA). CEDA then produces daily concatenated files before ingestion into the CEDA Archive.", "removedDataReason": "", "keywords": "E-PROFILE, ceilometer measurements", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-12-06T14:09:27", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3723, "bboxName": "Lisa-P7", "eastBoundLongitude": 2.3806309700012207, "westBoundLongitude": 2.3806309700012207, "southBoundLatitude": 48.827762603759766, "northBoundLatitude": 48.827762603759766 }, "verticalExtent": null, "result_field": { "ob_id": 39469, "dataPath": "/badc/eprofile/data/daily_files/france/lisa-p7/lisa-ipsl-vaisala-cl31_A", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1296870024, "numberOfFiles": 961, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 10947, "startTime": "2022-11-09T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 3890, "explanation": "The data are provided as-is with no quality control undertaken by the Centre for Environmental Data Analysis (CEDA). The data suppliers have not indicated if any quality control has been undertaken on these data.", "passesTest": true, "resultTitle": "E-PROFILE QC statement", "date": "2022-02-28" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 39471, "uuid": "23e49539a64b478481b06471614abd9d", "short_code": "acq", "title": "Laboratoire Inter-Universitaire Des Systèmes Atmosphériques (LISA/IPSL): vaisala-cl31 instrument deployed at Lisa-P7", "abstract": "vaisala-cl31 instrument instrument deployed at Lisa-P7 operated by Laboratoire Inter-Universitaire Des Systèmes Atmosphériques (LISA/IPSL) providing cloud base height and aerosol profile data." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 220 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2527, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 7, "licenceURL": "https://artefacts.ceda.ac.uk/licences/cuncgl", "licenceClassifications": [ { "ob_id": 6, "classification": "personal" }, { "ob_id": 4, "classification": "academic" }, { "ob_id": 5, "classification": "policy" } ] } } ], "projects": [ { "ob_id": 32779, "uuid": "16a48b5339ab48cd97bb680388c5cddf", "short_code": "proj", "title": "EUMETNET E-PROFILE", "abstract": "E-PROFILE is part of the EUMETNET Composite Observing System, EUCOS, managing the European networks of radar wind profilers (RWP) and automatic lidars and ceilometers (ALC) for the monitoring of vertical profiles of wind and aerosols including volcanic ash.\r\n \r\n\r\nE-PROFILE coordinates the measurements of vertical profiles of wind from radar wind profilers (vertically pointing Doppler radars) and weather radars from a network of locations across Europe and provides the data to the end users. The main goal is to improve the overall usability of wind profiler data for operational meteorology and to provide support and expertise to both profiler operators and end users.\r\nDue to technical advances of the last years ceilometers (automatic low cost lidars) provide nowadays not only cloud base height but also information on the vertical distribution of aerosols derived from the backscatter profile. To make available this new observation capacity E-PROFILE is developing a framework to produce and exchange profiles of attenuated backscatter profiles. Automatic lidars and ceilometers of stations across Europe are added to the operational network." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50358, 50359, 50360, 50361, 50362, 50363, 50365, 50366, 50367, 50368, 50370, 50371, 50372, 50373, 62345, 62346, 62347 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 34905, "uuid": "345c47d378b64c75b7957aef0c09c81f", "short_code": "coll", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from a network covering most of Europe with additional sites worldwide", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from a network of instruments within EUMETNET's E-PROFILE ALC network.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nMost datasets are available to registered CEDA users. For those not available to CEDA users application for access to those datasets under restricted access can be made using the links on one of the associated records. All use is made in accordance with the Closed-Use Non-Commercial General Licence. See datasets for further licencing links and for individual dataset citations.\r\n\r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.\r\n\r\nNote - the datasets listed on this collection are daily concatenated files produced from single time-step files for each instrument. CEDA holds an older archive of single time-step files (not linked to from the datasets or this collection) which will be aggregated together over time to extend these datasets further back to the start of the E-PROFILE holdings in the CEDA archives. Access to the older single time-step files ahead of their concatenation into daily files can be made via : https://data.ceda.ac.uk/badc/eprofile/data/. As these data are processed single time-step files will be removed from the archive.\r\n\r\nIt is not possible to support any requests for data that predates the CEDA holdings nor to back-fill any data gaps." } ], "responsiblepartyinfo_set": [ 192521, 192522, 192523, 192524, 192525, 192526, 192527, 192528, 192529 ], "onlineresource_set": [ 82540, 82541 ] }, { "ob_id": 39481, "uuid": "23f8ccb87b5d41e7b739cacf9c2968b2", "title": "Weighing trees with lasers project: terrestrial laser scanner data; The Grove of Old Trees reserve California (Plot CALI-01), September 2017", "abstract": "This dataset is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations, which have been re-surveyed at different times. The CALI-01 plot site was situated in the Grove of Old Trees, which is a 48-acre (19 ha) open space reserve woodland of mature coast redwood trees. The grove grows on a broad, flat ridgetop west of Occidental, California,\r\n\r\nThe project scanned all trees in the permanent sample plot (PSP) spanning a range of soil fertility and productivity gradients (24 x 1 ha PSPs in total). The aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents or whether they differ significantly and require continental-level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB (Above Ground Biomass) from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI (Global Ecosystem Dynamics Investigation