Result List
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{ "count": 11555, "next": "https://api.catalogue.ceda.ac.uk/api/v3/results/?format=api&limit=100&offset=8400", "previous": "https://api.catalogue.ceda.ac.uk/api/v3/results/?format=api&limit=100&offset=8200", "results": [ { "ob_id": 32583, "uuid": "90fba9e51daa4cf0a722dd9b9fb96bc1", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ness-aws/data/ness-aws", "numberOfFiles": 0, "volume": 0, "fileFormat": "Data are netCDF and BADC-CSV formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32582, "uuid": "f75c4b7739f34e02ae1a52b793c3b839", "short_code": "ob", "title": "University of Liverpool Botanical Gardens (Ness) Long-Term Monitoring: Automatic Weather Station 5 minute surface observations (2010 onwards)", "abstract": "This dataset contains 5 minute surface meteorological observations from an automatic weather station (AWS) deployed at the University of Liverpool's Ness Botanical Gardens in Cheshire, UK from 2010 onwards.\r\n\r\nThese data complement data from this site originally collected by the Met Office until 0900 UTC on 1st June 2011. Those earlier data are available within the MIDAS Open dataset collection provided under the Open Government Licence by the Met Office. Those data are also available via the Centre for Environmental Data (CEDA) Archive. This results in an overlap between the Met Office data for this site and the 5 minute AWS data within this dataset. \r\n\r\nBoth BADC-CSV and NetCDF formatted versions of the data have been provided to aid greatest usability possible of these data." }, "onlineresource_set": [] }, { "ob_id": 32591, "uuid": "ef6ee2d90a594541b46989f70fc0a13d", "short_code": "result", "curationCategory": "A", "dataPath": "/neodc/nceo_biomass_maps/data/kenya/v21.0/2015/", "numberOfFiles": 11, "volume": 575877009, "fileFormat": "GeoTiff \r\nPixel type: unsigned integer \r\nPixel Depth: 16 Bit NoData Value: 65535", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32592, "uuid": "653fdd814dba4103a301221955781e35", "short_code": "ob", "title": "NCEO Kenya forest aboveground biomass map 2015 v21.0", "abstract": "The NCEO Kenya forest aboveground biomass map shows aboveground woody biomass (AGB) in Kenyan forests. Forest areas include vegetated wetlands and wooded grassland for the year 2015. The map was generated by combining field inventory plots (KFS) with Advanced Land Observing Satellite (ALOS-2), Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) and multispectral optical data (NASA Landsat 8), by means of a Random Forests algorithm within a k-Fold calibration/validation framework. \r\n\r\nThe characterization of carbon stocks and dynamics at the national level is critical for countries engaging in climate change mitigation and adaptation strategies. However, several tropical countries, including Kenya, lack the essential information typically provided by a complete national forest inventory. These data were produced by the National Centre for Earth Observation (NCEO), University of Leicester, in collaboration with the Kenya Forest Service (KFS) with funding from the NCEO ODA Programme. \r\n\r\nKnown Issues: Residual scan line corrector (SLC) effects due to the use of the SLEEK land cover product as a retrieval mask (derived from Landsat imagery) are visible in some areas" }, "onlineresource_set": [] }, { "ob_id": 32599, "uuid": "ac0c6a6355fe41f8b9d317ead29af4f6", "short_code": "result", "curationCategory": "A", "dataPath": "/neodc/nceo_biomass_maps/data/mexico/v5.0/2010/", "numberOfFiles": 6, "volume": 540158114, "fileFormat": "GeoTIFF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32598, "uuid": "32274aca008b4f7799e8cea69ad3508e", "short_code": "ob", "title": "Yucatan Peninsula and Central Mexico above ground biomass maps 2010 v5.0", "abstract": "This dataset contains the biomass maps for epoch 2: 2010 (GlobBiomass project reference year) for Yucatan peninsula and Central Mexico with a 30m spatial resolution. \r\n\r\nThe dataset contains 4 raster files corresponding to 2 areas in Mexico; the Yucatan Peninsula AGB-MEX_Yucatan2010_v5.tif, AGB_MEX_Yucatan2010_QA_v5.tif, Central Mexico, AGBMEX_Central2010_v5.tif and AGB_MEX_Central2010_QA_v5.tif. \r\n\r\nThe maps show aboveground woody biomass in Mexican forests and were generated by combining the probabilistic outputs. from a Maximum Entropy (MaxEnt) algorithm. Field inventory plots (CONAFOR, INFyS) were used in combination with SAR (JAXA ALOS PALSAR) and optical data (NASA Landsat 7), as well as a digital elevation model (NASA SRTM).,An empirical linear regression model with AGB as dependant variable (Ln-transformed) and SAR backscatter intensity (DN values) of the HV polarization as the independent variable was fitted to estimate AGB over mangrove and other wetland vegetation\r\n\r\nFurther information can be found in the documnetation section and in a read me file archived with the data" }, "onlineresource_set": [] }, { "ob_id": 32606, "uuid": "045819681b2b4a8186fd3ef21e3f9990", "short_code": "result", "curationCategory": "B", "dataPath": "/badc/ukmo-nimrod/data/single-site/deanhill/raw-dual-polar/", "numberOfFiles": 652, "volume": 710833347529, "fileFormat": "Data are in NIMROD data format. See linked documentation for further details.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32605, "uuid": "5b22789f362c43f3b3d1c65bc30c30ee", "short_code": "ob", "title": "Deanhill C-band rain radar dual polar products", "abstract": "Dual-polar products from the Met Office's Deanhill C-band rain radar, Whiteparish, Wiltshire, England. Data include augmented ldr (linear depolarization ratio) and zdr (differential reflectivity) scan data (both long and short pulse). The radar is a C-band (5.3 cm wavelength) radar and data are received by the Nimrod system at 5 minute intervals." }, "onlineresource_set": [] }, { "ob_id": 32608, "uuid": "7c621b4acdee44a69f37c483e0cf9d00", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/ghg/data/cci_plus/CO2_TAN_OCFP/v1.1/", "numberOfFiles": 0, "volume": 0, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 32610, "uuid": "3ae1d321251b4338809ba8fafa21c68e", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/ghg/data/cci_plus/CO2_OC2_FOCA/v9.0/", "numberOfFiles": 1790, "volume": 8044862560, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32609, "uuid": "b0de069568a141b0b074ca0f7cee004b", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column averaged carbon dioxide from OCO-2 generated with the FOCAL algorithm, version 09", "abstract": "This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (XCO2), using the fast atmospheric trace gas retrieval for OCO2 (FOCAL-OCO2). The FOCAL-OCO2 algorithm which has been setup to retrieve XCO2 by analysing hyper spectral solar backscattered radiance measurements from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. FOCAL includes a radiative transfer model which has been developed to approximate light scattering effects by multiple scattering at an optically thin scattering layer. This reduces the computational costs by several orders of magnitude. FOCAL's radiative transfer model is utilised to simulate the radiance in all three OCO-2 spectral bands allowing the simultaneous retrieval of CO2, H2O, and solar induced chlorophyll fluorescence. The product is limited to cloud-free scenes on the Earth's day side. This dataset is also referred to as CO2_OC2_FOCA.\r\n\r\nThis version of the data (v09) was produced as part of the European Space Agency's (ESA) \r\nClimate Change Initiative (CCI) Greenhouse Gases (GHG) project (GHG-CCI+, http://cci.esa.int/ghg)\r\nand got co-funding from the Univ. Bremen and EU H2020 projects CHE (grant agreement no. 776186) and VERIFY (grant agreement no. 776810).\r\n\r\nWhen citing this data, please also cite the following peer-reviewed publications:\r\n\r\nM.Reuter, M.Buchwitz, O.Schneising, S.Noël, V.Rozanov, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 1: Radiative Transfer and a Potential OCO-2 XCO2 Retrieval Setup, Remote Sensing, 9(11), 1159; doi:10.3390/rs9111159, 2017\r\n\r\nM.Reuter, M.Buchwitz, O.Schneising, S.Noël, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 2: Application to XCO2 Retrievals from OCO-2, Remote Sensing, 9(11), 1102; doi:10.3390/rs9111102, 2017" }, "onlineresource_set": [] }, { "ob_id": 32613, "uuid": "953978b4331d4ca5af40de128c19f480", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/permafrost/data/active_layer_thickness/L4/area4/pp/v03.0/", "numberOfFiles": 24, "volume": 7139645166, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32612, "uuid": "67a3f8c8dc914ef99f7f08eb0d997e23", "short_code": "ob", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v3.0", "abstract": "This dataset contains permafrost active layer thickness data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v2). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v2 are released in annual files, covering the start to the end of the Julian year. The maximum depth of seasonal thaw is provided, which corresponds to the active layer thickness.\r\n\r\nCase A: This covers the Northern Hemisphere (north of 30°) for the period 2003-2019 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.\r\nCase B: This covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2019 using a pixel-specific statistics for each day of the year." }, "onlineresource_set": [] }, { "ob_id": 32615, "uuid": "a70fe536f0224d9ba8694d475af434e7", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0", "numberOfFiles": 24, "volume": 3617214388, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32614, "uuid": "6e2091cb0c8b4106921b63cd5357c97c", "short_code": "ob", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost extent for the Northern Hemisphere, v3.0", "abstract": "This dataset contains permafrost extent data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v2). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v2 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures (at 2 m depth) which forms the basis for the retrieval of yearly fraction of permafrost-underlain and permafrost-free area within a pixel. A classification according to the IPA (International Permafrost Association) zonation delivers the well-known permafrost zones, distinguishing isolated (0-10%) sporadic (10-50%), discontinuous (50-90%) and continuous permafrost (90-100%).\r\n\r\nCase A: This covers the Northern Hemisphere (north of 30°) for the period 2003-2019 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. \r\nCase B: This covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2019 using a pixel-specific statistics for each day of the year." }, "onlineresource_set": [] }, { "ob_id": 32617, "uuid": "43b1c13e9d904712a97ee893e7b7dd15", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/aphh/data/delhi/delhiflux/york-gcxgc-fid_voc-inventory/", "numberOfFiles": 46, "volume": 72624481321, "fileFormat": "Data are NASA Ames formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32616, "uuid": "fdb8960260a64c5faf652f8f47c4df81", "short_code": "ob", "title": "APHH: Non-methane volatile organic compound emission inventories from burning studies performed as part of the APHH-INDIA project (DelhiFlux).", "abstract": "This contains gridded non-methane volatile organic compound (NMVOC) emission inventories for India derived as part of burning studies performed during the APHH-INDIA campaign. For data files with more than 1 million rows, NASA AMES metadata headers have been provided as a separate document, which has the identical name of the data it applies to but also includes _metadata.\r\n\r\nFor years 1993, 1994, 1999, 2002, 2005, 2006, 2007, 2010, 2011 and 2016 inventories have been produced in terms of total NMVOC emission from each source sector (kg/km2). There are also two upper limit scenarios of emissions from cow dung cake combustion based on data from PPAC and PPAC supplemented with additional cow dung cake consumption for states now covered by this survey. The speciation factors of NMVOCs released from particular sources are also provided so that these years can be speciated by source simply by multiplying the total emission from each source by the ratio of species released from the source. This allows future users to produce speciated emission inventories for years other than 2011 if they need.\r\n\r\nGridded inventories are also provided for emissions of 21 polycyclic aromatic hydrocarbons for the year 2011 from fuelwood, cow dung cake, charcoal, liquefied petroleum gas and municipal solid waste. These are provided as total PAH emissions from a source with speciation factors also provided to allow speciation should it be required by multiplying the total NMVOC emission from a source by the speciation factors from that source. \r\n\r\nGridded inventories are provided for crop residue burning at 1km2 and 10km2. These were calculated with total agricultural area identified in a state from either NASA MODIS (1 km2) or Ramankutty et al. (2008) (10 km2). A second inventory was produced at 10km2 as it was felt that the NASA data offered little variation within respective states. These have been split into total emissions from each of the 5 emission factors applied, RiceEFyearlyVOCKG (for rice), WheatEFyearlyVOCKG (for wheat, coarse cereal and maize), JowarEFyearlyVOCKG (for Jowar and Bajra), MeanEFyearlyVOCKG (for 9 oilseeds, groundnut, rapeseed, mustard, sunflower, cotton, jute and mesta) and SugarcaneEFyearlyVOCKG (for sugarcane). \r\n\r\nThe inventories were produced using emission factors developed as part of the APHH-INDIA project as well as from a different publication focussed on the burning of crops. The inventories have been developed in the following manner. The emission factors used in this study come from a variety of recently published sources. All emission factors applied in this study included measurement by PTR-ToF-MS, a technique well suited to species released in significant quantities from solid fuel combustion such as small oxygenated species, phenolics and furanics. These species are often missed by GC measurement alone. Preference has been given to emission factors from studies which: (1) have many measurements (n), (2) use samples collected from India or (3) use samples collected from similar countries. Fully speciated emission factors are available from the references given. For residential fuel combustion, the emission factors measured by Stewart et al. (2021a) were used and were developed from 76 combustion experiments of fuel wood, cow dung cake, LPG and MSW samples collected from around Delhi. This study was extremely detailed and measured online, gas-phase, speciated NMVOC emission factors for up to 192 chemical species using dual-channel gas chromatography with flame ionisation detection (DC-GC-FID, n = 51), two-dimensional gas chromatography (GC×GC-FID, n = 74), proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS, n = 75) and solid-phase extraction two-dimensional gas chromatography with time-of-flight mass spectrometry (SPE-GC×GC-ToF-MS, n = 28). Comparison of these emission factors to those obtained in similar studies is provided in Stewart et al. (2021a). The emission factors used as part of this study are larger than those measured by Stockwell et al. (2016), Fleming et al. (2018) and several other studies which were based on gas chromatography techniques alone. The emission factors here measure many more NMVOC species, use techniques which target a range of species which more traditional GC analyses do not detect and make online measurements which minimise loss of intermediate-volatility and semi-volatile organic species, which may be lost through the collection of whole air samples, but have been shown to represent a large proportion of total emissions from biomass burning (Stockwell et al., 2015).\r\n\r\nEmission factors for combustion of crop residues on fields were taken from measurements by Stockwell et al. (2015) made using PTR-ToF-MS of 115 NMVOCs (Stockwell et al., 2015) for wheat straw (n = 6), sugarcane (n=2), rice straw (n=7) and millet (n=2). This study also included the mean crop residue emission factor for 19 food crops, for use when no current emission factor had been comprehensively measured using PTR-ToF-MS. The emission factor applied (38.8 g kg-1) was evaluated against that for crop residues used for domestic combustion in Delhi (37.9 g kg-1). Whilst the values measured by Stockwell et al. (2015) and Stewart et al. (2021a) were comparable, the value from Stockwell et al. (2015) was used as the crop types were more reflective of the crop residues burnt on fields after harvest, compared to those burnt to meet residential energy requirements. The mean emission factor for crop residue combustion on fields was used for specific crop types with smaller levels of cultivation.\r\nEmissions from coal burning were estimated using a mean emission factor from the combustion of bituminous coal from China (n = 14), a neighbouring Asian country, made using PTR-ToF-MS. Whilst the chemical composition of the coal may be more important than the development status of the country, there was overall a low level of reported residential coal use and this estimate was included for completeness. A total of 89 NMVOCs were identified, which represented 90-96% of the total mass spectra (Cai et al., 2019). \r\n\r\nIndian specific PAH emission factors were recently measured in gas- and particle-phases using PTR-ToF-MS and GC×GC-ToF-MS (Stewart et al., 2021). This dataset provided PAH emission factors collected from combustion of fuel wood (n = 16), cow dung cake (n = 3), crop residue from domestic combustion (n = 3), MSW (n = 3), LPG (n = 1) and charcoal (n = 1) samples. \r\n\r\nHigh resolution, gridded population data for India (WorldPop, 2017) was used at a resolution of 1 km2. Officially, urban populations in India are defined as having a population density > 400 people km-2, 75% of men employed in non-agricultural industries and a population of town > 5000 people. Rural populations in India cannot be identified simply by having a population density of < 400 people km-2, as some states such as Uttar Pradesh have an average population density of around 800 people km-2. Rural grid squares were therefore identified by calculating the population density threshold in each state in which the sum of the 1km2 grid squares below this threshold correctly reproduced the rural populations in these states from the 2001 and 2011 censuses (Government of India, 2014). A small uncertainty existed over the exact population of India and we used population statics indicated by the 2011 census. NMVOC and PAH emissions from domestic solid fuel combustion were plotted against this high-resolution population data in the R statistical programming language at 1 km2 for 2001 and 2011, with the population datasets scaled to the percentage changes in Indian population indicated by the World Bank for additional years of interest. \r\n\r\nPreference was given to large fuel usage surveys which included tens to hundreds of thousands of respondents. The Household Consumption of Goods and Services in India survey by the National Sample Survey Office (NSSO, 2007a, 2012a, 2014) gave state-wise kg capita-1 fuel wood, LPG, charcoal and coal burning statistics for rural and urban environments and was used for the years 2004-2005, 2009-2010 and 2011-2012. NMVOC emissions for these years were calculated by multiplying the NMVOC emission factor for the fuel, by the yearly fuel consumption per capita by the population of the grid cell. \r\n\r\n\r\nData were collected from additional large surveys previously conducted. These surveys collected data in terms of the number of households using specific fuels per 1000 households in different Indian states in rural and urban environments. The Fifth Quinquennial Survey on Consumer Expenditure provided data for 1993-1994 (NSSO, 1997), the Energy Sources of Indian Households for Cooking and Lighting provided data for years 2004-2005, 2009-2010 and 2010-2011 (NSSO, 2007b, 2012b, 2015) and the Household Consumer Expenditure and Employment-Unemployment Situation in India for 2002 and 2006-2007 (NSSO, 2003, 2008). The National Family Health Survey presented India-wide fuel use as a percentage of the population. To reflect spatial variation in fuel use, the raw data from these surveys were accessed (from the DHS Programme, U.S. Agency for International Development), extracted through the SPSS statistics software package and processed in the R programming language. This increased fuel usage data availability as the number of households per 1000 households using specific fuels in Indian states and covered the years 1992-1993, 1998-1999, 2005-2006 and 2015-2016 (International Institute for Population Sciences, 1995, 2000, 2007, 2017). These were extensive datasets with 1992-1993, 1998-1999 and 2005-2006 surveying just under 100,000 households and 2015-2016 around 600,000 households.\r\n\r\nTo allow the incorporation of data from years which were based on the number of households using a particular fuel per 1000 households (1993, 1994, 1999, 2002, 2006, 2007 and 2016), a scaling factor was developed. The scaling factor was based on the ratio of fuel use in the state from years where per capita data was available. It was possible to link the Household Consumption of Goods and Services in India and the Energy Sources of Indian Households for Cooking and Lighting surveys for the years 2005, 2010 and 2011. This was done using years where the number of households per 1000 households and kg capita-1 fuel usage statistics were available, as it was possible to calculate the amount of fuel a primary user would use. The fuel use of a primary user here was defined as the amount of fuel a person would burn who was recorded to use a specific fuel type. For example, if the per capita consumption in the Household Consumption of Goods and Services survey in India for fuel wood was 10 kg per capita per 30 days, and the Energy Sources of Indian Households for Cooking and Lighting survey showed 250 households per 1000 households used fuel wood, then the fuel use was estimated to be 40 kg per primary user per 30 days. This was achieved by multiplying the per capita usage for a particular fuel type by the inverse of the ratio of fuel usage in that state in rural or urban environments. The amount of fuel a primary user would use was then used to estimate the amount of fuel consumed per capita in years where only usage per 1000 household statistics were available.