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{ "count": 1153, "next": "https://api.catalogue.ceda.ac.uk/api/v3/relatedobservationinfos/?format=api&limit=100&offset=800", "previous": "https://api.catalogue.ceda.ac.uk/api/v3/relatedobservationinfos/?format=api&limit=100&offset=600", "results": [ { "ob_id": 746, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 38905, "uuid": "1030d40a071d4929bf04e08bfbd22c10", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure TS.25 v20221111", "abstract": "Data for Figure TS.25 from the Technical Summary 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 TS.25 shows the distribution of projected changes in selected climatic impact-driver (CID) indices for selected regions for Coupled Model Intercomparison Project Phases 5 and 6 (CMIP6, CMIP5) and Coordinated Regional Downscaling Experiment (CORDEX) model ensembles.\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 Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. 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. 33−144, doi:10.1017/9781009157896.002.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n For all the panels, the data provided consists of ensemble statistics (q5, median and q95) of the spatial averages over the IPCC AR6 regions of the list of indicators below for CMIP5, CMIP6 and CORDEX, for the recent past (1995-2014), the mid-term (2041-2060) and long-term (2081-2100) future horizons, as well as the +1.5, +2, and +4°C of global warming levels.\r\n The list of indicators shown on the figure is:\r\n - number of days per year with SWE > 100mm (North-America)\r\n - number of days with the NOAA Heat Index exceeding 41°C (Central-America and Asia)\r\n - the 100-yr return period stream flow (South-America, Europe, Africa)\r\n - the number of days per year with Maximum temperature exceeding 35°C (Asia)\r\n - the Shoreline position change (Asia, Australasia)\r\n\r\nSWE stands for snow water equivalent\r\nNOAA stands for National Oceanic and Atmospheric Administration.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nUpper panels of Panel (a):\r\nThe change of the number of days with SWE > 100mm are related with figure 12.10(d) with the corresponding file names:\r\n ** 'CMIP5_NORTH-AMERICA_snw_mask14_AR6_regional_averages.json' : regional averages for the CMIP5 multimodel ensemble of number of days per year with snow water equivalent over 100mm (SWE100) in North-America for recent past (1995-2014), mid-term (2041-2060) long-term (2081-2100) for RCP2.6 and RCP8.5, and for three global warming levels: 1.5, 2 and 4; the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars)\r\n ** 'NAM-22_CORDEX_NORTH-AMERICA_SWE_mask14_AR6_regional_averages.json' : same as previous file for the CORDEX-core NAM-22 multimodel ensemble\r\n ** 'CMIP6_NORTH-AMERICA_snw_mask14_AR6_regional_averages.json' : same as previous file for CMIP6 (ssp126 instead of RCP2.6 and ssp585 instead of RCP8.5)\r\n\r\nMiddle panels of Panel (a): \r\nThe change of the NOAA HI exceeding 41°C are related to figure 12.SM.2 with the corresponding file names:\r\n ** 'CMIP5_HI41_AR6_regional_averages.json' : data for the CMIP5 multi-model ensemble\r\n ** 'CMIP6_HI41_AR6_regional_averages.json' : data for the CMIP6 multi-model ensemble\r\n ** 'CORDEX_HI41_AR6_regional_averages.json' : data for the CORDEX multi-model ensemble\r\n see the description of the data associated with figure 12.SM.2 for more details on the structure of the files\r\n\r\nLower panels of Panel (a) and left panels of Panel (b):\r\n100-yr return period stream flow is shown for South America (figure 12.8(c)), Europe (figure 12.9(c)) and Africa (figure 12.5(c)) with corresponding file names: \r\n ** 'Q100_${ensemble}/Q100_${scenario}_${period}.nc_${CORDEX_domain}.txt': files containing the median and 5th/95th percentiles of each ensemble of the 1-in-100yr river discharge per unit catchment area (Q100) regional averages of time slices, with:\r\n - ${ensemble}: CMIP5, CMIP6 or CORDEX-core\r\n - ${scenario}: the name of the scenario : ssp126, ssp585, rcp26, rcp85\r\n - ${period}: the explicit period used to compute the temporal average: 1995-2014 (recent past), 2041-2060 (mid-term) and 2081-2099 (long term)\r\n - ${CORDEX_domain}: the CORDEX domain\r\n** 'Q100_${ensemble}/${GWL}_${CORDEX_domain}.txt' : files containing the Q100 regional averages of global warming levels with:\r\n - ${ensemble}: CMIP5, CMIP6 or CORDEX-core\r\n - ${GWL}: the Global Warming Level: 1.5, 2 or 4\r\n - ${CORDEX_domain}: the CORDEX domain\r\n\r\nRight panels of Panel (b):\r\nThe Maximum temperature exceeding 35°C (upper right) are related with figure 12.SM.1 with the corresponding file names:\r\n ** 'CMIP5_tx35isimip_AR6_regional_averages.json' : data for the CMIP5 multi-model ensemble\r\n ** 'CMIP6_tx35isimip_AR6_regional_averages.json' : data for the CMIP6 multi-model ensemble\r\n ** 'CORDEX_tx35isimip_AR6_regional_averages.json' : data for the CORDEX multi-model ensemble\r\n see the description of the data associated with figure 12.SM.1 for more details on the structure of the files\r\n\r\nThe Shoreline position change for EAS and RFE (upper middle right) (related to figure 12.6(d)), and in Australasia (lower right) (related to figure 12.7(d)) have corresponding data file names:\r\n ** 'globalErosionProjections_by_AR6_region_${scenario}_${horizon).json' : regional averages of shoreline position changes for Africa, for the RCP4.5 and RCP8.5 scenarios (${scenario} = RCP45 and ${scenario} = RCP85 respectively) and the 2050 (mid-term, in blue) and 2100 (long-term, in red) future horizons (${horizon}=2050 and ${horizon}=2100 respectively) against the recent past period (2010); the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars)\r\n\r\nThe four panels on the NOAA Heat Index exceeding 41°C (lower middle right) are related with figure 12.SM.2 with the corresponding file names:\r\n ** 'CMIP5_HI41_AR6_regional_averages.json': data for the CMIP5 multi-model ensemble\r\n ** 'CMIP6_HI41_AR6_regional_averages.json': data for the CMIP6 multi-model ensemble\r\n ** 'CORDEX_HI41_AR6_regional_averages.json': data for the CORDEX multi-model ensemble\r\n\r\nGWL stands for global warming levels.\r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100.\r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n You can find the scripts and the data to reproduce the figures on Github (link in Related Documents section), following the description below. Links to the catalogue records for relevant Chapter 12 figures are in the Related Records section of this catalogue record. \r\n\r\nPanel a:\r\n- the upper panels on the change of the number of days with SWE > 100mm are related with figure 12.10, panel d\r\n- the middle three panels on the change of the NOAA HI exceeding 41°C are related with figure 12.SM.2 \r\n- the lower panels on the 100-yr return period stream flow are related with figure 12.8, panel c\r\n\r\nPanel b:\r\n- upper left panels on the 100-yr return period stream flow in Europe are related with figure 12.9, panel c\r\n- upper right panels on the Maximum temperature exceeding 35°C are related with figure 12.SM.1 \r\n- middle right panels on Shoreline position change for EAS and RFE are associated with figure 12.6, panel d\r\n- the four panels right below on the NOAA Heat Index exceeding 41°C are related with figure 12.SM.2\r\n- the lower left panels on the 100-yr return period stream flow in Africa are related with figure 12.5, panel c\r\n- the lower right panels on the Shoreline position change in Australasia are related with figure 12.7, panel d\r\n\r\nThe final assembling of the panels to get the final figure was done with post-processing.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n - Link to Github for chapter 12 containing data and code\r\n - Link to code for Chapter 12 archived on Zenodo" }, "objectObservation": { "ob_id": 37858, "uuid": "b6a36a7fe12644bfa28bc4ec8bfcb028", "short_code": "ob", "title": "Chapter 12 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 12.10 (v20220804)", "abstract": "Input Data for Figure 12.10 from Chapter 12 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 12.10 shows projected changes in selected climatic impact-driver indices for North-America.\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 Ranasinghe, R., A.C. Ruane, R. Vautard, N. Arnell, E. Coppola, F.A. Cruz, S. Dessai, A.S. Islam, M. Rahimi, D. Ruiz Carrascal, J. Sillmann, M.B. Sylla, C. Tebaldi, W. Wang, and R. Zaaboul, 2021: Climate Change Information for Regional Impact and for Risk Assessment. 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. 1767–1926, doi:10.1017/9781009157896.014.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with general data provided in the central directory and specific data in 3 folders (Q100_CMIP5, Q100_CMIP6, Q1000_CORDEX-core).\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n - spatial field over North-America of mean change in 1-in-100 year river discharge per unit catchment area (Q100, m3 s-1 km-2) from CORDEX models for 2041-2060 relative to 1995-2014 for RCP8.5\r\n - spatial field of changes of number of days per year with snow water equivalent over 100mm (SWE100) from CORDEX-core models for 2041-2060 relative to 1995-2014 for RCP8.5; the grid points with less than 14 days per year with SWE100 during the reference (recent past) period are put to zero.\r\n - the associated mask showing the areas with more than 80% of model agreement in the sign of change\r\n - regional averages in North-America of Q100 (median value and the 10th-90th percentile range of model ensemble values across each model ensemble) over land areas for the WGI reference AR6 regions (defined in Chapter 1) for:\r\n - CMIP6 historical, ssp126 and ssp585\r\n - CMIP5 and CORDEX historical, RCP2.6 and RCP8.5\r\n - for the ‘recent past’ (1995-2014), mid-term (2041-2060) and long-term (2081-2100) time periods\r\n - and for three global warming levels (defined relative to the preindustrial period 1850-1900): 1.5°C, 2°C and 4°C\r\n - regional averages of number of days per year with snow water equivalent over 100mm (SWE100) in North-America for:\r\n - CMIP6 historical, ssp126 and ssp585\r\n - CMIP5 and CORDEX-core historical, RCP2.6 and RCP8.5\r\n - for the ‘recent past’ (1995-2014), mid-term (2041-2060) and long-term (2081-2100) time periods\r\n - and for three global warming levels (defined relative to the preindustrial period 1850-1900): 1.5°C, 2°C and 4°C\r\n The grid points with less than 14 days per year with SWE100 during the reference (recent past) period are put to zero.\r\n\r\nCAR, SCA, NWN, NEN, WNA, CNA, ENA and NCA are domains used in the model. \r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 12.9:\r\n \r\nPanel a:\r\n - Q100_map_panel_a_NAM_divdra.nc: Field (colors plotted on the map) of changes of 1-in-100yr river discharge per unit catchment area between 2041-2060 (mid-term) and 1995-2014 (recent past) for CORDEX RCP8.5; the file contains the data for the regions from the NAM CORDEX domain\r\n - Q100_map_panel_a_CAM_for_NAM_divdra.nc: same as above for the CAM CORDEX domain\r\n\r\n Panel b:\r\n - SWE_panel_b_RCP85_2041-2060_minus_1995-2014.nc: spatial field (colors) of changes of number of days per year with snow water equivalent over 100mm (SWE100) from CORDEX-core NAM-22 models for 2041-2060 relative to 1995-2014 for RCP8; the grid points with less than 14 days per year with SWE100 during the reference (recent past) period are put to zero\r\n - mask_80perc-agreement_SWE_panel_b_RCP85_2041-2060_minus_1995-2014.nc: spatial mask (for hatching) showing where at least 80% of the models agree in terms of sign of change (negative change, positive change or zero change); values are: 1 where true, 0 where false\r\n \r\nPanel c:\r\n - txt files containing the median and 5th/95th percentiles of each ensemble of the 1-in-100yr river discharge per unit catchment area (Q100) regional averages of time slices: Q100_${ensemble}/Q100_${scenario}_${period}.nc_${CORDEX_domain}.txt, with:\r\n - ${ensemble}: CMIP5, CMIP6 or CORDEX-core\r\n - ${scenario}: the name of the scenario : ssp126, ssp585, rcp26, rcp85\r\n - ${period}: the explicit period used to compute the temporal average: 1995-2014 (recent past), 2041-2060 (mid-term) and 2081-2099 (long term)\r\n - ${CORDEX_domain}: the CORDEX domain\r\n - txt files containing the Q100 regional averages of global warming levels: Q100_${ensemble}/${GWL}_${CORDEX_domain}.txt, with:\r\n - ${ensemble}: CMIP5, CMIP6 or CORDEX-core\r\n - ${GWL}: the Global Warming Level: 1.5, 2 and 4\r\n - ${CORDEX_domain}: the CORDEX domain\r\n\r\nPanel d:\r\n- CMIP5_NORTH-AMERICA_snw_mask14_AR6_regional_averages.json: regional averages for the CMIP5 multimodel ensemble of number of days per year with snow water equivalent over 100mm (SWE100) in North-America for recent past (1995-2014), mid-term (2041-2060) long-term (2081-2100) for RCP2.6 and RCP8.5, and for three global warming levels: 1.5, 2 and 4; the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars) - grid points with less than 14 days per year with SWE100 during the reference (recent past) period are put to zero.\r\n- CMIP6_NORTH-AMERICA_snw_mask14_AR6_regional_averages.json: same as previous file for CMIP6 (ssp126 instead of RCP2.6 and ssp585 instead of RCP8.5) - grid points with less than 14 days per year with SWE100 during the reference (recent past) period are put to zero.\r\n- NAM-22_CORDEX_NORTH-AMERICA_snw_mask14_AR6_regional_averages.json: same as previous file for the CORDEX-core NAM-22 multimodel ensemble - grid points with less than 14 days per year with SWE100 during the reference (recent past) period are put to zero.\r\n\r\n- NAM-22_CORDEX_NORTH-AMERICA_snw_mask30_AR6_regional_averages.json: same as previous file for the CORDEX-core NAM-22 multimodel ensemble, but grid points with less than 30 days per year with SWE100 during the reference (recent past) period are put to zero.\r\n- CMIP5_NORTH-AMERICA_snw_mask30_AR6_regional_averages.json: regional averages for the CMIP5 multimodel ensemble of number of days per year with snow water equivalent over 100mm (SWE100) in North-America for recent past (1995-2014), mid-term (2041-2060) long-term (2081-2100) for RCP2.6 and RCP8.5, and for three global warming levels: 1.5, 2 and 4; the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars) - grid points with less than 30 days per year with SWE100 during the reference (recent past) period are put to zero.\r\n- CMIP6_NORTH-AMERICA_snw_mask30_AR6_regional_averages.json: regional averages for the CMIP5 multimodel ensemble of number of days per year with snow water equivalent over 100mm (SWE100) in North-America for recent past (1995-2014), mid-term (2041-2060) long-term (2081-2100) for RCP2.6 and RCP8.5, and for three global warming levels: 1.5, 2 and 4; the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars) - grid points with less than 30 days per year with SWE100 during the reference (recent past) period are put to zero.\r\n\r\nCORDEX is The Coordinated Regional Downscaling Experiment from the WCRP. \r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project. \r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project. \r\n\r\nSSP stands for Shared Socioeconomic Pathway. SSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6. \r\n\r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5. \r\n\r\nRCP stands for Representative Concentration Pathway. \r\n\r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100. \r\n\r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n For panel a, the plotting script (see data tables and code on Github) draws the rivers and uses a subroutine to identify the rivers to plot them individually with lines; plotting the Q100 netcdf file will produce dots (and not rivers).\r\n\r\n\r\nFor panel c, the recent past values are plotted as absolute values (left column on each regional subpanel) and the future changes are plotted as differences against the recent past values (differences are computed when plotting the values).\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 12)\r\n - Link to the Supplementary Material for Chapter 12, which contains details on the input data used in Table 12.SM.1\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to the Chapter 12 GitHub repository." } }, { "ob_id": 747, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 38905, "uuid": "1030d40a071d4929bf04e08bfbd22c10", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure TS.25 v20221111", "abstract": "Data for Figure TS.25 from the Technical Summary 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 TS.25 shows the distribution of projected changes in selected climatic impact-driver (CID) indices for selected regions for Coupled Model Intercomparison Project Phases 5 and 6 (CMIP6, CMIP5) and Coordinated Regional Downscaling Experiment (CORDEX) model ensembles.\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 Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. 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. 33−144, doi:10.1017/9781009157896.002.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n For all the panels, the data provided consists of ensemble statistics (q5, median and q95) of the spatial averages over the IPCC AR6 regions of the list of indicators below for CMIP5, CMIP6 and CORDEX, for the recent past (1995-2014), the mid-term (2041-2060) and long-term (2081-2100) future horizons, as well as the +1.5, +2, and +4°C of global warming levels.\r\n The list of indicators shown on the figure is:\r\n - number of days per year with SWE > 100mm (North-America)\r\n - number of days with the NOAA Heat Index exceeding 41°C (Central-America and Asia)\r\n - the 100-yr return period stream flow (South-America, Europe, Africa)\r\n - the number of days per year with Maximum temperature exceeding 35°C (Asia)\r\n - the Shoreline position change (Asia, Australasia)\r\n\r\nSWE stands for snow water equivalent\r\nNOAA stands for National Oceanic and Atmospheric Administration.