Lidar) missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data, including: UAVs (unmanned aerial vehicle) for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2020-03-19T12:56:01", "updateFrequency": "", "dataLineage": "Provided by Andrew Burt UCL to CEDA for publication", "removedDataReason": "", "keywords": "Terrestrial Laser Scanner, Grove of old trees, California, Redwood", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-06-19T10:29:33", "doiPublishedTime": "2025-06-26T14:08:41.957706", "removedDataTime": null, "geographicExtent": { "ob_id": 3726, "bboxName": "TLS -CALI-01 plot USA", "eastBoundLongitude": -122.9915, "westBoundLongitude": -122.9915, "southBoundLatitude": 38.398, "northBoundLatitude": 38.398 }, "verticalExtent": null, "result_field": { "ob_id": 39482, "dataPath": "/neodc/tls/data/raw/usa/CALI-01/2017-09-05.001.riproject", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 47010934541, "numberOfFiles": 1768, "fileFormat": "The scan folder contains a number of open and proprietary formats file formats CSV, ASCII, text, PNG , RXP and PAT" }, "timePeriod": { "ob_id": 7312, "startTime": "2016-09-05T00:00:00", "endTime": "2016-09-06T00:00:00" }, "resultQuality": { "ob_id": 3184, "explanation": "validated by UCL", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-11-02" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 39483, "uuid": "5936a0971f014c49a5eaba158dce1900", "short_code": "acq", "title": "The Grove of Old Trees reserve California 05/09/2017", "abstract": "The data was collected from the Weighing trees with lasers project: terrestrial laser scanner data; The Grove of Old Trees reserve California on the 05/09/2017 using the RIEGL VZ-400 Terrestrial Laser Scanner" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 26683, "uuid": "70b2a6b0163747778ee85b4f7f86d8c0", "short_code": "proj", "title": "Weighing Trees with Lasers", "abstract": "Measuring the volume and structure of a tree accurately allows us to calculate the total above-ground carbon (C) stored in the tree, a very important property. Trees remove CO2 from the atmosphere during photosynthesis and can store this C for decades or even centuries until the tree dies, when some of it is released back to the atmosphere through decomposition. Tropical forests store around half of all above-ground terrestrial C, but are at particular risk due to deforestation and degradation, as well as from changing rainfall and temperature patterns. Surprisingly, our knowledge of tropical forest C stocks is quite poor, and errors in these stocks are large and uncertain. This uncertainty feeds into estimates of CO2 emissions due to deforestation, degradation and land use change. We will address this major uncertainty in the terrestrial C cycle by deploying a new, NERC-funded terrestrial laser scanner (TLS) to scan 1000s of trees in tropical forests on three continents: Amazonia, the Congo Basin and SE Asia. The laser data will allow us to measure 3D tree volume and biomass non-destructively to within a few percent of the best current estimates, made by destructive harvesting and weighing. The current, large uncertainties arise because weighing a tree is extremely difficult: tropical trees may be over 50m tall, and weigh 100 tonnes or more. Harvesting also precludes revisiting trees over time to measure change. In practice, a small sample of trees that have been harvested and weighed are related to easy-to-measure parameters of diameter and height, using empirical 'allometric' (size-to-mass) relationships. These relationships are then used to translate diameter and height measurements made over wider areas into estimates of biomass. Allometry is also the only way to infer biomass at very large (pan-tropical) scales, from remote sensing measurements. Unfortunately, the sample of harvested trees underpinning global allometric relationships is geographically limited, and contains very few large trees. Current estimates of tropical forest C stocks from satellite and ground data, all based on these very limited allometry samples, diverge significantly in size and pattern, leading to heated debate as to why this should be.\r\n\r\nThe project hopes to settle this debate, given that our lidar-derived estimates of biomass are completely independent of allometry and unbiased in terms of tree size. We will 'weigh' more trees than are currently included in all global pan-tropical allometries and quantify uncertainty in the allometry models. We will also test assumptions made in allometric models regarding tree shape and wood density. Our measurements will also answer fundamental questions about geographical differences in structural characteristics across tropical forests. Our data will be vital for testing new estimates of biomass from remote sensing; the UK-led ESA BIOMASS RADAR and NASA GEDI laser missions will both estimate pan-tropical C stocks by relying on allometric relationships between forest height and biomass. Our work will feed into these two missions through long-standing collaborations with the lead scientists. More generally, the large number of tree measurements we will collect would be of great interest to researchers in tropical ecology, forestry, biodiversity, remote sensing and C mapping, among others.\r\n\r\nA key aim of the project is to ensure the widest use of our results, by making our data and tools publicly available. We will work with partners to explore routes for commercial developments and input into government policy, particularly relating to forest management and C mapping and mitigation. Lastly, we will make our work accessible through a range of outreach activities, including developing links between a school in the Amazon and UK schools, to raise awareness of scientific, conservation and policy issues surrounding tropical forests.\r\n\r\nThis project was funded by NERC through grant: NE/N00373X/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 13432 ], "observationcollection_set": [ { "ob_id": 26677, "uuid": "ca0729ec30514a64a6ccd393eacff5f0", "short_code": "coll", "title": "Weighing trees with lasers project: Terrestrial Laser Scanner data collection", "abstract": "This dataset collection is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations which, have been re-surveyed at different times. The terrestrial laser scanner (TLS) was able to scan 1000s of trees in tropical forests on three continents: including Amazonia, the Congo Basin and SE Asia. The laser data measured 3D tree volume and biomass non-destructively to within a few percent of the best current estimates, made by destructive harvesting and weighing.