\r\n\r\nCow dung cake consumption was only reported as number of households per 1000 in these surveys and the amount of cow dung cake burnt per primary user was determined based on the energy density compared to fuel wood. This was done using calorimetry data which showed that cow dung cake was 1.3-1.9 times less efficient than fuel wood (EPA, 2000; Gadi et al., 2012). For this reason, the amount of fuel per primary user for fuel wood in a state has been multiplied by 1.6 to give the equivalent amount of cow dung cake a user would need to burn for their cooking needs. Upper and lower estimates for cow dung cake consumption were based on the range 1.3-1.9. This was then converted to fuel use per capita in kg per user per 30 days by rearranging E2. This has been evaluated to validate this approach, which estimated Indian cow dung cake consumption to be in the range 25.7-79.7 Tg yr-1 from 1993-2016. This was generally towards the lower end of consumption values previously reported of 35-128 Tg yr-1 (Habib et al., 2004). For this reason, emission inventory estimates were also compared to those produced using cow dung cake consumption based on the TERI Energy Data Directory and Yearbook (TEDDY) 2012/2013 data and a study from the Petroleum Planning & Analysis Cell (PPAC) from 2016 with population indicated at the 2011 level (TEDDY, 2012; PPAC, 2016).\r\n\r\n\r\nThe amount of MSW burnt was estimated using an established approach (IPCC, 2006; Wiedinmyer et al., 2014) with revised inputs for India based on per capita MSW generation from over 300 Indian cities (Annepu et al., 2012), state wise MSW collection figures (CPCB, 2013) as well as estimates of the amount of urban (NEERI, 2010) and rural MSW burnt (World Bank, 2012). This estimate does not include incineration for electrical power generation. \r\n\r\nWiedinmyer et al. (2014) assessed worldwide emissions from MSW burning based on IPCC guidelines (IPCC, 2006). The approach used here was similar, with modifications to the input data which made them more specific to India. The approach split the amount of MSW burnt into the MSW burnt by rural and urban populations in the country. For rural populations this was given by per capita rural MSW generation multiplied by the population of rural grid cell multiplied by the fraction of MSW burnt residentially. Per capita rural MSW generation was set at the lower limit indicated by the World Bank for South Asia of 0.12 kg capita-1 day-1 and evaluated in the range 0.08 kg capita-1 day-1 (Parmar and Pamnani, 2018) to 0.12 kg capita-1 day-1 (World Bank, 2012). The fraction of MSW burnt rurally was set to 0.6 which was the IPCC estimate (IPCC, 2006) and was further supported by a recent study which showed that only around 40% of rural MSW was collected in South Asia (Kaza et al., 2018).\r\n\r\nThe fraction of MSW burnt for an urban population was estimated by the sum of two calculations. The first was for street MSW burning which was calculated by per capita urban MSW generation multiplied by the population of urban grid cell multiplied by the fraction of MSW which was not collected multiplied by the fraction burnt.\r\n\r\nThe weighted per capita urban MSW generation was calculated by averaging per capita MSW generation statistics from 366 Indian cities by state (Annepu et al., 2012). The fraction of MSW which was uncollected was calculated from the Central Pollution Control Board (CPCB), as the difference in the amount of MSW generated and collected (CPCB, 2013). Urban per capita MSW generation was scaled to its estimated change for different years of interest.\r\n\r\nThe second calculation was for the MSW burnt on landfill sites, which was calculated by the MSW per capita produced in urban environments, multiplied by the urban population, multiplied by the fraction collected in an urban environment multiplied by the fraction burnt at the landfill site. The fraction of MSW collected came from CPCB statistics, but was reduced by 17-50% due to the informal recycling sector, based on very limited data from studies focussed on MSW recovery by the informal sector which showed 17% recovery in Delhi (Talyan et al., 2008), 20% recovery at a landfill site in Pune (Annepu et al., 2012), 4% in Pondicherry (Rajamanikam et al., 2014) and up to 40-50% in Mohali (Nandy et al., 2015). This was due to the large contribution of the informal recycling sector to recycling in India, where waste was collected by waste merchants, garbage collectors and waste pickers from highways, waste depots and landfill sites. This was an important consideration in India as studies have shown recovery of between 8.5-80 kg of material per picker per day and large cities such as Delhi having 80,000-100,000 pickers (Nandy et al., 2015). The fraction of waste burnt in a dump (Bfrac,dump) was given by NEERI who estimated that 10% of landfill MSW in Mumbai was burnt (NEERI, 2010). This was reinforced by a further study which examined the amount of waste burnt based on satellite studies of a landfill site in India which showed that approximately 10% of the waste that entered the site each day ended up being burnt (Sharma et al., 2019). Bfrac,dump was notably lower here (0.1) than in Wiedinmyer et al. (2014) (0.6) which was based on the 2006 IPCC Guidelines for National GHG Inventories. The estimate used in this study represented a conservative estimate of NMVOC emissions from landfill fires. Due to lack of reliable data in establishing Bfrac,dump, and the associated uncertainty, the sensitivity of urban landfill burning emissions over the range 0.1-0.6 was evaluated as part of the uncertainty range given in this study. This provided the upper limit to the uncertainty range of the potential amount of landfill waste burnt. This depicts scenarios before the new MSW management rules in 2016. \r\n\r\nNMVOC emissions from crop residue burning on fields in India were estimated to evaluate the relative importance of different burning sources using the most up-to-date input data currently available. A calculation was carried out for 2011, as NMVOC emissions from crop-residue burning on fields showed little year-on-year variation from 1995-2009 (Jain et al., 2014). The residue generated from the cultivation of four main categories of crops was estimated. The amount of crop types produced in each state (Ministry of Agriculture, 2012) was collated for cereals (rice, wheat, coarse cereals, maize, jowar, bajra), oilseeds (groundnut, rapeseed, mustard, sunflower and 9 oilseeds), fibres (cotton, jute and mesta) and sugarcane. The amount burnt was calculated using India specific estimates of the residue to crop ratio, dry matter fraction and fraction burnt (Jain et al., 2014). Emissions were estimated using factors from recent studies of crop residues routinely burnt on fields using PTR-ToF-MS (Stockwell et al., 2015). When the exact residue was measured (e.g., rice straw, wheat straw, sugarcane and millet) the correct emission factor was used. For cases where the exact residue was not measured, the mean reported crop residue emission factor was used. The spatial distribution of croplands was then either indicated using agricultural land identified by the high-resolution 500 m NASA MODIS land use product reduced to 1 km2 resolution or through croplands identified at 10 km2 through evaluation of the distribution of agricultural lands (Ramankutty et al., 2008). The total amount of crop residue burnt in a state was calculated using the approach given in Jain et al. (2014) but with the up-to-date inputs discussed. \r\n\r\nThe inventories were produced by Gareth Stewart at the University of York. Full details of the methodology are provided in the publication associated with these inventories. \r\n\r\nThe inventories provided here cover most of the land mass of India, but may vary slightly compared to those presented in the publication. This is associated with the North of India, particularly around the Pakistan and Chinese borders. This is due to how the boundaries of India were defined in the base data used for this study (WorldPop and GADM) and changes to states in the North of India after the period of interest (i.e. formation of Ladakh in 2019).\r\nAn inventory for coal has not been included due to low total emissions. \r\n\r\nAcronyms\r\n\r\nPAH = Polycyclic aromatic hydrocarbon\r\n\r\nNMVOCs = non-methane volatile organic compounds\r\n\r\nMSW = Municipal solid waste\r\n\r\nCrop = agricultural crop residue \r\n\r\nWood = Fuel wood\r\n\r\nDung = Cow dung cakes\r\n\r\nCharcoal = Charcoal fuel\r\n\r\nCoal = Coal fuel\r\nAPHH-INDIA = Atmospheric Pollution and Human Health in an Indian Megacity project\r\n\r\n\r\nRiceEFyearlyVOCKG = Total NMVOC emission in 2011 from agricultural on field burning of agricultural rice residues\r\n\r\nWheatEFyearlyVOCKG = Total NMVOC emission in 2011 from agricultural on field burning of agricultural wheat, coarse cereal and maize residues\r\n\r\nJowarEFyearlyVOCKG = Total NMVOC emission in 2011 from agricultural on field burning of agricultural jowar and bajra residues\r\n\r\nMeanEFyearlyVOCKG = Total NMVOC emission in 2011 from agricultural on field burning of agricultural 9 oilseeds, groundnut, rapeseed, mustard, sunflower, cotton, jute and mesta residues\r\n\r\nSugarcaneEFyearlyVOCKG (for sugarcane) = Total NMVOC emission in 2011 from agricultural on field burning of agricultural sugarcane residues\r\n\r\nBfracdump = fraction of waste burnt in the dump" }, "onlineresource_set": [] }, { "ob_id": 32620, "uuid": "2567b41805d5403ab6c39ab853b73e17", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/permafrost/data/ground_temperature/L4/area4/pp/v03.0/", "numberOfFiles": 24, "volume": 35291367976, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32619, "uuid": "b25d4a6174de4ac78000d034f500a268", "short_code": "ob", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Ground Temperature for the Northern Hemisphere, v3.0", "abstract": "This dataset contains permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v2). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v2 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m , 10m).\r\n\r\nCase A: This covers the Northern Hemisphere (north of 30°) for the period 2003-2019 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.\r\nCase B: This covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2019 using a pixel-specific statistics for each day of the year." }, "onlineresource_set": [] }, { "ob_id": 32621, "uuid": "682cca08f34d4e71848064827acde563", "short_code": "result", "curationCategory": "", "dataPath": "/badc/deposited2021/WAT-UV296_water_line/", "numberOfFiles": 3, "volume": 121029527, "fileFormat": "Data are Hirtan formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32623, "uuid": "f6c384ae730744d2ba929e0fad77160a", "short_code": "ob", "title": "Spectroscopic data for transitions in water (1H 16O2) extending into the near-ultraviolet", "abstract": "This dataset contains spectroscopic data for transitions in water (1H 16O2 (H2O with oxygen 16 and hydrogen 1)) extending into the near-ultraviolet. These data were calculated using a mixture of first principle quantum mechanics and input from the experiment. \r\n\r\nThe data is provided in HITRAN format as described in the associated readme file. The data were collected as part of the NERC grant Short wavelength absorption by water vapour (NE/T000767/1)." }, "onlineresource_set": [] }, { "ob_id": 32670, "uuid": "d875d8fe9ef0468cbde62b2053050c0f", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/deposited2021/adverse_met_scenarios_electricity/data", "numberOfFiles": 2624, "volume": 689690747173, "fileFormat": "Data are NetCDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32669, "uuid": "7beeed0bc7fa41feb10be22ee9d10f00", "short_code": "ob", "title": "Adverse Weather Scenarios for Future Electricity Systems", "abstract": "This dataset contains gridded meteorological data associated with challenging periods of weather for highly-renewable UK and European electricity systems of the future collected during the Adverse Weather Scenarios for Future Electricity Systems project. This project is a collaboration between the Met Office, the National Infrastructure Commission and the Climate Change Committee. More details about the project can be found in the associated documentation.\r\n\r\nTwo categories of challenging weather conditions; long duration adverse events and short duration wind ramping events, are provided.\r\n\r\nLong duration events\r\n\r\nThe long duration event component of the dataset provides daily time series at 60 x 60 km spatial resolution, covering a European domain, for surface temperature, 100 m wind speed and net surface solar radiation data, representative of a selection of adverse weather scenarios. Each adverse weather scenario is contained within a time slice of data. For summer-time events, one calendar year (January - December) of data is provided, with the summer-time event occurring in the summer of that year. For winter-time events, two calendar years of data are provided, with the winter-time event occurring in the winter (October-March) intersecting the two calendar years. In all cases, the start date, duration and severity of the adverse weather event, contained within the time slice of data, are given in the NetCDF global ttributes.\r\n\r\nThree types of long-duration adverse weather scenarios are represented: winter-time wind-drought-peak-demand events, summer-time wind-drought-peak-demand events, and summer-time surplus generation events. These are provided at various extreme levels (1 in 2, 5, 10, 20 ,50 and 100-year events); and for a range of current and nominal future climate change warming levels (1.2 [current day, early 2020s], 1.5, 2, 3, and 4 degrees Celsius above pre-industrial level), representative of events impacting either just the UK, or Europe as a whole.\r\n\r\nThe data provided are derived from the Met Office decadal prediction system hindcast (https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model/climate-models/depresys) according to the climate change impacts identified from UKCP18 (https://www.metoffice.gov.uk/research/approach/collaboration/ukcp/index).\r\n\r\nShort duration events\r\nThe short duration event component of the dataset provides hourly time series at 4 x 4 km spatial resolution, covering a UK and surrounding offshore area domain, for 100 m wind speed, representative of a selection of wind generation ramping events. Each adverse weather scenario is contained within a time slice of data with up to one week before and one week after the day on which the event occurs (up to 15 days in total) provided. For the majority of events provided, the full 15 days are available, however for a small number of events which occur less than one week from the beginning or end of the underlying data used to derive this dataset, this is not possibly to supply, and these events are listed below. The start date and time along with the direction and magnitude of the ramp (change in wind capacity factor) contained\r\nwithin the time slice of data, are given in the NetCDF global attributes.\r\n\r\nThe short duration wind generation ramping events are representative of events impacting five separate regions of Great Britain and surrounding offshore areas, as defined in the accompanying documentation. These regions are Scotland, the East England, West England and Wales offshore North and offshore South. The events are defined by changes in wind capacity factors occurring over different length time windows (1-hour, 3-hour, 6-hour, 12-hour and 24-hour windows). These are provided at various extreme levels (1 in 2, 5, 10, 20 ,50 and 100-year events) for the 1.2 degrees Celsius above pre-industrial level (I.e. representative of early 2020s climate) and through the analysis outlined in the accompanying documentation are though to also be representative of the 2, 3, and 4 degrees Celsius above pre-industrial level nominal future climate change warming levels.\r\n\r\nThe data provided are derived from the UKCP18 local projections (https://www.metoffice.gov.uk/research/approach/collaboration/ukcp/index).\r\n\r\nThe methods developed for characterising and representing these adverse weather scenarios, and the approach used to compile the final dataset are presented in the accompanying documentation.\r\n\r\nUse of this data is subject to the terms of the Open Government Licence (http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/) The following acknowledgment must be given when using the data: © Crown Copyright 2021, Met Office, funded by the National Infrastructure Commission." }, "onlineresource_set": [] }, { "ob_id": 32689, "uuid": "ac5f1fbd11a5475c8c2bfee0e68d03df", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/deposited2021/spei-central-asia/data", "numberOfFiles": 51, "volume": 54700431523, "fileFormat": "Data are NetCDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32688, "uuid": "feb1e0b5426d4f5c80f791909a3a2d37", "short_code": "ob", "title": "High resolution Standardized Precipitation Evapotranspiration Index (SPEI) dataset for Central Asia", "abstract": "This dataset contains high-resolution (5 km) Standardized Precipitation Evaporation Index (SPEI-HR) drought data for Central Asia. There are forty-eight different SPEI time scales and the available period is from 1981 - 2018, the data was produced using Climate Hazards group InfraRed Precipitation with Station’s (CHIRPS) precipitation dataset and Global Land Evaporation Amsterdam Model’s (GLEAM) potential evaporation dataset. The SPEI-HR dataset, over time and space, correlates fairly well with SPEI values estimated from coarse-resolution Climate Research Unit (CRU) dataset. Furthermore, the SPEI-HR dataset, for 6-month timescale, displayed a good correlation of 0.66 with GLEAM root zone soil moisture and a positive correlation of 0.26 with normalized difference vegetation index (NDVI) from Global Inventory Monitoring and Modelling System (GIMMS)." }, "onlineresource_set": [] }, { "ob_id": 32703, "uuid": "1d214d97eb3544f0bda6e455caa676bc", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/oltraj/data/v2.1/", "numberOfFiles": 31, "volume": 6497487670, "fileFormat": "NetCDF file \r\nA reformatted version of the v2.0 dataset. In this version V2.1 the int64 time value was changed to double to allow Jupyter Notebook access via THREDDS/OpenDAP.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32702, "uuid": "12eae5e708e541f390898af4187a1c20", "short_code": "ob", "title": "Global Ocean Lagrangian Trajectories based on AVISO velocities, v2.1", "abstract": "The National Centre for Earth Observation (NCEO) Long Term Science Single Centre (LTSS) Global Ocean Lagrangian Trajectories (OLTraj) provides 30-day forward and backward Lagrangian trajectories based on AVISO (Satellite Altimetry Data project) surface velocities. Each trajectory represents the path that a water mass would move along starting at a given pixel and a given day. OLTraj can be thus used to implement analyses of oceanic data in a Lagrangian framework. The purpose of OLTraj is to allow non-specialists to conduct Lagrangian analyses of surface ocean data.\r\n\r\nThe dataset has global coverage and spans the year 2018 with a daily temporal resolution. The trajectories were generated starting from zonal and meridional model velocity fields that were integrated using the LAMTA (6-hour time step - part of ) as described in Nencioli et al., 2018 and SPASSO (Software package for and adaptive satellite-based sampling for ocean graphic cruises containing LAMTA) software user guide. Please see the documentation section below for further information.\r\n\r\nVersion 2.1 has the same resolution as version V2.0 but has double value for time variables to permit access via THREDDS" }, "onlineresource_set": [] }, { "ob_id": 32707, "uuid": "335c7a44128e4f31bd86d96eba55e704", "short_code": "result", "curationCategory": "C", "dataPath": "/badc/ecmwf-era5/data/invariants", "numberOfFiles": 15, "volume": 7338834, "fileFormat": "Data are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32706, "uuid": "2c8f38fac04945b89cf12d6e9c928c6f", "short_code": "ob", "title": "ECMWF ERA5: surface level invariant parameter data", "abstract": "This dataset contains ERA5 surface level invariant parameter data. ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nModel level analysis and surface analysis and forecast data to complement this dataset are also available. Data from a 10 member ensemble, run at lower spatial and temporal resolution, were also produced to provide an uncertainty estimate for the output from the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation producing data in this dataset.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.\r\n\r\nAn initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that \"ERA5.1 is very close to ERA5 in the lower and middle troposphere.