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nUpper panels of Panel (a):\r\nThe change of the number of days with SWE > 100mm are related with figure 12.10(d) with the corresponding file names:\r\n ** 'CMIP5_NORTH-AMERICA_snw_mask14_AR6_regional_averages.json' : regional averages for the CMIP5 multimodel ensemble of number of days per year with snow water equivalent over 100mm (SWE100) in North-America for recent past (1995-2014), mid-term (2041-2060) long-term (2081-2100) for RCP2.6 and RCP8.5, and for three global warming levels: 1.5, 2 and 4; the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars)\r\n ** 'NAM-22_CORDEX_NORTH-AMERICA_SWE_mask14_AR6_regional_averages.json' : same as previous file for the CORDEX-core NAM-22 multimodel ensemble\r\n ** 'CMIP6_NORTH-AMERICA_snw_mask14_AR6_regional_averages.json' : same as previous file for CMIP6 (ssp126 instead of RCP2.6 and ssp585 instead of RCP8.5)\r\n\r\nMiddle panels of Panel (a): \r\nThe change of the NOAA HI exceeding 41°C are related to figure 12.SM.2 with the corresponding file names:\r\n ** 'CMIP5_HI41_AR6_regional_averages.json' : data for the CMIP5 multi-model ensemble\r\n ** 'CMIP6_HI41_AR6_regional_averages.json' : data for the CMIP6 multi-model ensemble\r\n ** 'CORDEX_HI41_AR6_regional_averages.json' : data for the CORDEX multi-model ensemble\r\n see the description of the data associated with figure 12.SM.2 for more details on the structure of the files\r\n\r\nLower panels of Panel (a) and left panels of Panel (b):\r\n100-yr return period stream flow is shown for South America (figure 12.8(c)), Europe (figure 12.9(c)) and Africa (figure 12.5(c)) with corresponding file names: \r\n ** 'Q100_${ensemble}/Q100_${scenario}_${period}.nc_${CORDEX_domain}.txt': files containing the median and 5th/95th percentiles of each ensemble of the 1-in-100yr river discharge per unit catchment area (Q100) regional averages of time slices, with:\r\n - ${ensemble}: CMIP5, CMIP6 or CORDEX-core\r\n - ${scenario}: the name of the scenario : ssp126, ssp585, rcp26, rcp85\r\n - ${period}: the explicit period used to compute the temporal average: 1995-2014 (recent past), 2041-2060 (mid-term) and 2081-2099 (long term)\r\n - ${CORDEX_domain}: the CORDEX domain\r\n** 'Q100_${ensemble}/${GWL}_${CORDEX_domain}.txt' : files containing the Q100 regional averages of global warming levels with:\r\n - ${ensemble}: CMIP5, CMIP6 or CORDEX-core\r\n - ${GWL}: the Global Warming Level: 1.5, 2 or 4\r\n - ${CORDEX_domain}: the CORDEX domain\r\n\r\nRight panels of Panel (b):\r\nThe Maximum temperature exceeding 35°C (upper right) are related with figure 12.SM.1 with the corresponding file names:\r\n ** 'CMIP5_tx35isimip_AR6_regional_averages.json' : data for the CMIP5 multi-model ensemble\r\n ** 'CMIP6_tx35isimip_AR6_regional_averages.json' : data for the CMIP6 multi-model ensemble\r\n ** 'CORDEX_tx35isimip_AR6_regional_averages.json' : data for the CORDEX multi-model ensemble\r\n see the description of the data associated with figure 12.SM.1 for more details on the structure of the files\r\n\r\nThe Shoreline position change for EAS and RFE (upper middle right) (related to figure 12.6(d)), and in Australasia (lower right) (related to figure 12.7(d)) have corresponding data file names:\r\n ** 'globalErosionProjections_by_AR6_region_${scenario}_${horizon).json' : regional averages of shoreline position changes for Africa, for the RCP4.5 and RCP8.5 scenarios (${scenario} = RCP45 and ${scenario} = RCP85 respectively) and the 2050 (mid-term, in blue) and 2100 (long-term, in red) future horizons (${horizon}=2050 and ${horizon}=2100 respectively) against the recent past period (2010); the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars)\r\n\r\nThe four panels on the NOAA Heat Index exceeding 41°C (lower middle right) are related with figure 12.SM.2 with the corresponding file names:\r\n ** 'CMIP5_HI41_AR6_regional_averages.json': data for the CMIP5 multi-model ensemble\r\n ** 'CMIP6_HI41_AR6_regional_averages.json': data for the CMIP6 multi-model ensemble\r\n ** 'CORDEX_HI41_AR6_regional_averages.json': data for the CORDEX multi-model ensemble\r\n\r\nGWL stands for global warming levels.\r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100.\r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n You can find the scripts and the data to reproduce the figures on Github (link in Related Documents section), following the description below. Links to the catalogue records for relevant Chapter 12 figures are in the Related Records section of this catalogue record. \r\n\r\nPanel a:\r\n- the upper panels on the change of the number of days with SWE > 100mm are related with figure 12.10, panel d\r\n- the middle three panels on the change of the NOAA HI exceeding 41°C are related with figure 12.SM.2 \r\n- the lower panels on the 100-yr return period stream flow are related with figure 12.8, panel c\r\n\r\nPanel b:\r\n- upper left panels on the 100-yr return period stream flow in Europe are related with figure 12.9, panel c\r\n- upper right panels on the Maximum temperature exceeding 35°C are related with figure 12.SM.1 \r\n- middle right panels on Shoreline position change for EAS and RFE are associated with figure 12.6, panel d\r\n- the four panels right below on the NOAA Heat Index exceeding 41°C are related with figure 12.SM.2\r\n- the lower left panels on the 100-yr return period stream flow in Africa are related with figure 12.5, panel c\r\n- the lower right panels on the Shoreline position change in Australasia are related with figure 12.7, panel d\r\n\r\nThe final assembling of the panels to get the final figure was done with post-processing.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n - Link to Github for chapter 12 containing data and code\r\n - Link to code for Chapter 12 archived on Zenodo" }, "objectObservation": { "ob_id": 37875, "uuid": "156e4a10ddfb418aa24aadf244fbadf6", "short_code": "ob", "title": "Chapter 12 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 12.SM.1 (v20220808)", "abstract": "Data for Figure 12.SM.1 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 12.SM.1 shows regional projections for the number of days per year with maximum temperature exceeding 35°C for different scenarios, time horizons and global warming levels. \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\nRanasinghe, R., A.C. Ruane, R. Vautard, N. Arnell, E. Coppola, F.A. Cruz, S. Dessai, A.S. Islam, M. Rahimi, D. Ruiz Carrascal, J. Sillmann, M.B. Sylla, C. Tebaldi, W. Wang, and R. Zaaboul, 2021: Climate Change Information for Regional Impact and for Risk Assessment Supplementary Material. 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.)]. Available from https://www.ipcc.ch/\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\nThis figure has 43 subpanels (AR6 regions).\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- regional averages over 43 AR6 regions of the number of days per year with maximum daily temperature exceeding 35°C (median value and the 10th-90th percentile range of model ensemble values across each model ensemble) over land areas for the WGI reference AR6 regions (defined in Chapter 1) for:\r\n\r\n - CMIP6 historical, ssp126 and ssp585\r\n\r\n - CMIP5 and CORDEX historical, RCP2.6 and RCP8.5\r\n\r\n - for the ‘recent past’ (1995-2014), mid-term (2041-2060) and long-term (2081-2100) time periods\r\n\r\n - and for three global warming levels (defined relative to the preindustrial period 1850-1900): 1.5°C, 2°C and 4°C\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 12.SM.1:\r\n \r\nThe regional averages for all the subpanels (AR6 regions) are stored in three json files:\r\n\r\n- CMIP5_tx35isimip_AR6_regional_averages.json: data for the CMIP5 multi-model ensemble\r\n\r\n- CMIP6_tx35isimip_AR6_regional_averages.json: data for the CMIP6 multi-model ensemble\r\n\r\n- CORDEX_tx35isimip_AR6_regional_averages.json: data for the CORDEX multi-model ensemble\r\n\r\nThe content of the files is organized as follows:\r\n\r\n - level 1 key:\r\n - GWL: string: 1.5, 2, 3, 4\r\n or\r\n - name of the time slice: baseline or ${scenario}_${horizon}, with:\r\n - ${scenario}: the scenario: ssp126 or ssp585 for CMIP6, rcp26 or rcp85 for CMIP5 and CORDEX\r\n - ${horizon}: mid (mid-term) or far (long-term)\r\n - level 2 keys: name of the AR6 region\r\n - value: list with:\r\n - first element: the multi-model ensemble 10th percentile (lower bounds of the vertical lines)\r\n - second element: the multi-model ensemble median (the dots)\r\n - third element: the multi-model ensemble 90th percentile (upper bounds of the vertical lines)\r\n\r\n CMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\n CMIP6 is the sixth phase of the Coupled Model Intercomparison Project\r\n CORDEX is The Coordinated Regional Downscaling Experiment from the WCRP.\r\n SSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6.\r\n SSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5. \r\n RCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100. \r\n RCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nJupyter notebooks containing the data files and code used to plot this figure are stored in the 'scripts' GitHub repository linked in the documentation. \r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 12)\r\n - Link to the Supplementary Material for Chapter 12, which contains details on the input data used in Table 12.SM.1\r\n- Link to the master GitHub repository containing the Juptyer notebooks to run the code for the figure, as well as the other figures in Chapter 12.\r\n- Link to the code for the figure, archived on Zenodo." } }, { "ob_id": 748, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 38905, "uuid": "1030d40a071d4929bf04e08bfbd22c10", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure TS.25 v20221111", "abstract": "Data for Figure TS.25 from the Technical Summary 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 TS.25 shows the distribution of projected changes in selected climatic impact-driver (CID) indices for selected regions for Coupled Model Intercomparison Project Phases 5 and 6 (CMIP6, CMIP5) and Coordinated Regional Downscaling Experiment (CORDEX) model ensembles.\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 Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. 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. 33−144, doi:10.1017/9781009157896.002.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n For all the panels, the data provided consists of ensemble statistics (q5, median and q95) of the spatial averages over the IPCC AR6 regions of the list of indicators below for CMIP5, CMIP6 and CORDEX, for the recent past (1995-2014), the mid-term (2041-2060) and long-term (2081-2100) future horizons, as well as the +1.5, +2, and +4°C of global warming levels.\r\n The list of indicators shown on the figure is:\r\n - number of days per year with SWE > 100mm (North-America)\r\n - number of days with the NOAA Heat Index exceeding 41°C (Central-America and Asia)\r\n - the 100-yr return period stream flow (South-America, Europe, Africa)\r\n - the number of days per year with Maximum temperature exceeding 35°C (Asia)\r\n - the Shoreline position change (Asia, Australasia)\r\n\r\nSWE stands for snow water equivalent\r\nNOAA stands for National Oceanic and Atmospheric Administration.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nUpper panels of Panel (a):\r\nThe change of the number of days with SWE > 100mm are related with figure 12.10(d) with the corresponding file names:\r\n ** 'CMIP5_NORTH-AMERICA_snw_mask14_AR6_regional_averages.json' : regional averages for the CMIP5 multimodel ensemble of number of days per year with snow water equivalent over 100mm (SWE100) in North-America for recent past (1995-2014), mid-term (2041-2060) long-term (2081-2100) for RCP2.6 and RCP8.5, and for three global warming levels: 1.5, 2 and 4; the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars)\r\n ** 'NAM-22_CORDEX_NORTH-AMERICA_SWE_mask14_AR6_regional_averages.json' : same as previous file for the CORDEX-core NAM-22 multimodel ensemble\r\n ** 'CMIP6_NORTH-AMERICA_snw_mask14_AR6_regional_averages.json' : same as previous file for CMIP6 (ssp126 instead of RCP2.6 and ssp585 instead of RCP8.5)\r\n\r\nMiddle panels of Panel (a): \r\nThe change of the NOAA HI exceeding 41°C are related to figure 12.SM.2 with the corresponding file names:\r\n ** 'CMIP5_HI41_AR6_regional_averages.json' : data for the CMIP5 multi-model ensemble\r\n ** 'CMIP6_HI41_AR6_regional_averages.json' : data for the CMIP6 multi-model ensemble\r\n ** 'CORDEX_HI41_AR6_regional_averages.json' : data for the CORDEX multi-model ensemble\r\n see the description of the data associated with figure 12.SM.2 for more details on the structure of the files\r\n\r\nLower panels of Panel (a) and left panels of Panel (b):\r\n100-yr return period stream flow is shown for South America (figure 12.8(c)), Europe (figure 12.9(c)) and Africa (figure 12.5(c)) with corresponding file names: \r\n ** 'Q100_${ensemble}/Q100_${scenario}_${period}.nc_${CORDEX_domain}.txt': files containing the median and 5th/95th percentiles of each ensemble of the 1-in-100yr river discharge per unit catchment area (Q100) regional averages of time slices, with:\r\n - ${ensemble}: CMIP5, CMIP6 or CORDEX-core\r\n - ${scenario}: the name of the scenario : ssp126, ssp585, rcp26, rcp85\r\n - ${period}: the explicit period used to compute the temporal average: 1995-2014 (recent past), 2041-2060 (mid-term) and 2081-2099 (long term)\r\n - ${CORDEX_domain}: the CORDEX domain\r\n** 'Q100_${ensemble}/${GWL}_${CORDEX_domain}.txt' : files containing the Q100 regional averages of global warming levels with:\r\n - ${ensemble}: CMIP5, CMIP6 or CORDEX-core\r\n - ${GWL}: the Global Warming Level: 1.5, 2 or 4\r\n - ${CORDEX_domain}: the CORDEX domain\r\n\r\nRight panels of Panel (b):\r\nThe Maximum temperature exceeding 35°C (upper right) are related with figure 12.SM.1 with the corresponding file names:\r\n ** 'CMIP5_tx35isimip_AR6_regional_averages.json' : data for the CMIP5 multi-model ensemble\r\n ** 'CMIP6_tx35isimip_AR6_regional_averages.json' : data for the CMIP6 multi-model ensemble\r\n ** 'CORDEX_tx35isimip_AR6_regional_averages.json' : data for the CORDEX multi-model ensemble\r\n see the description of the data associated with figure 12.SM.1 for more details on the structure of the files\r\n\r\nThe Shoreline position change for EAS and RFE (upper middle right) (related to figure 12.6(d)), and in Australasia (lower right) (related to figure 12.7(d)) have corresponding data file names:\r\n ** 'globalErosionProjections_by_AR6_region_${scenario}_${horizon).json' : regional averages of shoreline position changes for Africa, for the RCP4.5 and RCP8.5 scenarios (${scenario} = RCP45 and ${scenario} = RCP85 respectively) and the 2050 (mid-term, in blue) and 2100 (long-term, in red) future horizons (${horizon}=2050 and ${horizon}=2100 respectively) against the recent past period (2010); the file contains the median (dots in the subpanels) and the 5th (q5) and 95th (q95) uncertainty estimates (used to plot the vertical bars)\r\n\r\nThe four panels on the NOAA Heat Index exceeding 41°C (lower middle right) are related with figure 12.SM.2 with the corresponding file names:\r\n ** 'CMIP5_HI41_AR6_regional_averages.json': data for the CMIP5 multi-model ensemble\r\n ** 'CMIP6_HI41_AR6_regional_averages.json': data for the CMIP6 multi-model ensemble\r\n ** 'CORDEX_HI41_AR6_regional_averages.json': data for the CORDEX multi-model ensemble\r\n\r\nGWL stands for global warming levels.\r\nRCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100.\r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n You can find the scripts and the data to reproduce the figures on Github (link in Related Documents section), following the description below. Links to the catalogue records for relevant Chapter 12 figures are in the Related Records section of this catalogue record. \r\n\r\nPanel a:\r\n- the upper panels on the change of the number of days with SWE > 100mm are related with figure 12.10, panel d\r\n- the middle three panels on the change of the NOAA HI exceeding 41°C are related with figure 12.SM.2 \r\n- the lower panels on the 100-yr return period stream flow are related with figure 12.8, panel c\r\n\r\nPanel b:\r\n- upper left panels on the 100-yr return period stream flow in Europe are related with figure 12.9, panel c\r\n- upper right panels on the Maximum temperature exceeding 35°C are related with figure 12.SM.1 \r\n- middle right panels on Shoreline position change for EAS and RFE are associated with figure 12.6, panel d\r\n- the four panels right below on the NOAA Heat Index exceeding 41°C are related with figure 12.SM.2\r\n- the lower left panels on the 100-yr return period stream flow in Africa are related with figure 12.5, panel c\r\n- the lower right panels on the Shoreline position change in Australasia are related with figure 12.7, panel d\r\n\r\nThe final assembling of the panels to get the final figure was done with post-processing.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n - Link to Github for chapter 12 containing data and code\r\n - Link to code for Chapter 12 archived on Zenodo" }, "objectObservation": { "ob_id": 37873, "uuid": "660a0224eee04d0880b78f538510f416", "short_code": "ob", "title": "Chapter 12 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 12.SM.2 (v20220808)", "abstract": "Data for Figure 12.SM.2 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 12.SM.2 shows regional projections for the number of days per year with NOAA Heat Index exceeding 41°C for different scenarios, time horizons and global warming levels. \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\nRanasinghe, R., A.C. Ruane, R. Vautard, N. Arnell, E. Coppola, F.A. Cruz, S. Dessai, A.S. Islam, M. Rahimi, D. Ruiz Carrascal, J. Sillmann, M.B. Sylla, C. Tebaldi, W. Wang, and R. Zaaboul, 2021: Climate Change Information for Regional Impact and for Risk Assessment Supplementary Material. 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.)]. Available from https://www.ipcc.ch/\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\nThis figure has 43 subpanels (AR6 regions).\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- regional averages over 43 AR6 regions of the number of days per year with NOAA Heat Index exceeding 41°C (median value and the 10th-90th percentile range of model ensemble values across each model ensemble) over land areas for the WGI reference AR6 regions (defined in Chapter 1) for:\r\n\r\n - CMIP6 historical, ssp126 and ssp585\r\n\r\n - CMIP5 and CORDEX historical, RCP2.6 and RCP8.5\r\n\r\n - for the ‘recent past’ (1995-2014), mid-term (2041-2060) and long-term (2081-2100) time periods\r\n\r\n - and for three global warming levels (defined relative to the preindustrial period 1850-1900): 1.5°C, 2°C and 4°C\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 12.SM.2\r\n \r\nThe regional averages for all the subpanels (AR6 regions) are stored in three json files:\r\n\r\n- CMIP5_HI41_AR6_regional_averages.json: data for the CMIP5 multi-model ensemble\r\n\r\n- CMIP6_HI41_AR6_regional_averages.json: data for the CMIP6 multi-model ensemble\r\n\r\n- CORDEX_HI41_AR6_regional_averages.json: data for the CORDEX multi-model ensemble\r\n\r\nThe content of the files is organized as follows:\r\n\r\n - level 1 key:\r\n - GWL: string: 1.5, 2, 3, 4\r\n or\r\n - name of the time slice: baseline or ${scenario}_${horizon}, with:\r\n - ${scenario}: the scenario: ssp126 or ssp585 for CMIP6, rcp26 or rcp85 for CMIP5 and CORDEX\r\n - ${horizon}: mid (mid-term) or far (long-term)\r\n - level 2 keys: name of the AR6 region\r\n - value: list with:\r\n - first element: the multi-model ensemble 10th percentile (lower bounds of the vertical lines)\r\n - second element: the multi-model ensemble median (the dots)\r\n - third element: the multi-model ensemble 90th percentile (upper bounds of the vertical lines)\r\n\r\n NOAA is the National Oceanic and Atmospheric Administration.\r\n CMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\n CMIP6 is the sixth phase of the Coupled Model Intercomparison Project\r\n CORDEX is The Coordinated Regional Downscaling Experiment from the WCRP.\r\n SSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6.\r\n SSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5. \r\n RCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100. \r\n RCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nJupyter notebooks containing the data files and code used to plot this figure are stored in the 'scripts' GitHub repository linked in the documentation. \r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 12)\r\n - Link to the Supplementary Material for Chapter 12, which contains details on the input data used in Table 12.SM.1\r\n - Link to the master GitHub repository containing the Juptyer notebooks to run the code for the figure, as well as the other figures in Chapter 12.