\r\n\r\nThe project scanned all trees in multiple permanent sample plots (PSPs) spanning a range of soil fertility and productivity gradients (24 x 1 ha PSPs in total). The aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents, or whether they differ significantly, and require continental level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data including: UAVs for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy." }, { "ob_id": 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": [ 192569, 192570, 192571, 192572, 192573, 192574, 192575, 192576, 192577, 192578, 192583 ], "onlineresource_set": [ 82547 ] }, { "ob_id": 39484, "uuid": "7498da3969884bb9a2c7bac20bf5c96d", "title": "Weighing trees with lasers project: terrestrial laser scanner data; The Grove of Old Trees reserve California (Plot CALI-02), September 2017", "abstract": "This dataset is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations, which have been re-surveyed at different times. The CALI-02 plot site was situated in the Grove of Old Trees, which is a 48-acre (19 ha) open space reserve woodland of mature coast redwood trees. The grove grows on a broad, flat ridgetop west of Occidental, California,\r\n\r\nThe project scanned all trees in the permanent sample plot (PSP) spanning a range of soil fertility and productivity gradients (24 x 1 ha PSPs in total). The aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents or whether they differ significantly and require continental-level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB (Above Ground Biomass) from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI (Global Ecosystem Dynamics Investigation Lidar) missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data, including: UAVs (unmanned aerial vehicle) for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2020-03-02T06:05:03", "updateFrequency": "", "dataLineage": "Provided by Andrew Burt UCL to CEDA for publication", "removedDataReason": "", "keywords": "Terrestrial Laser Scanner, Grove of old trees, California, Redwood", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-06-19T10:59:12", "doiPublishedTime": "2025-06-26T14:09:08.773947", "removedDataTime": null, "geographicExtent": { "ob_id": 3727, "bboxName": "TLS -CALI-02 plot USA", "eastBoundLongitude": -122.9915, "westBoundLongitude": -122.9915, "southBoundLatitude": 38.398, "northBoundLatitude": 38.398 }, "verticalExtent": null, "result_field": { "ob_id": 39486, "dataPath": "/neodc/tls/data/raw/usa/CALI-02/2017-09-09.001.riproject", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 12937428305, "numberOfFiles": 447, "fileFormat": "The scan folder contains a number of open and proprietary formats file formats CSV, ASCII, text, PNG , RXP and PAT" }, "timePeriod": { "ob_id": 10949, "startTime": "2017-08-14T00:00:00", "endTime": "2017-08-15T00:00:00" }, "resultQuality": { "ob_id": 3184, "explanation": "validated by UCL", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-11-02" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 39489, "uuid": "092c7c1afcea47618a9ca1f8451826f8", "short_code": "acq", "title": "The Grove of Old Trees reserve California 09/09/2017", "abstract": "The data was collected from the Weighing trees with lasers project: terrestrial laser scanner data; The Grove of Old Trees reserve California on the 09/09/2017 using the RIEGL VZ-400 Terrestrial Laser Scanner" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 26683, "uuid": "70b2a6b0163747778ee85b4f7f86d8c0", "short_code": "proj", "title": "Weighing Trees with Lasers", "abstract": "Measuring the volume and structure of a tree accurately allows us to calculate the total above-ground carbon (C) stored in the tree, a very important property. Trees remove CO2 from the atmosphere during photosynthesis and can store this C for decades or even centuries until the tree dies, when some of it is released back to the atmosphere through decomposition. Tropical forests store around half of all above-ground terrestrial C, but are at particular risk due to deforestation and degradation, as well as from changing rainfall and temperature patterns. Surprisingly, our knowledge of tropical forest C stocks is quite poor, and errors in these stocks are large and uncertain. This uncertainty feeds into estimates of CO2 emissions due to deforestation, degradation and land use change. We will address this major uncertainty in the terrestrial C cycle by deploying a new, NERC-funded terrestrial laser scanner (TLS) to scan 1000s of trees in tropical forests on three continents: Amazonia, the Congo Basin and SE Asia. The laser data will allow us to measure 3D tree volume and biomass non-destructively to within a few percent of the best current estimates, made by destructive harvesting and weighing. The current, large uncertainties arise because weighing a tree is extremely difficult: tropical trees may be over 50m tall, and weigh 100 tonnes or more. Harvesting also precludes revisiting trees over time to measure change. In practice, a small sample of trees that have been harvested and weighed are related to easy-to-measure parameters of diameter and height, using empirical 'allometric' (size-to-mass) relationships. These relationships are then used to translate diameter and height measurements made over wider areas into estimates of biomass. Allometry is also the only way to infer biomass at very large (pan-tropical) scales, from remote sensing measurements. Unfortunately, the sample of harvested trees underpinning global allometric relationships is geographically limited, and contains very few large trees. Current estimates of tropical forest C stocks from satellite and ground data, all based on these very limited allometry samples, diverge significantly in size and pattern, leading to heated debate as to why this should be.