\" but users of data from this period should read the technical memo 859 for further details." }, "onlineresource_set": [] }, { "ob_id": 32708, "uuid": "b5f6ca4d35124942befe7845875efd2f", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/ice_sheets_antarctica/data/grounding_line_locations/key_glaciers/v2.0/", "numberOfFiles": 6, "volume": 7999482, "fileFormat": "Shapefiles", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32600, "uuid": "7b3bddd5af4945c2ac508a6d25537f0a", "short_code": "ob", "title": "ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Grounding line location for key glaciers, Antarctica, 1994-2020, v2.0", "abstract": "This dataset contains grounding line locations (GLL) for key glaciers in Antarctica, produced as part of the ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci) project. The data have been derived from satellite observations from the ERS-1/2, TerraSAR-X and Copernicus Sentinel-1 satellites, acquired between 1994 and 2020." }, "onlineresource_set": [] }, { "ob_id": 32712, "uuid": "c749cd43ef004c3983ba7aec46869548", "short_code": "result", "curationCategory": "C", "dataPath": "/badc/ecmwf-e40/data/li/ap/", "numberOfFiles": 0, "volume": 0, "fileFormat": "Data are Grib formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 32739, "uuid": "260cb2f21bfe4e45ac649f4a6858e9b0", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/oltraj/data/v2.2/", "numberOfFiles": 8044, "volume": 6858992869823, "fileFormat": "NetCDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32738, "uuid": "5c2b70d069cb467ab73e80b84c3e395a", "short_code": "ob", "title": "Global ocean lagrangian trajectories based on AVISO velocities, v2.2", "abstract": "The National Centre for Earth Observation (NCEO) Long Term Science Single Centre (LTSS) Global Ocean Lagrangian Trajectories (OLTraj) provide 30-day forward and backward Lagrangian trajectories based on AVISO (Satellite Altimetry Data project) surface velocities. Each trajectory represents the path that a water mass would move along starting at a given pixel and a given day. OLTraj can be thus used to implement analyses of oceanic data in a Lagrangian framework. The purpose of OLTraj is to allow non-specialists to conduct Lagrangian analyses of surface ocean data.\r\n\r\nThe dataset has global coverage and spans 1998-2019 with a daily temporal resolution. The trajectories were generated starting from zonal and meridional model velocity fields that were integrated using the LAMTA (6-hour time step - part of ) as described in Nencioli et al., 2018 and SPASSO (Software package for and adaptive satellite-based sampling for ocean graphic cruises containing LAMTA) software user guide. Please see the documentation section below for further information.\r\n\r\nVersion 2.2 is a higher resolution version of V2.0 and also has double value for time variables to permit access via THREDDS" }, "onlineresource_set": [] }, { "ob_id": 32741, "uuid": "33b99e97f0a14b14b5fb3e05cb288443", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/ice_sheets_antarctica/data/gravimetric_mass_balance/gridded/v3.0/", "numberOfFiles": 2, "volume": 17719816, "fileFormat": "Data are in NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32692, "uuid": "36dae49c76f845a18062fa96599be719", "short_code": "ob", "title": "ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Antarctic Ice Sheet monthly Gravimetric Mass Balance gridded product, v3.0, 2002 - 2020", "abstract": "This dataset contains the Gravimetric Mass Balance (GMB) gridded product for the Antarctic Ice Sheet (AIS), generated by TU Dresden as part of the ESA Antarctic Ice Sheet Climate Change Initiatve (Antarctic_Ice_Sheet_cci). \r\n\r\nThe Gravimetric Mass Balance (GMB) product for the Antarctic Ice Sheet (AIS) is based on monthly snapshots of the Earth’s gravity field provided by the Gravity Recovery and Climate Experiment (GRACE) and its follow-on satellite mission (GRACE-FO). The product relies on monthly gravity field solutions (L2) of release 06 generated at the Center for Space Research (University of Texas at Austin) and spans the period from April 2002 through July 2020. The GMB product covers the full GRACE mission period (April 2002 - June 2017) and is extended by means of GRACE-FO data starting from June 2018, thus including 187 monthly solutions. The mass change estimation is based on the tailored sensitivity kernel approach developed at TU Dresden. (Groh & Horwath, 2021)\r\n\r\nThe GMB gridded product comprises time series of ice mass changes for cells of polar-stereographic grid with a sampling of 50x50 km² covering the entire AIS. A GMB basin product is also available as a separate dataset.\r\n\r\nGroh, A. & Horwath, M. (2021). Antarctic Ice Mass Change Products from GRACE/GRACE-FO Using Tailored Sensitivity Kernels. Remote Sens., 13(9), 1736. doi:10.3390/rs13091736" }, "onlineresource_set": [] }, { "ob_id": 32742, "uuid": "3b333d8702484b8e84a86737e4297150", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/ice_sheets_antarctica/data/gravimetric_mass_balance/basin/v3.0/", "numberOfFiles": 2, "volume": 147610, "fileFormat": "Data are in NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32602, "uuid": "e1dfd0ee655944b8a82ce0479c518747", "short_code": "ob", "title": "ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Antarctic Ice Sheet monthly Gravimetric Mass Balance basin product, v3.0, 2002-2020", "abstract": "This dataset contains the Gravimetric Mass Balance (GMB) basin product for the Antarctic Ice Sheet (AIS), generated by TU Dresden as part of the ESA Antarctic Ice Sheet Climate Change Initiatve (Antarctic_Ice_Sheet_cci). \r\n\r\nThe Gravimetric Mass Balance (GMB) product for the Antarctic Ice Sheet (AIS) is based on monthly snapshots of the Earth’s gravity field provided by the Gravity Recovery and Climate Experiment (GRACE) and its follow-on satellite mission (GRACE-FO). The product relies on monthly gravity field solutions (L2) of release 06 generated at the Center for Space Research (University of Texas at Austin) and spans the period from April 2002 through July 2020. The GMB product covers the full GRACE mission period (April 2002 - June 2017) and is extended by means of GRACE-FO data starting from June 2018, thus including 187 monthly solutions. The mass change estimation is based on the tailored sensitivity kernel approach developed at TU Dresden. (Groh & Horwath, 2021)\r\n\r\nThe GMB basin product provides time series of integrated mass changes for 26 drainage basins and the aggregations of the Antarctic Peninsula, East Antarctica, West Antarctica and the entire AIS. Based on the GMB basin product, ice mass balance estimates, i.e. linear trend in the change in ice mass, were derived for all drainage basins and aggregations. A gridded GMB product is also available as a separate dataset.\r\n\r\nGroh, A. & Horwath, M. (2021). Antarctic Ice Mass Change Products from GRACE/GRACE-FO Using Tailored Sensitivity Kernels. Remote Sens., 13(9), 1736. doi:10.3390/rs13091736" }, "onlineresource_set": [] }, { "ob_id": 32743, "uuid": "d4e0a2f974aa4184878d62837eaf60f3", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/ice_sheets_antarctica/data/ice_velocity/antarctic_ice_sheet/sentinel_1/monthly/v1.0/", "numberOfFiles": 45, "volume": 101295328588, "fileFormat": "Data are in NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32601, "uuid": "00fe090efc58446e8980992a617f632f", "short_code": "ob", "title": "ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Antarctic Ice Sheet monthly velocity from 2017 to 2020, derived from Sentinel-1, v1", "abstract": "This dataset contains monthly gridded ice velocity maps of the Antarctic Ice Sheet derived from Sentin\r\nel-1 data acquired between 2017-01-01 and 2020-08-31. It was generated by ENVEO, as part of the ESA Antarctic Ice Sheet Climate Change Initiative project (Antarctic_Ice_Sheet_cci).\r\n\r\nThe surface velocity is derived by applying feature tracking techniques using Sentinel-1 synthetic aperture radar (SAR) data acquired in the Interferometric Wide (IW) swath mode. Ice velocity is provided at 200m grid spacing in Polar Stereographic projection (EPSG: 3031). The horizontal velocity components are provided in true meters per day, towards easting and northing direction of the grid. The vertical displacement is derived from a digital elevation model. Provided is a NetCDF file with the velocity components: vx, vy, vz, along with maps showing the magnitude of the horizontal components, the valid pixel count and uncertainty. The product combines all ice velocity maps, based on 6- and 12-day repeats, acquired within a single month in a monthly averaged product." }, "onlineresource_set": [] }, { "ob_id": 32745, "uuid": "058f999880274a3d8cf4d11cc13e4732", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/ghg/data/cci_plus/CH4_S5P_WFMD/v1.2_extended_to_july2020/", "numberOfFiles": 953, "volume": 48435508668, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32744, "uuid": "1c9c816d0b8a4fbf878e7e0bfef5d79f", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from Sentinel-5P, generated with the WFM-DOAS algorithm, version 1.2, November 2017 - July 2020", "abstract": "This product is the column-average dry-air mole fraction of atmospheric methane, denoted XCH4. It has been retrieved from radiance measurements from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite in the 2.3 µm spectral range of the solar spectral range, using the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS or WFMD) retrieval algorithm. This dataset is also referred to as CH4_S5P_WFMD. This version of the product is version 1.2, and covers the period from November 2017 - July 2020. \r\n\r\nThe WFMD algorithm is based on iteratively fitting a simulated radiance spectrum to the measured spectrum using a least-squares method. The algorithm is very fast as it is based on a radiative transfer model based look-up table scheme. The product is limited to cloud-free scenes on the Earth's day side.\r\n\r\nThese data were produced as part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project.\r\n\r\nWhen citing this dataset, please also cite the following peer-reviewed publication: \r\nSchneising, O., Buchwitz, M., Reuter, M., Bovensmann, H., Burrows, J. P., Borsdorff, T., Deutscher, N. M., Feist, D. G., Griffith, D. W. T., Hase, F., Hermans, C., Iraci, L. T., Kivi, R., Landgraf, J., Morino, I., Notholt, J., Petri, C., Pollard, D. F., Roche, S., Shiomi, K., Strong, K., Sussmann, R., Velazco, V. A., Warneke, T., and Wunch, D.: A scientific algorithm to simultaneously retrieve carbon monoxide and methane from TROPOMI onboard Sentinel-5 Precursor, Atmos. Meas. Tech., 12, 6771–6802, https://doi.org/10.5194/amt-12-6771-2019, 2019." }, "onlineresource_set": [] }, { "ob_id": 32749, "uuid": "7cbd1567ffb34c90b80e15b9829dda69", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/ghg/data/cci_plus/CH4_GO2_SRFP/v1.0/", "numberOfFiles": 249, "volume": 266017657, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32748, "uuid": "fdd90615b0df45489d9ca47708d98325", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from GOSAT-2, generated with the SRFP (RemoTeC) full physics retrieval algorithm, version 1.0.0", "abstract": "This dataset contains column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) Near Infrared (NIR) and Shortwave Infrared (SWIR) spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the RemoTeC SRFP Full Physics Retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.\r\n\r\nThese data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme." }, "onlineresource_set": [] }, { "ob_id": 32754, "uuid": "8c1a07475a5b4ed2a83c3258865c3206", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/sentinel1a/data/IW/L2_OCN/IPF_v2/", "numberOfFiles": 620049, "volume": 1055164946685, "fileFormat": "These data are provided in the ESA safe format as downloaded from the Sentinel data hubs.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32753, "uuid": "e972cb1afd34494c94d9b22c1b66daca", "short_code": "ob", "title": "Sentinel 1A C-band Synthetic Aperture Radar (SAR): Interferometric Wide (IW) mode Ocean (OCN) Level 2 data, Instrument Processing Facility (IPF) v2", "abstract": "This dataset contains level-2 Interferometric Wide swath (IW) Ocean (OCN) C-band Synthetic Aperture Radar (SAR) data from the European Space Agency (ESA) Sentinel 1A satellite. Level-2 data consists of geolocated geophysical products derived from Level-1. Sentinel 1A was launched on 3rd April 2014 and provides continuous all-weather, day and night imaging radar data. The IW mode is the main operational mode. These level 2 OCN products provide Ocean Wind field (OWI) and Surface Radial Velocity (RVL).\r\n\r\nThe OWI component is a ground range gridded estimate of the surface wind speed and direction at 10 m above the surface, derived from IW mode. The OWI component contains a set of wind vectors for each processed Level-1 input product. The norm is the wind speed in m/s and the argument is wind direction in degrees (meteorological convention = clockwise direction from where the wind blows with respect to the North). The spatial resolution of the SAR wind speed is 1 km for IW mode.\r\n\r\nThe RVL surface radial velocity component is a ground range gridded difference between the measured Level-2 Doppler grid and the Level-1 calculated geometrical Doppler. The RVL component provides continuity of the ASAR Doppler grid. The RVL estimates are produced on a ground-range grid.\r\n\r\nThese data are available via CEDA to any registered CEDA user." }, "onlineresource_set": [] }, { "ob_id": 32758, "uuid": "32d69751847f4146968d10080e1946d6", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/deposited2021/bbubl/data/wrf_WestMidlands_2015_1km", "numberOfFiles": 13, "volume": 105379533569, "fileFormat": "Data are CF-compliant NetCDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32759, "uuid": "465d86a5750447128f24f79c4f2ecdd4", "short_code": "ob", "title": "BBUBL: 1 km gridded output from WRF v3.6.1 model runs for the Birmingham conurbation for 2015", "abstract": "This dataset contains a range of parameters from a 1 km gridded output from runs of version 3.6.1 of the Weather Research and Forecasting (WRF) model deployed on the ARCHER UK National Supercomputing Service. These runs were part of the NERC funded BBUBL project (Biotelemetry/Bio-aerial-platforms for the Urban Boundary Layer - also known as City Flocks, NERC grant award NE/N003195/1). The domain of the model runs was over the set over Birmingham conurbation for all of 2015. This geo-temporal domain encompasses measurements of the urban boundary layer obtained from instrumentation attached to birds flown around the area. See related dataset.\r\n\r\nThe WRF model set up followed that used by Heaviside et al. (2015) - see linked documentation for details - and was run on the ARCHER UK National Supercomputing Service. Meteorology data from the European Centre for Medium-range Weather Forecasts (ECMWF) ERA-interim reanalysis data for initial and lateral boundary conditions.\r\n\r\nThe WRF v3.6.1 model set up implemented in this study included four nested domains. The domains had grid resolutions of 36 km x 36 km, 12 km x 12 km, 3 km x 3 km and 1 km x 1 km. The finest domain covered the West Midlands, centering over Birmingham. The multi-layer building energy parametrization (BEP) scheme with three land-use types (low-intensity residential, high-intensity residential and industrial/commercial) was also used." }, "onlineresource_set": [] }, { "ob_id": 32763, "uuid": "fa921cb9b27b45d59ee46dcbcaf247a2", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/sentinel1b/data/IW/L2_OCN/IPF_v3/", "numberOfFiles": 552782, "volume": 935560923847, "fileFormat": "These data are provided in the ESA safe format as downloaded from the Sentinel data hubs.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32762, "uuid": "18851d1f4454455dad76141c02ad740e", "short_code": "ob", "title": "Sentinel 1B C-band Synthetic Aperture Radar (SAR): Interferometric Wide (IW) mode Ocean (OCN) Level 2 data, Instrument Processing Facility (IPF) v3", "abstract": "This dataset contains level-2 Interferometric Wide swath (IW) Ocean (OCN) C-band Synthetic Aperture Radar (SAR) data from the European Space Agency (ESA) Sentinel 1B satellite. Level-2 data consists of geolocated geophysical products derived from Level-1. Sentinel 1B was launched on 25th April 2016 and provides continuous all-weather, day and night imaging radar data. The IW mode is the main operational mode. These level 2 OCN products provide Ocean Wind field (OWI) and Surface Radial Velocity (RVL). \r\n\r\nThe OWI component is a ground range gridded estimate of the surface wind speed and direction at 10 m above the surface, derived from IW mode. The OWI component contains a set of wind vectors for each processed Level-1 input product. The norm is the wind speed in m/s and the argument is wind direction in degrees (meteorological convention = clockwise direction from where the wind blows with respect to the North). The spatial resolution of the SAR wind speed is 1km for IW mode.\r\n\r\nThe RVL surface radial velocity component is a ground range gridded difference between the measured Level-2 Doppler grid and the Level-1 calculated geometrical Doppler. The RVL component provides continuity of the ASAR Doppler grid. The RVL estimates are produced on a ground-range grid.\r\n\r\nThese data are available via CEDA to any registered CEDA user." }, "onlineresource_set": [] }, { "ob_id": 32765, "uuid": "d08bb833e39847178181823feb3a8aba", "short_code": "result", "curationCategory": "", "dataPath": "/badc/cru/data/cru_cy/cru_cy_3.24/", "numberOfFiles": 2, "volume": 2026, "fileFormat": "The CRU CY data are provided as text files with the extension \".per\", most text editors will open these files. See the linked file formats guide for more information.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 20044, "uuid": "a38c710ef63c4e8c9a295501f1122dbf", "short_code": "ob", "title": "CRU CY3.24: Climatic Research Unit (CRU) Year-by-Year Variation of Selected Climate Variables by CountrY (CY) version 3.24 (Jan. 1901 - Dec. 2015)", "abstract": "The CRU CY version 3.24 dataset consists of country averages at a monthly, seasonal and annual frequency, for ten climate variables, including cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, vapour pressure and Potential Evapo-transpiration. \r\n\r\nThis dataset was produced in 2016 by the Climatic Research Unit (CRU) at the University of East Anglia. The data are available as text files with the extension '.per' and can be opened by most text editors.\r\n\r\nSpatial averages are calculated using area-weighted means. CRU CY3.24 is derived directly from the CRU TS3.24 dataset. CRU CY version 3.24 spans the period 1901-2015 for 289 countries.\r\n\r\nTo understand the CRU-CY3.24 dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS3.24. It is therefore recommended that all users read the Harris et al, 2014 paper listed in the online documentation on this record.\r\n\r\nCRU CY data are available for download to all CEDA users." }, "onlineresource_set": [] }, { "ob_id": 32768, "uuid": "8d0c573da715484293ddfcff3ec173b0", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/sentinel1a/data/IW/L0_RAW/", "numberOfFiles": 1626, "volume": 837020898568, "fileFormat": "These data are provided in the ESA safe format as downloaded from the Sentinel data hubs.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32767, "uuid": "76202b367af44b82a1c7ba1d1a39e7b9", "short_code": "ob", "title": "Sentinel 1A C-band Synthetic Aperture Radar (SAR): Interferometric Wide (IW) mode Single Look Complex (SLC) Level 0 data", "abstract": "This dataset contains level 0, raw Interferometric Wide swath (IW) Single Look Complex (SLC) C-band Synthetic Aperture Radar (SAR) data from the European Space Agency (ESA) Sentinel 1A satellite. Sentinel 1A was launched on 3rd April 2014 and provides continuous all-weather, day and night imaging radar data. The IW mode is the main operational mode. The IW mode supports single (HH or VV) and dual (HH+HV or VV+VH) polarisation. These data were archived as a test - CEDA does not regularly archive these products.\r\n\r\nThese data are available via CEDA to any registered CEDA user." }, "onlineresource_set": [] }, { "ob_id": 32770, "uuid": "691ef889c0414f42903a67b5d806f29d", "short_code": "result", "curationCategory": "A", "dataPath": "/neodc/atsrubt", "numberOfFiles": 5803528, "volume": 25033378589868, "fileFormat": "SADIST", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 32772, "uuid": "a823dcf019ac41bf8b8c2676203e620a", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/qa4ecv/data/bi-directional_reflectance_factor/", "numberOfFiles": 13162, "volume": 191746904474, "fileFormat": "These data are provided in NetCDF file format.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32771, "uuid": "8130ee8d2e33439aa85669b620e7b532", "short_code": "ob", "title": "QA4ECV Bi-directional reflectance factor", "abstract": "QA4ECV Bi-directional reflectance factor data. This data provides masks for the AVHRR data." }, "onlineresource_set": [] }, { "ob_id": 32776, "uuid": "69b6af4f68d349c78e80159db4cc4d9b", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/sentinel1a/data/SM/L1_SLC/IPF_v2/", "numberOfFiles": 12131, "volume": 6563765716239, "fileFormat": "Image data files are in a binary format. Quicklook images are in png format. Manifest files with relevant metadata are in SAFE format.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32775, "uuid": "6ae95449b899409790e64e23120b48e8", "short_code": "ob", "title": "Sentinel 1A C-band Synthetic Aperture Radar (SAR): SM mode SLC Level 1 data, Instrument Processing Facility (IPF) v2", "abstract": "This dataset contains Stripmap Mode (SM) C-band Synthetic Aperture Radar (SAR) Single Look Complex (SLC) data from the European Space Agency (ESA) Sentinel 1A satellite. Sentinel 1A was launched on 3rd April 2014 and provides continuous all-weather, day and night imaging radar data. The SM mode is used only on special request for extraordinary events such as emergency management. The SM mode supports single (HH or VV) and dual (HH+HV or VV+VH) polarisation. \r\n\r\nStripmap SLCs contain one image per polarisation band from one of six overlapping beams. Each beam covers 80.1 km, covering a combined range of 375 km. Pixel spacing is determined, in azimuth by the pulse repetition frequency (PRF), and in range by the radar range sampling frequency, providing natural pixel spacing.