\r\n - Link to the code for the figure, archived on Zenodo." } }, { "ob_id": 749, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 38315, "uuid": "169c76a05fa247eebc5ee53f239871a7", "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 (CO2_GO2_SRFP), version 2.0.0", "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 Remote Sensing of Greenhouse Gases for Carbon Cycle Modeling (RemoTeC) SRON Full Physics (SRFP) 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." }, "objectObservation": { "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." } }, { "ob_id": 750, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 38314, "uuid": "c8037223dfff49db9fcdd8a9f6dd8d41", "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 (CH4_GO2_SRFP), version 2.0.0", "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 Remote Sensing of Greenhouse Gases for Carbon Cycle Modeling (RemoTeC) SRON Full Physics (SRFP) 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." }, "objectObservation": { "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." } }, { "ob_id": 751, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 38313, "uuid": "6ecb706ac16c4e05aab75ed2cc3ec119", "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 (CH4_GO2_SRPR), version 2.0.0", "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 Remote Sensing of Greenhouse Gases for Carbon Cycle Modeling (RemoTeC) SRON Proxy (SRPR) 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." }, "objectObservation": { "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." } }, { "ob_id": 752, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 38257, "uuid": "cc32622e68ad40bcac14fc6cd69ae4b7", "short_code": "ob", "title": "MOSAiC: Wind profiles from Galion G4000 Lidar Wind Profiler - Version 2", "abstract": "Wind profiles from a Galion G4000 Doppler lidar for the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) project, derived from conical scans at 30 degree and 50 degree beam elevation angles.\r\n\r\nThe University of Leeds participation in the project- MOSAiC Boundary Layer -was funded by the Natural Environment Research Council (NERC, grant: NE/S002472/1) and involved instrumentation from the Atmospheric Measurement and Observations Facility of the UK's National Centre for Atmospheric Science (NCAS AMOF). This was a year-long project on the German icebreaker Polarstern to study Arctic climate focused on measurements of atmospheric boundary layer dynamics and turbulent structure. The Galion wind profiler provides high resolution (~15m vertical and 5 minute temporal) measurements of wind profiles. Data are only available where sufficient particles are available to backscatter the laser light - in the clean arctic environment, this requires cloud or precipitation.\r\n\r\nThis is version 2 of this dataset." }, "objectObservation": { "ob_id": 37046, "uuid": "c4abd037c7ad4019ad02d0c802e2f27e", "short_code": "ob", "title": "MOSAiC: Wind profiles from Galion G4000 Lidar Wind Profiler", "abstract": "Wind profiles from a Galion G4000 Doppler lidar for the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) project, derived from conical scans at 30 degree and 50 degree beam elevation angles.\r\n\r\nThe University of Leeds participation in the project- MOSAiC Boundary Layer -was funded by the Natural Environment Research Council (NERC, grant: NE/S002472/1) and involved instrumentation from the Atmospheric Measurement and Observations Facility of the UK's National Centre for Atmospheric Science (NCAS AMOF). This was a year-long project on the German icebreaker Polarstern to study Arctic climate focused on measurements of atmospheric boundary layer dynamics and turbulent structure. The Galion wind profiler provides high resolution (~15m vertical and 5 minute temporal) measurements of wind profiles. Data are only available where sufficient particles are available to backscatter the laser light - in the clean arctic environment, this requires cloud or precipitation." } }, { "ob_id": 753, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 23991, "uuid": "aeedfad6a2f345a8a0b50f40f4c0787f", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1931-2016, v2.0.1.2016f", "abstract": "This is version 2.0.1.2016f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data that extends HadISD v2.0.0.2015p to span 1931-2016 and includes an increase in the number of stations and an updated methodology and is the final version of the 2016 data. \r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19310101-20151231_v2-0-1-2016p.nc. The station codes can be found under the docs tab or on the archive beside the station_data folder. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016.\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014." }, "objectObservation": { "ob_id": 34674, "uuid": "f0f32cf3e7884950a585b7a4fda9dcb6", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1973-2015, v1.0.4.2015f", "abstract": "This is version 1.0.4.2015f of HadISD (27 April 2015) the Met Office Hadley Centre's global sub-daily data, extending v1.0.3.2014f to span 1/1/1973 - 31/12/2015. \r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed in the quality control process are also provided along with a station listing with IDs, names and location information. The data are provided as one NetCDF file per station. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19310101-20151231_v1-0-4-2015f.nc (note, the filenames incorrectly show the start date of 19310101, instead of 19730101). The station codes can be found under the docs tab or on the archive beside the station_data folder. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Climate of the Past\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014." } }, { "ob_id": 754, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 19744, "uuid": "289126a92b7a4b8a9ac69714263c1c1d", "short_code": "ob", "title": "ATSR-1: Multimission land and sea surface removal data, v2.1", "abstract": "Along-Track Scanning Radiometer (ATSR) mission was funded jointly by the UK Department of Energy and Climate Change External Link (DECC) and the Australian Department of Innovation, Industry, Science and Research External Link (DIISR).\r\n\r\nThis dataset contains version 2.1 ATSR Multimission land and sea surface removal data. These data were taken during calibration phase." }, "objectObservation": { "ob_id": 32766, "uuid": "4d10319960f442528a8f365ad5dfbcb4", "short_code": "ob", "title": "ATSR-1 Ungridded Brightness Temperature (UBT) Data", "abstract": "Data from the Along Track Scanning Radiometer (ATSR-1) instrument on the ERS-1 platform operational between 1991 and 1996. The ATSR is an imaging radiometer providing images of the Earth from space. The ERS (Earth Resources Satellite) program was funded by and operated by ESA and was the first main ESA EO data campaign. ATSR-1 was placed on the ERS1 platform and ATSR-2 was on the ERS-2 platform. The ATSR-1 and 2 instruments were followed by the Advanced Along Track Scanning Radiometer (AATSR) on the ENVISAT platform in 2002.\r\n\r\nThe ATSR-1 instrument has been designed for exceptional sensitivity and stability of calibration, which are achieved through the incorporation of several innovative features in the instrument design. This design has, among other things, enabled the accurate measurement of sea surface temperature to an accuracy of +/- 0.3K. The design of the ATSR instrument incorporates a dual view made possible by the rotating scan mirror. There is a nadir view and then a subsequent along track view. These provide 2 images per scan and allow improved estimate of atmospheric attenuation. This coupled with the inclusion of consistent calibration using on-board black bodies allows for the collection of extremely radiometrically accurate data. \r\n\r\nThe data are Level1 Ungridded Brightness Temperatures (UBT). The data are in SADIST-2 format and CEDA is the primary archive for this data. The UBT product provides scenes for both nadir and forward views with a swath width of 512km and a ground pixel distance of 1km. \r\n\r\nThis dataset is superseded by the AATSR Multimission ATSR-1 data set that involved reprocessing this data with improved calibration and cloud masking and is available in a number of reprocessings so consistent with ENVISAT format data." } }, { "ob_id": 755, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 39323, "uuid": "edd6991e06024f7d9ee06460ec7cd7f6", "short_code": "ob", "title": "Solar-induced Chlorophyll Fluorescence from GOSAT's TANSO-FTS by the University of Leicester (v3.0)", "abstract": "Solar-induced Chlorophyll Fluorescence (SIF) data created from the Greenhouse Gas Observing Satellite (GOSAT) Level 1B data using an adapted version of the University of Leicester Full-Physics retrieval scheme (UoL-FP). These dataset contains both Level 2 and Level 3 S-polarised SIF. The Level 2 data are in daily files and are not averaged, whilst the Level 3 data are averaged spatially and temporally on both a monthly and weekly timescale.\r\n\r\nThe SIF data was derived from L1B data from the TANSO-FTS ( Thermal and Near Infrared Sensor for carbon Observation - Fourier Transform Spectrometer) instrument on the GOSAT satellite. For each GOSAT sounding, the S-polarised spectra have been extracted from two narrow micro-windows outside the Oxygen A-band, at around 755 nm and 772 nm. For more information on the retrieval setup and bias correction, please see \"Novel Methods for Atmospheric Carbon Dioxide Retrieval from the JAXA GOSAT and NASA OCO-2 Satellites - Part II: Remote Sensing of Chlorophyll Fluorescence\" by Peter Somkuti." }, "objectObservation": { "ob_id": 28009, "uuid": "3d8678091ba247e58e19a6fe5543487d", "short_code": "ob", "title": "Solar-induced Chlorophyll Fluorescence from GOSAT's TANSO-FTS by the University of Leicester", "abstract": "Solar-induced Chlorophyll Fluorescence (SIF) data created from the Greenhouse Gas Observing Satellite (GOSAT) Level 1B data using an adapted version of the University of Leicester Full-Physics retrieval scheme (UoL-FP). These dataset contains both Level 2 and Level 3 S-polarised SIF. The Level 2 data are in daily files and are not averaged, whilst the Level 3 data are averaged spatially and temporally on both a monthly and weekly timescale.\r\n\r\nThe SIF data was derived from L1B data from the TANSO-FTS ( Thermal and Near Infrared Sensor for carbon Observation - Fourier Transform Spectrometer) instrument on the GOSAT satellite. For each GOSAT sounding, the S-polarised spectra have been extracted from two narrow micro-windows outside the Oxygen A-band, at around 755 nm and 772 nm. For more information on the retrieval setup and bias correction, please see \"Novel Methods for Atmospheric Carbon Dioxide Retrieval from the JAXA GOSAT and NASA OCO-2 Satellites - Part II: Remote Sensing of Chlorophyll Fluorescence\" by Peter Somkuti." } }, { "ob_id": 757, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 37561, "uuid": "85168e39bfff444ba02bf55e7682f73d", "short_code": "ob", "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.26 (v20220616)", "abstract": "Data for Figure 3.26 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.26 shows global ocean heat content in CMIP6 simulations and observations.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n ---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n Technically the figure has 4 panels, but they are not named, so the datasets are stored in the parent directory. \r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The dataset contains simulated and observed ocean heat content timeseries:\r\n \r\n - from CMIP6 models at full depth (1850-2014)\r\n - from observations at full depth (1971-2018)\r\n - from CMIP6 models at 0-700 m (1850-2014)\r\n - from observations at 700-200 m (1971-2018)\r\n - from CMIP6 models at 700-200 m (1850-2014)\r\n - from observations at 700-200 m (1971-2018)\r\n - from CMIP6 models at deeper than 2000 m (1850-2014)\r\n - from observations at deeper than 2000 m (1992-2018)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - ocean_heat_content_anomalies_full_depth.csv has the data for the read lines and shadings (CMIP6) from 1850 to 2014 and black lines and shadings (observations) from 1971 to 2018\r\n - ocean_heat_content_anomalies_0_700_m.csv has the data for the read lines and shadings (CMIP6) from 1850 to 2014 and black lines and shadings (observations) from 1971 to 2018\r\n - ocean_heat_content_anomalies_700_2000_m.csv has the data for the read lines and shadings (CMIP6) from 1850 to 2014 and black lines and shadings (observations) from 1971 to 2018\r\n - ocean_heat_content_anomalies_over_2000_m.csv has the data for the read lines and shadings (CMIP6) from 1850 to 2014 and black lines and shadings (observations) from 1992 to 2018\r\n\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The observational data for this figure is taken from the file 'AR6_FGD_assessment_timeseries_OHC.csv' from Cross-Chapter Box1 figure 1, Chapter 9. The link to this dataset is provided in the Related Documents section of this catalogue record.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to dataset for figure CCB1 Chapter 9\r\n - Link to the figure on the IPCC AR6 website" }, "objectObservation": { "ob_id": 40089, "uuid": "c622adfeb4cc4ae181dc4cca82c2311c", "short_code": "ob", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Cross-Chapter Box 9.1, Figure 1 (v20230523)", "abstract": "Data for Cross-Chapter Box 9.1, Figure 1 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nCross-Chapter Box 9.1, Figure 1 shows observed and simulated regional probability ratio of marine heatwaves (MHWs) for the 1985-2014 period and for the end of the 21st century under two different greenhouse gas emissions 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 Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels with data provided for all panels in the main directory. \r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n Main assessment timeseries for GMSL change, OHC and ThSL. Timeseries are global integrals over the following vertical layers: 0-300 m; 0-700 m; 0-2000 m; 700-2000 m; > 2000 m; Full-depth.\r\n\r\n\r\nThis dataset are also used in the following figures:\r\na) AR6 FGD assessment timeseries GMSL satellite altimeter: Figure 2.28; \r\nb) AR6 FGD assessment timeseries GMSL tide gauge: Figure 2.28;\r\nc) AR6 FGD assessment timeseries OHC: Figure 3.26, Box 7.2, Figure 1; \r\n\r\nOther figures/tables: Figure 2.26, Table 2.7; Figure 3.26; Box 7.2 Figure 1, Table 9.5; Figure TS.8; Figure TS.13.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a: \r\n Data file: “AR6_FGD_assessment_timeseries_OHC.csv” => column 2 is used to plot the light blue shaded region, column 4 is used to plot the medium blue shaded region, column 6 is used to plot the dark blue shaded region in CCBox9.1 Figure 1 panel a). \r\n\r\n\r\nPanel b: \r\n Data file: “AR6_FGD_assessment_timseries_GMSL_satellite_altimeter.csv” => column 2 is used to plot the dashed black line in CCBox9.1 Figure 1 panel b)\r\n Data file: “AR6_FGD_assesssment_timeseries_GMSL_tide_gauge.csv” => column 2 is used to to plot the dashed black line in CCBox9.1 Figure 1 panel b)\r\n Data file: “AR6_FGD_assessment_timeseries_ThSL.csv” => column 2 is used to plot the light blue shaded region, column 4 is used to plot the medium blue shaded region, column 6 is used to plot the dark blue shaded region in CCBox9.1 Figure 1 panel b).\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to the code for the figure, archived on github repository for chapter 9.\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to Chapter 2 Figure 2.26 \r\n - Link to Chapter 2 Figure 2.28\r\n - Link to Chapter 3 Figure 3.26\r\n - Link to Chapter 7 Box 7.2, Figure 1\r\n - Link to Technical Summary Figure TS.13\r\n - Link to input data for Cross-Chapter Box 9.1, Figure 1" } }, { "ob_id": 758, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 10926, "uuid": "a905a0c1a6522c9b1c8221703b0986e0", "short_code": "ob", "title": "ATSR-1: Multimission land and sea surface data, v1.1", "abstract": "Along-Track Scanning Radiometer (ATSR) mission was funded jointly by the UK Department of Energy and Climate Change External Link (DECC) and the Australian Department of Innovation, Industry, Science and Research External Link (DIISR).\r\n\r\nThis dataset collection contains version 1.1 ATSR Multimission land and sea surface temperature data.\r\n\r\nThe instrument uses thermal channels at 3.7, 10.8, and 12 microns wavelength; and reflected visible/near infra-red channels at 0.555, 0.659, 0.865, and 1.61 microns wavelength. Level 1b products contain gridded brightness temperature and reflectance. Level 2 products contain land and sea-surface temperature, and NDVI at a range of spatial resolutions. The third reprocessing was done to implement updated algorithms, processors, and auxiliary files. The data were acquired by the European Space Agency's (ESA) Envisat satellite, and the NERC Earth Observation Data Centre (NEODC) mirrors the data for UK users." }, "objectObservation": { "ob_id": 32766, "uuid": "4d10319960f442528a8f365ad5dfbcb4", "short_code": "ob", "title": "ATSR-1 Ungridded Brightness Temperature (UBT) Data", "abstract": "Data from the Along Track Scanning Radiometer (ATSR-1) instrument on the ERS-1 platform operational between 1991 and 1996. The ATSR is an imaging radiometer providing images of the Earth from space. The ERS (Earth Resources Satellite) program was funded by and operated by ESA and was the first main ESA EO data campaign. ATSR-1 was placed on the ERS1 platform and ATSR-2 was on the ERS-2 platform. The ATSR-1 and 2 instruments were followed by the Advanced Along Track Scanning Radiometer (AATSR) on the ENVISAT platform in 2002.\r\n\r\nThe ATSR-1 instrument has been designed for exceptional sensitivity and stability of calibration, which are achieved through the incorporation of several innovative features in the instrument design. This design has, among other things, enabled the accurate measurement of sea surface temperature to an accuracy of +/- 0.3K. The design of the ATSR instrument incorporates a dual view made possible by the rotating scan mirror. There is a nadir view and then a subsequent along track view. These provide 2 images per scan and allow improved estimate of atmospheric attenuation. This coupled with the inclusion of consistent calibration using on-board black bodies allows for the collection of extremely radiometrically accurate data. \r\n\r\nThe data are Level1 Ungridded Brightness Temperatures (UBT). The data are in SADIST-2 format and CEDA is the primary archive for this data. The UBT product provides scenes for both nadir and forward views with a swath width of 512km and a ground pixel distance of 1km. \r\n\r\nThis dataset is superseded by the AATSR Multimission ATSR-1 data set that involved reprocessing this data with improved calibration and cloud masking and is available in a number of reprocessings so consistent with ENVISAT format data." } }, { "ob_id": 759, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 10922, "uuid": "8bca3f5042da65dbb05d13404cac7d6c", "short_code": "ob", "title": "ATSR-2: Multimission land and sea surface temperature data, v1.1", "abstract": "Along-Track Scanning Radiometer (ATSR) mission was funded jointly by the UK Department of Energy and Climate Change External Link (DECC) and the Australian Department of Innovation, Industry, Science and Research External Link (DIISR).\r\n\r\nThis dataset collection contains version 1.1 ATSR2 Multimission land and sea surface temperature data.\r\n\r\nThe instrument uses thermal channels at 3.7, 10.8, and 12 microns wavelength; and reflected visible/near infra-red channels at 0.555, 0.659, 0.865, and 1.61 microns wavelength. Level 1b products contain gridded brightness temperature and reflectance. Level 2 products contain land and sea-surface temperature, and NDVI at a range of spatial resolutions. The third reprocessing was done to implement updated algorithms, processors, and auxiliary files. The data were acquired by the European Space Agency's (ESA) Envisat satellite, and the NERC Earth Observation Data Centre (NEODC) mirrors the data for UK users." }, "objectObservation": { "ob_id": 38727, "uuid": "a96880386de0431f97ed53baa1667ca1", "short_code": "ob", "title": "ATSR-2 Ungridded Brightness Temperature (UBT) Data", "abstract": "Data from the Along Track Scanning Radiometer (ATSR) instrument on the ERS-2 platform operational between 1995 and 2011. The ATSR is an imaging radiometer providing images of the Earth from space on the ERS2- platform. The ERS (Earth Resources Satellite) programme was funded by and operated by ESA and was the first main ESA EO data campaign. ATSR-1 was placed on the ERS-1 platform and ATSR-2 was on the ERS-2 platform. The ATSR-1 and 2 instruments were followed by the Advanced Along Track Scanning Radiometer (AATSR) on the ENVISAT platform in 2002.