\r\n\r\nThe project hopes to settle this debate, given that our lidar-derived estimates of biomass are completely independent of allometry and unbiased in terms of tree size. We will 'weigh' more trees than are currently included in all global pan-tropical allometries and quantify uncertainty in the allometry models. We will also test assumptions made in allometric models regarding tree shape and wood density. Our measurements will also answer fundamental questions about geographical differences in structural characteristics across tropical forests. Our data will be vital for testing new estimates of biomass from remote sensing; the UK-led ESA BIOMASS RADAR and NASA GEDI laser missions will both estimate pan-tropical C stocks by relying on allometric relationships between forest height and biomass. Our work will feed into these two missions through long-standing collaborations with the lead scientists. More generally, the large number of tree measurements we will collect would be of great interest to researchers in tropical ecology, forestry, biodiversity, remote sensing and C mapping, among others.\r\n\r\nA key aim of the project is to ensure the widest use of our results, by making our data and tools publicly available. We will work with partners to explore routes for commercial developments and input into government policy, particularly relating to forest management and C mapping and mitigation. Lastly, we will make our work accessible through a range of outreach activities, including developing links between a school in the Amazon and UK schools, to raise awareness of scientific, conservation and policy issues surrounding tropical forests.\r\n\r\nThis project was funded by NERC through grant: NE/N00373X/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 13433 ], "observationcollection_set": [ { "ob_id": 26677, "uuid": "ca0729ec30514a64a6ccd393eacff5f0", "short_code": "coll", "title": "Weighing trees with lasers project: Terrestrial Laser Scanner data collection", "abstract": "This dataset collection is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations which, have been re-surveyed at different times. The terrestrial laser scanner (TLS) was able to scan 1000s of trees in tropical forests on three continents: including Amazonia, the Congo Basin and SE Asia. The laser data measured 3D tree volume and biomass non-destructively to within a few percent of the best current estimates, made by destructive harvesting and weighing.\r\n\r\nThe project scanned all trees in multiple permanent sample plots (PSPs) spanning a range of soil fertility and productivity gradients (24 x 1 ha PSPs in total). The aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents, or whether they differ significantly, and require continental level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data including: UAVs for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy." }, { "ob_id": 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": [ 192584, 192585, 192586, 192587, 192588, 192589, 192590, 192591, 192592, 192593, 192594 ], "onlineresource_set": [ 82577 ] }, { "ob_id": 39487, "uuid": "422a95d694bf4963854a2f0ab5d166d6", "title": "Weighing trees with lasers project: terrestrial laser scanner data; The Sea Ranch woods California (Plot CALI-07), September 2017", "abstract": "This dataset is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations, which have been re-surveyed at different times. The CALI-07 plot site was situated in the Sea Ranch Woods near Sonoma California.\r\n\r\nThe project scanned all trees in the permanent sample plot (PSP) spanning a range of soil fertility and productivity gradients (24 x 1 ha PSPs in total). The aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents or whether they differ significantly and require continental-level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB (Above Ground Biomass) from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI (Global Ecosystem Dynamics Investigation Lidar) missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data, including: UAVs (unmanned aerial vehicle) for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2018-09-11T15:16:22.103233", "updateFrequency": "", "dataLineage": "Provided by Andrew Burt UCL to CEDA for publication", "removedDataReason": "", "keywords": "Terrestrial Laser Scanner, The Sea Ranch, California, Redwood", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-06-19T10:45:53", "doiPublishedTime": "2025-06-26T14:09:31.844308", "removedDataTime": null, "geographicExtent": { "ob_id": 3729, "bboxName": "TLS-CALI-07", "eastBoundLongitude": -123.45, "westBoundLongitude": -123.45, "southBoundLatitude": 38.7152, "northBoundLatitude": 38.7152 }, "verticalExtent": null, "result_field": { "ob_id": 39493, "dataPath": "/neodc/tls/data/raw/usa/CALI-07/2017-09-18.001.riproject", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 42515072754, "numberOfFiles": 1617, "fileFormat": "The scan folder contains a number of open and proprietary formats file formats CSV, ASCII, text, PNG , RXP and PAT" }, "timePeriod": { "ob_id": 10949, "startTime": "2017-08-14T00:00:00", "endTime": "2017-08-15T00:00:00" }, "resultQuality": { "ob_id": 3184, "explanation": "validated by UCL", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-11-02" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 39485, "uuid": "28f16cfc0d984ce4b5ce62502fd847a7", "short_code": "acq", "title": "The Sea Ranch Woods California 08/09/2017", "abstract": "The data was collected from the Weighing trees with lasers project: terrestrial laser scanner data; The Sea Ranch woods California on 09/09/2017 using the RIEGL VZ-400 Terrestrial Laser Scanner" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 26683, "uuid": "70b2a6b0163747778ee85b4f7f86d8c0", "short_code": "proj", "title": "Weighing Trees with Lasers", "abstract": "Measuring the volume and structure of a tree accurately allows us to calculate the total above-ground carbon (C) stored in the tree, a very important property. Trees remove CO2 from the atmosphere during photosynthesis and can store this C for decades or even centuries until the tree dies, when some of it is released back to the atmosphere through decomposition. Tropical forests store around half of all above-ground terrestrial C, but are at particular risk due to deforestation and degradation, as well as from changing rainfall and temperature patterns. Surprisingly, our knowledge of tropical forest C stocks is quite poor, and errors in these stocks are large and uncertain. This uncertainty feeds into estimates of