\r\n\r\nThese data are available via CEDA to any registered user." }, "onlineresource_set": [] }, { "ob_id": 32781, "uuid": "e9ea9c30906e4cedac9b7a664ddd86a5", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/sentinel1a/data/IW/L1_SLC/IPF_v3", "numberOfFiles": 5714231, "volume": 5766391293430236, "fileFormat": "Image data files are in a binary format. Quicklook images are in png format. Manifest files with relevant metadata are in SAFE format.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32780, "uuid": "9cafde3874d54e2794fcfb372311883e", "short_code": "ob", "title": "Sentinel 1A C-band Synthetic Aperture Radar (SAR): Interferometric Wide (IW) mode Single Look Complex (SLC) Level 1 data, Instrument Processing Facility (IPF) v3", "abstract": "This dataset contains level 1 Interferometric Wide swath (IW) Single Look Complex (SLC) C-band Synthetic Aperture Radar (SAR) data from the European Space Agency (ESA) Sentinel 1A satellite. Sentinel 1A was launched on 3rd April 2014 and provides continuous all-weather, day and night imaging radar data. The IW mode is the main operational mode. The IW mode supports single (HH or VV) and dual (HH+HV or VV+VH) polarisation. \r\n\r\nThe IW SLC product contains one image per sub-swath, per polarisation channel, for a total of three or six images. Each sub-swath image consists of a series of bursts, where each burst was processed as a separate SLC image. The individually focused complex burst images are included, in azimuth-time order, into a single sub-swath image, with black-fill demarcation in between\r\n\r\nUnlike SM and WV SLC products, which are sampled at the natural pixel spacing, the images for all bursts in all sub-swaths of an IW SLC product are re-sampled to a common pixel spacing grid in range and azimuth. The resampling to a common grid eliminates the need for further interpolation in case, in later processing stages, the bursts are merged to create a contiguous ground range, detected image.\r\n\r\nThese data are available via CEDA to any registered CEDA user." }, "onlineresource_set": [] }, { "ob_id": 32786, "uuid": "81611e8fd2db4b1199a708372cccf898", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/sentinel3a/data/SRAL/L1_SRA_A__/", "numberOfFiles": 13802, "volume": 11349097935027, "fileFormat": "Data are provided by ESA in zipped SAFE format.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32785, "uuid": "67b646c363fc4f9289486ffd8c4c6b07", "short_code": "ob", "title": "Sentinel 3A Synthetic Aperture Radar Altimeter (SRAL) Level 1A data", "abstract": "This dataset contains level 1a altimetry data from the Synthetic Aperture Radar Altimeter (SRAL) aboard the European Space Agency (ESA) Sentinel 3A Satellite. Sentinel 3A was launched on the 16th of February 2016. These data contain geo-located bursts of echoes with all calibrations applied. Level 1A (L1A) is an intermediate output of the Synthetic Aperture Radar (SAR) processor. L1A complex waveforms should be fully calibrated (including both instrumental gains and calibration corrections) and aligned in range within each burst. The time tag is given at the surface (that is when the middle of the burst reaches the surface). L1A is the starting point for the SAR processing which provides high-resolution products. Data are provided by ESA and are made available via CEDA to any registered user." }, "onlineresource_set": [] }, { "ob_id": 32788, "uuid": "935e812d791940f29d6b5f3a0f86e34f", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/sentinel3a/data/SRAL/L1_SRA_BS_/", "numberOfFiles": 9688, "volume": 5415290909817, "fileFormat": "Data are provided by ESA in zipped SAFE format.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32787, "uuid": "41526384c3dd463eb0fc0117b87d08f6", "short_code": "ob", "title": "Sentinel 3A Synthetic Aperture Radar Altimeter (SRAL) Level 1B-S data", "abstract": "This dataset contains level 1b (L1B-S) altimetry data from the Synthetic Aperture Radar Altimeter (SRAL) aboard the European Space Agency (ESA) Sentinel 3A Satellite. Sentinel 3A was launched on the 16th of February 2016. These data are fully SAR-processed and calibrated High-Resolution (HR) complex echoes arranged in stacks after slant range correction and prior to echo multi-look (multi-look processing reduces noise by averaging of adjacent pixels, and thereby reduces the standard deviation of the noise level).\r\n\r\nThe L1B-S HR product contains information from Doppler beams data. Hence, it has only been defined for the Synthetic Aperture Radar (SAR) processing chain. The Doppler beams associated with a given surface location (also called stack data) are formed through the selection of all the beams that illuminate a given surface location, and that contribute to each L1B HR waveform. Beams are the result of applying Doppler processing to the waveform bursts, which allows division of the conventional altimeter footprint into a certain number of stripes, thus creating a Delay Doppler Map (DDM). With this, contributions coming from different stripes can be identified and collected separately. When all the contributions from different bursts are collected, a stack is formed. The stack waveforms are provided in In-phase (I) and Quadrature-phase (I/Q) samples (complex waveforms) in the frequency domain. Apart from the Doppler processing, the beams of a stack have also been fully calibrated and range aligned. The L1B-S also includes characterisation parameters about the stack itself. The time tag is given at each surface location (defined throughout the L1 processing chain).\r\n\r\nData are provided by ESA and are made available via CEDA to any registered user." }, "onlineresource_set": [] }, { "ob_id": 32790, "uuid": "c0c965e8e54648af9be66ea8d2d7f7cb", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/sentinel1a/data/WV/L2_OCN/IPF_v3/", "numberOfFiles": 158861, "volume": 1591499378956, "fileFormat": "Data are provided in SAFE format. With the data products inside the SAFE zip in NetCDF.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32789, "uuid": "ab33998624364d63be7471a30cee635b", "short_code": "ob", "title": "Sentinel 1A C-band Synthetic Aperture Radar (SAR): Wave (WV) mode Ocean (OCN) Level 2 data, Instrument Processing Facility (IPF) v3", "abstract": "This dataset contains Level-2, Wave mode (WV) Ocean (OCN) C-band Synthetic Aperture Radar (SAR) data from the European Space Agency (ESA) Sentinel 1A satellite. Level-2 data consists of geolocated geophysical products derived from Level-1. \r\n\r\nFrom WV modes, the OCN product will only contain Ocean Swell Spectra (OSW) and Surface Radial Velocity (RVL). \r\n\r\nThe OSW component is a two-dimensional ocean surface swell spectrum and includes an estimate of wind speed and direction per swell spectrum. The OSW component provides continuity measurement of SAR swell spectra at C-band. OSW is estimated from Sentinel-1 SLC images by inversion of the corresponding image cross-spectra.\r\n\r\nThe OSW is generated from Stripmap and Wave modes only and is not available from the TOPSAR IW and EW modes. For Stripmap mode, there are multiple spectra derived from the Level-1 SLC image. For Wave mode, there is one spectrum per vignette.\r\n\r\nOcean wave height spectra are provided in units of m4 and given on a polar grid of wavenumber in rad/m and direction in degrees with respect to North.\r\n\r\nThe OSW product also contains one estimate of the wind speed in m/s and direction in degrees (meteorological convention) per ocean wave spectrum, as well as parameters derived from the ocean wave spectra (integrated wave parameters) and from the imagette (image statistics).\r\n\r\nThe spatial coverage of the OSW product is equal to the spatial coverage of the corresponding Level-1 WV SLC or Level-1 SM SLC product, limited to ocean areas.\r\n\r\nThe RVL surface radial velocity component is a ground range gridded difference between the measured Level-2 Doppler grid and the Level-1 calculated geometrical Doppler. The RVL component provides continuity of the ASAR Doppler grid. The RVL estimates are produced on a ground-range grid.\r\n\r\nThe Level-2 Doppler is computed on a grid similar to the OWI component grid and provides an estimate of the Doppler frequency and the Doppler spectral width. For TOPS, one grid is provided by swath (additional dimension in the NetCDF). The uncertainties of the estimates are also provided for both the Doppler and radial velocity. The Doppler frequency and the Doppler spectral width are estimated based on fitting the azimuth spectral profile of the data to the antenna model taking into account additive noise, aliasing, and sideband effects. The Doppler frequency provided in the product is the pure Doppler frequency estimated from the SLC data without correcting for geometry and mispointing errors.\r\n\r\nSentinel 1A was launched on 3rd April 2014 and provides continuous all-weather, day and night imaging radar data. These data are available via CEDA to any registered CEDA user." }, "onlineresource_set": [] }, { "ob_id": 32794, "uuid": "04325278a10d4ebfae603aad67a30fe6", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/lakes/data/lake_products/L3S/v1.1/", "numberOfFiles": 9970, "volume": 362653685408, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32169, "uuid": "ef1627f523764eae8bbb6b81bf1f7a0a", "short_code": "ob", "title": "ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 1.1", "abstract": "This dataset contains various global lake products (1992-2019) produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. This is version 1.1 of the dataset.\r\n\r\nLakes are of significant interest to the scientific community, local to national governments, industries and the wider public. A range of scientific disciplines including hydrology, limnology, climatology, biogeochemistry and geodesy are interested in distribution and functioning of the millions of lakes (from small ponds to inland seas), from the local to the global scale. Remote sensing provides an opportunity to extend the spatio-temporal scale of lake observation. \r\n\r\nThe five thematic climate variables included in this dataset are:\r\n•\tLake Water Level (LWL): a proxy fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate changes.\r\n•\tLake Water Extent (LWE): a proxy for change in glacial regions (lake expansion) and drought in many arid environments, water extent relates to local climate for the cooling effect that water bodies provide.\r\n•\tLake Surface Water temperature (LSWT): correlated with regional air temperatures and a proxy for mixing regimes, driving biogeochemical cycling and seasonality. \r\n•\tLake Ice Cover (LIC): freeze-up in autumn and advancing break-up in spring are proxies for gradually changing climate patterns and seasonality. \r\n•\tLake Water-Leaving Reflectance (LWLR): a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).\r\n\r\nData generated in the Lakes_cci project are derived from data from multiple instruments and multiple satellites including; TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel, Landsat, ERS, Terra/Aqua, Suomi NPP, Metop and Orbview. For more information please see the product user guide in the documents." }, "onlineresource_set": [] }, { "ob_id": 32799, "uuid": "5079b7a3b5b6438fa937562d293ec903", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/sentinel1b/data/WV/L2_OCN/IPF_v3/", "numberOfFiles": 110186, "volume": 1099563983359, "fileFormat": "Data are provided in SAFE format. With the data products inside the SAFE zip in NetCDF.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32798, "uuid": "4ecd5242cde24b2bb9c0572218da9861", "short_code": "ob", "title": "Sentinel 1B C-band Synthetic Aperture Radar (SAR): Wave (WV) mode Ocean (OCN) Level 2 data, Instrument Processing Facility (IPF) v3", "abstract": "This dataset contains Level-2, Wave mode (WV) Ocean (OCN) C-band Synthetic Aperture Radar (SAR) data from the European Space Agency (ESA) Sentinel 1B satellite. Level-2 data consists of geolocated geophysical products derived from Level-1. \r\n\r\nFrom WV modes, the OCN product will only contain Ocean Swell Spectra (OSW) and Surface Radial Velocity (RVL). \r\n\r\nThe OSW component is a two-dimensional ocean surface swell spectrum and includes an estimate of wind speed and direction per swell spectrum. The OSW component provides continuity measurement of SAR swell spectra at C-band. OSW is estimated from Sentinel-1 SLC images by inversion of the corresponding image cross-spectra.\r\n\r\nThe OSW is generated from Stripmap and Wave modes only and is not available from the TOPSAR IW and EW modes. For Stripmap mode, there are multiple spectra derived from the Level-1 SLC image. For Wave mode, there is one spectrum per vignette.\r\n\r\nOcean wave height spectra are provided in units of m4 and given on a polar grid of wavenumber in rad/m and direction in degrees with respect to North.\r\n\r\nThe OSW product also contains one estimate of the wind speed in m/s and direction in degrees (meteorological convention) per ocean wave spectrum, as well as parameters derived from the ocean wave spectra (integrated wave parameters) and from the imagette (image statistics).\r\n\r\nThe spatial coverage of the OSW product is equal to the spatial coverage of the corresponding Level-1 WV SLC or Level-1 SM SLC product, limited to ocean areas.\r\n\r\nThe RVL surface radial velocity component is a ground range gridded difference between the measured Level-2 Doppler grid and the Level-1 calculated geometrical Doppler. The RVL component provides continuity of the ASAR Doppler grid. The RVL estimates are produced on a ground-range grid.\r\n\r\nThe Level-2 Doppler is computed on a grid similar to the OWI component grid and provides an estimate of the Doppler frequency and the Doppler spectral width. For TOPS, one grid is provided by swath (additional dimension in the NetCDF). The uncertainties of the estimates are also provided for both the Doppler and radial velocity. The Doppler frequency and the Doppler spectral width are estimated based on fitting the azimuth spectral profile of the data to the antenna model taking into account additive noise, aliasing, and sideband effects. The Doppler frequency provided in the product is the pure Doppler frequency estimated from the SLC data without correcting for geometry and mispointing errors.\r\n\r\nSentinel 1A was launched on 25th April 2016 and provides continuous all-weather, day and night imaging radar data. These data are available via CEDA to any registered CEDA user." }, "onlineresource_set": [] }, { "ob_id": 32802, "uuid": "fdbc85605a6d4562ab3565b7d12d2978", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/sentinel1b/data/SM/L1_SLC/IPF_v2/", "numberOfFiles": 2336, "volume": 1427172708154, "fileFormat": "Image data files are in a binary format. Quicklook images are in png format. Manifest files with relevant metadata are in SAFE format.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32801, "uuid": "8305ad215f0f48c994bdb37df1bcf773", "short_code": "ob", "title": "Sentinel 1B C-band Synthetic Aperture Radar (SAR): SM mode SLC Level 1 data, Instrument Processing Facility (IPF) v2", "abstract": "This dataset contains Stripmap Mode (SM) C-band Synthetic Aperture Radar (SAR) Single Look Complex (SLC) data from the European Space Agency (ESA) Sentinel 1B satellite. Sentinel 1B was launched on 25th April 2016 and provides continuous all-weather, day and night imaging radar data. The SM mode is used only on special request for extraordinary events such as emergency management. The SM mode supports single (HH or VV) and dual (HH+HV or VV+VH) polarisation. \r\n\r\nStripmap SLCs contain one image per polarisation band from one of six overlapping beams. Each beam covers 80.1 km, covering a combined range of 375 km. Pixel spacing is determined, in azimuth by the pulse repetition frequency (PRF), and in range by the radar range sampling frequency, providing natural pixel spacing.\r\n\r\nThese data are available via CEDA to any registered user." }, "onlineresource_set": [] }, { "ob_id": 32806, "uuid": "eedb4a03453a4d688f3c0310ea087076", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ccmi/data/post-cmip6/ccmi-2022/ETH-PMOD/SOCOL/refD1", "numberOfFiles": 426, "volume": 361060547370, "fileFormat": "netCDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32809, "uuid": "f088e7a33a25409197ea9b6aa3b90864", "short_code": "ob", "title": "CCMI-2022: refD1 data produced by the SOCOL model at ETH-PMOD", "abstract": "This dataset contains model data for CCMI-2022 experiment refD1 produced by the SOCOL (SOlar Climate Ozone Links) model run by the modelling team at ETH-PMOD (Swiss Federal Institute of Technology Zurich and the Physical-Meteorology Observatory Davos).\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- Atmosphere-Ocean-Aerosol-Chemistry-Climate Model SOCOLv4.0: description and evaluation.\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" }, "onlineresource_set": [] }, { "ob_id": 32810, "uuid": "ce23352d4bdc4978a0679028562ae6b1", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/sentinel1b/data/IW/L1_SLC/IPF_v3/", "numberOfFiles": 1834079, "volume": 1514497644380227, "fileFormat": "Image data files are in a binary format. Quicklook images are in png format. Manifest files with relevant metadata are in SAFE format.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32807, "uuid": "d69ef5ff221a45f38c35cd77c0ca9352", "short_code": "ob", "title": "Sentinel 1B C-band Synthetic Aperture Radar (SAR): Interferometric Wide (IW) mode Single Look Complex (SLC) Level 1 data, Instrument Processing Facility (IPF) v3", "abstract": "This dataset contains Interferometric Wide swath (IW) Single Look Complex (SLC) C-band Synthetic Aperture Radar (SAR) data from the European Space Agency (ESA) Sentinel 1B satellite. Sentinel 1B was lanched on 25th April 2016 and provides continuous all-weather, day and night imaging radar data. The IW mode is the main operational mode. The IW mode supports single (HH or VV) and dual (HH+HV or VV+VH) polarisation. \r\n\r\nThe IW SLC product contains one image per sub-swath, per polarisation channel, for a total of three or six images. Each sub-swath image consists of a series of bursts, where each burst was processed as a separate SLC image. The individually focused complex burst images are included, in azimuth-time order, into a single sub-swath image, with black-fill demarcation in between\r\n\r\nUnlike SM and WV SLC products, which are sampled at the natural pixel spacing, the images for all bursts in all sub-swaths of an IW SLC product are re-sampled to a common pixel spacing grid in range and azimuth. The resampling to a common grid eliminates the need for further interpolation in case, in later processing stages, the bursts are merged to create a contiguous ground range, detected image.\r\n\r\n\r\nThese data are available via CEDA to any registered user." }, "onlineresource_set": [] }, { "ob_id": 32815, "uuid": "003b4ee078e541b8a9a8e8fe19109e65", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/sentinel1b/data/SM/L1_SLC/IPF_v3/", "numberOfFiles": 6621, "volume": 3727643585008, "fileFormat": "Image data files are in a binary format. Quicklook images are in png format. Manifest files with relevant metadata are in SAFE format.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32814, "uuid": "171d5d0e6ef44fc3addad6ba45c8fb17", "short_code": "ob", "title": "Sentinel 1B C-band Synthetic Aperture Radar (SAR): SM mode SLC Level 1 data, Instrument Processing Facility (IPF) v3", "abstract": "This dataset contains Stripmap Mode (SM) C-band Synthetic Aperture Radar (SAR) Single Look Complex (SLC) data from the European Space Agency (ESA) Sentinel 1B satellite. Sentinel 1B was launched on 25th April 2016 and provides continuous all-weather, day and night imaging radar data. The SM mode is used only on special request for extraordinary events such as emergency management. The SM mode supports single (HH or VV) and dual (HH+HV or VV+VH) polarisation. \r\n\r\nStripmap SLCs contain one image per polarisation band from one of six overlapping beams. Each beam covers 80.1 km, covering a combined range of 375 km. Pixel spacing is determined, in azimuth by the pulse repetition frequency (PRF), and in range by the radar range sampling frequency, providing natural pixel spacing.\r\n\r\nThese data are available via CEDA to any registered user." }, "onlineresource_set": [] }, { "ob_id": 32821, "uuid": "a0f2f01f43f048939eaa7b841681bbeb", "short_code": "result", "curationCategory": "", "dataPath": "/badc/cru/data/cru_jra/cru_jra_2.2/", "numberOfFiles": 1202, "volume": 406024456466, "fileFormat": "The data are provided as gzipped NetCDF files, with one file per variable, per year.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32817, "uuid": "4bdf41fc10af4caaa489b14745c665a6", "short_code": "ob", "title": "CRU JRA v2.2: A forcings dataset of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data; Jan.1901 - Dec.2020.", "abstract": "The CRU JRA V2.2 dataset is a 6-hourly, land surface, gridded time series of ten meteorological variables produced by the Climatic Research Unit (CRU) at the University of East Anglia (UEA), and is intended to be used to drive models. The variables are provided on a 0.5 deg latitude x 0.5 deg longitude grid, the grid is near global but excludes Antarctica (this is same as the CRU TS grid, though the set of variables is different). The data are available at a 6 hourly time-step from January 1901 to December 2020.