\r\n\r\nThe ATSR-2 instrument has been designed for exceptional sensitivity and stability of calibration, which are achieved through the incorporation of several innovative features in the instrument design. This design has, among other things, enabled the accurate measurement of sea surface temperature to an accuracy of +/- 0.3K. The design of the ATSR instrument incorporates a dual view made possible by the rotating scan mirror. There is a nadir view and then a subsequent along track view. These provide 2 images per scan and allow improved estimate of atmospheric attenuation. This coupled with the inclusion of consistent calibration using on-board black bodies allows for the collection of extremely radiometrically accurate data. \r\n\r\nThe data are Level1 Ungridded Brightness Temperatures (UBT). The data are in SADIST-2 format and CEDA is the primary archive for this data. The UBT product provides scenes for both nadir and forward views with a swath width of 512km and a ground pixel distance of 1km. \r\n\r\nThis dataset is superceded by the AATSR Multimission ATSR-2 data set that involved reprocessing this data with improved calibration and cloud masking and is available in a number of reprocessings so consistent with ENVISAT format data." } }, { "ob_id": 760, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 19742, "uuid": "7913089bdbf447188f66697cbca9da1e", "short_code": "ob", "title": "ATSR-1: Multimission land and sea surface data, v2.0", "abstract": "Along-Track Scanning Radiometer (ATSR) mission was funded jointly by the UK Department of Energy and Climate Change External Link (DECC) and the Australian Department of Innovation, Industry, Science and Research External Link (DIISR).\r\n\r\nThis dataset collection contains version 2.0 ATSR Multimission land and sea surface temperature data.\r\n\r\nThe instrument uses thermal channels at 3.7, 10.8, and 12 microns wavelength; and reflected visible/near infra-red channels at 0.555, 0.659, 0.865, and 1.61 microns wavelength. Level 1b products contain gridded brightness temperature and reflectance. Level 2 products contain land and sea-surface temperature, and NDVI at a range of spatial resolutions. The third reprocessing was done to implement updated algorithms, processors, and auxiliary files. The data were acquired by the European Space Agency's (ESA) Envisat satellite, and the NERC Earth Observation Data Centre (NEODC) mirrors the data for UK users." }, "objectObservation": { "ob_id": 10926, "uuid": "a905a0c1a6522c9b1c8221703b0986e0", "short_code": "ob", "title": "ATSR-1: Multimission land and sea surface data, v1.1", "abstract": "Along-Track Scanning Radiometer (ATSR) mission was funded jointly by the UK Department of Energy and Climate Change External Link (DECC) and the Australian Department of Innovation, Industry, Science and Research External Link (DIISR).\r\n\r\nThis dataset collection contains version 1.1 ATSR Multimission land and sea surface temperature data.\r\n\r\nThe instrument uses thermal channels at 3.7, 10.8, and 12 microns wavelength; and reflected visible/near infra-red channels at 0.555, 0.659, 0.865, and 1.61 microns wavelength. Level 1b products contain gridded brightness temperature and reflectance. Level 2 products contain land and sea-surface temperature, and NDVI at a range of spatial resolutions. The third reprocessing was done to implement updated algorithms, processors, and auxiliary files. The data were acquired by the European Space Agency's (ESA) Envisat satellite, and the NERC Earth Observation Data Centre (NEODC) mirrors the data for UK users." } }, { "ob_id": 761, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 38003, "uuid": "0ca27ce794324ec086d6a6c60d5567ac", "short_code": "ob", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 6.22 and Figure 6.24 (v20220824)", "abstract": "Input data for figures 6.22 and 6.24 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 6.22 shows time evolution of the effects of changes in short-lived climate forcers (SLCFs) and hydrofluorocarbons (HFCs) on global surface air temperature (GSAT) across the WGI core set of Shared Socio-economic Pathways (SSPs). \r\n\r\nFigure 6.24 shows effects of changes in short-lived climate forcers (SLCFs) and hydrofluorocarbons (HFCs) on global surface air temperature (GSAT) across the WGI core set of Shared Socio-economic Pathways (SSPs).\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\nSzopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, H. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers. 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. 817–922, doi:10.1017/9781009157896.008.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\nFigure 6.22 has 1 panel, with input data provided for this panel.\r\n\r\nFigure 6.24 has 2 subpanels, with input data provided for both panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Effects of net aerosols, methane, tropospheric ozone and hydrofluorocarbons (HFCs; with lifetimes <50years), and the sum of these, relative to the year 2019 and to the year 1750. \r\n\r\n- The GSAT changes are based on the assessed historic and future evolution of effective radiative forcing (ERF; Section 7.3.5). The temperature responses to the ERFs are calculated with an impulse response function with an equilibrium climate sensitivity of 3.0°C for a doubling of atmospheric CO2 (feedback parameter of –1.31 W m–2 °C–1, see Cross-Chapter Box 7.1). The vertical bars to the right in each panel show the uncertainties (5–95% ranges) for the GSAT change between 2019 and 2100. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 6.SM.3).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figures\r\n ---------------------------------------------------\r\n Data provided in relation to figures 6.22 and 6.24:\r\n \r\n - Data file: AR6_ERF_1750-2019.csv: ERF derived from FAiR\r\n - Data file: AR6_ERF_minorGHGs_1750-2019.csv: ERF derived from FAiR\r\n - Data file: recommended_irf_from_2xCO2_2021_02_25_222758.csv: Impulse response function (IRF) from AR6\r\n\r\nThe folder SSPs (SSP scenario ERF from FAIR) contains the following file formats:\r\n\r\nERF_${scenario}$_${component}$_1750-2500.csv, with:\r\n\r\n- $(scenario): the name of the scenario : ssp119, ssp126, ssp245, ssp334, ssp370, ssp370-low-nTCF-aerchemmip, ssp370-low-nTCF-gidden, ssp434, ssp460, ssp534-over, ssp585\r\n- $(component): blank, or 'minor GHGs'\r\n\r\nThe folder slcf_warming_ranges (uncertainties in dGSAT from FAIR) contains the following file formats:\r\n\r\nslcf_warming_ranges_${scenario)_$(uncertainty).csv, with:\r\n\r\n- ${scenario}: the name of the scenario : ssp119, ssp126, ssp245, ssp334, ssp370, ssp370-lowNTCF-aerchemmip, ssp370-lowNTCF-gidden, ssp434, ssp460, ssp534-over, ssp585\r\n- ${uncertainty}: percentiles of warming: p05, p16, p50, p84, p95\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figures from the provided data\r\n ---------------------------------------------------\r\nPanels were plotted using Python and the code has been embedded in Jupyter notebooks for reproducibility - code is available in the GitHub repository linked in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to Figure 6.22 on the IPCC AR6 website\r\n - Link to Figure 6.24 on the IPCC AR6 website\r\n - Link to the report component containing the figures (Chapter 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3\r\n - Link to the GitHub repository containing the Jupyter notebooks used to run the code associated with these figures.\r\n - Link to the code for the figures, archived on Zenodo." }, "objectObservation": { "ob_id": 37886, "uuid": "288d2cfa740f4e60a369b5778064bd5a", "short_code": "ob", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 6.22 (v20220815)", "abstract": "Data for Figure 6.22 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 6.22 shows time evolution of the effects of changes in short-lived climate forcers (SLCFs) and hydrofluorocarbons (HFCs) on global surface air temperature (GSAT) across the WGI core set of Shared Socio-economic Pathways (SSPs). \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\nSzopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, H. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers. 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. 817–922, doi:10.1017/9781009157896.008.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 1 panel, with data provided for this panel.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Effects of net aerosols, methane, tropospheric ozone and hydrofluorocarbons (HFCs; with lifetimes <50years), and the sum of these, relative to the year 2019 and to the year 1750. \r\n\r\nThe GSAT changes are based on the assessed historic and future evolution of effective radiative forcing (ERF; Section 7.3.5). The temperature responses to the ERFs are calculated with an impulse response function with an equilibrium climate sensitivity of 3.0°C for a doubling of atmospheric CO2 (feedback parameter of –1.31 W m–2 °C–1, see Cross-Chapter Box 7.1). The vertical bars to the right in each panel show the uncertainties (5–95% ranges) for the GSAT change between 2019 and 2100. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 6.SM.3).\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 6.22:\r\n \r\n - Data file: fig_timeseries_dT_p95-p50_HFCs_2019-2100_refyear2019.csv\r\n - Data file: fig_timeseries_dT_p95-p50_Sum_SLCF_Aerosols_Methane_Ozone_HFCs_2019-2100_refyear2019.csv\r\n - Data file: fig_timeseries_dT_p95-p50_aerosol-total-with_bc-snow_2019-2100_refyear2019.csv\r\n - Data file: fig_timeseries_dT_p95-p50_ch4_2019-2100_refyear2019.csv\r\n - Data file: fig_timeseries_dT_p95-p50_o3_2019-2100_refyear2019.csv\r\n - Data file: fig_timeseries_dT_recommendation_HFCs_2019-2100_refyear2019.csv\r\n - Data file: fig_timeseries_dT_recommendation_Sum_SLCF_Aerosols_Methane_Ozone_HFCs_2019-2100_refyear2019.csv\r\n - Data file: fig_timeseries_dT_recommendation_aerosol-total-with_bc-snow_2019-2100_refyear2019.csv\r\n - Data file: fig_timeseries_dT_recommendation_ch4_2019-2100_refyear2019.csv\r\n - Data file: fig_timeseries_dT_recommendation_o3_2019-2100_refyear2019.csv\r\n\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nPanels were plotted using Python and the code has been embedded in Jupyter notebooks for reproducibility - code is available in the GitHub repository linked in the documentation.\r\n\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3\r\n- Link to the GitHub repository containing the Jupyter notebooks used to run the code associated with this figure.\r\n- Link to the code for the figure, archived on Zenodo." } }, { "ob_id": 763, "relationType": "IsVariantFormOf", "subjectObservation": { "ob_id": 20361, "uuid": "3df7562727314bab963282e6a0284f24", "short_code": "ob", "title": "CRU TS3.24.01: Climatic Research Unit (CRU) Time-Series (TS) Version 3.24.01 of High Resolution Gridded Data of Month-by-month Variation in Climate (Jan. 1901- Dec. 2015)", "abstract": "The gridded CRU TS (time-series) 3.24.01 data are month-by-month variations in climate over the period 1901-2015, on high-resolution (0.5x0.5 degree) grids, produced by the Climatic Research Unit (CRU) at the University of East Anglia.\r\n\r\nCRU TS 3.24.01 variables are cloud cover, diurnal temperature range, frost day frequency, PET, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, and vapour pressure for the period Jan. 1901 - Dec. 2015.\r\n\r\nCRU TS 3.24.01 data were produced using the same methodology as for the 3.21 datasets. In addition to updating the dataset with 2015 data, some new stations have been added for TMP and PRE only. This release should be used in place of v3.24 which has been withdrawn. Known issues predating this release remain.\r\n\r\nThe CRU TS 3.24.01 data are monthly gridded fields based on monthly observational data, which are 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.\r\n\r\nAll CRU TS output files are actual values - NOT anomalies." }, "objectObservation": { "ob_id": 20046, "uuid": "492c792f417c452db1a5946b9c3bc5fe", "short_code": "ob", "title": "CRU TS3.24: Climatic Research Unit (CRU) Time-Series (TS) Version 3.24 of High Resolution Gridded Data of Month-by-month Variation in Climate (Jan. 1901- Dec. 2015)", "abstract": "The gridded CRU TS (time-series) 3.24 data are month-by-month variations in climate over the period 1901-2015, on high-resolution (0.5x0.5 degree) grids, produced by the Climatic Research Unit (CRU) at the University of East Anglia.\r\n\r\nCRU TS 3.24 variables are cloud cover, diurnal temperature range, frost day frequency, PET, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, and vapour pressure for the period Jan. 1901 - Dec. 2015.\r\n\r\nCRU TS 3.24 data were produced using the same methodology as for the 3.21 datasets. In addition to updating the dataset with 2015 data, some new stations have been added for TMP and PRE only. Known issues predating this release remain.\r\n\r\nThe CRU TS 3.24 data are monthly gridded fields based on monthly observational data, which are 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.\r\n\r\nAll CRU TS output files are actual values - NOT anomalies.\r\n\r\nNote, these data were found to be in error and a new version, v3.24.01, should be used instead." } }, { "ob_id": 764, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 38003, "uuid": "0ca27ce794324ec086d6a6c60d5567ac", "short_code": "ob", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 6.22 and Figure 6.24 (v20220824)", "abstract": "Input data for figures 6.22 and 6.24 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 6.22 shows time evolution of the effects of changes in short-lived climate forcers (SLCFs) and hydrofluorocarbons (HFCs) on global surface air temperature (GSAT) across the WGI core set of Shared Socio-economic Pathways (SSPs). \r\n\r\nFigure 6.24 shows effects of changes in short-lived climate forcers (SLCFs) and hydrofluorocarbons (HFCs) on global surface air temperature (GSAT) across the WGI core set of Shared Socio-economic Pathways (SSPs).\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\nSzopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, H. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers. 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. 817–922, doi:10.1017/9781009157896.008.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\nFigure 6.22 has 1 panel, with input data provided for this panel.\r\n\r\nFigure 6.24 has 2 subpanels, with input data provided for both panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Effects of net aerosols, methane, tropospheric ozone and hydrofluorocarbons (HFCs; with lifetimes <50years), and the sum of these, relative to the year 2019 and to the year 1750. \r\n\r\n- The GSAT changes are based on the assessed historic and future evolution of effective radiative forcing (ERF; Section 7.3.5). The temperature responses to the ERFs are calculated with an impulse response function with an equilibrium climate sensitivity of 3.0°C for a doubling of atmospheric CO2 (feedback parameter of –1.31 W m–2 °C–1, see Cross-Chapter Box 7.1). The vertical bars to the right in each panel show the uncertainties (5–95% ranges) for the GSAT change between 2019 and 2100. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 6.SM.3).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figures\r\n ---------------------------------------------------\r\n Data provided in relation to figures 6.22 and 6.24:\r\n \r\n - Data file: AR6_ERF_1750-2019.csv: ERF derived from FAiR\r\n - Data file: AR6_ERF_minorGHGs_1750-2019.csv: ERF derived from FAiR\r\n - Data file: recommended_irf_from_2xCO2_2021_02_25_222758.csv: Impulse response function (IRF) from AR6\r\n\r\nThe folder SSPs (SSP scenario ERF from FAIR) contains the following file formats:\r\n\r\nERF_${scenario}$_${component}$_1750-2500.csv, with:\r\n\r\n- $(scenario): the name of the scenario : ssp119, ssp126, ssp245, ssp334, ssp370, ssp370-low-nTCF-aerchemmip, ssp370-low-nTCF-gidden, ssp434, ssp460, ssp534-over, ssp585\r\n- $(component): blank, or 'minor GHGs'\r\n\r\nThe folder slcf_warming_ranges (uncertainties in dGSAT from FAIR) contains the following file formats:\r\n\r\nslcf_warming_ranges_${scenario)_$(uncertainty).csv, with:\r\n\r\n- ${scenario}: the name of the scenario : ssp119, ssp126, ssp245, ssp334, ssp370, ssp370-lowNTCF-aerchemmip, ssp370-lowNTCF-gidden, ssp434, ssp460, ssp534-over, ssp585\r\n- ${uncertainty}: percentiles of warming: p05, p16, p50, p84, p95\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figures from the provided data\r\n ---------------------------------------------------\r\nPanels were plotted using Python and the code has been embedded in Jupyter notebooks for reproducibility - code is available in the GitHub repository linked in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to Figure 6.22 on the IPCC AR6 website\r\n - Link to Figure 6.24 on the IPCC AR6 website\r\n - Link to the report component containing the figures (Chapter 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3\r\n - Link to the GitHub repository containing the Jupyter notebooks used to run the code associated with these figures.\r\n - Link to the code for the figures, archived on Zenodo." }, "objectObservation": { "ob_id": 37888, "uuid": "ca4127fb1be14ea68fefd5643fe3677f", "short_code": "ob", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 6.24 (v20220815)", "abstract": "Data for Figure 6.24 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 6.24 shows effects of changes in short-lived climate forcers (SLCFs) and hydrofluorocarbons (HFCs) on global surface air temperature (GSAT) across the WGI core set of Shared Socio-economic Pathways (SSPs). \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\nSzopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, H. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers. 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. 817–922, doi:10.1017/9781009157896.008.\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 2 subpanels, with data provided for both panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Effects of net aerosols, methane, tropospheric ozone and hydrofluorocarbons (HFCs; with lifetimes <50years) compared with those of total anthropogenic forcing for 2040 and 2100 relative to the year 2019. \r\n\r\nThe GSAT changes are based on the assessed historic and future evolution of effective radiative forcing (ERF; Section 7.3.5). The temperature responses to the ERFs are calculated with an impulse response function with an equilibrium climate sensitivity of 3.0°C for a doubling of atmospheric CO2 (feedback parameter of –1.31 W m–2 °C–1; Cross-Chapter Box 7.1). Uncertainties are 5–95% ranges. The scenario total (grey bar) includes all anthropogenic forcings (long- and short-lived climate forcers, and land-use changes) whereas the white diamonds and bars show the net effects of SLCFs and HFCs and their uncertainties. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 6.SM.3)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 6.24:\r\n \r\n - Data file: fig_dT_2040_2100_stacked_bar_5th-50th__2020-2040_refyear2020.csv\r\n - Data file: fig_dT_2040_2100_stacked_bar_5th-50th__2020-2100_refyear2020.csv\r\n - Data file: fig_dT_2040_2100_stacked_bar_95th-50th__2020-2040_refyear2020.csv\r\n - Data file: fig_dT_2040_2100_stacked_bar_95th-50th__2020-2100_refyear2020.csv\r\n - Data file: fig_dT_2040_2100_stacked_bar_mean__2019-2040_refyear2019.csv\r\n - Data file: fig_dT_2040_2100_stacked_bar_mean__2019-2100_refyear2019.csv\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nPanels were plotted using Python and the code has been embedded in Jupyter notebooks for reproducibility - code is available in the GitHub repository linked in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3\r\n- Link to the GitHub repository containing the Jupyter notebooks used to run the code associated with this figure.\r\n- Link to the code for the figure, archived on Zenodo." } }, { "ob_id": 765, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 39513, "uuid": "fe6074fee8a64a738cf89f0294bd9fb9", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Box TS.7, Figure 1 (v20221216)", "abstract": "Data for Box TS.7, Figure 1 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nBox TS.7 figure 1 shows the effects of short-lived climate forcers (SLCFs) on global surface temperature and air pollution across the WGI core set of Shared Socio-economic Pathways (SSPs).\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 Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. 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. 33−144, doi:10.1017/9781009157896.002.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has three panels, data provided for the right part of panels b and c in one single file. \r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains global changes in air pollutant concentrations (ozone and PM2.5), based on multimodel CMIP6 simulations:\r\n \r\n - changes in 5-year mean surface continental concentrations for 2040, relative to 2019.\r\n - changes in 5-year mean surface continental concentrations for 2098, relative to 2019.\r\n\r\n Data are for the SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP3-7.0 lowSLCF highCH4, SSP3-7.0 lowSLCF lowCH4 and SSP5-8.5 scenarios.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data file Box_TS7_figure1.csv relates to the right part of panels b and c.\r\n \r\n - Column 5 is used for the green bars\r\n - Columns 4 and 6 are used for the standard deviation plotted over the green bars.\r\n - Column 8 is used for the purple bars.\r\n - Columns 7 and 9 are used for the standard deviation plotted over the purple bars.\r\n\r\nPM2.5 refers to particulate matter that are 2.5 micrometers or less in diameter.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nSSP1-2.6 is based on Shared Socioeconomic Pathway SSP1 with low climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 2.6 W/m2 in the year 2100.\r\nSSP2-4.5 is based on Shared Socioeconomic Pathway SSP2 with medium challenges to climate change mitigation and adaptation and RCP4.5, a future pathway with a radiative forcing of 4.5 W/m2 in the year 2100.\r\nSSP5-8.5 is based on Shares Socioeconomic Pathway SSP5 where climate change mitigation challenges dominate and RCP8.5, a future pathway with a radiative forcing of 8.5 W/m2 in the year 2100.\r\nSLCF stands for short-lived climate forcers.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n - Data Sources for effect on GSAT are the same as in Figure 6.24 (see Chapter 6 Supplementary Material Table 6.SM.3). \r\n\r\n- Data Sources for effect on surface PM2.5 and O3 are the same as in Figures 6.20 and 6.21 (see Chapter 6 Supplementary Material Table 6.SM.3)\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n - Links to the report components of the underlying chapter figures from which part of this figure was generated (Chapter 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3" }, "objectObservation": { "ob_id": 38241, "uuid": "022d449b91eb453eb56228c17fdce725", "short_code": "ob", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 6.20 (v20220928)", "abstract": "Data for Figure 6.20 from Chapter 6 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 6.20 shows projected changes in regional annual mean surface ozone (O3; ppb) from 2015 to 2100 in different shared socio-economic pathways (SSPs)\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 Szopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, H. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers. 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. 817–922, doi:10.1017/9781009157896.008.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figures 11 panels, with data provided for all panels in three files in the main directory.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains precomputed values of surface ozone concentrations across world regions for:\r\n - A 10-year mean period (2005 to 2014) from the historical simulation to represent present day regional mean values. Regional multi-model annual mean and standard deviation values are calculated across 5 different CMIP6 models\r\n - Annual multi-model mean values of surface ozone from 5 different CMIP6 models projected for 7 different future scenarios covering the period 2015 to 2100\r\n - Standard deviation values of surface ozone from 5 different CMIP6 models projected for 7 different future scenarios covering the period 2015 to 2100\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n All the data files provided are used to create the time series plots for each region. The numbers in each panel for each region are obtained from 'Surf_O3_data_05_14_mean_for_IPCC_figure_V1_5mods.csv', with the time series line for each scenario from 'Surf_O3_data_fut_mean_for_IPCC_figure_V1_5mods.csv' and the shading obtained by using the values in 'Surf_O3_SD_data_fut_mean_for_IPCC_figure_V1_5mods.csv'.\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nSSP stands for Shared Socioeconomic Pathway.\r\nppb stands for parts per billion.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The plotting code that is provided along with this dataset should just be able to read in each of the precomputed regional mean .csv files and then reproduce the time series figures. A link to the code archived on Zenodo is provided in the Related Documents section of this catalogue record.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3\r\n - Link to the code for the figure, archived on Zenodo." } }, { "ob_id": 766, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 39513, "uuid": "fe6074fee8a64a738cf89f0294bd9fb9", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Box TS.7, Figure 1 (v20221216)", "abstract": "Data for Box TS.7, Figure 1 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nBox TS.7 figure 1 shows the effects of short-lived climate forcers (SLCFs) on global surface temperature and air pollution across the WGI core set of Shared Socio-economic Pathways (SSPs).\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 Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. 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. 33−144, doi:10.1017/9781009157896.002.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has three panels, data provided for the right part of panels b and c in one single file. \r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains global changes in air pollutant concentrations (ozone and PM2.5), based on multimodel CMIP6 simulations:\r\n \r\n - changes in 5-year mean surface continental concentrations for 2040, relative to 2019.\r\n - changes in 5-year mean surface continental concentrations for 2098, relative to 2019.\r\n\r\n Data are for the SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP3-7.0 lowSLCF highCH4, SSP3-7.0 lowSLCF lowCH4 and SSP5-8.5 scenarios.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data file Box_TS7_figure1.csv relates to the right part of panels b and c.\r\n \r\n - Column 5 is used for the green bars\r\n - Columns 4 and 6 are used for the standard deviation plotted over the green bars.\r\n - Column 8 is used for the purple bars.\r\n - Columns 7 and 9 are used for the standard deviation plotted over the purple bars.\r\n\r\nPM2.5 refers to particulate matter that are 2.5 micrometers or less in diameter.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nSSP1-2.6 is based on Shared Socioeconomic Pathway SSP1 with low climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 2.6 W/m2 in the year 2100.\r\nSSP2-4.5 is based on Shared Socioeconomic Pathway SSP2 with medium challenges to climate change mitigation and adaptation and RCP4.5, a future pathway with a radiative forcing of 4.5 W/m2 in the year 2100.\r\nSSP5-8.5 is based on Shares Socioeconomic Pathway SSP5 where climate change mitigation challenges dominate and RCP8.5, a future pathway with a radiative forcing of 8.5 W/m2 in the year 2100.\r\nSLCF stands for short-lived climate forcers.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n - Data Sources for effect on GSAT are the same as in Figure 6.24 (see Chapter 6 Supplementary Material Table 6.SM.3). \r\n\r\n- Data Sources for effect on surface PM2.5 and O3 are the same as in Figures 6.20 and 6.21 (see Chapter 6 Supplementary Material Table 6.SM.3)\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n - Links to the report components of the underlying chapter figures from which part of this figure was generated (Chapter 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3" }, "objectObservation": { "ob_id": 38244, "uuid": "572c9744ddab47fc8a5b938c4a4f7387", "short_code": "ob", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 6.21 (v20220928)", "abstract": "Data for Figure 6.21 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 6.21 shows future changes in regional five-year mean surface PM2.5 from 2015 to 2100 in different shared socio-economic pathways (SSPs).\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 Szopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, H. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers. 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. 817–922, doi:10.1017/9781009157896.008.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 11 panels with data provided for all panels in three files placed in the main directory.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains precomputed values of surface PM2.5 concentrations across world regions for:\r\n \r\n - A 10-year mean period (2005 to 2014) from the historical simulation to represent present day regional mean values. Regional multi-model annual mean and standard deviation values are calculated across 5 different CMIP6 models\r\n - Annual 5-year multi-model mean values of surface PM2.5 from 5 different CMIP6 models projected for 7 different future scenarios covering the period 2015 to 2100\r\n - Standard deviation values of surface PM2.5 from 5 different CMIP6 models projected for 7 different future scenarios covering 5-year mean periods from 2015 to 2100\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nPM2.5 refers to fine particulate matter air pollution with diameter of less than 2.5 microns.\r\nSSP stands for Shared Socioeconomic Pathway.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n All the data files provided are used to create the time series plots for each region. The numbers in each panel for each region are obtained from 'Surf_PM2pt5_data_05_14_mean_for_IPCC_figure_V1_5mods.csv', with the time series line for each scenario from 'Surf_PM2pt5_data_fut_mean_for_IPCC_figure_V1_5mods.csv' and the shading obtained by using the values in 'Surf_PM2pt5_SD_data_fut_mean_for_IPCC_figure_V1_5mods.csv'.\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The plotting code that is provided along with this dataset should just be able to read in each of the precomputed regional mean .csv files and then reproduce the time series figures.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3\r\n - Link to the code for the figure, archived on Zenodo." } }, { "ob_id": 767, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 39513, "uuid": "fe6074fee8a64a738cf89f0294bd9fb9", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Box TS.7, Figure 1 (v20221216)", "abstract": "Data for Box TS.7, Figure 1 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nBox TS.7 figure 1 shows the effects of short-lived climate forcers (SLCFs) on global surface temperature and air pollution across the WGI core set of Shared Socio-economic Pathways (SSPs).\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 Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. 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. 33−144, doi:10.1017/9781009157896.002.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has three panels, data provided for the right part of panels b and c in one single file. \r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains global changes in air pollutant concentrations (ozone and PM2.5), based on multimodel CMIP6 simulations:\r\n \r\n - changes in 5-year mean surface continental concentrations for 2040, relative to 2019.\r\n - changes in 5-year mean surface continental concentrations for 2098, relative to 2019.\r\n\r\n Data are for the SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP3-7.0 lowSLCF highCH4, SSP3-7.0 lowSLCF lowCH4 and SSP5-8.5 scenarios.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data file Box_TS7_figure1.csv relates to the right part of panels b and c.\r\n \r\n - Column 5 is used for the green bars\r\n - Columns 4 and 6 are used for the standard deviation plotted over the green bars.\r\n - Column 8 is used for the purple bars.\r\n - Columns 7 and 9 are used for the standard deviation plotted over the purple bars.\r\n\r\nPM2.5 refers to particulate matter that are 2.5 micrometers or less in diameter.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nSSP1-2.6 is based on Shared Socioeconomic Pathway SSP1 with low climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 2.6 W/m2 in the year 2100.\r\nSSP2-4.5 is based on Shared Socioeconomic Pathway SSP2 with medium challenges to climate change mitigation and adaptation and RCP4.5, a future pathway with a radiative forcing of 4.5 W/m2 in the year 2100.\r\nSSP5-8.5 is based on Shares Socioeconomic Pathway SSP5 where climate change mitigation challenges dominate and RCP8.5, a future pathway with a radiative forcing of 8.5 W/m2 in the year 2100.\r\nSLCF stands for short-lived climate forcers.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n - Data Sources for effect on GSAT are the same as in Figure 6.24 (see Chapter 6 Supplementary Material Table 6.SM.3). \r\n\r\n- Data Sources for effect on surface PM2.5 and O3 are the same as in Figures 6.20 and 6.21 (see Chapter 6 Supplementary Material Table 6.SM.3)\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n - Links to the report components of the underlying chapter figures from which part of this figure was generated (Chapter 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3" }, "objectObservation": { "ob_id": 37888, "uuid": "ca4127fb1be14ea68fefd5643fe3677f", "short_code": "ob", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 6.24 (v20220815)", "abstract": "Data for Figure 6.24 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 6.24 shows effects of changes in short-lived climate forcers (SLCFs) and hydrofluorocarbons (HFCs) on global surface air temperature (GSAT) across the WGI core set of Shared Socio-economic Pathways (SSPs). \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\nSzopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, H. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers. 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. 817–922, doi:10.1017/9781009157896.008.\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 2 subpanels, with data provided for both panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Effects of net aerosols, methane, tropospheric ozone and hydrofluorocarbons (HFCs; with lifetimes <50years) compared with those of total anthropogenic forcing for 2040 and 2100 relative to the year 2019. \r\n\r\nThe GSAT changes are based on the assessed historic and future evolution of effective radiative forcing (ERF; Section 7.3.5). The temperature responses to the ERFs are calculated with an impulse response function with an equilibrium climate sensitivity of 3.0°C for a doubling of atmospheric CO2 (feedback parameter of –1.31 W m–2 °C–1; Cross-Chapter Box 7.1). Uncertainties are 5–95% ranges. The scenario total (grey bar) includes all anthropogenic forcings (long- and short-lived climate forcers, and land-use changes) whereas the white diamonds and bars show the net effects of SLCFs and HFCs and their uncertainties. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 6.SM.3)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 6.24:\r\n \r\n - Data file: fig_dT_2040_2100_stacked_bar_5th-50th__2020-2040_refyear2020.csv\r\n - Data file: fig_dT_2040_2100_stacked_bar_5th-50th__2020-2100_refyear2020.csv\r\n - Data file: fig_dT_2040_2100_stacked_bar_95th-50th__2020-2040_refyear2020.csv\r\n - Data file: fig_dT_2040_2100_stacked_bar_95th-50th__2020-2100_refyear2020.csv\r\n - Data file: fig_dT_2040_2100_stacked_bar_mean__2019-2040_refyear2019.csv\r\n - Data file: fig_dT_2040_2100_stacked_bar_mean__2019-2100_refyear2019.csv\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nPanels were plotted using Python and the code has been embedded in Jupyter notebooks for reproducibility - code is available in the GitHub repository linked in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3\r\n- Link to the GitHub repository containing the Jupyter notebooks used to run the code associated with this figure.\r\n- Link to the code for the figure, archived on Zenodo." } }, { "ob_id": 768, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 38227, "uuid": "f99ec964a6f345beadb000e295ac2e5b", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure TS.9 v20220922", "abstract": "Data for Figure TS.9 from the Technical Summary 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 TS.9 shows changes in well-mixed greenhouse gas (WMGHG) concentrations and effective radiative forcing (EFR).\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 Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. 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. 33−144, doi:10.1017/9781009157896.002.\r\n\r\nPlease also include citations of the related publications for Figure 2.4b provided at the end of this abstract.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with data provided for panels b and c. Links to the code which contains the data for other panels are provided in the Related Documents section of this catalogue record.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n - Panel TS.9a => Figure 2.3 c\r\n - Panel TS.9b => Figure 2.4 b\r\n - Panel TS.9c => Figure 2.5 a,b,c\r\n - Panel TS.9d => Figure 2.10\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - Panel TS.9b => Figure 2.4 b\r\n - Panel TS.9c => Figure 2.5 a,b,c\r\n\r\n\r\n---------------------------------------------------\r\nTemporal Range of Paleoclimate Data\r\n---------------------------------------------------\r\nThis dataset covers a paleoclimate timespan from 450 Ma to 2020. \r\nMa refers to millions of years before present.\r\n\r\n\r\n---------------------------------------------------\r\nNotes on reproducing the figure from the provided data\r\n---------------------------------------------------\r\nData for figures 2.3 c and 2.10 are contained within the code to generate the figures which is linked in the Related Documents section of this catalogue record and data for Figure 2.5 and 2.4 panel b are provided. The corresponding catalogue records for Figure 2.5 and 2.4 are linked in the Related Records section below.