CO2 emissions due to deforestation, degradation and land use change. We will address this major uncertainty in the terrestrial C cycle by deploying a new, NERC-funded terrestrial laser scanner (TLS) to scan 1000s of trees in tropical forests on three continents: Amazonia, the Congo Basin and SE Asia. The laser data will allow us to measure 3D tree volume and biomass non-destructively to within a few percent of the best current estimates, made by destructive harvesting and weighing. The current, large uncertainties arise because weighing a tree is extremely difficult: tropical trees may be over 50m tall, and weigh 100 tonnes or more. Harvesting also precludes revisiting trees over time to measure change. In practice, a small sample of trees that have been harvested and weighed are related to easy-to-measure parameters of diameter and height, using empirical 'allometric' (size-to-mass) relationships. These relationships are then used to translate diameter and height measurements made over wider areas into estimates of biomass. Allometry is also the only way to infer biomass at very large (pan-tropical) scales, from remote sensing measurements. Unfortunately, the sample of harvested trees underpinning global allometric relationships is geographically limited, and contains very few large trees. Current estimates of tropical forest C stocks from satellite and ground data, all based on these very limited allometry samples, diverge significantly in size and pattern, leading to heated debate as to why this should be.\r\n\r\nThe project hopes to settle this debate, given that our lidar-derived estimates of biomass are completely independent of allometry and unbiased in terms of tree size. We will 'weigh' more trees than are currently included in all global pan-tropical allometries and quantify uncertainty in the allometry models. We will also test assumptions made in allometric models regarding tree shape and wood density. Our measurements will also answer fundamental questions about geographical differences in structural characteristics across tropical forests. Our data will be vital for testing new estimates of biomass from remote sensing; the UK-led ESA BIOMASS RADAR and NASA GEDI laser missions will both estimate pan-tropical C stocks by relying on allometric relationships between forest height and biomass. Our work will feed into these two missions through long-standing collaborations with the lead scientists. More generally, the large number of tree measurements we will collect would be of great interest to researchers in tropical ecology, forestry, biodiversity, remote sensing and C mapping, among others.\r\n\r\nA key aim of the project is to ensure the widest use of our results, by making our data and tools publicly available. We will work with partners to explore routes for commercial developments and input into government policy, particularly relating to forest management and C mapping and mitigation. Lastly, we will make our work accessible through a range of outreach activities, including developing links between a school in the Amazon and UK schools, to raise awareness of scientific, conservation and policy issues surrounding tropical forests.\r\n\r\nThis project was funded by NERC through grant: NE/N00373X/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 13434 ], "observationcollection_set": [ { "ob_id": 26677, "uuid": "ca0729ec30514a64a6ccd393eacff5f0", "short_code": "coll", "title": "Weighing trees with lasers project: Terrestrial Laser Scanner data collection", "abstract": "This dataset collection is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations which, have been re-surveyed at different times. The terrestrial laser scanner (TLS) was able to scan 1000s of trees in tropical forests on three continents: including Amazonia, the Congo Basin and SE Asia. The laser data measured 3D tree volume and biomass non-destructively to within a few percent of the best current estimates, made by destructive harvesting and weighing.\r\n\r\nThe project scanned all trees in multiple permanent sample plots (PSPs) spanning a range of soil fertility and productivity gradients (24 x 1 ha PSPs in total). The aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents, or whether they differ significantly, and require continental level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data including: UAVs for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy." }, { "ob_id": 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": [ 192596, 192598, 192599, 192600, 192601, 192602, 192603, 192604, 192605, 192606, 192608 ], "onlineresource_set": [ 82578 ] }, { "ob_id": 39495, "uuid": "f4654030223445b0bac63a23aaa60620", "title": "ESA Snow Climate Change Initiative (Snow_cci): Fractional Snow Cover in CryoClim, v1.0", "abstract": "This dataset contains the CryoClim Daily Snow Cover Fraction (snow on ground) product, produced by the Snow project of the ESA Climate Change Initiative programme.\r\n\r\nFractional snow cover (FSC) on the ground indicates the area of snow observed from space on land surfaces, in forested areas compensated for the effect of trees hiding the ground surface snow cover under the forest canopy. The FSC is given in percentage (%) per grid cell. \r\n\r\nThe global snow_cci CryoClim fractional snow cover (FSC) product is available at 0.05° grid size (about 5 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included. \r\n\r\nThe CryoClim FSC time series provides daily products for the period 1982 – 2019. \r\n\r\nThe CryoClim FSC product is based on a multi-sensor time-series fusion algorithm combining observations by optical and passive microwave radiometer (PMR) data. The product combines an historical record of AVHRR sensor data with PMR data from the SMMR, SSM/I and SSMIS sensors. \r\n\r\nThe overall aim of the CryoClim FSC climate data record is to provide one of the longest snow cover extent time series available with global coverage and without hindrance from clouds and polar night. This has been achieved by utilising the best features of optical and passive microwave radiometer observations of snow using a sensor-fusion algorithm generating a consistent time series of global FSC products (Solberg et al. 2014, 2015; Rudjord et al. 2015). \r\n\r\nThe snow_cci project has advanced the original CryoClim binary product to an FSC product. The thematic variable represents snow on the ground (SCFG). \r\n\r\nAVHRR sensors aboard the satellites NOAA-7, -9, -11, -14, -16, -18, -19 have been used as the optical data source, and SMMR, SSM/I and SSMIS sensors aboard the Nimbus-7, DMSP F8, DMSP F10, DMSP F11, DMSP F13, DMSP F14, DMSP F15, DMSP F16, DMSP F17 and DMSP F18 satellites, respectively, have been used as PMR data source. To have the best possible input data quality, we have used fundamental climate data records (FCDRs) developed by EUMETSAT CM SAF for AVHRR (Karlson et al. 2020) and PMR (Fenning et al. 2017).