\r\n\r\nThe dataset is constructed by regridding data from the Japanese Reanalysis data (JRA) produced by the Japanese Meteorological Agency (JMA), adjusting where possible to align with the CRU TS 4.05 data (see the Process section and the ReadMe file for full details).\r\n\r\nThe CRU JRA data consists of the following ten meteorological variables: 2-metre temperature, 2-metre maximum and minimum temperature, total precipitation, specific humidity, downward solar radiation flux, downward long wave radiation flux, pressure and the zonal and meridional components of wind speed (see the ReadMe file for further details).\r\n\r\nThe CRU JRA dataset is intended to be a replacement of the CRU NCEP forcing dataset. The CRU JRA dataset follows the style of Nicolas Viovy's original CRU NCEP dataset rather than that which is available from UCAR. A link to the CRU NCEP documentation for comparison is provided in the documentation section. \r\n\r\nIf this dataset is used in addition to citing the dataset as per the data citation string users must also cite the following:\r\n\r\nHarris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS\r\nmonthly high-resolution gridded multivariate climate dataset.\r\nSci Data 7, 109 (2020). https://doi.org/10.1038/s41597-020-0453-3\r\n\r\nHarris, I., Jones, P.D., Osborn, T.J. and Lister, D.H. (2014), Updated\r\nhigh-resolution grids of monthly climatic observations - the CRU TS3.10\r\nDataset. International Journal of Climatology 34, 623-642.\r\n\r\nKobayashi, S., et. al., The JRA-55 Reanalysis: General Specifications and\r\nBasic Characteristics. J. Met. Soc. Jap., 93(1), 5-48\r\nhttps://dx.doi.org/10.2151/jmsj.2015-001" }, "onlineresource_set": [] }, { "ob_id": 32822, "uuid": "6c527dba7e334a2eb629114e0cdf412b", "short_code": "result", "curationCategory": "", "dataPath": "/badc/cru/data/cru_ts/cru_ts_4.05", "numberOfFiles": 379, "volume": 6911362666, "fileFormat": "Data are provided in ASCII and NetCDF formats.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32804, "uuid": "c26a65020a5e4b80b20018f148556681", "short_code": "ob", "title": "CRU TS4.05: Climatic Research Unit (CRU) Time-Series (TS) version 4.05 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2020)", "abstract": "The gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.05 data are month-by-month variations in climate over the period 1901-2020, provided on high-resolution (0.5x0.5 degree) grids, produced by CRU at the University of East Anglia and funded by the UK National Centre for Atmospheric Science (NCAS), a NERC collaborative centre.\r\n\r\nThe CRU TS4.05 variables are cloud cover, diurnal temperature range, frost day frequency, wet day frequency, potential evapotranspiration (PET), precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, and vapour pressure for the period January 1901 - December 2020.\r\n\r\nThe CRU TS4.05 data were produced using angular-distance weighting (ADW) interpolation. All versions prior to 4.00 used triangulation routines in IDL. Please see the release notes for full details of this version update. \r\n\r\nThe CRU TS4.05 data are monthly gridded fields based on monthly observational data calculated from daily or sub-daily data by National Meteorological Services and other external agents. The ASCII and NetCDF data files both contain monthly mean values for the various parameters. The NetCDF versions contain an additional integer variable, ’stn’, which provides, for each datum in the main variable, a count (between 0 and 8) of the number of stations used in that interpolation. The missing value code for 'stn' is -999.\r\n\r\nAll CRU TS output files are actual values - NOT anomalies." }, "onlineresource_set": [] }, { "ob_id": 32823, "uuid": "8a4efe37ae724bfdbb4450ccfd57d3f5", "short_code": "result", "curationCategory": "", "dataPath": "/badc/cru/data/cru_cy/cru_cy_4.05/", "numberOfFiles": 2924, "volume": 50992332, "fileFormat": "The CRU CY data are provided as text files with the extension \".per\", most text editors will open these files. See the linked file formats guide for more information.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32803, "uuid": "7a5529a8758041eb83b9c32f8461e50d", "short_code": "ob", "title": "CRU CY4.05: Climatic Research Unit year-by-year variation of selected climate variables by country version 4.05 (Jan. 1901 - Dec. 2020)", "abstract": "The Climatic Research Unit (CRU) Country (CY) data version 4.05 dataset consists of ten climate variables for country averages at a monthly, seasonal and annual frequency: including cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, vapour pressure, potential evapotranspiration and wet day frequency. This version uses the updated set of country definitions, please see the appropriate Release Notes.\r\n\r\nThis dataset was produced in 2021 by CRU at the University of East Anglia and extends the CRU CY4.04 data to include 2020. The data are available as text files with the extension '.per' and can be opened by most text editors.\r\n\r\nSpatial averages are calculated using area-weighted means. CRU CY4.05 is derived directly from the CRU time series (TS) 4.05 dataset. CRU CY version 4.05 spans the period 1901-2020 for 292 countries.\r\n\r\nTo understand the CRU CY4.05 dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS4.05. It is therefore recommended that all users read the Harris et al, 2020 paper and the CRU TS4.05 release notes listed in the online documentation on this record.\r\n\r\nCRU CY data are available for download to all CEDA users." }, "onlineresource_set": [] }, { "ob_id": 32826, "uuid": "4d5ec73e911141e3ba95bf65465c3708", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ipcc-ddc/data/legacy/download_data/is92/echam4", "numberOfFiles": 113, "volume": 102387736, "fileFormat": "plain text", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 5540, "uuid": "36e875d8bd9625e0f0efdb4e7fc09fe2", "short_code": "ob", "title": "Data from ECHAM4 ocean coupled general circulation model at Max Planck Institute für Meteorologie computing facility for the IPCC Second Assessment Report Collection", "abstract": "The Intergovernmental Panel on Climate Change (IPCC) Data Distribution Centre provides four main types of data and guidance:\r\n1. Observed Climate Data Sets;\r\n2. Global Climate Model Data;\r\n3. Socio-economic data and scenarios;\r\n4. Data and scenarios for other environmental changes." }, "onlineresource_set": [] }, { "ob_id": 32827, "uuid": "169b0348bd4c43238f16f72d85971f21", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ipcc-ddc/data/legacy/download_data/is92/cccsr", "numberOfFiles": 27, "volume": 4725047, "fileFormat": "Plain Text", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 5547, "uuid": "c417b52e9695a3cf51e54aad7278ee12", "short_code": "ob", "title": "Data from CCSR/NIES Global Circulation Model at Centre for Climate System Research / National Institude for Environmental Studies (Japan) computer for the IPCC Second Assessment Report Collection", "abstract": "The Intergovernmental Panel on Climate Change (IPCC) Data Distribution Centre provides four main types of data and guidance:\r\n1. Observed Climate Data Sets;\r\n2. Global Climate Model Data;\r\n3. Socio-economic data and scenarios;\r\n4. Data and scenarios for other environmental changes." }, "onlineresource_set": [] }, { "ob_id": 32828, "uuid": "785167ff15d9422f9dc88eac215d94cf", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ipcc-ddc/data/legacy/download_data/is92/hadcm2", "numberOfFiles": 737, "volume": 603642519, "fileFormat": "Plain Text", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 5526, "uuid": "0105466f9995ac3f63b8d46060ccc20a", "short_code": "ob", "title": "Data from Hadley Centre Coupled Model 2 (HadCM2) at Met Office Hadley Centre Computers for the IPCC Second Assessment Report Collection", "abstract": "The Intergovernmental Panel on Climate Change (IPCC) Data Distribution Centre provides four main types of data and guidance:\r\n1. Observed Climate Data Sets;\r\n2. Global Climate Model Data;\r\n3. Socio-economic data and scenarios;\r\n4. Data and scenarios for other environmental changes." }, "onlineresource_set": [] }, { "ob_id": 32829, "uuid": "b789ef5b549d405cabe38808b5c72c1c", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ipcc-ddc/data/legacy/download_data/is92/csiromk2", "numberOfFiles": 118, "volume": 66728419, "fileFormat": "Plain Text", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 5545, "uuid": "2315b16f6cd4e5bab43dc572baae8079", "short_code": "ob", "title": "Data from CSIRO-Mk2 Global Circulation Model at Commonwealth Scientific and Industrial Research Organisation computing facility for the IPCC Second Assessment Report Collection", "abstract": "The Intergovernmental Panel on Climate Change (IPCC) Data Distribution Centre provides four main types of data and guidance:\r\n1. Observed Climate Data Sets;\r\n2. Global Climate Model Data;\r\n3. Socio-economic data and scenarios;\r\n4. Data and scenarios for other environmental changes." }, "onlineresource_set": [] }, { "ob_id": 32830, "uuid": "f670def5c7744c588cf82bf217cd91d2", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ipcc-ddc/data/legacy/download_data/is92/gfdl", "numberOfFiles": 55, "volume": 25668636, "fileFormat": "Plain Text", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 5556, "uuid": "dded4c40bcc6c3a2c18baf384aadf83c", "short_code": "ob", "title": "Data from GFDL-R15 Global Circulation Model at Geophysical Fluid Dynamics Laboratory (USA) computing facility for the IPCC Second Assessment Report Collection", "abstract": "The Intergovernmental Panel on Climate Change (IPCC) Data Distribution Centre provides four main types of data and guidance:\r\n1. Observed Climate Data Sets;\r\n2. Global Climate Model Data;\r\n3. Socio-economic data and scenarios;\r\n4. Data and scenarios for other environmental changes." }, "onlineresource_set": [] }, { "ob_id": 32831, "uuid": "1e40a125829841319f0c2e2acd7eea92", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ipcc-ddc/data/legacy/download_data/is92/ncardoe", "numberOfFiles": 15, "volume": 2288925, "fileFormat": "Plain Text", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 5534, "uuid": "68acb28215e1215f014d0a42713fd8fd", "short_code": "ob", "title": "Data from NCAR-DoE global coupled model at National Centre for Atmospheric Research (USA) computing facility for the IPCC Second Assessment Report Collection", "abstract": "The Intergovernmental Panel on Climate Change (IPCC) Data Distribution Centre provides four main types of data and guidance:\r\n1. Observed Climate Data Sets;\r\n2. Global Climate Model Data;\r\n3. Socio-economic data and scenarios;\r\n4. Data and scenarios for other environmental changes." }, "onlineresource_set": [] }, { "ob_id": 32832, "uuid": "765de6c9f26f43bfa8f46df844e62b6f", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ipcc-ddc/data/legacy/download_data/is92/cccma", "numberOfFiles": 361, "volume": 255172477, "fileFormat": "Plain Text", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 5558, "uuid": "64385a98975dca1ec08719cba96fd480", "short_code": "ob", "title": "Data from CGCM1 Canadian Global Coupled Model Version 1 at Canadian Centre for Climate Modelling and Analysis computing facility for the IPCC Second Assessment Report Collection", "abstract": "The Intergovernmental Panel on Climate Change (IPCC) Data Distribution Centre provides four main types of data and guidance:\r\n1. Observed Climate Data Sets;\r\n2. Global Climate Model Data;\r\n3. Socio-economic data and scenarios;\r\n4. Data and scenarios for other environmental changes." }, "onlineresource_set": [] }, { "ob_id": 32833, "uuid": "cfd960b43081439dbfe0c4ca147e3d5c", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ipcc-ddc/data/legacy/download_data/sres", "numberOfFiles": 1570, "volume": 256000000, "fileFormat": "Plain Text", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 32834, "uuid": "3548c218f64c4e84a55288ebbff332ec", "short_code": "result", "curationCategory": "", "dataPath": "/badc/ipcc-ddc/data/legacy/download_data/sres/GFDL99_A2a", "numberOfFiles": 37, "volume": 10659557, "fileFormat": "Plain Text", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 5553, "uuid": "6059f6e6b08333795540fbf9a0b57a7f", "short_code": "ob", "title": "Data from GFDL-R15 Global Circulation Model at Geophysical Fluid Dynamics Laboratory (USA) computing facility for the IPCC Third Assessment Report Collection", "abstract": "The Intergovernmental Panel on Climate Change (IPCC) Data Distribution Centre provides four main types of data and guidance:\r\n1. Observed Climate Data Sets;\r\n2. Global Climate Model Data;\r\n3. Socio-economic data and scenarios;\r\n4. Data and scenarios for other environmental changes." }, "onlineresource_set": [] }, { "ob_id": 32835, "uuid": "56c8413656934bf48ecde447d91df721", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ipcc-ddc/data/legacy/download_data/sres/ECHAM4_A2a", "numberOfFiles": 60, "volume": 19399200, "fileFormat": "Plain Text", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 5537, "uuid": "dc33eeca0acf1dd69fa3930344824160", "short_code": "ob", "title": "Data from ECHAM4 ocean coupled general circulation model at Max Planck Institute für Meteorologie computing facility for the IPCC Third Assessment Report Collection", "abstract": "The Intergovernmental Panel on Climate Change (IPCC) Data Distribution Centre provides four main types of data and guidance:\r\n1. Observed Climate Data Sets;\r\n2. Global Climate Model Data;\r\n3. Socio-economic data and scenarios;\r\n4. Data and scenarios for other environmental changes." }, "onlineresource_set": [] }, { "ob_id": 32836, "uuid": "69c2ef9995d2494d804c19a37895c406", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ipcc-ddc/data/legacy/download_data/sres/csiro_a1a", "numberOfFiles": 122, "volume": 19737136, "fileFormat": "Plain Text", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 5542, "uuid": "a192e98ae80dd369f3e0f2293a45a4e6", "short_code": "ob", "title": "Data from CSIRO-Mk2 Global Circulation Model at Commonwealth Scientific and Industrial Research Organisation computing facility for the IPCC Third Assessment Report Collection", "abstract": "The Intergovernmental Panel on Climate Change (IPCC) Data Distribution Centre provides four main types of data and guidance:\r\n1. Observed Climate Data Sets;\r\n2. Global Climate Model Data;\r\n3. Socio-economic data and scenarios;\r\n4. Data and scenarios for other environmental changes." }, "onlineresource_set": [] }, { "ob_id": 32837, "uuid": "19cd5373babc4ff5a814beca16d20059", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ipcc-ddc/data/legacy/download_data/sres/cccma_a2a", "numberOfFiles": 58, "volume": 14008319, "fileFormat": "Plain Text", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 5550, "uuid": "4541470a3ecd4b3d3eb11cb58213ec12", "short_code": "ob", "title": "Data from CGCM2 Canadian Global Coupled Model Version 2 at Canadian Centre for Climate Modelling and Analysis computing facility for the IPCC Third Assessment Report Collection", "abstract": "The Intergovernmental Panel on Climate Change (IPCC) Data Distribution Centre provides four main types of data and guidance:\r\n1. Observed Climate Data Sets;\r\n2. Global Climate Model Data;\r\n3. Socio-economic data and scenarios;\r\n4. Data and scenarios for other environmental changes." }, "onlineresource_set": [] }, { "ob_id": 32838, "uuid": "3f780f5b8a7948109dfebcead2fdcb00", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ipcc-ddc/data/legacy/download_data/sres/hadcm3_a1f", "numberOfFiles": 45, "volume": 14212332, "fileFormat": "Plain Text", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 5530, "uuid": "053af44e98190715670b19de24e30e38", "short_code": "ob", "title": "Data from Hadley Centre Coupled Model 3 (HadCM3) at Met Office Hadley Centre Computers for the IPCC Third Assessment Report Collection", "abstract": "The Intergovernmental Panel on Climate Change (IPCC) Data Distribution Centre provides four main types of data and guidance:\r\n1. Observed Climate Data Sets;\r\n2. Global Climate Model Data;\r\n3. Socio-economic data and scenarios;\r\n4. Data and scenarios for other environmental changes." }, "onlineresource_set": [] }, { "ob_id": 32845, "uuid": "5a7cc4fd729b4413b92fa24c9a4bed27", "short_code": "result", "curationCategory": "A", "dataPath": "/bodc/BAS210039/CORE2NYF-ORCH0083-LIM3", "numberOfFiles": 66605, "volume": 39378733805708, "fileFormat": "Data are CF-compliant NetCDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32844, "uuid": "2e982e6692e3427dbe35e64ad9dee12d", "short_code": "ob", "title": "1/12 degree Nucleus for European Modelling of the Ocean (NEMO) model of the Southern Ocean: CORE2 normal year forced control run (1951-1987)", "abstract": "The dataset is a 37 year control run of a NEMO-based 1/12 degree grid spacing model of the Southern Ocean as part of the ORCHESTRA LTS-M project. It uses the NEMO \"extended\" grid, although ice cavities are closed. The model was run on Archer, the national HPC platform. The dataset covers the full length of the model run (excluding a three year spinup period) and includes regular (5 day mean) output of the model state, as well as more frequent (1 day mean) output of surface variables and fluxes and 1 month mean of more extensive transport diagnostics.\r\n\r\nForced by the GFDL (Geophysical Fluid Dynamics Laboratory) CORE2 (corrected normal year forcing version 2.0) normal year forcing. With some additional forcing as supplied by the UK Met Office (freshwater runoff, tidal friction, geothermal heating) and additional freshwater runoff to suppress polynya formation. Initialised from January of a climatology of ECCOv4r2 (Estimating the Circulation and Climate of the Ocean) in nominal year 1948." }, "onlineresource_set": [] }, { "ob_id": 32849, "uuid": "9bacf3863a4646ce90f16dad89eb0276", "short_code": "result", "curationCategory": "", "dataPath": "/badc/ukcp18/data/marine-sim/worldwide-proj", "numberOfFiles": 205, "volume": 12614183, "fileFormat": "Data are NetCDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32848, "uuid": "65bcd04dde2e4f8383a0cca4f69ac79e", "short_code": "ob", "title": "UKCP18 Local Time-Mean Sea Level Projections for selected Tide Gauge Locations around the World", "abstract": "The UKCP18 worldwide sea level projections are provided for the tide gauge locations presented in Palmer et al (2020). They follow the same methods as the UK projections with the exceptions that make use of globally complete GIA estimates and a larger set of GRD fingerprints. The data consist of annual time series of the projected change in the time-mean coastal water level relative to the average value for the period 1986-2005, consistent with the IPCC Fifth Assessment Report (AR5) and the Special Report on the Ocean and Cryosphere in a Changing Climate. Projections are available for the RCP2.6, RCP4.5 and RCP8.5 climate change scenarios (Meinshausen et al, 2011). The 5th, 50th and 95th percentile projections are provided for the total sea level change and the individual components. Further details are available in Palmer et al (2020), which can be found in the documentation section." }, "onlineresource_set": [] }, { "ob_id": 32851, "uuid": "fe6b5c74909745b3adbf128fb9c529aa", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/dem/kruger/v1.0/sep2018_oct2018/", "numberOfFiles": 338, "volume": 3633786348379, "fileFormat": "GEOtiff", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32852, "uuid": "deab4235f1ef4cd79b73d0cbf2655bd7", "short_code": "ob", "title": "Sub-meter resolution digital elevation models and orthomosaics of the Kruger National Park, South Africa, v1.0, September-October 2018", "abstract": "This dataset contains sub-meter resolution digital elevation models and orthomosaics of the Kruger National Park, South Africa, generated from aerial images captured by Digital Mapping Camera (DMC) during September and October 2018.\r\n\r\n\r\nThe use of digital elevation models has proven to be crucial in a large number of studies related to savanna ecosystem research. However, the insufficient spatial resolution of the input data is often considered to be a limiting factor when conducting local to regional ecosystem analysis. The elevation models and orthorectified imagery created in this dataset represent the first wall-to-wall digital elevation products of the Kruger National Park (KNP) in South Africa at 25 cm pixel posting. In the light of regular flight campaigns carried out by the South African government, the workflow of the presented data sets can be reused to create height models and orthorectified images of a vulnerable ecosystem in the future. Flight campaigns were carried out by GeoSpace International, Pretoria. Data processing and preparation as well as validation of the final products was carried out by Kai Heckel (Friedrich Schiller University Jena, Germany) with the strong support of all co-authors of the related study.\r\n\r\nThe methodology is described in the following publication:\r\n\r\nHeckel, K.; Urban, M.; Bouffard, J.-S.; Baade, J.; Boucher, P.; Davies, A.; Hockridge, E.G.; Lück, W.; Ziemer, J.; Smit, I.; Jacobs, B.; Norris-Rogers, M.; Schmullius, C. (2021): The first sub-meter resolution digital elevation model of the Kruger National Park, South Africa. Koedoe." }, "onlineresource_set": [] }, { "ob_id": 32853, "uuid": "705c9a04cb7b4002bd9a07f0356ab1ac", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/ghg/data/cci_plus/CH4_GO2_SRPR/v1.0/", "numberOfFiles": 249, "volume": 405913974, "fileFormat": "Data are in NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32750, "uuid": "722fe4748da2487ead0a755f6d09e6ab", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from GOSAT-2, generated with the SRPR (RemoTeC) proxy retrieval algorithm, version 1.0.0", "abstract": "This dataset contains column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2(TANSO-FTS-2) Near Infrared (NIR) and Shortwave Infrared (SWIR) spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the RemoTeC SRPR Proxy Retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.