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n - Link to the report component of the underlying chapter figures from which this figure was generated (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n- Links to catalogue records of relevant figures the data is taken from in the Related Records section of this catalogue record\r\n- Link to code which contains data for figure 2.3 and 2.10\r\n\r\n\r\n---------------------------------------------------\r\nRelated publications for figure 2.4 panel b datasets\r\n---------------------------------------------------\r\nPlease include the following citations of related publications from which the figure 2.4 panel b datasets originate. Relations to individual datasets are listed at the top of each dataset. Links are provided in the Related Documents section of the figure 2.4 catalogue record which is linked to this record.\r\n\r\nAhn, J., Brook, E. J., Mitchell, L., Rosen, J. McConnell, J. R., Taylor, K., Etheridge, D., and Rubino, M. (2012b). Atmospheric CO2 over the last 1000 years: A high-resolution record from the West Antarctic Ice Sheet (WAIS) Divide ice core, Global Biogeochemical Cycles, 26, GB2027 , doi:10.1029/2011GB004247.\r\n\r\nBauska, T. K., Joos, F., Mix, A. C., Roth, R., Ahn, J., & Brook, E. J. (2015). Links between atmospheric carbon dioxide, the land carbon reservoir and climate over the past millennium. Nature Geoscience. https://doi.org/10.1038/ngeo2422\r\n\r\nRubino, M., Etheridge, D. M., Thornton, D. P., Howden, R., Allison, C. E., Francey, R. J., Langenfelds, R. L., Steele, L. P., Trudinger, C. M., Spencer, D. A., Curran, M. A. J., van Ommen, T. D., & Smith, A. M. (2019). Revised records of atmospheric trace gases CO2, CH4, N2O, and d13C-CO2 over the last 2000 years from Law Dome, Antarctica. Earth System Science Data, 11(2), 473–492. https://doi.org/10.5194/essd-11-473-2019\r\n\r\nSIEGENTHALER, U. R. S., MONNIN, E., KAWAMURA, K., SPAHNI, R., SCHWANDER, J., STAUFFER, B., STOCKER, T. F., BARNOLA, J.-M., & FISCHER, H. (2005). Supporting evidence from the EPICA Dronning Maud Land ice core for atmospheric CO2 changes during the past millennium. Tellus B, 57(1), 51–57. https://doi.org/10.1111/j.1600-0889.2005.00131.x\r\n\r\nMitchell, L., Brook, E., Lee, J. E., Buizert, C., & Sowers, T. (2013). Constraints on the late Holocene anthropogenic contribution to the atmospheric methane budget. Science. https://doi.org/10.1126/science.1238920\r\n\r\nFlückiger, J., Dällenbach, A., Blunier, T., Stauffer, B., Stocker, T. F., Raynaud, D., & Barnola, J. M. (1999). Variations in atmospheric N2O concentration during abrupt climatic changes. Science. https://doi.org/10.1126/science.285.5425.227\r\n\r\nMachida, T., Nakazawa, T., Fujii, Y., Aoki, S., & Watanabe, O. (1995). Increase in the atmospheric nitrous oxide concentration during the last 250 years. Geophysical Research Letters, 22(21), 2921–2924. https://doi.org/10.1029/95GL02822\r\n\r\nRyu, Y., Ahn, J., Yang, J.-W., Jang, Y., Brook, E., Timmermann, A., Hong, S., Han, Y., Hur, S., & Kim, S. (2020). Atmospheric nitrous oxide during the past two millennia, Global Biogeochemical Cycles, 34, e2020GB006568. https://doi.org/10.1029/2020GB006568\r\n\r\nSowers, T. (2001). N2O record spanning the penultimate deglaciation from the Vostok ice core. Journal of Geophysical Research: Atmospheres, 106(D23), 31903–31914. https://doi.org/10.1029/2000JD900707" }, "objectObservation": { "ob_id": 39521, "uuid": "60eeb3cce51a457cb5ee1c577a0c8674", "short_code": "ob", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.4 (v20221219)", "abstract": "Data for Figure 2.4 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 2.4 shows Atmospheric well-mixed greenhouse gases concentration from ice cores. \r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\n Gulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson- Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\nPlease also include citations of the related publications provided at the end of this abstract.\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 the main directory.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n \r\n - Atmospheric WMGHG concentration records during the last 800 kyr with the LGM to Holocene transition as inset.\r\n - Multiple high-resolution records over the CE\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nSubdirectory 'fig2_4_data_Feb24_2021' contains figure data with listed publications from which the datasets originate. Datasets are also provided in BADC-CSV formats as described below:\r\n\r\n Panel a: \r\n - Data file: fig2_4a_main figure_data.csv; column 2: red line (main figure); column 4: green line (main figure); column six: purple line (main figure).\r\n - Data file: fig2_4a_inset_data.csv: column 3: red line (inset); column 6: orange line (inset)\r\n\r\n Panel b:\r\n - Data file: fig2_4b_data_v2.csv: column 2: pink dot (top panel); column 5: brown dot (top panel); column 8: orange dot (top panel); column 11: red line (top panel); column 13: sky blue line (middle panel); column 15: green line (middle panel); column 18: purple square (bottom panel); column 21: blue circle N2O (bottom panel); column 24: brown dot (bottom panel); column 26: green circle (bottom panel); column 29: red circle (bottom panel); column 32: orange square (bottom panel); column 35: blue line (bottom panel)\r\n\r\nWMGHG stands for well-mixed green-house gases.\r\nLGM stands for Last Glacial Maximum.\r\n\r\n\r\n---------------------------------------------------\r\nTemporal range of data\r\n---------------------------------------------------\r\nThis dataset covers a timespan from 800kyr ago to 2000 CE.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure from the IPCC AR6 website \r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Links to related publications listed below\r\n\r\n\r\n---------------------------------------------------\r\nRelated publications for figure datasets\r\n---------------------------------------------------\r\nPlease include the following citations of related publications from which the figure datasets originate. Relations to individual datasets are listed at the top of each dataset. Links are provided in the Related Documents section of this catalogue record. \r\n\r\nBereiter, B., Eggleston, S., Schmitt, J., Nehrbass-Ahles, C., Stocker, T. F., Fischer, H., Kipfstuhl, S., & Chappellaz, J. (2015). Revision of the EPICA Dome C CO2 record from 800 to 600-kyr before present. Geophysical Research Letters. https://doi.org/10.1002/2014GL061957\r\n\r\nLoulergue, L. et al. Orbital and millennial-scale features of atmospheric CH4 over the past 800,000 years. Nature 453, 383–386 (2008)\r\n\r\nSchilt, A., Baumgartner, M., Blunier, T., Schwander, J., Spahni, R., Fischer, H., and Stocker, T. F.: Glacial-interglacial and millennialscale variations in the atmospheric nitrous oxide concentration uring the last 800 000 years, Quaternary Science Reviews, 29, 182–192, doi:10.1016/j.quascirev.2009.03.011 (2010)\r\n\r\nKöhler, P., Nehrbass-Ahles, C., Schmitt, J., Stocker, T. F., & Fischer, H. (2017). A 156 kyr smoothed history of the atmospheric greenhouse gases CO2, CH4, and N2O and their radiative forcing. Earth System Science Data. https://doi.org/10.5194/essd-9-363-2017\r\n\r\nLüthi, D., M. Le Floch, B. Bereiter, T. Blunier, J.-M. Barnola, U. Siegenthaler, D. Raynaud, J. Jouzel, H. Fischer, K. Kawamura, and T.F. Stocker. 2008. High-resolution carbon dioxide concentration record 650,000-800,000 years before present. Nature, Vol. 453, pp. 379-382, 15 May 2008.\r\n\r\nMarcott, S. A., Bauska, T. K., Buizert, C., Steig, E. J., Rosen, J. L., Cuffey, K. M., Fudge, T. J., Severinghaus, J. P., Ahn, J., Kalk, M. L., McConnell, J. R., Sowers, T., Taylor, K. C., White, J. W. C., & Brook, E. J. (2014). Centennial-scale changes in the global carbon cycle during the last deglaciation. Nature, 514(7524), 616–619.\r\n\r\nBereiter, B., Eggleston, S., Schmitt, J., Nehrbass-Ahles, C., Stocker, T. F., Fischer, H., Kipfstuhl, S., & Chappellaz, J. (2015). Revision of the EPICA Dome C CO2 record from 800 to 600-kyr before present. Geophysical Research Letters. https://doi.org/10.1002/2014GL061957\r\n\r\nMonnin E, Indermühle A, Dällenbach A, Flückiger J, Stauffer B, Stocker TF, Raynaud D, Barnola JM. Atmospheric CO2 concentrations over the last glacial termination. Science. 2001 Jan 5;291(5501):112-4. doi: 10.1126/science.291.5501.112. PMID: 11141559.\r\n\r\nAhn, J., Brook, E. J., Mitchell, L., Rosen, J. McConnell, J. R., Taylor, K., Etheridge, D., and Rubino, M. (2012b). Atmospheric CO2 over the last 1000 years: A high-resolution record from the West Antarctic Ice Sheet (WAIS) Divide ice core, Global Biogeochemical Cycles, 26, GB2027 , doi:10.1029/2011GB004247.\r\n\r\nBauska, T. K., Joos, F., Mix, A. C., Roth, R., Ahn, J., & Brook, E. J. (2015). Links between atmospheric carbon dioxide, the land carbon reservoir and climate over the past millennium. Nature Geoscience. https://doi.org/10.1038/ngeo2422\r\n\r\nRubino, M., Etheridge, D. M., Thornton, D. P., Howden, R., Allison, C. E., Francey, R. J., Langenfelds, R. L., Steele, L. P., Trudinger, C. M., Spencer, D. A., Curran, M. A. J., van Ommen, T. D., & Smith, A. M. (2019). Revised records of atmospheric trace gases CO2, CH4, N2O, and d13C-CO2 over the last 2000 years from Law Dome, Antarctica. Earth System Science Data, 11(2), 473–492. https://doi.org/10.5194/essd-11-473-2019\r\n\r\nSIEGENTHALER, U. R. S., MONNIN, E., KAWAMURA, K., SPAHNI, R., SCHWANDER, J., STAUFFER, B., STOCKER, T. F., BARNOLA, J.-M., & FISCHER, H. (2005). Supporting evidence from the EPICA Dronning Maud Land ice core for atmospheric CO2 changes during the past millennium. Tellus B, 57(1), 51–57. https://doi.org/10.1111/j.1600-0889.2005.00131.x\r\n\r\nMitchell, L., Brook, E., Lee, J. E., Buizert, C., & Sowers, T. (2013). Constraints on the late Holocene anthropogenic contribution to the atmospheric methane budget. Science. https://doi.org/10.1126/science.1238920\r\n\r\nFlückiger, J., Dällenbach, A., Blunier, T., Stauffer, B., Stocker, T. F., Raynaud, D., & Barnola, J. M. (1999). Variations in atmospheric N2O concentration during abrupt climatic changes. Science. https://doi.org/10.1126/science.285.5425.227\r\n\r\nMachida, T., Nakazawa, T., Fujii, Y., Aoki, S., & Watanabe, O. (1995). Increase in the atmospheric nitrous oxide concentration during the last 250 years. Geophysical Research Letters, 22(21), 2921–2924. https://doi.org/10.1029/95GL02822\r\n\r\nRyu, Y., Ahn, J., Yang, J.-W., Jang, Y., Brook, E., Timmermann, A., Hong, S., Han, Y., Hur, S., & Kim, S. (2020). Atmospheric nitrous oxide during the past two millennia, Global Biogeochemical Cycles, 34, e2020GB006568. https://doi.org/10.1029/2020GB006568\r\n\r\nSowers, T. (2001). N2O record spanning the penultimate deglaciation from the Vostok ice core. Journal of Geophysical Research: Atmospheres, 106(D23), 31903–31914. https://doi.org/10.1029/2000JD900707" } }, { "ob_id": 769, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 39532, "uuid": "a7e811fe11d34df5abac6f18c920bbeb", "short_code": "ob", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): All-weather MicroWave Land Surface Temperature (MW-LST) global data record (1996-2020), v2.33", "abstract": "MW-LST is a data record of land surface temperature (LST) derived from the microwave instruments Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager / Sounder (SSMIS). Observations available at frequencies close to 18, 22, 26, and 85 GHz are used as an input to a retrieval algorithm that produces LST over all continental surfaces, twice per day (6 am/pm), at a spatial resolution of ~25 km, and over 25 years (1996-2020). \r\n\r\nThe data record has been produced by the company Estellus working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci). Compared with the remaining infrared LST data records of the LST_cci, the spatial resolution of the MW-LST is coarser, and the associated retrieval errors are larger. However, it offers LST estimates for clear-sky and cloudy conditions, therefore complementing the IR LST data records, which can only provide LST for clear skies. The data record is temporally and spatially complete, although in rare occasions some data can be missing due to missing observations, e.g., due to satellite maintenance operations or anomalous behavior. The data record is provided on a regular grid of 0.25x0.25 degrees, saved as daily, monthly, and yearly netcdf files. The reader is referred to the LST_cci website for more information about how the data record was derived, and how to use the data and associated quality flags and estimated uncertainty.\r\n\r\nThis version of the data is v2.33. It fixes an issue that was found with the variable 'lst_unc_time_correction' in the previous v2.23, but is otherwise identical." }, "objectObservation": { "ob_id": 33362, "uuid": "058d3ad17a084f0bb8dc9b5dc5efdb7f", "short_code": "ob", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): All-weather MicroWave Land Surface Temperature (MW-LST) global data record (1996-2020)", "abstract": "MW-LST is a data record of land surface temperature (LST) derived from the microwave instruments Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager / Sounder (SSMIS). Observations available at frequencies close to 18, 22, 26, and 85 GHz are used as an input to a retrieval algorithm that produces LST over all continental surfaces, twice per day (6 am/pm), at a spatial resolution of ~25 km, and over 25 years (1996-2020). \r\n\r\nThe data record has been produced by the company Estellus working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci). Compared with the remaining infrared LST data records of the LST_cci, the spatial resolution of the MW-LST is coarser, and the associated retrieval errors are larger. However, it offers LST estimates for clear-sky and cloudy conditions, therefore complementing the IR LST data records, which can only provide LST for clear skies. The data record is temporally and spatially complete, although in rare occasions some data can be missing due to missing observations, e.g., due to satellite maintenance operations or anomalous behavior. The data record is provided on a regular grid of 0.25x0.25 degrees, saved as daily, monthly, and yearly netcdf files. The reader is referred to the LST_cci website for more information about how the data record was derived, and how to use the data and associated quality flags and estimated uncertainty." } }, { "ob_id": 770, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 39557, "uuid": "60c28523d8c54c58831b2608164cf35e", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1931-2022, v3.3.0.2022f", "abstract": "This is version v3.3.0.2022f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data.\r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19310101-20230101_v3.3.1.2022f.nc. The station codes can be found under the docs tab. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., (2019), HadISD version 3: monthly updates, Hadley Centre Technical Note.\r\n\r\nDunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016.\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014." }, "objectObservation": { "ob_id": 34664, "uuid": "5fb94c8e37f64c95b671278b0e55cdd4", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1931-2021, v3.2.0.2021f", "abstract": "This is version v3.2.0.2021f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data.\r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19310101-20220101_v3.2.1.2021f.nc. The station codes can be found under the docs tab. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., (2019), HadISD version 3: monthly updates, Hadley Centre Technical Note.\r\n\r\nDunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016.\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014." } }, { "ob_id": 771, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 39752, "uuid": "62b675f929974746bbf72fdc773cf0ec", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Cross-Section Box TS.1, Figure 1 (v20230203)", "abstract": "Data for Figure CSB TS.1, 1 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nCross-Section Box TS.1 Figure 1, shows Earth's surface temperature history and projections to 2100\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 Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. 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. 33−144, doi:10.1017/9781009157896.002.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has five panels with data provided for panels a, b and c from the underlying chapter figures (2.11, 4.19). Data for panel e is from Figure 4.11 and a link is provided to data and code in the Related Documents section.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n \r\n - Global surface temperature changer over the Holocene divided into three time scales: (i) 12,000 to 1000 years ago (10,000 BCE to 1000 CE) in 100-year time steps, (ii) 1000 to 1900 CE, 10-year smooth, and (iii) 1900 to 2020 CE. All temperatures are relative to 1850–1900.\r\n - Spatially resolved temperature trends (°C per decade) for HadCRUTv5 over 1981–2020\r\n - Multi-model temperature mean projected changes from 1995–2014 to 2081–2100 in the SST3-7.0 scenario.\r\n - Temperature from instrumental data for 1850–2020.\r\n - Assessed projected temperature change in 20-year running mean global surface temperature for five scenarios (central estimate, very likely range for SSP1-2.6 and SSP3-7.0), relative to 1995–2014 and 1850–1900.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - Panels (a), (c) and the top part of (b) are from Figure 2.11.\r\n - The bottom part of (b) is from Figure 4.19.\r\n - Panel (e) is from Figure 4.11.\r\n\r\nPanel a (from Figure 2.11 panel a):\r\n- Figure_2_11a-PAGES_2k_Consortium.csv (center) \r\n- Figure_2_11_panel_a.csv (right)\r\n\r\n\r\n Panel b (upper part from Figure 2.11 input data)\r\n- IndermediateData_Figure-2_11-HadCRUT_significance_overlay_1981-2020.txt\r\n- IntermediateData_Figure-2_11-HadCRUT_significance_overlay_1900-1980.txt\r\n- IntermediateData_Figure-2_11-HadCRUT_trends_1900-1980.txt\r\n- IntermediateData_Figure-2_11-HadCRUT_trends_1981-2020.txt\r\n- Figure_2_11-notes_on_HadCRUT_trend_files.pdf\r\n\r\n Panel b (lower part from Figure 4.19)\r\n- ‘Data_shown_in_figure.nc’\r\n\r\n\r\n Panel c (from Figure 2.11 panel c input data)\r\n- Figure_2_11c-lower_panel.csv (lower)\r\n- Figure_2_11c-lower_panel.xlsx : same as Figure_2_11c-lower_panel.csv but with the format used by the code that generates the figure.\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Panel d:\r\n\r\nThe data lines are single ensemble members of SSP2-4.5 from BCC-CSM2-MR, FGOALS-f3-L, MPI-ESM1-2-HR, MPI-ESM1-2-LR, selected on the criterium that they have a diagnosed ECS less than 0.1K away from 3.0.\r\n\r\nThe CMIP6 repository is identical to the one used for Box 4.1 figure 1. The same 39 models with historical simulations are plotted. HadCRUT5 is the same reference as for Box 4.1 figure 1. Assessed GSAT is the same as produced by the figure 4.11 code.\r\n\r\nNotes on HadCRUT trend maps\r\nFor each time period, there are two plain text files: one showing the trend values (in °C per decade), the other an indicator of significant (1 = significant, 0 = non-significant, -99 = missing data). Gridpoints with insufficient data for trend calcula)on are shown with a trend of -999.0. The values are on a 5-degree grid, with rows from longitude -177.5 to 177.5 and columns from latitudes -87.5 to 87.5.\r\n\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n - Links to the report components of the underlying chapter figures from which part of this figure was generated (Chapter 2 and Chapter 4)\r\n - Link to the Supplementary Material for Chapter 4, which contains details on the input data used in Table 4.SM.1\r\n- Link to figure 4.11 data stored on WDC-Climate\r\n - Link to the code for figure 4.11, archived on Zenodo." }, "objectObservation": { "ob_id": 37280, "uuid": "f3515388768344bfb2be0521f82388be", "short_code": "ob", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.11 (v20220510)", "abstract": "Data for Figure 2.11 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 2.11 includes mapped and time-series data showing global surface temperature relative to 1850 - 1900 over multiple time scales.