\r\n\r\nThe optical algorithm component processes all available swaths from AVHRR GAC. The calculations are based on a Bayesian approach using a set of signatures (instrument channel combinations) and statistical coefficients. For each pixel of the swath, the probabilities for the surface classes snow, bare ground and cloud are estimated. The statistical coefficients are based on pre-knowledge of the typical behaviour of the surface classes in the different parts of the electromagnetic spectrum.\r\n\r\nThe algorithm for PMR is also based on a Bayesian estimation approach. For SSM/I and SSMIS four snow classes were defined to model the snow surface state. For SMMR two classes were considered. The algorithm estimates the probability for each snow class given the PMR measurements. Land cover data are included to improve the performance of the Bayesian algorithm. This made it possible to construct a Bayesian estimator for each land cover regime. \r\n\r\nThe multi-sensor multi-temporal fusion algorithm (Rudjord et al. 2015; Solberg et al. 2017) is based on a hidden Markov model (HMM) simulating the snow states based on observations with PMR and optical sensors. The basic idea is to simulate the states the snow surface goes through during the snow season with a state model. The states are not directly observable, but the remote sensing observations give data describing the snow conditions, which are related to the snow states. The HMM solution represents not only a multi-sensor model but also a multi-temporal model. The sequence of states over time is conditioned to follow certain optimisation criteria.\r\n\r\nThe advancement from binary to fractional snow cover carried out by snow_cci has followed two main paths: First, we introduced more HMM states to be able to classify the snow cover into 10% FSC intervals. However, introducing 100 primary states to obtain 1% FSC intervals would not give a stable model. For obtaining higher precision, we have interpolated between HMM states using a secondary Viterbi sequence. The two probabilities are used as weights to estimate the FSC.\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 FSC 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 FSC 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 together with the Norwegian Meteorological Institute (MET Norway) responsible for the FSC product development and generation from satellite data. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation.\r\n\r\nFor the whole time series, there are 27 days with neither optical nor PMR retrieval. These are individual days and not series of days in a row. The multi-sensor time-series algorithm handles this by making a best estimate of snow cover, based on days both prior to and following after the lack of data. This will not reduce the quality of the snow maps much for days without data as long as they are just individual days.\r\nThe algorithm estimating the uncertainty associated with the FSC maps needs observations of covariates from the same day as the time stamp of the FSC product. These covariates are partly based on data from PMR sensors. Hence, estimates of uncertainty could not be produced for days lacking PMR acquisitions. Most days lacking PMR are in the period 1982-1988 (53 days), and there are only two cases after that (in 2008).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-01-10T01:56:07", "updateFrequency": "notPlanned", "dataLineage": "The snow_cci CryoClim FSC products are based on fundamental climate data records (FCDRs) developed by EUMETSAT CM SAF for AVHRR (Karlson et al. 2020) and PMR (Fenning et al. 2017). These have been used to assure having the best possible temporal quality of input data. The FCDRs are based on optical data from AVHRR sensors aboard the satellites NOAA-7, -9, -11, -14, -16, -18, -19, and PMR data from SMMR, SSM/I and SSMIS sensors aboard the DMSP F8, DMSP F11, DMSP F13 and DMSP F17 satellites, respectively. \r\n\r\nThe snow_cci CryoClim FSC processing chain includes retrieval of fractional snow cover per grid cell for all grid cells. Permanent snow and ice areas as well as water bodies are masked in the FSC products using the corresponding classes from the Land Cover CCI map of the year 2000 as auxiliary layers. All FSC products are prepared according to the CCI data standards.\r\n\r\nThe processing chain was developed by Norwegian Computing Center (Norsk Regnesentral, NR) and Norwegian Meteorological Institute (MET Norway), 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.\r\n\r\nReferences:\r\n\r\nFennig, K., Schröder, M. and Hollmann, R., 2017. Fundamental Climate Data Record of Microwave Imager Radiances, Edition 3, Satellite Application Facility on Climate Monitoring. https://doi.org/10.5676/EUM_SAF_CM/FCDR_MWI/V003\r\n\r\nKarlsson, K.-G., Anttila, K., Trentmann, J., Stengel, M., Solodovnik, I., Meirink, J. F., Devasthale, A., Kothe, S., Jääskeläinen, E., Sedlar, J., Benas, N. van Zadelhoff, G.-J., Stein, D., Finkensieper, S., Håkansson, N., Hollmann, R., Kaiser, J., and Werscheck, M. 2020. CLARA-A2.1: CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data - Edition 2.1, Satellite Application Facility on Climate Monitoring. https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V002_01", "removedDataReason": "", "keywords": "ESA, CCI, Snow, Snow Cover Fraction, Snow Cover, Sensor Fusion, AVHRR, SMMR, SSM/I, SSMIS", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-08-08T13:15:33", "doiPublishedTime": "2023-08-08T13:17:01", "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": 39794, "dataPath": "/neodc/esacci/snow/data/scfg/CryoClim/v1.