\r\n\r\nThese data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme." }, "onlineresource_set": [] }, { "ob_id": 32862, "uuid": "43b7612e0b614d8f927f09d1c6ef9262", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/ghg/data/cci_plus/CO2_GO2_SRFP/v1.0/", "numberOfFiles": 249, "volume": 264108937, "fileFormat": "Data are in NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32751, "uuid": "f1b19872c12d477abfeb229f060a0969", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from GOSAT-2, derived using the SRFP (RemoTeC) full physics algorithm, version 1.0.0", "abstract": "This dataset contains column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) Near Infrared(NIR) and Shortwave Infrared (SWIR) spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the RemoTeC SRFP Full Physics Retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.\r\n\r\nThese data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme." }, "onlineresource_set": [] }, { "ob_id": 32887, "uuid": "04d6a6eedf7d49c5b5944f9aecf2ee4e", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukcp18/data/land-cpm/derived/future-extremes", "numberOfFiles": 151, "volume": 80307963, "fileFormat": "Data are provided as Shapefiles and CSV", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32886, "uuid": "18f83caf9bdf4cb4803484d8dce19eef", "short_code": "ob", "title": "Extreme precipitation return level changes at 1, 3, 6, 12, 24 hours for 2050 and 2070, derived from UKCP Local Projections on a 5km grid for the FUTURE-DRAINAGE Project", "abstract": "Extreme short-duration precipitation changes, derived from the UKCP Local projections at 5km resolution (Kendon et al 2021) have been estimated using a spatial statistical model as part of the NERC-funded Future-Drainage project. Future changes (\"\"uplifts\"\") are estimated for 2050 and 2070 for RCP8.5 compared to the baseline of 1990 for precipitation durations of 1-, 3-, 6-, 12-, 24-hours. 2070 is the central year for 2060-2080 (\"\"UKCP Local TS3\"\") time-slice, and 2050 value is an interpolation between TS3 and 2020-2040 (\"\"UKCP Local TS2\"\") time-slice. 2050 is an important date for the UK water industry in its delivery of Drainage and Wastewater Management Plans (DWMPs; Water UK, 2019). Return level changes are provided for 2, 30, and 100-year return periods. The data is on the OSGB 1936/EPSG:27700 projection at 5km resolution. The underlying statistical model is described in Youngman (2018, 2020) and is applied individually to each of the twelve UKCP Local ensemble members. Future changes plus their uncertainties from each ensemble member are then combined following the method described in Fosser et al (2020).\r\n\r\nTwo estimates of future changes are provided from this \"\"super-ensemble\"\" by estimating percentiles from the distribution obtained using the Fosser et al (2020) method - a central (50%) and high (95%) estimate. Values are rounded to nearest 5%. The future changes are available for each 5km grid point within the borders of the United Kingdom, provided as ERSI shapefiles and a CSV (comma-separated values) file, with separate files for different durations.\"" }, "onlineresource_set": [] }, { "ob_id": 32892, "uuid": "c4cb3ebb8dbb43c3a02c623388c22d3f", "short_code": "result", "curationCategory": "C", "dataPath": "/neodc/iasi_metop_a/data/l2", "numberOfFiles": 59131, "volume": 3673032085102, "fileFormat": "Data are in EUMETSAT native format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [ { "ob_id": 8573, "uuid": "0a569ddaa6254777a052a9c18bb3883f", "short_code": "result", "title": null, "abstract": null } ], "observation": { "ob_id": 8572, "uuid": "a7f8fdc2ed8bfd16651d201e44384029", "short_code": "ob", "title": "IASI: Atmospheric sounding Level 2 data products", "abstract": "This dataset contains level 2 data products from the Infrared Atmospheric Sounding Interferometer (IASI) instrument on board the Eumetsat EPS Metop-A satellite. \r\n\r\nIASI was designed to measure the infrared spectrum emitted by the earth. IASI provides infrared soundings of the temperature profiles in the troposphere and lower stratosphere, moisture profiles in the troposphere, as well as some of the chemical components playing a key role in the climate monitoring, global change and atmospheric chemistry." }, "onlineresource_set": [ 43078 ] }, { "ob_id": 32893, "uuid": "0d90b768e73b41a6843b0670b78b9bc9", "short_code": "result", "curationCategory": "C", "dataPath": "/neodc/iasi/data/l1c", "numberOfFiles": 0, "volume": 0, "fileFormat": "Data are in Eumetsat native format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [ 43079 ] }, { "ob_id": 32894, "uuid": "5138512e3c50485b8f6a71a4b83830e2", "short_code": "result", "curationCategory": "A", "dataPath": "/neodc/gome2_metop_a/data/l1b/", "numberOfFiles": 88732, "volume": 56068709863464, "fileFormat": "Data are EPS native formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [ { "ob_id": 8204, "uuid": "37eaa34b203f4f7790f0bcba32ac73da", "short_code": "result", "title": null, "abstract": null } ], "observation": { "ob_id": 8203, "uuid": "bde3e07047e99cdf61908c3e93d51c66", "short_code": "ob", "title": "Global Ozone Monitoring Experiment-2 (GOME-2): Atmospheric Spectral METOP-A data at Level 1b", "abstract": "The Global Ozone Monitoring Experiment–2 (GOME–2), is an optical spectrometer, fed by a scan mirror which enables across–track scanning in nadir, as well as sideways viewing for polar coverage and instrument characterisation measurements using the moon. The scan mirror directs light into a telescope, designed to match the field of view of the instrument to the dimensions of the entrance slit. This scan mirror can also be directed towards internal calibration sources or towards a diffuser plate for calibration measurements using the sun.\r\n\r\nThis dataset contains atmospheric spectra (Level 1b data) from the GOME-2 instrument on-board the Eumetsat Polar System (EPS) Metop-A satellite." }, "onlineresource_set": [ 43080 ] }, { "ob_id": 32895, "uuid": "028842b2c5624e87813495c2eaa029d6", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/avhrr3_metop_a/data/l1b", "numberOfFiles": 75157, "volume": 31941089779577, "fileFormat": "Data are in EUMETSAT native format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [ { "ob_id": 10887, "uuid": "74b2e1261cd64ce89e9650dbcbc6f6c7", "short_code": "result", "title": null, "abstract": null } ], "observation": { "ob_id": 10886, "uuid": "b2e553261448fcbed284612e5b4bae58", "short_code": "ob", "title": "Data from AVHRR-3 at Metop-A for the Eumetsat Polar System Project", "abstract": "AVHRR-3 scans the Earth's surface in six spectral bands in the range of 0.58-12.5 microns, to provide day and night imaging of land, water and clouds and measurements of sea surface temperature, ice snow and vegetation cover. The instruments were provided by the National Oceanic and Atmospheric Administration (NOAA) and is flown on the EPS-METOP series of satellites\r\n\r\nThis dataset contains data from the Advanced Very High Resolution Radiometer-3 (AVHRR-3) on board the Eumetsat Polar System (EPS) MetOp-A satellite." }, "onlineresource_set": [] }, { "ob_id": 32897, "uuid": "c0fc4a5d4c1b4e09815f011053dc53b4", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/hyrex/data/Common/drn", "numberOfFiles": 135, "volume": 13601146, "fileFormat": "Text", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32896, "uuid": "48e1c8759a034a7298ada6ab18ef5134", "short_code": "ob", "title": "HYREX project: Data from Purpose-built Dense Raingauge Network", "abstract": "Data from the purpose-built dense raingauge network was established in Somerset as part of the HYREX project. HYREX (Hydrological Radar Experiment) was a NERC (Natural Environment Research Council) special topic running from May 1993 to April 1997. Field experiments with an emphasis on radar, plus related interpretation and modelling, were carried out to investigate the short term forecasting and hydrological implications of precipitation." }, "onlineresource_set": [] }, { "ob_id": 32928, "uuid": "1bc9e17a3978477daf877c51bd792aac", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/cloud/data/version3/L2/ATSR2-AATSR/CLOUD/v3.0/", "numberOfFiles": 253085, "volume": 56169148862303, "fileFormat": "Data are in NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32927, "uuid": "b69d7604f031485bb515eff2de322468", "short_code": "ob", "title": "ESA Cloud Climate Change Initiative (Cloud_cci): ATSR2-AATSR cloud properties on the satellite swath (L2), version 3.0", "abstract": "The Cloud_cci ATSR2-AATSRv3 Level 2 dataset (covering 1995-2012) was generated within the Cloud_cci project, which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. \r\n\r\nThis dataset is based on measurements from the ATSR2 and AATSR instruments (onboard the ERS2 and ENVISAT satellites) and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework. The core cloud properties contained in the Cloud_cci ATSR2-AATSRv3 dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. This particular dataset contains Level 2 data on the satellite swath. Level-3U (globally gridded, unaveraged data fields) and L3C (monthly gridded) is also available as a separate dataset. Pixel-based uncertainty estimates come along with all properties." }, "onlineresource_set": [] }, { "ob_id": 32930, "uuid": "0944323332a94068904bccc763408ea0", "short_code": "result", "curationCategory": "A", "dataPath": "/neodc/esacci/cloud/data/version3/L2/ATSR2-AATSR/FLUX/v3.0/", "numberOfFiles": 246683, "volume": 99143962759941, "fileFormat": "Data are in NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32929, "uuid": "420feb0e4f364ed2bdf0bc92171a1374", "short_code": "ob", "title": "ESA Cloud Climate Change Initiative (Cloud_cci): ATSR2-AATSR flux data on the satellite swath (L2), version 3.0", "abstract": "The Cloud_cci ATSR2-AATSRv3 Level 2 dataset (covering 1995-2012) was generated within the Cloud_cci project, which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. \r\n\r\nThis dataset is based on measurements from the ATSR2 and AATSR instruments (onboard the ERS2 and ENVISAT satellites) and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework. This particular dataset contains Level 2 flux data on the satellite swath." }, "onlineresource_set": [] }, { "ob_id": 32945, "uuid": "19373069e9374299a4da07152eb01053", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ar6_wg1/data/spm/spm_08/v20210809", "numberOfFiles": 24, "volume": 86756, "fileFormat": "Data are CSV formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32899, "uuid": "98af2184e13e4b91893ab72f301790db", "short_code": "ob", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.8 (v20210809)", "abstract": "Data for Figure SPM.8 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure SPM.8 shows selected indicators of global climate change under the five core scenarios used in this report.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n\r\nIPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001.\r\n\r\n\r\n ---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has five panels, with data provided for all panels in subdirectories named panel_a, panel_b, panel_c, panel_d and panel_e.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n - Historical, SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 Global Surface Air Temperature (GSAT) anomalies relative to 1850-1900 (20 year means)\r\n - Historical, SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 September sea-ice area\r\n - Historical, SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 Global ocean surface pH\r\n - Historical sea level relative to 1900 from gauges (to 1992) and altimeters (1993 on) (offset 0.158 m vs. 1995-2014)\r\n - AR6 sea level projections relative to 1900 (offset 0.158 m vs. 1995-2014)\r\n - AR6 assessed global mean sea level at 2300 relative to 1900 (offset 0.158 m vs. 1995-2014)\r\n\r\nThe five illustrative SSP (Shared Socio-economic Pathway) scenarios are described in Box SPM.1 of the Summary for Policymakers and Section 1.6.1.1 of Chapter 1.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a: Near-Surface Air Temperature\r\n\r\n - Data file: panel_a/tas_global_Historical.csv (black line and grey shading)\r\n - Data file: panel_a/tas_global_SSP1_1_9.csv (cyan line)\r\n - Data file: panel_a/tas_global_SSP1_2_6.csv (blue line and blue shading)\r\n - Data file: panel_a/tas_global_SSP2_4_5.csv (orange line)\r\n - Data file: panel_a/tas_global_SSP3_7_0.csv (red line and red shading)\r\n - Data file: panel_a/tas_global_SSP5_8_5.csv (brown line)\r\n\r\n\r\nPanel b: Sea-Ice Area\r\n\r\n - Data file: panel_b/sia_arctic_september_Historical.csv (black line and grey shading)\r\n - Data file: panel_b/sia_arctic_september_SSP1_1_9.csv (cyan line)\r\n - Data file: panel_b/sia_arctic_september_SSP1_2_6.csv (blue line and blue shading)\r\n - Data file: panel_b/sia_arctic_september_SSP2_4_5.csv (orange line)\r\n - Data file: panel_b/sia_arctic_september_SSP3_7_0.csv (red line and red shading)\r\n - Data file: panel_b/sia_arctic_september_SSP5_8_5.csv (brown line)\r\n\r\n\r\nPanel c: Ocean Surface pH\r\n\r\n - Data file: panel_c/phos_global_Historical.csv (black line and grey shading\r\n - Data file: panel_c/phos_global_SSP1_1_9.csv (cyan line\r\n - Data file: panel_b/phos_global_SSP1_2_6.csv (blue line and blue shading)\r\n - Data file: panel_c/phos_global_SSP2_4_5.csv (orange line)\r\n - Data file: panel_c/phos_global_SSP3_7_0.csv (red line and red shading)\r\n - Data file: panel_c/phos_global_SSP5_8_5.csv (brown line)\r\n\r\n\r\nPanel d: Sea Level\r\n\r\n - Data file: panel_d/global_sea_level_observed.csv (black line)\r\n - Data file: panel_d/global_sea_level_projected.csv (cyan, blue, orange, red and brown lines, red and blue shading)\r\n\r\n\r\nPanel e: Sea Level\r\n\r\n - Data file: panel_e: global_sea_level_2300_assessed.csv (columns 2 and 3, SSP1-2.6 scenario; columns 4 to 6 SSP5-8.5 scenario)\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n\r\n - Link to the report component containing the figure (Summary for Policymakers)" }, "onlineresource_set": [] }, { "ob_id": 32955, "uuid": "5178d886286445dbacb73443345c77ac", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ar6_wg1/data/spm/spm_06/v20210809", "numberOfFiles": 12, "volume": 24076, "fileFormat": "Data are CSV formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32906, "uuid": "93d1b84fbb144901809eaf67b35eb5c4", "short_code": "ob", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.6 (v20210809)", "abstract": "Data for Figure SPM.6 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure SPM.6 shows projected changes in the intensity and frequency of extreme temperature, extreme precipitation and droughts.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n\r\nIPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with data provided for all panels in subdirectories named panel_a, panel_b, panel_c and panel_d.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\nThis dataset contains:\r\n- Changes in annual maximum temperature (TXx) extremes for intensity (°C) and frequency (-) for 1 in 10 year and 1 in 50 year events (relative to 1850-1900)\r\n- Changes in annual maximum 1-day precipitation (Rx1day) extremes for intensity (%) and frequency (-) for 1 in 10 year events (relative to 1850-1900)\r\n- Changes in soil moisture-based drought events for intensity (standard deviation) and frequency (-) for 1 in 10 year events (relative to 1850-1900)\r\n\r\n---------------------------------------------------\r\nData provided in relation to figure\r\n---------------------------------------------------\r\nPanel a:\r\n- Data file: panel_a/TXx_freq_change_10_year_event.csv ('Hot temperature extremes') [column 2 dark dots, columns 5 and 6 light dots]\r\n- Data file: panel_a/TXx_intens_change_10_year_event.csv ('Hot temperature extremes') [column 2 dark bars, columns 5 and 6 light bars]\r\nPanel b:\r\n- Data file: panel_b/TXx_freq_change_50_year_event.csv ('Hot temperature extremes') [column 2 dark dots, columns 5 and 6 light dots]\r\n- Data file: panel_b/TXx_intens_change_50_year_event.csv ('Hot temperature extremes') [column 2 dark bars, columns 5 and 6 light bars]\r\n \r\nPanel c:\r\n- Data file: panel_c/Rx1day_freq_change_10_year_event.csv ('Extreme precipitation over land') [column 2 dark dots, columns 5 and 6 light dots]\r\n- Data file: panel_c/Rx1day_intens_change_10_year_event.csv ('Extreme precipitation over land') [column 2 dark bars, columns 5 and 6 light bars]\r\nPanel d:\r\n- Data file: panel_d/drought_freq_change_10_year_event.csv ('Drought') [column 2 dark dots, columns 5 and 6 light dots]\r\n- Data file: panel_d/drought_intens_change_10_year_event.csv ('Drought') [column 2 dark bars, columns 5 and 6 light bars]\r\n\r\n---------------------------------------------------\r\nNotes on reproducing the figure from the provided data\r\n---------------------------------------------------\r\n- The 50th, 5th, and 95th percentiles are shown on the figure (lines on the bars).\r\n- The drought intensity shows 'drying' while the data file shows the change in soil moisture (i.e., a negative soil moisture change corresponds to a positive drying signal).\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblink is provided in the Related Documents section of this catalogue record:\r\n - - Link to the report webpage, which includes the report component containing the figure (Summary for Policymakers) and the Supplementary Material for Chapter 11, which contains details on the input data used in Table 11.SM.9. (Figures 11.15, 11.6, 11.7, 11.12, and 11.18)" }, "onlineresource_set": [] }, { "ob_id": 32957, "uuid": "e553096c2ce344feaae3663e61618663", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ar6_wg1/data/spm/spm_05/v20210809", "numberOfFiles": 13, "volume": 35754261, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32874, "uuid": "2787230b963942009e452255a3880609", "short_code": "ob", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.5 (v20210809)", "abstract": "Data for Figure SPM.5 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure SPM.5 shows changes in annual mean surface temperatures, precipitation, and total column soil moisture.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n\r\nIPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels with 11 maps. All data is provided, except for panel a1.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n\r\n- Annual mean temperature change (°C) (relative to 1850-1900)\r\n- Annual mean precipitation change (%) (relative to 1850-1900)\r\n- Annual mean soil moisture change (standard deviation of interannual variability) (relative to 1850-1900)\r\n\r\n \r\nThe data is given for global warming levels (GWLs), namely +1.0°C (temperature only), +1.5°C, 2.0°C, and +4.0°C.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nPanel a:\r\n- Data file: Panel_a2_Simulated_temperature_change_at_1C.nc, simulated annual mean temperature change (°C) at 1°C global warming relative to 1850-1900 (right).\r\n\r\nPanel b:\r\n- Data file: Panel_b1_Simulated_temperature_change_at_1_5C.nc, simulated annual mean temperature change (°C) at 1.5°C global warming relative to 1850-1900 (left).\r\n- Data file: Panel_b2_Simulated_temperature_change_at_2C.nc, simulated annual mean temperature change (°C) at 2.0°C global warming relative to 1850-1900 (center).\r\n- Data file: Panel_b3_Simulated_temperature_change_at_4C.nc, simulated annual mean temperature change (°C) at 4.0°C global warming relative to 1850-1900 (right).\r\n\r\nPanel c:\r\n- Data file: Panel_c1_Simulated_precipitation_change_at_1_5C.nc, simulated annual mean precipitation change (%) at 1.5°C global warming relative to 1850-1900 (left).\r\n- Data file: Panel_c2_Simulated_precipitation_change_at_2C.nc, simulated annual mean precipitation change (%) at 2.0°C global warming relative to 1850-1900 (center).\r\n- Data file: Panel_c3_Simulated_precipitation_change_at_4C.nc, simulated annual mean precipitation change (%) at 4.0°C global warming relative to 1850-1900 (right).\r\n\r\nPanel d:\r\n- Data file: Figure_SPM5_d1_cmip6_SM_tot_change_at_1_5C.nc, simulated annual mean total column soil moisture change (standard deviation) at 1.5°C global warming relative to 1850-1900 (left).\r\n- Data file: Figure_SPM5_d2_cmip6_SM_tot_change_at_2C.nc, simulated annual mean total column soil moisture change (standard deviation) at 2.0°C global warming relative to 1850-1900 (center).\r\n- Data file: Figure_SPM5_d3_cmip6_SM_tot_change_at_4C.nc, simulated annual mean total column soil moisture change (standard deviation) at 4.0°C global warming relative to 1850-1900 (right).\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblink is provided in the Related Documents section of this catalogue record:\r\n\r\n- Link to the report webpage, which includes the component containing the figure (Summary for Policymakers), the Technical Summary (Figures TS.3 and TS.5) and the Supplementary Material for Chapters 1, 4 and 11, which contains details on the input data used in Tables 1.SM.1 (Figure 1.14), 4.SM.1 (Figures 4.31 and 4.32) and 11.SM.9 (Figure 11.19)." }, "onlineresource_set": [] }, { "ob_id": 32959, "uuid": "63d0b6c13154487997ae2a5cf2a41e96", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ar6_wg1/data/spm/spm_01/v20210809", "numberOfFiles": 7, "volume": 66276, "fileFormat": "Data are CSV formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32909, "uuid": "76cad0b4f6f141ada1c44a4ce9e7d4bd", "short_code": "ob", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.1 (v20210809)", "abstract": "Data for Figure SPM.1 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure SPM.1 shows global temperature history and causes of recent warming.