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n---------------------------------------------------\r\n Figure has three panels, with data provided for panel (a) (center and right part), and panel (c).\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n---------------------------------------------------\r\n Global surface temperature, relative to 1850 - 1900 for:\r\n\r\n Panel a: \r\n \r\n - 1000 to 1900 CE - from PAGES 2k Consortium (modified from the version 2019: 10.1038/s41561-019-0400-0)\r\n - 1850 to 2020 from AR6 assessed mean (same as Figure 2.11c).\r\n\r\n Panel c: \r\n \r\n - Annual and decadal means from instrumental data for 1850–2020, along with the uncertainty range from HadCRUT5.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n---------------------------------------------------\r\n Panel a:\r\n \r\n - Data file: Figure_2_11a-PAGES_2k_Consortium.csv (yearly data, 1000 to 1900); relates to the center part of the figure showing global surface temperature relative to 1850 -1900. (bold solid green line, column 2, median 10-yr smooth adjusted (+0.37°C), thin solid green lines: 5th (column 3) and 95th (column 4) percentiles of the ensemble members).\r\n - Data file: Figure2_11_panel_a.csv (yearly data, 1850 to 2020); relates to the right part of the figure showing global temperature anomaly AR6 assessed mean. (bold solid violet line, column 2)\r\n\r\nPanel c: \r\n \r\n - Data file: Figure_2_11c-land_and_ocean_time_series.csv (yearly data, 1850 to 2020); relates to the upper part of the figure showing global surface temperature relative to 1850 -1900. (Land, column 2, red line; Ocean, column 3, blue line).\r\n\r\n---------------------------------------------------\r\nNotes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nInput data and code to reproduce panel b and panel c (lower part) plots are provided in the Related Documents section of this catalogue record.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to input data figure 2.11.\r\n - Link to the code for the figure, archived on Zenodo." } }, { "ob_id": 772, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 39752, "uuid": "62b675f929974746bbf72fdc773cf0ec", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Cross-Section Box TS.1, Figure 1 (v20230203)", "abstract": "Data for Figure CSB TS.1, 1 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nCross-Section Box TS.1 Figure 1, shows Earth's surface temperature history and projections to 2100\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 Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. 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. 33−144, doi:10.1017/9781009157896.002.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has five panels with data provided for panels a, b and c from the underlying chapter figures (2.11, 4.19). Data for panel e is from Figure 4.11 and a link is provided to data and code in the Related Documents section.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n \r\n - Global surface temperature changer over the Holocene divided into three time scales: (i) 12,000 to 1000 years ago (10,000 BCE to 1000 CE) in 100-year time steps, (ii) 1000 to 1900 CE, 10-year smooth, and (iii) 1900 to 2020 CE. All temperatures are relative to 1850–1900.\r\n - Spatially resolved temperature trends (°C per decade) for HadCRUTv5 over 1981–2020\r\n - Multi-model temperature mean projected changes from 1995–2014 to 2081–2100 in the SST3-7.0 scenario.\r\n - Temperature from instrumental data for 1850–2020.\r\n - Assessed projected temperature change in 20-year running mean global surface temperature for five scenarios (central estimate, very likely range for SSP1-2.6 and SSP3-7.0), relative to 1995–2014 and 1850–1900.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - Panels (a), (c) and the top part of (b) are from Figure 2.11.\r\n - The bottom part of (b) is from Figure 4.19.\r\n - Panel (e) is from Figure 4.11.\r\n\r\nPanel a (from Figure 2.11 panel a):\r\n- Figure_2_11a-PAGES_2k_Consortium.csv (center) \r\n- Figure_2_11_panel_a.csv (right)\r\n\r\n\r\n Panel b (upper part from Figure 2.11 input data)\r\n- IndermediateData_Figure-2_11-HadCRUT_significance_overlay_1981-2020.txt\r\n- IntermediateData_Figure-2_11-HadCRUT_significance_overlay_1900-1980.txt\r\n- IntermediateData_Figure-2_11-HadCRUT_trends_1900-1980.txt\r\n- IntermediateData_Figure-2_11-HadCRUT_trends_1981-2020.txt\r\n- Figure_2_11-notes_on_HadCRUT_trend_files.pdf\r\n\r\n Panel b (lower part from Figure 4.19)\r\n- ‘Data_shown_in_figure.nc’\r\n\r\n\r\n Panel c (from Figure 2.11 panel c input data)\r\n- Figure_2_11c-lower_panel.csv (lower)\r\n- Figure_2_11c-lower_panel.xlsx : same as Figure_2_11c-lower_panel.csv but with the format used by the code that generates the figure.\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Panel d:\r\n\r\nThe data lines are single ensemble members of SSP2-4.5 from BCC-CSM2-MR, FGOALS-f3-L, MPI-ESM1-2-HR, MPI-ESM1-2-LR, selected on the criterium that they have a diagnosed ECS less than 0.1K away from 3.0.\r\n\r\nThe CMIP6 repository is identical to the one used for Box 4.1 figure 1. The same 39 models with historical simulations are plotted. HadCRUT5 is the same reference as for Box 4.1 figure 1. Assessed GSAT is the same as produced by the figure 4.11 code.\r\n\r\nNotes on HadCRUT trend maps\r\nFor each time period, there are two plain text files: one showing the trend values (in °C per decade), the other an indicator of significant (1 = significant, 0 = non-significant, -99 = missing data). Gridpoints with insufficient data for trend calcula)on are shown with a trend of -999.0. The values are on a 5-degree grid, with rows from longitude -177.5 to 177.5 and columns from latitudes -87.5 to 87.5.\r\n\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n - Links to the report components of the underlying chapter figures from which part of this figure was generated (Chapter 2 and Chapter 4)\r\n - Link to the Supplementary Material for Chapter 4, which contains details on the input data used in Table 4.SM.1\r\n- Link to figure 4.11 data stored on WDC-Climate\r\n - Link to the code for figure 4.11, archived on Zenodo." }, "objectObservation": { "ob_id": 39648, "uuid": "dce10ff4596241e190aaea9291cc4249", "short_code": "ob", "title": "Chapter 4 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 4.19 (v20230203)", "abstract": "Data for Figure 4.19 from Chapter 4 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 4.19 shows the projected mid- and long-term change of annual mean surface temperature.\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 Lee, J.-Y., J. Marotzke, G. Bala, L. Cao, S. Corti, J.P. Dunne, F. Engelbrecht, E. Fischer, J.C. Fyfe, C. Jones, A. Maycock, J. Mutemi, O. Ndiaye, S. Panickal, and T. Zhou, 2021: Future Global Climate: Scenario-Based Projections and Near-Term Information. 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. 553–672, doi:10.1017/9781009157896.006.\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 one NetCDF file. \r\na) Global projected spatial patterns of multi-model mean change in annual mean near-surface air temperature (°C) in 2041-2060 relative to 1995-2014 in SSP1-2.6 \r\nb) Global projected spatial patterns of multi-model mean change in annual mean near-surface air temperature (°C) in 2081-2100 relative to 1995-2014 in SSP1-2.6 \r\nc) Global projected spatial patterns of multi-model mean change in annual mean near-surface air temperature (°C) in 2041-2060 relative to 1995-2014 in SSP3-7.0 \r\nd) Global projected spatial patterns of multi-model mean change in annual mean near-surface air temperature (°C) in 2081-2100 relative to 1995-2014 in SSP3-7.0\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n CMIP6 multi-model mean projected change in surface air temperature (2041–2060 and 2081-2100) from SSP1‑2.6 and SSP3‑7.0 relative to 1995–2014.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n The variable tas includes the map information as a function of latitude and longitude and has a dimension named panel, which includes the data for all panels a-d.\r\n\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nSSP1-2.6 is based on Shared Socioeconomic Pathway SSP1 with low climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 2.6 W/m2 in the year 2100.\r\nSSP3-7.0 is based on Shared Socioeconomic Pathway SSP3 which is characterized by high challenges to both mitigation and adaptation and RCP7.0, a future pathway with a radiative forcing of 7.0 W/m2 in the year 2100.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 4)\r\n - Link to the Supplementary Material for Chapter 4, which contains details on the input data used in Table 4.SM.1" } }, { "ob_id": 773, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 39752, "uuid": "62b675f929974746bbf72fdc773cf0ec", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Cross-Section Box TS.1, Figure 1 (v20230203)", "abstract": "Data for Figure CSB TS.1, 1 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nCross-Section Box TS.1 Figure 1, shows Earth's surface temperature history and projections to 2100\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 Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. 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. 33−144, doi:10.1017/9781009157896.002.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has five panels with data provided for panels a, b and c from the underlying chapter figures (2.11, 4.19). Data for panel e is from Figure 4.11 and a link is provided to data and code in the Related Documents section.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n \r\n - Global surface temperature changer over the Holocene divided into three time scales: (i) 12,000 to 1000 years ago (10,000 BCE to 1000 CE) in 100-year time steps, (ii) 1000 to 1900 CE, 10-year smooth, and (iii) 1900 to 2020 CE. All temperatures are relative to 1850–1900.\r\n - Spatially resolved temperature trends (°C per decade) for HadCRUTv5 over 1981–2020\r\n - Multi-model temperature mean projected changes from 1995–2014 to 2081–2100 in the SST3-7.0 scenario.\r\n - Temperature from instrumental data for 1850–2020.\r\n - Assessed projected temperature change in 20-year running mean global surface temperature for five scenarios (central estimate, very likely range for SSP1-2.6 and SSP3-7.0), relative to 1995–2014 and 1850–1900.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - Panels (a), (c) and the top part of (b) are from Figure 2.11.\r\n - The bottom part of (b) is from Figure 4.19.\r\n - Panel (e) is from Figure 4.11.\r\n\r\nPanel a (from Figure 2.11 panel a):\r\n- Figure_2_11a-PAGES_2k_Consortium.csv (center) \r\n- Figure_2_11_panel_a.csv (right)\r\n\r\n\r\n Panel b (upper part from Figure 2.11 input data)\r\n- IndermediateData_Figure-2_11-HadCRUT_significance_overlay_1981-2020.txt\r\n- IntermediateData_Figure-2_11-HadCRUT_significance_overlay_1900-1980.txt\r\n- IntermediateData_Figure-2_11-HadCRUT_trends_1900-1980.txt\r\n- IntermediateData_Figure-2_11-HadCRUT_trends_1981-2020.txt\r\n- Figure_2_11-notes_on_HadCRUT_trend_files.pdf\r\n\r\n Panel b (lower part from Figure 4.19)\r\n- ‘Data_shown_in_figure.nc’\r\n\r\n\r\n Panel c (from Figure 2.11 panel c input data)\r\n- Figure_2_11c-lower_panel.csv (lower)\r\n- Figure_2_11c-lower_panel.xlsx : same as Figure_2_11c-lower_panel.csv but with the format used by the code that generates the figure.\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Panel d:\r\n\r\nThe data lines are single ensemble members of SSP2-4.5 from BCC-CSM2-MR, FGOALS-f3-L, MPI-ESM1-2-HR, MPI-ESM1-2-LR, selected on the criterium that they have a diagnosed ECS less than 0.1K away from 3.0.\r\n\r\nThe CMIP6 repository is identical to the one used for Box 4.1 figure 1. The same 39 models with historical simulations are plotted. HadCRUT5 is the same reference as for Box 4.1 figure 1. Assessed GSAT is the same as produced by the figure 4.11 code.\r\n\r\nNotes on HadCRUT trend maps\r\nFor each time period, there are two plain text files: one showing the trend values (in °C per decade), the other an indicator of significant (1 = significant, 0 = non-significant, -99 = missing data). Gridpoints with insufficient data for trend calcula)on are shown with a trend of -999.0. The values are on a 5-degree grid, with rows from longitude -177.5 to 177.5 and columns from latitudes -87.5 to 87.5.\r\n\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n - Links to the report components of the underlying chapter figures from which part of this figure was generated (Chapter 2 and Chapter 4)\r\n - Link to the Supplementary Material for Chapter 4, which contains details on the input data used in Table 4.SM.1\r\n- Link to figure 4.11 data stored on WDC-Climate\r\n - Link to the code for figure 4.11, archived on Zenodo." }, "objectObservation": { "ob_id": 37308, "uuid": "033cd690801741c9bc745b8da55faef4", "short_code": "ob", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 2.11 (v20220428)", "abstract": "Input Data for Figure 2.11 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 2.11 shows observed global temperature change over a wide range of timescales.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has three subpanels. Input data are provided for panel b and panel c (lower panel).\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n Panel b:\r\n - gridded file of observed trends (as ASCII text) and significance overlay. Separate notes document.\r\n \r\n Panel c (lower panel):\r\n - Global surface temperature, relative to 1850 - 1900 for annual and decadal means from instrumental data for 1850–2020, along with the uncertainty range from HadCRUT5.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel b:\r\n - IndermediateData_Figure-2_11-HadCRUT_significance_overlay_1981-2020.txt\r\n - IntermediateData_Figure-2_11-HadCRUT_significance_overlay_1900-1980.txt\r\n - IntermediateData_Figure-2_11-HadCRUT_trends_1900-1980.txt\r\n - IntermediateData_Figure-2_11-HadCRUT_trends_1981-2020.txt\r\n \r\n Panel c:\r\n - Figure_2_11c-lower_panel.csv; relates to the lower part of the figure. (black line, column 2, HadCRUT 5.0; cyan line, column 3, NOAA Global Temp; pink line, column 4, Berkeley Earth; orange line, column 5, Kadow et al.; grey shadow, columns 6 and 7, HadCRUT confidence limit)\r\n\r\nHadCRUT5 is a gridded dataset of global historical surface temperature anomalies relative to a 1961-1990 reference period produced by the Met Office Hadley Centre. \r\nNOAA Global Temp is a gridded dataset of global historical surface temperature anomalies relative to a 1971-2000 reference period produced by the National Oceanic and Atmospheric Administration. \r\nBerkeley Earth is a global historical land-ocean temperature index produced by Berkeley Earth.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Figure 2.11b - this is an ASCII grid (described in Figure_2_11-notes_on_HadCRUT_trend_files.txt) with a significance overlay. Should be approximately reproducible with any standard software to produce maps from gridded data.\r\n\r\n\r\nFigure 2.11c (lower panel), link to the code to reproduce this part of the figure is provided in the Related Documents section of this catalogue record.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to the code for the figure, archived on Zenodo." } }, { "ob_id": 777, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 39804, "uuid": "91d1fbc572224b9c86f6c14ba9109479", "short_code": "ob", "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): Regional coastline profile of Vertical Land Motions in Europe and SE Asia/Oceania, v1", "abstract": "This dataset contains a regional coastline profile of Vertical Land Motions in Europe and SE Asia/Oceania produced as part of the ESA Climate Change Initiative Sea Level project.\r\n\r\nVertical Land Motions have been estimated as the difference between the altimeter coastal sea level v1.1 dataset (available from https://catalogue.ceda.ac.uk/uuid/222cf11f49a94d2da8a6da239df2efc4 ) and tide gauge measurements from the Permanent Service for Mean Sea Level (PMSML) network. Spatial interpolation has allowed the production of a regularly spaced coastline profile of vertical land movements together with their uncertainties.\r\n\r\nThe altimeter input data are from the Jason-1, Jason-2 and Jason-3 missions during the period Jan. 2002 - May 2018." }, "objectObservation": { "ob_id": 31822, "uuid": "222cf11f49a94d2da8a6da239df2efc4", "short_code": "ob", "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): Altimeter along-track high resolution sea level anomalies in some coastal regions (2002-2018) from the JASON satellites, v1.1", "abstract": "This dataset contains along-track sea level anomalies derived from satellite altimetry. Altimeter along-track sea level measurements from the Jason-1, Jason -2 and Jason-3 satellite missions have been processed to produce high resolution (20 Hz, corresponding to an along-track distance of ~300m) sea level anomalies, in order to provide long-term homogeneous sea level time series as close to the coast as possible in six different coastal regions (North-East Atlantic, Mediterranean Sea, Western Africa, North Indian Ocean, South-East Asia and Australia). These six time series cover the period from 15 January 2002 to 30 May 2018.\r\n\r\nThe product benefits from the spatial resolution provided by high-rate data, the Adaptive Leading Edge Subwaveform Retracker (ALES) and the post-processing strategy of the along-track (X-TRACK) algorithm, both developed for the processing of coastal altimetry data, as well as the best possible set of geophysical corrections. \r\n\r\nThe main objective of this product is to provide accurate altimeter Sea Level Anomalies (SLA) time series as close to the coast as possible in order to assess whether the coastal sea level trends experienced at the coast are similar to the observed sea level trends in the open ocean and to determine the causes of the potential discrepancies.\r\n\r\nThe product has been developed within the sea level project of the extension phase of the European Space Agency (ESA) Climate Change Initiative (SL_cci+). During the project, the product will be extended in spatial coverage and with additional altimeter missions. This version of the dataset is v1.1. (DOI: 10.5270/esa-sl_cci-xtrack_ales_sla-200206_201805-v1.1-202005)" } }, { "ob_id": 780, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 39750, "uuid": "bb759548ee444168b4554569741c8e61", "short_code": "ob", "title": "UKCP18 projected future extreme sea levels at selected tide gauge locations for 2020-2300, using exploratory extended time-mean sea level projections", "abstract": "The data are projected extreme sea levels at 46 UK tide gauge locations. The data were produced by the Met Office, using estimates of present-day extreme sea levels provided by the Environment Agency and projections of exploratory extended time-mean sea level change prepared at the Met Office.\r\n\r\nProjected extreme sea level values are described for 16 different annual probabilities of exceedance (return levels), ranging from 1:1 to 1:10,000. Confidence levels relating to the 5% and 95% lower and upper bounds of confidence are included.\r\n\r\nThe data were produced to put the projected future mean sea level change in the context of the present-day extremes. The data were produced by combining the best estimates of present-day extreme sea levels with projections of mean sea level change. The data covers the period from 2020 to 2300 and is available for each decade (i.e., 2020, 2030... 