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 32341088431, "numberOfFiles": 13696, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 8858, "startTime": "1982-01-01T00:00:00", "endTime": "2019-06-30T23:59:59" }, "resultQuality": { "ob_id": 3649, "explanation": "The unbiased estimate of the root mean square error of the snow cover fraction is adapted from the approach of Salberg et al. (2022) and is added as an uncertainty layer in each product. The snow_cci CryoClim FSC products are matching the CCI data standards version 2.3, released in July 2021. Salberg, A.-B., Solberg, R. (2022) ESA CCI+ Snow ECV, Option 2 - Fractional Snow Cover in CryoClim: Annex to End-to-End ECV Uncertainty Budget, version 2.0, July 2022.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-04-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 39708, "uuid": "f1fd006ea7b74b8abe2dd9bd2404125b", "short_code": "cmppr", "title": "Composite process for the ESA Snow Climate Change Initiative Fractional Snow Cover in CryoClim, v1.0", "abstract": "The global snow_cci CryoClim fractional snow cover (FSC) product is available at 0.05° grid size (about 5 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included. \r\n\r\nThe CryoClim FSC time series provides daily products for the period 1982 – 2019. \r\n\r\nThe CryoClim FSC product is based on a multi-sensor time-series fusion algorithm combining observations by optical and passive microwave radiometer (PMR) data. The product combines an historical record of AVHRR sensor data with PMR data from SMMR, SSM/I and SSMIS sensors. \r\nThe overall aim of the CryoClim FSC climate data record is to provide one of the longest snow cover extent time series available with global coverage and without hindrance from clouds and polar night. This has been achieved by utilising the best features of optical and passive microwave radiometer observations of snow using a sensor-fusion algorithm generating a consistent time series of global FSC products (Solberg et al. 2014, 2015; Rudjord et al. 2015). \r\n\r\nThe snow_cci project has advanced the original CryoClim binary product to an FSC product. The thematic variable represents snow on the ground (SCFG). \r\n\r\nAVHRR sensors aboard the satellites NOAA-7, -9, -11, -14, -16, -18, -19 have been used as the optical data source, and SMMR, SSM/I and SSMIS sensors aboard the DMSP F8, DMSP F11, DMSP F13 and DMSP F17 satellites, respectively, have been used as PMR data source. To have the best possible input data quality, we have used fundamental climate data records (FCDRs) developed by EUMETSAT CM SAF for AVHRR (Karlson et al. 2020) and PMR (Fenning et al. 2017).\r\n\r\nThe optical algorithm component processes all available swaths from AVHRR GAC. The calculations are based on a Bayesian approach using a set of signatures (instrument channel combinations) and statistical coefficients. For each pixel of the swath, the probabilities for the surface classes snow, bare ground and cloud are estimated. The statistical coefficients are based on pre-knowledge of the typical behaviour of the surface classes in the different parts of the electromagnetic spectrum.\r\n\r\nThe algorithm for PMR is also based on a Bayesian estimation approach. For SSM/I and SSMIS four snow classes were defined to model the snow surface state. For SMMR two classes were considered. The algorithm estimates the probability for each snow class given the PMR measurements. Land cover data are included to improve the performance of the Bayesian algorithm. This made it possible to construct a Bayesian estimator for each land cover regime. \r\n\r\nThe multi-sensor multi-temporal fusion algorithm (Rudjord et al. 2015; Solberg et al. 2017) is based on a hidden Markov model (HMM) simulating the snow states based on observations with PMR and optical sensors. The basic idea is to simulate the states the snow surface goes through during the snow season with a state model. The states are not directly observable, but the remote sensing observations give data describing the snow conditions, which are related to the snow states. The HMM solution represents not only a multi-sensor model but also a multi-temporal model. The sequence of states over time is conditioned to follow certain optimisation criteria.\r\n\r\nThe advancement from binary to fractional snow cover carried out by snow_cci has followed two main paths: First, we introduced more HMM states to be able to classify the snow cover into 10% FSC intervals. However, introducing 100 primary states to obtain 1% FSC intervals would not give a stable model. For obtaining higher precision, we have interpolated between HMM states using a secondary Viterbi sequence. The two probabilities are used as weights to estimate the FSC.\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 FSC 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, 61130, 61131, 62643, 62644, 62645 ], "vocabularyKeywords": [], "identifier_set": [ 12668 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 192622, 192623, 192624, 192625, 192626, 192627, 192629, 192628, 192630, 192631, 192632, 192633, 192634, 192635, 193176, 193177, 193178 ], "onlineresource_set": [ 82586, 82588, 82589, 82587, 83190, 83410, 83411 ] }, { "ob_id": 39499, "uuid": "6b143f0feab14045b91556438b48cceb", "title": "TCOM-CH4: TOMCAT CTM and Occultation Measurements based daily zonal stratospheric methane profile dataset (1991-2021) constructed using machine-learning", "abstract": "This dataset contains daily zonal stratospheric methane profile outputs between 1991-2021 simulated by the TOMCAT model.\r\n\r\nThe TOMCAT simulation is performed at T64L32 resolution that is similar to the one used in Dhomse et al., (2021, 2022) for 1991-2021 time period. \r\n\r\nCollocated methane profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, differences are calculated for each zonal bins for 46 height levels (15km to 60km). Then separate XGBoost regression models are trained for the methane differences between TOMCAT and measurements at each level for a given latitude bin. \r\n\r\nThe same model is used for all day/night time (2 X11323 days) TOMCAT output sampled at 1.30 am and 1.30 pm local time at the equator. Thus bias corrections for a given model grid are added to the original TOMCAT day and night time profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values. For more details regarding this methodology see the associated presentation on Zenodo.