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n\r\nIPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu and B. Zhou (eds.)]. Cambridge University Press. In Press.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\nThe figure has two panels, with data provided for all panels in subdirectories named panel_a and panel_b.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\nPanel a\r\n\r\nThe dataset contains:\r\n\r\n - Estimated temperature during the warmest multi-century period in at least the last 100,000 years, which occurred around 6500 years ago (4500 BCE), multi-centennial average, from AR6 WGI Chapter 2\r\n - Global surface temperature change time series relative to 1850-1900 for 1-2020 from:\r\n• 1-2000 CE reconstruction from paleoclimate archives, decadal smoothed, from PAGES2k Consortium (2019, DOI: 10.1038/s41561-019-0400-0)\r\n• 1850-2020 CE, observations, decadal smoothed, from AR6 WGI Chapter 2 assessed mean\r\n\r\nPanel b:\r\n\r\nThe dataset contains global surface temperature change time series relative to 1850-1900 for 1850-2020 from simulations from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and observations:\r\n\r\n- CMIP6 historical+ssp245 simulations (simulations with human and natural forcing, 1850-2019)\r\n- CMIP6 hist-nat simulations (simulations with natural forcing, 1850-2019)\r\n- Global Surface Temperature Anomalies (GSTA) relative to 1850-1900 from observations assessed in IPCC AR6 WG1 Chapter 2 (1850-2020)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n---------------------------------------------------\r\nPanel a:\r\n\r\n- panel_a/SPM1_1-2000_recon.txt, 1-2000 time series, decadal smoothed, for years centered on 5-1996 CE [column 1 grey line, columns 2 and 3 grey shading]\r\n- panel_a/SPM1_1850-2020_obs.txt, 1850-2020 time series, decadal smoothed, for years centered on 1855-2016 CE [black line]\r\n- panel_a/SPM1_6500_recon.txt, bar for the warmest multi-century period in more than 100,000 years (around 6500 years ago: 4500 BCE) [grey bar]\r\n\r\nPanel b:\r\n\r\n- panel_b/gmst_changes_model_and_obs.csv. Global surface temperature change time series relative to 1850-1900 for 1850-2020 from:\r\n• CMIP6 historical+ssp245 simulations (1850-2019) [mean, brown line]\r\n• CMIP6 historical+ssp245 simulations (1850-2019) [5% range, brown shading, bottom]\r\n• CMIP6 historical+ssp245 simulations (1850-2019) [95% range, brown shading, top]\r\n• CMIP6 hist-nat simulations (1850-2019) [mean, green line]\r\n• CMIP6 hist-nat simulations (1850-2019) [5% range, green shading, bottom]\r\n• CMIP6 hist-nat simulations (1850-2019) [95% range, green shading, top]\r\n• Global Surface Temperature Anomalies (GSTA) relative to 1850-1900 from observations assessed in IPCC AR6 WG1 Chapter 2 (1850-2020) [black line]\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\nThe following weblinks are provided in the Related Documents section of this catalogue record:\r\n\r\n- Link to the report webpage, which includes the report component containing the figure (Summary for Policymakers), the Technical Summary (Cross-Section Box TS.1, Figure 1a) and the Supplementary Material for Chapters 2 and 3, which contains details on the input data used in Tables 2.SM.1 (Figure 2.11a) and 3.SM.1 (Figure 3.2c; FAQ 3.1, Figure 1).\r\n- Link to related publication for input data" }, "onlineresource_set": [] }, { "ob_id": 32962, "uuid": "07cd291bb4ee4a818a9a3b9be9aff30b", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ar6_wg1/data/spm/spm_09/v20210809", "numberOfFiles": 4, "volume": 6540, "fileFormat": "Data are CSV formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32913, "uuid": "35a7ee81a50c4b95ab59f9bd128f9b63", "short_code": "ob", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.9 (v20210809)", "abstract": "Data for Figure SPM.9 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure SPM.9 provides a synthesis of the number of AR6 WGI reference regions where climatic impact-drivers are projected to change.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n\r\nIPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001.\r\n\r\n\r\n---------------------------------------------------\r\nTemporal range\r\n---------------------------------------------------\r\nChanges refer to a 20–30 year period centred around 2050 and/or consistent with 2°C global warming compared to a similar period within 1960-2014 or 1850-1900.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels, with data provided for all panels in a single file named consolidated_data_figure_SPM9.csv\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\nThis dataset contains the number of AR6 WGI regions where climatic impact-drivers are projected to change if a global warming level of 2°C is reached compared to a climatological reference period included within 1960-2014.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nData file: consolidated_data_figure_SPM.9.csv (count of regions with increasing or decreasing changes in climatic impact-drivers); relates to panel (a) and panel (b) and it's shown by the bars in the figure. The first row of data relates to the darker purple bars, the second row to the lighter purple bars, the third row to the lighter brown bars and the fourth row to the darker brown bars. Row 5 represents the maximum number of regions for which each climatic impact-driver is relevant. It is shown on the figure as the lighter-shaded ‘envelope’.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n\r\n - Link to the report webpage, which includes the report component containing the figure (Summary for Policymakers)" }, "onlineresource_set": [] }, { "ob_id": 32963, "uuid": "82f713be40ec469aa138f65a69f0a345", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ar6_wg1/data/spm/spm_10/v20210809", "numberOfFiles": 11, "volume": 46050, "fileFormat": "Data are CSV formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32916, "uuid": "cfe938e70f8f4e98b0622296743f7913", "short_code": "ob", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.10 (v20210809)", "abstract": "Data for Figure SPM.10 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure SPM.10 shows global warming as a function of cumulative emissions of carbon dioxide.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\nWhen citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n\r\nIPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels that are closely linked. Data files for the top panel are labelled with 'Top_panel' while data files for the bottom panel are labelled with 'Bottom_panel'. \r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n---------------------------------------------------\r\nThis dataset contains:\r\n\r\nTop panel:\r\n\r\n- Cumulative global total anthropogenic carbon dioxide emissions (1850-2019)\r\n- Global surface temperature increase relative to 1850-1900 (1850-2019)\r\n- Estimated human-caused warming relative to 1850-1900 (1850-2019)\r\n- Projected global total anthropogenic carbon dioxide emissions for the five scenarios of the AR6 WGI core set of scenarios (2015-2050)\r\n- Assessed global surface temperature increase relative to 1850-1900 for the five scenarios of the AR6 WGI core set of scenarios (2015-2050)\r\n\r\nBottom panel:\r\n\r\n- Cumulative global total anthropogenic carbon dioxide emissions (1850-2019)\r\n- Projected global total anthropogenic carbon dioxide emissions for the five scenarios of the AR6 WGI core set of scenarios (2015-2050)\r\n\r\nThe illustrative SSP (Shared Socio-economic Pathway) scenarios (referred to here as core scenarios) are described in Box SPM.1 of the Summary for Policymakers and Section 1.6.1.1 of Chapter 1.\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nTop panel: \r\n•\tTop_panel_HISTORY.csv: historical CO2 emissions, global surface temperature increase since 1850-1900 for the 1850-2019 period, estimated human-caused warming since 1850-1900 over the 1850-2019 period. [row 1 for black line, grey line and grey range, row 2 for black line, row 3 to 5 range and central grey range]\r\n•\tTop_panel_SSP1-19.csv: projected CO2 emissions, assessed projections of global surface temperature increase relative to the 1850-1900 period for the period 2015-2050 [row 1 and 2 for central lines, row 1, 3, and 4 for ranges]\r\n•\tTop_panel_SSP1-26.csv: projected CO2 emissions, assessed projections of global surface temperature increase relative to the 1850-1900 period for the period 2015-2050 [row 1 and 2 for central lines, row 1, 3, and 4 for ranges]\r\n•\tTop_panel_SSP2-45.csv: projected CO2 emissions, assessed projections of global surface temperature increase relative to the 1850-1900 period for the period 2015-2050 [row 1 and 2 for central lines, row 1, 3, and 4 for ranges]\r\n•\tTop_panel_SSP3-70.csv: projected CO2 emissions, assessed projections of global surface temperature increase relative to the 1850-1900 period for the period 2015-2050 [row 1 and 2 for central lines, row 1, 3, and 4 for ranges]\r\n•\tTop_panel_SSP5-85.csv: projected CO2 emissions, assessed projections of global surface temperature increase relative to the 1850-1900 period for the period 2015-2050 [row 1 and 2 for central lines, row 1, 3, and 4 for ranges]\r\n\r\nBottom panel: \r\n•\tBottom_panel_GtCO2_historical.csv: historical CO2 emissions [grey bars]\r\n•\tBottom_panel_GtCO2_projections.csv; projected CO2 emissions for the five scenarios in the core set of IPCC AR6 WG1 scenarios [coloured bars]\r\n\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n\r\n - Link to the report webpage, which includes the report component containing the figure (Summary for Policymakers) the Technical Summary (Section TS.3.3). and the Supplementary Material for Chapter 5, which contains details on the input data used in Table 5.SM.6 (Figure 5.31)\r\n - Link to related publications for input data" }, "onlineresource_set": [] }, { "ob_id": 32965, "uuid": "986ffdeab5804c7ea19026a86610f4da", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ar6_wg1/data/spm/spm_04/v20210809", "numberOfFiles": 8, "volume": 19280, "fileFormat": "Data are CSV formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32921, "uuid": "bd65331b1d344ccca44852e495d3a049", "short_code": "ob", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.4 (v20210809)", "abstract": "Data for Figure SPM.4 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure SPM.4 panel a shows global emissions projections for CO2 and a set of key non-CO2 climate drivers, for the core set of five IPCC AR6 scenarios. Figure SPM.4 panel b shows attributed warming in 2081-2100 relative to 1850-1900 for total anthropogenic, CO2, other greenhouse gases, and other anthropogenic forcings for five Shared Socio-economic Pathway (SSP) scenarios.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n\r\nIPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n---------------------------------------------------\r\nThe figure has two panels, with data provided for all panels in subdirectories named panel_a and panel_b.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n---------------------------------------------------\r\n This dataset contains:\r\n\r\n - Projected emissions from 2015 to 2100 for the five scenarios of the AR6 WGI core scenario set (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5)\r\n - Projected warming for all anthropogenic forcers, CO2 only, non-CO2 greenhouse gases (GHGs) only, and other anthropogenic components for 2081-2100 relative to 1850-1900, for SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5.\r\n\r\nThe five illustrative SSP (Shared Socio-economic Pathway) scenarios are described in Box SPM.1 of the Summary for Policymakers and Section 1.6.1.1 of Chapter 1.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a:\r\n\r\n\r\n The first column includes the years, while the next columns include the data per scenario and per climate forcer for the line graphs.\r\n\r\n - Data file: Carbon_dioxide_Gt_CO2_yr.csv. relates to Carbon dioxide emissions panel\r\n - Data file: Methane_Mt_CO2_yr.csv. relates to Methane emissions panel\r\n - Data file: Nitrous_oxide_Mt N2O_yr.csv. relates to Nitrous oxide emissions panel\r\n - Data file: Sulfur_dioxide_Mt SO2_yr.csv. relates to Sulfur dioxide emissions panel\r\n\r\n Panel b:\r\n\r\n - Data file: ts_warming_ranges_1850-1900_base_panel_b.csv. [Rows 2 to 5 relate to the first bar chart (cyan). Rows 6 to 9 relate to the second bar chart (blue). Rows 10 to 13 relate to the third bar chart (orange). Rows 14 to 17 relate to the fourth bar chart (red). Rows 18 to 21 relate to the fifth bar chart (brown).].\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblink are provided in the Related Documents section of this catalogue record:\r\n- Link to the report webpage, which includes the report component containing the figure (Summary for Policymakers) and the Supplementary Material for Chapter 1, which contains details on the input data used in Table 1.SM.1..(Cross-Chapter Box 1.4, Figure 2).\r\n- Link to related publication for input data used in panel a." }, "onlineresource_set": [] }, { "ob_id": 32966, "uuid": "8060856eaa6940a9a26a42cd275313a8", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ar6_wg1/data/spm/spm_07/v20210809", "numberOfFiles": 4, "volume": 4587, "fileFormat": "Data are CSV formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32924, "uuid": "b1ad4c02319b438884a72fea34cb5a18", "short_code": "ob", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.7 (v20210809)", "abstract": "Data for Figure SPM.7 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure SPM.7 shows the cumulative anthropogenic CO2 emissions taken up by land and ocean sinks by 2100 under the five core scenarios.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n---------------------------------------------------\r\nWhen citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n\r\nIPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n---------------------------------------------------\r\nThis dataset contains cumulative anthropogenic (human-caused) carbon dioxide (CO2) emissions taken up by the land and ocean sinks under the five core scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5), simulated from 1850 to 2100 by Earth System Models that contributed to the sixth phase of the Coupled Model Intercomparison Project (CMIP6).\r\n\r\nThe five illustrative SSP (Shared Socio-economic Pathway) scenarios are described in Box SPM.1 of the Summary for Policymakers and Section 1.6.1.1 of Chapter 1.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n---------------------------------------------------\r\nData file: SPM7_data.csv: each column corresponds to a single scenario, in which rows 2-7 are the bar values, rows 8-10 are the pie chart values and row 11 is the central value in the pie chart.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n---------------------------------------------------\r\nThe following weblink is provided in the Related Documents section of this catalogue record:\r\n- Link to the report webpage, which includes the report component containing the figure (Summary for Policymakers)." }, "onlineresource_set": [] }, { "ob_id": 32967, "uuid": "746335eaef72481098571634e1ba34f0", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ar6_wg1/data/spm/spm_02/v20210809", "numberOfFiles": 6, "volume": 10279, "fileFormat": "Data are CSV formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32935, "uuid": "c1eb6dad1598427f8f9f3eae346ece2f", "short_code": "ob", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.2 (v20210809)", "abstract": "Data for Figure SPM.2 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure SPM.2 relates to assessed contributions to observed warming.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n---------------------------------------------------\r\nWhen citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n\r\nIPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n---------------------------------------------------\r\nThe figure has three panels, with data provided for all panels in subdirectories named panel_a, panel_b and panel_c. \r\n\r\n---------------------------------------------------\r\n List of data provided\r\n---------------------------------------------------\r\n This data set contains:\r\n\r\n- Observed warming (2010-2019 relative to 1850-1900)\r\n - Aggregated contributions to 2010-2019 warming relative 1850 -1900, assessed from attribution studies\r\n - Contributions to 2010-2019 warming relative to 1850-1900, assessed from radiative studies\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n---------------------------------------------------\r\n Panel a:\r\n\r\n- Data file: panel_a/SPM2a.csv (Observed warming). Mean value is used for the bar plot and top and bottom values are used for the error bars and they represent borders of the very likely range.\r\n\r\nPanel b:\r\n\r\n - Data file: panel_b/SPM2b.csv (Aggregated contributions assessed from attribution studies). Mean values are used for the bar plot and top and bottom values are used for the error bars and represent the borders of the very likely range\r\n\r\nPanel c:\r\n\r\n - Data file: panel_c/SPM2c_data.csv (Contributions assessed from radiative studies). Total global surface air temperature (GSAT) effect values are used for the bar plots and 5% and 95% very likely limit values are used for the error bars.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n---------------------------------------------------\r\n The following weblink is provided in the Related Documents section of this catalogue record:\r\n\r\n- Link to the report webpage, which includes the report component containing the figure (Summary for Policymakers) and the Supplementary Material for Chapters 3, 6 and 7, which contain details on the input data used in Tables 3.SM.1 (Figure 3.8), 6.SM.1 (Figure 6.12) and 7.SM.14 (Figure 7.7)." }, "onlineresource_set": [] }, { "ob_id": 32976, "uuid": "ad351472db8d4735af4f9920051d3eb2", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-midas-open/data/uk-daily-rain-obs/dataset-version-202107/", "numberOfFiles": 51431, "volume": 1086544333, "fileFormat": "Data are BADC-CSV formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32971, "uuid": "d6bcf4171c2f4754a7455d00deda0f72", "short_code": "ob", "title": "MIDAS Open: UK daily rainfall data, v202107", "abstract": "The UK daily rainfall data contain rainfall accumulation and precipitation amounts over a 24 hour period. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSDLY, DLY3208 and SSER. The data spans from 1853 to 2020. Over time a range of rain gauges have been used - see section 5.6 and the relevant message type information in the linked MIDAS User Guide for further details.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. A large proportion of the UK raingauge observing network (associated with WAHRAIN, WADRAIN and WAMRAIN for hourly, daily and monthly rainfall measurements respectively) is operated by other agencies beyond the Met Office, and are consequently currently excluded from the Midas-open dataset. Currently this represents approximately 13% of available daily rainfall observations within the full MIDAS collection." }, "onlineresource_set": [] }, { "ob_id": 32977, "uuid": "a8da67ba88c94efc8ae85bd7d31c4dbb", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-midas-open/data/uk-daily-weather-obs/dataset-version-202107/", "numberOfFiles": 46758, "volume": 3006018865, "fileFormat": "Data are BADC-CSV formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32973, "uuid": "d399794d81fa41779a925b6d4758a5cd", "short_code": "ob", "title": "MIDAS Open: UK daily weather observation data, v202107", "abstract": "The UK daily weather observation data contain meteorological values measured on a 24 hour time scale. The measurements of sunshine duration, concrete state, snow depth, fresh snow depth, and days of snow, hail, thunder and gail were attained by observation stations operated by the Met Office across the UK operated and transmitted within DLY3208, NCM, AWSDLY and SYNOP messages. The data span from 1887 to 2020. For details of observations see the relevant sections of the MIDAS User Guide linked from this record for the various message types.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. Of particular note, however, is that as well as including data for 2020, historical data recovery has added further data for Eastbourne (1887-1910).\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Currently this represents approximately 95% of available daily weather observations within the full MIDAS collection." }, "onlineresource_set": [] }, { "ob_id": 32978, "uuid": "02f759b6954a454188bbf22867f9ed4a", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-midas-open/data/uk-hourly-rain-obs/dataset-version-202107/", "numberOfFiles": 15263, "volume": 4952556253, "fileFormat": "Data are BADC-CSV formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32970, "uuid": "f7ae919f96b44a1c9695f40a9cf988dd", "short_code": "ob", "title": "MIDAS Open: UK hourly rainfall data, v202107", "abstract": "The UK hourly rainfall data contain the rainfall amount (and duration from tilting syphon gauges) during the hour (or hours) ending at the specified time. The data also contains precipitation amounts, however precipitation measured over 24 hours are not stored. Over time a range of rain gauges have been used - see the linked MIDAS User Guide for further details.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nThe data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSHRLY, DLY3208, SREW and SSER. The data spans from 1915 to 2020.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. A large proportion of the UK raingauge observing network (associated with WAHRAIN, WADRAIN and WAMRAIN for hourly, daily and monthly rainfall measurements respectively) is operated by other agencies beyond the Met Office, and are consequently currently excluded from the Midas-open dataset." }, "onlineresource_set": [] }, { "ob_id": 32979, "uuid": "3e5f11c3084340b187df7d358a5ca084", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-midas-open/data/uk-daily-temperature-obs/dataset-version-202107/", "numberOfFiles": 62229, "volume": 2120207136, "fileFormat": "Data are BADC-CSV formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32968, "uuid": "92e823b277cc4f439803a87f5246db5f", "short_code": "ob", "title": "MIDAS Open: UK daily temperature data, v202107", "abstract": "The UK daily temperature data contain maximum and minimum temperatures (air, grass and concrete slab) measured over a period of up to 24 hours. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM, DLY3208 or AWSDLY messages. The data span from 1853 to 2020. For details on measurement techniques, including calibration information and changes in measurements, see section 5.2 of the MIDAS User Guide linked to from this record. Soil temperature data may be found in the UK soil temperature datasets linked from this record.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. Of particular note, however, is that as well as including data for 2020, historical data recovery has added further data for Eskdalemuir (1915-1948) and Eastbourne (1887-1910).\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Currently this represents approximately 95% of available daily temperature observations within the full MIDAS collection." }, "onlineresource_set": [] }, { "ob_id": 32980, "uuid": "cb2294581cdf4f94b7c22744da8954bb", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-midas-open/data/uk-hourly-weather-obs/dataset-version-202107/", "numberOfFiles": 50518, "volume": 30652786376, "fileFormat": "Data are BADC-CSV formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32974, "uuid": "3bd7221d4844435dad2fa030f26ab5fd", "short_code": "ob", "title": "MIDAS Open: UK hourly weather observation data, v202107", "abstract": "The UK hourly weather observation data contain meteorological values measured on an hourly time scale. The measurements of the concrete state, wind speed and direction, cloud type and amount, visibility, and temperature were recorded by observation stations operated by the Met Office across the UK and transmitted within SYNOP, DLY3208, AWSHRLY and NCM messages. The sunshine duration measurements were transmitted in the HSUN3445 message. The data spans from 1875 to 2020.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. Of particular note, however, is that as well as including data for 2020, historical data recovery has added further data for Eskdalemuir (1914-1944) and Eastbourne (1887-1910).\r\n\r\nFor details on observing practice see the message type information in the MIDAS User Guide linked from this record and relevant sections for parameter types.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Note, METAR message types are not included in the Open version of this dataset. Those data may be accessed via the full MIDAS hourly weather data." }, "onlineresource_set": [] }, { "ob_id": 32981, "uuid": "a2fedaeb2dd34587883c9bc4154caa4a", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-midas-open/data/uk-mean-wind-obs/dataset-version-202107/", "numberOfFiles": 14406, "volume": 7589141537, "fileFormat": "Data are BADC-CSV formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32972, "uuid": "4d48efaaeb7f47a7963df75d6d1dbdc5", "short_code": "ob", "title": "MIDAS Open: UK mean wind data, v202107", "abstract": "The UK mean wind data contain the mean wind speed and direction, and the direction, speed and time of the maximum gust, all during 1 or more hours, ending at the stated time and date. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, DLY3208, HWNDAUTO and HWND6910. The data spans from 1949 to 2020.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nFor further details on observing practice, including measurement accuracies for the message types, see relevant sections of the MIDAS User Guide linked from this record (e.g. section 3.3 details the wind network in the UK, section 5.5 covers wind measurements in general and section 4 details message type information).\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record." }, "onlineresource_set": [] }, { "ob_id": 32982, "uuid": "55c51558faf74d908cbdbb10a0d05013", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-midas-open/data/uk-soil-temperature-obs/dataset-version-202107/", "numberOfFiles": 23308, "volume": 3953950041, "fileFormat": "Data are BADC-CSV formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32969, "uuid": "cabc37d867fa4f2a84302350df908693", "short_code": "ob", "title": "MIDAS Open: UK soil temperature data, v202107", "abstract": "The UK soil temperature data contain daily and hourly values of soil temperatures at depths of 5, 10, 20, 30, 50, and 100 centimetres. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM or DLY3208 messages. The data spans from 1900 to 2020.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nAt many stations temperatures below the surface are measured at various depths. The depths used today are 5, 10, 20, 30 and 100cm, although measurements are not necessarily made at all these depths at a station and exceptionally measurements may be made at other depths. When imperial units were in general use, typically before 1961, the normal depths of measurement were 4, 8, 12, 24 and 48 inches.\r\n\r\nLiquid-in-glass soil thermometers at a depth of 20 cm or less are unsheathed and have a bend in the stem between the bulb and the lowest graduation. At greater depths the thermometer is suspended in a steel tube and has its bulb encased in wax.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record." }, "onlineresource_set": [] }, { "ob_id": 32983, "uuid": "9f9f5aa53b3f4df7a8df9d45e7a10f90", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-midas-open/data/uk-radiation-obs/dataset-version-202107/", "numberOfFiles": 6781, "volume": 2947770418, "fileFormat": "Data are BADC-CSV formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32975, "uuid": "625f5ea4ddac4578a2aacf47bcf39657", "short_code": "ob", "title": "MIDAS Open: UK hourly solar radiation data, v202107", "abstract": "The UK hourly solar radiation data contain the amount of solar irradiance received during the hour ending at the specified time. All sites report 'global' radiation amounts. This is also known as 'total sky radiation' as it includes both direct solar irradiance and 'diffuse' irradiance as a result of light scattering. Some sites also provide separate diffuse and direct irradiation amounts, depending on the instrumentation at the site. For these the sun's path is tracked with two pyrometers - one where the path to the sun is blocked by a suitable disc to allow the scattered sunlight to be measured to give the diffuse measurement, while the other has a tube pointing at the sun to measure direct solar irradiance whilst blanking out scattered sun light.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nThe data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, MODLERAD, ESAWRADT and DRADR35 messages. The data spans from 1947 to 2020.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record." }, "onlineresource_set": [] }, { "ob_id": 32992, "uuid": "f5ea40ee390846cc9132538e5b874cbf", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.0.3.0/river", "numberOfFiles": 133, "volume": 25938984, "fileFormat": "Data are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32984, "uuid": "0cb035c7598a4dcb8aecb6b6558c83e9", "short_code": "ob", "title": "HadUK-Grid Climate Observations by UK river basins, v1.0.3.0 (1862-2020)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK river basins consistent with data from UKCP18 climate projections. The dataset spans the period from 1862 to 2020, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." }, "onlineresource_set": [] }, { "ob_id": 32993, "uuid": "4dcfed420f4146d0ab7e4d8f3f23b7ad", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.0.3.0/country", "numberOfFiles": 133, "volume": 12739647, "fileFormat": "Data are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32985, "uuid": "489e22fb2961482bb76711cedbeecedd", "short_code": "ob", "title": "HadUK-Grid Climate Observations by UK countries, v1.0.3.0 (1862-2020)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK countries consistent with data from UKCP18 climate projections. The dataset spans the period from 1862 to 2020, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." }, "onlineresource_set": [] }, { "ob_id": 32994, "uuid": "61480880794947e69b33a09e733c000e", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.0.3.0/region", "numberOfFiles": 133, "volume": 19805658, "fileFormat": "Data are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32991, "uuid": "97bc0b64bc354898a242a42238e1b45c", "short_code": "ob", "title": "HadUK-Grid Climate Observations by Administrative Regions over the UK, v1.0.3.0 (1862-2020)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK administrative regions consistent with data from UKCP18 climate projections. The dataset spans the period from 1862 to 2020, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." }, "onlineresource_set": [] }, { "ob_id": 32995, "uuid": "af534783364b44169e0e99ddfa970f94", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.0.3.0/60km", "numberOfFiles": 6163, "volume": 538615627, "fileFormat": "Data are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32986, "uuid": "bc774f1b83524437a8046d8b9a9e3c6d", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 60km grid over the UK, v1.0.3.0 (1862-2020)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 60 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1862 to 2020, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." }, "onlineresource_set": [] }, { "ob_id": 32996, "uuid": "98527b90fc57407dae569bfdcfcf3221", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.0.3.0/25km", "numberOfFiles": 6163, "volume": 2142938841, "fileFormat": "Data are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32987, "uuid": "616e6194a8c742d790f3b43bf66a534d", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 25km grid over the UK, v1.0.3.0 (1862-2020)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 25 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1862 to 2020, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." }, "onlineresource_set": [] }, { "ob_id": 32997, "uuid": "93362d1d2b084f09a2a04c4baeedc8f1", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.0.3.0/12km", "numberOfFiles": 6158, "volume": 9151135715, "fileFormat": "Data are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32988, "uuid": "54a99222c1e741a4a70ef1caa8f10c7e", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 12km grid over the UK, v1.0.3.0 (1862-2020)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 12 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from climate projections. The dataset spans the period from 1862 to 2020, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation). \r\n\r\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." }, "onlineresource_set": [] }, { "ob_id": 32998, "uuid": "0548257f7a7e45e0a602bd1d18a2ea76", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.0.3.0/1km", "numberOfFiles": 6163, "volume": 1275636193559, "fileFormat": "Data are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32989, "uuid": "786b3ce6be54468496a3e11ce2f2669c", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 1km grid over the UK, v1.0.3.0 (1862-2020)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The datasets cover the UK at 1 km x 1 km resolution. These 1 km x 1 km data have been used to provide a range of other resolutions and across countries, administrative regions and river basins to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1862 to 2020, but the start time is dependent on climate variable and temporal resolution. \r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." }, "onlineresource_set": [] }, { "ob_id": 32999, "uuid": "4c93e8726caf46da9cb8b28b45eb28d1", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.0.3.0/5km", "numberOfFiles": 6163, "volume": 51229517277, "fileFormat": "Data are NetCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32990, "uuid": "f2da35c56afb4fa6aebf44094b65dff3", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 5km grid over the UK, v1.0.3.0 (1862-2020)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 5 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1862 to 2020, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." }, "onlineresource_set": [] }, { "ob_id": 33016, "uuid": "a9815b71261c4633bf74e6c395223f01", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/ghg/data/cci_plus/CO2_TAN_OCFP/v1.0_global_land/", "numberOfFiles": 60, "volume": 518692308, "fileFormat": "Data are in NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32607, "uuid": "9252ff9ddeb249a2bd8433e9ae9dfe13", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from TANSAT, generated with the OCFP algorithm, for global land areas, version 1.0", "abstract": "This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (CO2), derived from the TANSAT satellite, using the University of Leicester Full-Physics Retrieval Algorithm (UoL-FP, also known as OCFP). This dataset is also referred to as CO2_TAN_OCFP. This version of the dataset provides data globally over land. For further information on the dataset, please see the linked documentation.\r\n\r\nInitially this dataset contains two months of data (June and August 2017), delivered as part of the GHG_cci Climate Research Data Package 6. Additional time periods will be added in the future.\r\n\r\n\r\nThis data has been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme, with support from the UK's National Centre for Earth Observation (NCEO)." }, "onlineresource_set": [] }, { "ob_id": 33017, "uuid": "a7b6b4c998914e2cb9a55669ac17e323", "short_code": "result", "curationCategory": "A", "dataPath": "/neodc/esacci/fire/data/burned_area/SFD/Africa/Sentinel2/pixel/v2.0/", "numberOfFiles": 6962, "volume": 87998288670, "fileFormat": "The pixel product is composed of 4 files:\r\n *JD.tif: Day of first detection (Julian Day) of the burned area; \r\n*CL.tif: Confidence level of burned area detection; \r\n *LC.tif: Land cover of the pixel detected as burned as defined by the C3S Land Cover map of 2018 (https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover).\r\n *.xml: Metadata of the product.\r\n\r\nIn the Compressed folder these have been compressed into a single .tar.gz file.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32259, "uuid": "4c5feb539f1f44308ca7ec26e0bb7316", "short_code": "ob", "title": "ESA Fire Climate Change Initiative (Fire_cci): Small Fire Dataset (SFD) Burned Area pixel product for Sub-Saharan Africa, version 2.0", "abstract": "The ESA Fire Disturbance Climate Change Initiative (Fire_cci) project has produced maps of global burned area developed from satellite observations. The Small Fire Dataset (SFD) pixel products have been obtained by combining spectral information from Sentinel-2 MSI data and thermal information from VIIRS VNP14IMGML active fire products.\r\n\r\nThis dataset is part of v2.0 of the Small Fire Dataset (also known as FireCCISFD11), which covers Sub-Saharan Africa for the year 2019. Data is available here at pixel resolution (0.00017966259 degrees, corresponding to approximately 20m at the Equator). Gridded data products are also available in a separate dataset." }, "onlineresource_set": [] }, { "ob_id": 33018, "uuid": "e68863a20618487a91edceecf220be02", "short_code": "result", "curationCategory": "A", "dataPath": "/neodc/esacci/fire/data/burned_area/SFD/Africa/Sentinel2/grid/v2.0/", "numberOfFiles": 26, "volume": 27482604516, "fileFormat": "Data are in NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 32258, "uuid": "01b00854797d44a59d57c8cce08821eb", "short_code": "ob", "title": "ESA Fire Climate Change Initiative (Fire_cci): Small Fire Database (SFD) Burned Area grid product for Sub-Saharan Africa, version 2.0", "abstract": "The ESA Fire Disturbance Climate Change Initiative (Fire_cci) project has produced maps of global burned area developed from satellite observations. The Small Fire Database (SFD) pixel products have been obtained by combining spectral information from Sentinel-2 MSI data and thermal information from VIIRS VNP14IMGML active fire products.\r\n\r\nThis gridded dataset has been derived from the Small Fire Database (SFD) Burned Area pixel product for Sub-Saharan Africa, v2.0 (also available), which covers Sub-Saharan Africa for the year 2019, by summarising its burned area information into a regular grid covering the Earth at 0.05 x 0.05 degrees resolution and at monthly temporal resolution." }, "onlineresource_set": [] }, { "ob_id": 33020, "uuid": "82bfb75285ab404cb2ce5567da65e249", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ccmi/data/post-cmip6/ccmi-2022/NIWA/NIWA-UKCA2/refD1", "numberOfFiles": 409, "volume": 160155577528, "fileFormat": "CF-netCDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 33019, "uuid": "9d93bed3b24648fcade5e427903c7da7", "short_code": "ob", "title": "CCMI-2022: REF-D1 data produced by the NIWA-UKCA2 model at NIWA", "abstract": "This dataset contains model data for CCMI-2022 experiment refD1 produced by the NIWA-UKCA2 chemistry-climate model run by the modelling team at NIWA (National Institute of Water and Atmospheric Research) in New Zealand.\r\n\r\nThe refD1 experiment is a hindcast of the atmospheric state, using a prescribed evolution of sea surface temperature and sea ice from observations along with forcings for the extra-terrestrial solar flux, long-lived greenhouse gases and ozone depleting substances, stratospheric aerosols and an imposed quasi-biennial oscillation that approximate the observed variations over the historical period to the fullest extent possible.\r\n\r\nThe CCMI-2022 Chemistry-climate model initiative is a set of model experiments focused on the stratosphere, with the goals of providing updated projections towards the future evolution of the ozone layer and improving our understanding of chemistry-climate interactions from models.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Review of the global models used within phase 1 of the Chemistry-Climate Model Initiative (CCMI)\r\n- A new set of Chemistry-Climate Model Initiative (CCMI) Community Simulations to Update the Assessment of Models and Support Upcoming Ozone Assessment Activities, David Plummer and Tatsuya Nagashima and Simone Tilmes and Alex Archibald and Gabriel Chiodo and Suvarna Fadnavis and Hella Garny and Beatrice Josse and Joowan Kim and Jean-Francois Lamarque and Olaf Morgenstern and Lee Murray and Clara Orbe and Amos Tai and Martyn Chipperfield and Bernd Funke and Martin Juckes and Doug Kinnison and Markus Kunze and Beiping Luo and Katja Matthes and Paul A. Newman and Charlotte Pascoe and Thomas Peter (2021), SPARC Newsletter, volume 57, pp 22-30" }, "onlineresource_set": [] }, { "ob_id": 33035, "uuid": "b3e8c789eef441adbb3d35a3f00b758e", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/weather_at_home/data/b778-845_archive", "numberOfFiles": 41213, "volume": 5355157184461, "fileFormat": "Data are netCDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 33034, "uuid": "dae723e78f9f4a0b9712f352f0a0231d", "short_code": "ob", "title": "weather@home 2010 global simulations and climatology (1986-2016)", "abstract": "This dataset contains atmospheric data, such as wind field and surface air temperature, obtained by running the weather@home general circulation model for the year 2010. The aim of this dataset is to study the 2010 weather extremes which have affected Western Russian and Pakistan. The dataset contains data for both the regional and global GCM models provided as NetCDF v3 files. The gridded global model output is at 1.25°x1.875° (N96) horizontal resolution. The regional model output is provided on a rotated longitude-latitude grid at a 50 km (0.44°x0.44°) horizontal resolution centred over South Asia. In order to compare 2010 data to the climatology of the model, the climatology is calculated for the years 1986 to 2016. This dataset has been produced by the University of Oxford, in cooperation the Potsdam Institute for Climate Impact Research and the VU University of Amsterdam in the context of the GOTHAM project, funded by the Belmont Forum through the Natural Environment Research Council (NE/P006779/1). The weather@home model runs on the climateprecictions.net platform, which provides a volunteer distributed computational system.\r\n\r\nThis dataset contains weather@home global and regional simulations for the year 2010 and for the climatology of both models for the period 1986-2016.\r\n\r\nThe data comprises of:\r\n~700 ensemble members for weather@home (global model) for 2010 (batch 778)\r\n~700 ensemble members for weather@home (regional model, South Asia) for 2010 (nested on batch 778)\r\n~170 ensemble members per year for the period (1987-2016) for weather@home (global model) (batch 845)\r\n~100 ensemble members per year for the period (1986-2015) for weather@home (regional model, South Asia) (batch 697, not nested on batch 845)\r\nextracted variables:\r\nAt daily resolution: geopotential height 300 hPa (item16202), meridional wind velocity at 300 hPa (item15202), surface air temperature at 1.5m (item 3236), precipitation (item5216)\r\nAt monthly resolution: soil moisture (item8208)" }, "onlineresource_set": [] } ] }