\r\n\r\nDataset also includes two files containing daily mean zonal mean methane profiles on height (15-60 km) and pressure (300-0.1 hPa) levels:\r\n\r\n- zmch4_TCOM_hlev_T2Dz_1991_2021.nc – height level data (15 to 60 km)\r\n- zmch4_TCOM_plev_T2Dz_1991_2021.nc – pressure level data (300 to 0.1 hPa)\r\n\r\nThe exact cause of unusual methane variations during 1991-1994 is unknown, however some recent studies argue that it could be due to sudden changes in methane loss processes following Mount Pinatubo eruption as well as significant changes in methane emissions following collapse of the Soviet Union.", "creationDate": "2022-12-14T12:37:22.589672", "lastUpdatedDate": "2022-12-14T12:36:29", "latestDataUpdateTime": "2022-12-14T12:36:29", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "stratosphere, methane profiles, satellite, machine-learning", "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": null, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39527, "uuid": "7bb74f45412947aeb044e59d3bd653f4", "short_code": "comp", "title": "TOMCAT CTM and Occultation Measurements based daily zonal stratospheric methane profile dataset (1991-2021) constructed using machine-learning", "abstract": "TOMCAT simulation is performed at T64L32 resolution that is similar to the one used in Dhomse et al., (2021, 2022) for 1991-2021 time period. Collocated methane profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, differences are calculated for each zonal bins for 46 height levels (15km to 60km). Then separate XGBoost regression models are trained for the methane differences between TOMCAT and measurements at each level for a given latitude bin. Same model is used for all day/night time (2 X11323 days) TOMCAT output sampled at 1.30 am and 1.30 pm local time at the equator. This way we get bias corrections for a given model grid that are added to the original TOMCAT day and night time profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values. For more details see attached presentation.\r\n\r\nDataset also includes two files containing daily mean zonal mean methane profiles on height (15-60 km) and pressure (300-0.1 hPa) levels" }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "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": [], "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": [ 192647, 192648, 192649, 192650, 192651, 192652, 192653 ], "onlineresource_set": [ 82603 ] }, { "ob_id": 39500, "uuid": "a35f1fba6c1b43579f6ea9f7c0d00314", "title": "TCOM-N2O: TOMCAT CTM and Occultation Measurements based daily zonal stratospheric nitrous oxide profile dataset (1991-2021) constructed using machine-learning", "abstract": "This dataset contains daily zonal stratospheric nitrous oxide profile outputs between 1991-2021 simulated by the TOMCAT model.\r\n\r\nThe TOMCAT simulation is performed at T64L32 resolution that is similar to the one used in Dhomse et al., (2021, 2022) for 1991-2021 time period. Model profile are sample at ACE-FTS (2004-present) measurement collocation, so that model output is at the nearest lat/lon and time. Then collocated N2O profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Corrections for overlapping latitude are averaged to ensure that mean correction terms do not have sharp edges\r\n\r\nInitially, differences are calculated for each zonal bins for 51 height levels (10km to 60km). Then separate XGBoost regression models are trained for the N2O differences between TOMCAT and measurements at each level for a given latitude bin. Same model is used for all day/night time (2 X11323 days) TOMCAT output sampled at 1.30 am and 1.30 pm local time at the equator. Bias corrections for a given model grid are calculated using XGBoost and are added to the original TOMCAT day and night time profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values. For more details see associated presentation on Zenodo.\r\n\r\nDataset also includes two files containing daily mean zonal mean N2O profiles on height (15-60 km) and pressure (300-0.1 hPa) levels:\r\n\r\nzmn2o_TCOM_hlev_T2Dz_1991_2021.nc – height level data (15 to 60 km)\r\nzmn2o_TCOM_plev_T2Dz_1991_2021.nc – pressure level data (300 to 0.1 hPa)\r\n\r\nNote that there is no observational constrain for 1991-2003 time period, hence correction terms assume that there are no significant discontinuities in ERA5 reanalysis fields that are used drive TOMCAT transport.", "creationDate": "2022-12-14T12:37:22.589672", "lastUpdatedDate": "2022-12-14T12:36:29", "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": "machine learning, stratosphere, nitrous oxide profiles, chemical model", "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": null, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39526, "uuid": "0dae9a32133941daa422df4e1c8598db", "short_code": "comp", "title": "TOMCAT CTM and Occultation Measurements based daily zonal stratospheric nitrous oxide profile dataset [1991-2021] constructed using machine-learning", "abstract": "TOMCAT simulation is performed at T64L32 resolution that is similar to the one used in Dhomse et al., (2021, 2022) for 1991-2021 time period. Model profile are sample at ACE-FTS (2004-present) measurement collocation, so that we get model output at nearest lat/lon and time. Then collocated N2O profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Corrections for overlapping latitude are averaged to ensure that mean correction terms do not have sharp edges\r\n\r\nInitially, differences are calculated for each zonal bins for 51 height levels (10km to 60km). Then separate XGBoost regression models are trained for the N2O differences between TOMCAT and measurements at each level for a given latitude bin. Same model is used for all day/night time (2 X11323 days) TOMCAT output sampled at 1.30 am and 1.30 pm local time at the equator. Bias corrections for a given model grid are calculated using XGBoost and are added to the original TOMCAT day and night time profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values. For more details see attached presentation." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "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": [], "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": [ 192658, 192659, 192660, 192654, 192655, 192656, 192657 ], "onlineresource_set": [ 82614 ] } ] }