2300).\r\n\r\nFurther information on this dataset and UKCP18 can be found in the documentation section." }, "objectObservation": { "ob_id": 26693, "uuid": "242066d230a44986bf199f782e7f9008", "short_code": "ob", "title": "UKCP18 projected future extreme still water level at selected tide gauge locations for 2100-2300", "abstract": "The data are projected future still water return levels. The data were produced by the Met Office using projections of future mean sea level change prepared at the Met Office and estimates of present-day still water return levels which were provided by the Environment Agency. The data were produced as a simple indication of the relative sizes and uncertainties in present day extreme water levels and projected future mean sea level change. The data were produced by combining projections of mean sea level change with best estimates of present day extreme still water levels. The data in marine strand 4.10 cover the period from 2100 to 2300 and are available for 46 UK tide gauge locations.\r\n\r\nFurther information on this dataset and UKCP18 can be found in the documentation section." } }, { "ob_id": 781, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 39750, "uuid": "bb759548ee444168b4554569741c8e61", "short_code": "ob", "title": "UKCP18 projected future extreme sea levels at selected tide gauge locations for 2020-2300, using exploratory extended time-mean sea level projections", "abstract": "The data are projected extreme sea levels at 46 UK tide gauge locations. The data were produced by the Met Office, using estimates of present-day extreme sea levels provided by the Environment Agency and projections of exploratory extended time-mean sea level change prepared at the Met Office.\r\n\r\nProjected extreme sea level values are described for 16 different annual probabilities of exceedance (return levels), ranging from 1:1 to 1:10,000. Confidence levels relating to the 5% and 95% lower and upper bounds of confidence are included.\r\n\r\nThe data were produced to put the projected future mean sea level change in the context of the present-day extremes. The data were produced by combining the best estimates of present-day extreme sea levels with projections of mean sea level change. The data covers the period from 2020 to 2300 and is available for each decade (i.e., 2020, 2030... 2300).\r\n\r\nFurther information on this dataset and UKCP18 can be found in the documentation section." }, "objectObservation": { "ob_id": 26690, "uuid": "5f739395b79a439f94aa36e2aca4d7c6", "short_code": "ob", "title": "UKCP18 projected future extreme still water level at selected tide gauge locations for 2020-2100", "abstract": "The data are projected future still water return levels. The data were produced by the Met Office using projections of future mean sea level change prepared at the Met Office and estimates of present-day still water return levels which were provided by the Environment Agency. The data were produced as a simple indication of the relative sizes and uncertainties in present day extreme water levels and projected future mean sea level change. The data were produced by combining projections of mean sea level change with best estimates of present day extreme still water levels. The data in marine strand 4.09 cover the period from 2020 to 2100 and are available for 46 UK tide gauge locations.\r\n\r\nFurther information on this dataset and UKCP18 can be found in the documentation section." } }, { "ob_id": 784, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 39899, "uuid": "af60720c1e404a9e9d2c145d2b2ead4e", "short_code": "ob", "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017, 2018, 2019 and 2020, v4", "abstract": "This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017, 2018, 2019 and 2020. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. \r\n\r\nThis release of the data is version 4. Compared to version 3, version 4 consists of an update of the three maps of AGB for the years 2010, 2017 and 2018 and new AGB maps for 2019 and 2020. New AGB change maps have been created for consecutive years (2018-2017, 2019-2018 and 2020-2019) and for a decadal interval (2020-2010). The pool of remote sensing data now includes multi-temporal observations at L-band for all biomes and for all years. The AGB maps rely on revised allometries which are now based on a longer record of spaceborne LiDAR data from the GEDI and ICESat-2 missions. Temporal information is now implemented in the retrieval algorithm to preserve biomass dynamics as expressed in the remote sensing data. Biases between 2010 and more recent years have been reduced.\r\n\r\n\r\n\r\nThe data products consist of two (2) global layers that include estimates of:\r\n1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots\r\n2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)\r\n\r\nIn addition, files describing the AGB change between two consecutive years (i.e., 2018-2017, 2019-2018 and 2020-2010) and over a decade (2020-2010) are provided (labelled as 2018_2017, 2019_2018, 2020_2019 and 2020_2010). Each AGB change product consists of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.\r\n\r\n\r\nData are provided in both netcdf and geotiff format." }, "objectObservation": { "ob_id": 33218, "uuid": "5f331c418e9f4935b8eb1b836f8a91b8", "short_code": "ob", "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017 and 2018, v3", "abstract": "This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017 and 2018. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. \r\n\r\nThis release of the data is version 3. Compared to version 2, this is a consolidated version of the Above Ground Biomass (AGB) maps. This version also includes a preliminary estimate of AGB changes for two epochs.\r\n\r\nThe data products consist of two (2) global layers that include estimates of:\r\n1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots\r\n2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)\r\n\r\nIn addition, files describing the AGB change between 2018 and the other two years are provided (labelled as 2018_2010 and 2018_2017). These consist of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.\r\n\r\nData are provided in both netcdf and geotiff format." } }, { "ob_id": 785, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 39942, "uuid": "a0782135bcd04d77a1dae4aa71fba47c", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a geographic projection at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 6.0 Remote Sensing Reflectance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, this dataset is also contained within the 'All Products' dataset. \r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection)." }, "objectObservation": { "ob_id": 32137, "uuid": "5ab5267b17254152bcdbc055747faa02", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a geographic projection, Version 5.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 5.0 Remote Sensing Reflectance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, this dataset is also contained within the 'All Products' dataset. \r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection).\r\n\r\nPlease note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d)\r\n\r\nVersion 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0" } }, { "ob_id": 786, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 39945, "uuid": "0875b4675f1e46ebadb526e0b95505c5", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a geographic projection (All Products) at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains all their Version 6.0 generated ocean colour products on a geographic projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Data are also available as monthly climatologies.\r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.\r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)" }, "objectObservation": { "ob_id": 32138, "uuid": "016f577b631a429a8558796a74983154", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a geographic projection (All Products), Version 5.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains all their Version 5.0 generated ocean colour products on a geographic projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Data are also available as monthly climatologies.\r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.\r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)\r\n\r\nPlease note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d)\r\n\r\nVersion 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0" } }, { "ob_id": 787, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 39947, "uuid": "474ac06235e54e6cb0ec6eed635e1213", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a sinusoidal projection at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 6.0 chlorophyll-a product (in mg/m3) on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Note, the chlorophyll-a data are also included in the 'All Products' dataset. \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)" }, "objectObservation": { "ob_id": 32140, "uuid": "66534da90ed44abebfc1b08adca4f9c3", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a sinusoidal projection, Version 5.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 5.0 chlorophyll-a product (in mg/m3) on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Note, the chlorophyll-a data are also included in the 'All Products' dataset. \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)\r\n\r\nPlease note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d)\r\n\r\nVersion 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0" } }, { "ob_id": 788, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 39949, "uuid": "8b9d461f245b4efd8ea9fa080366e3b1", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a sinusoidal projection at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 6.0 Remote Sensing Reflectance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, these data are also contained within the 'All Products' dataset. \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection)." }, "objectObservation": { "ob_id": 32141, "uuid": "f30495d4425f46c489765a2f84dd6862", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a sinusoidal projection, Version 5.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 5.0 Remote Sensing Reflectance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, these data are also contained within the 'All Products' dataset. \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).\r\n\r\nPlease note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d)\r\n\r\nVersion 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0" } }, { "ob_id": 789, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 39959, "uuid": "024292dcda5d42ceb326850f89f8b40d", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a sinusoidal projection at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 6.0 inherent optical properties (IOP) product (in mg/m3) on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Note, the IOP data are also included in the 'All Products' dataset. \r\n\r\nThe inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)" }, "objectObservation": { "ob_id": 32142, "uuid": "e2f9d8f61a02431997361a8827eaf558", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a sinusoidal projection, Version 5.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 5.0 inherent optical properties (IOP) product (in mg/m3) on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Note, the IOP data are also included in the 'All Products' dataset. \r\n\r\nThe inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)\r\n\r\nPlease note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d)\r\n\r\nVersion 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0" } }, { "ob_id": 790, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 39961, "uuid": "8ecae26f390b4938b67a97cbce3ecd8b", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a geographic projection at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 6.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. It is computed from the Ocean Colour CCI Version 6.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset.\r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection)." }, "objectObservation": { "ob_id": 32144, "uuid": "e94f2810c0794175b834153a71ac3182", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a geographic projection, Version 5.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 5.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. It is computed from the Ocean Colour CCI Version 5.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset.\r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection).\r\n\r\nPlease note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d)\r\n\r\nVersion 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0" } }, { "ob_id": 791, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 39963, "uuid": "b0ec72a28b6a4829a33ed9adc215d5bc", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a geographic projection at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 6.0 chlorophyll-a product (in mg/m3) on a geographic projection at 4 km spatial resolution and at number of time resolutions (daily, 5day, 8day, monthly and yearly composites) covering the period 1997 - 2022. Note, this chlor_a data is also included in the 'All Products' dataset. \r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)" }, "objectObservation": { "ob_id": 32145, "uuid": "e9f82908fd9c48138b31e5cfaa6d692b", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a geographic projection, Version 5.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 5.0 chlorophyll-a product (in mg/m3) on a geographic projection at 4 km spatial resolution and at number of time resolutions (daily, 5day, 8day and monthly composites) covering the period 1997 - 2020. Note, this chlor_a data is also included in the 'All Products' dataset. \r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)\r\n\r\nPlease note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d)\r\n\r\nVersion 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0" } }, { "ob_id": 792, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 39965, "uuid": "90682bac7d0e4e418085f30eba43dfba", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a geographic projection at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 6.0 inherent optical properties (IOP) product (in mg/m3) on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Note, the IOP data is also included in the 'All Products' dataset. \r\n\r\nThe inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). \r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)" }, "objectObservation": { "ob_id": 32146, "uuid": "de75072edfca44bfaaec0ed171d86bde", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a geographic projection, Version 5.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 5.0 inherent optical properties (IOP) product (in mg/m3) on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Note, the IOP data is also included in the 'All Products' dataset. \r\n\r\nThe inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). \r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)\r\n\r\nPlease note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d)\r\n\r\nVersion 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0" } }, { "ob_id": 793, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 39967, "uuid": "3bdb21a4cd004e5f8cc148fea5f1d4e3", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a sinusoidal projection at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 6.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. It is computed from the Ocean Colour CCI Version 6.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset. \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection)." }, "objectObservation": { "ob_id": 32139, "uuid": "e2c223cdcb4844f9a1ffe9759b61eaf4", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a sinusoidal projection, Version 5.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 5.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. It is computed from the Ocean Colour CCI Version 5.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset. \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).\r\n\r\nPlease note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d)\r\n\r\nVersion 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0" } }, { "ob_id": 794, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 39969, "uuid": "86d360431f3b4184b89cdd1cd707bb33", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a sinusoidal projection (All Products) at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains all their Version 6.0 generated ocean colour products on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. \r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.\r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)" }, "objectObservation": { "ob_id": 32143, "uuid": "8154e881452f49c1ba86982ed88b20f0", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a sinusoidal projection (All Products), Version 5.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains all their Version 5.0 generated ocean colour products on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. \r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.\r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)\r\n\r\nPlease note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d)\r\n\r\nVersion 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0" } }, { "ob_id": 795, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 39971, "uuid": "690fdf8f229c4d04a2aa68de67beb733", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Monthly climatology of global ocean colour data products at 4km resolution, Version 6.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains a monthly climatology of the generated ocean colour products covering the period 1997 - 2022.\r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided." }, "objectObservation": { "ob_id": 32136, "uuid": "612a615afb5d48459b385380b440b545", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Monthly climatology of global ocean colour data products, Version 5.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains a monthly climatology of the generated ocean colour products covering the period 1997 - 2020.\r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.\r\n\r\nPlease note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d)\r\n\r\nVersion 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0" } }, { "ob_id": 796, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 39978, "uuid": "86d55a4576284365906f661aa547e566", "short_code": "ob", "title": "Copernicus Climate Change Service Dataset: Sea Surface Temperature Integrated Climate Data Record (ICDR) from the Advanced Very High Resolution Radiometer (AVHRR), Level 3C (L3C), version 2.1", "abstract": "This dataset provides gridded Sea Surface Temperature data derived from the Advance Very High Resolution Radiometer (AVHRR) series of satellites. Data is available separately for the AVHRR instruments on NOAA-19, METOP-A and METOP-B.\r\n\r\nThis dataset is produced as an Intermediate Climate Data Record for the Copernicus Climate Change Service (C3S). V2.1 extends from 2017-2022.\r\n\r\nA historic Climate Data Record (CDR) has also been produced under the ESA Climate Change Initiative Sea Surface Temperature (CCI_sst). This is available as a separate dataset in the CEDA catalgoue and through the ESA CCI Open Data Portal." }, "objectObservation": { "ob_id": 26429, "uuid": "f194fe3a5c4f4d0180f133535096b43b", "short_code": "ob", "title": "Copernicus Climate Change Service Dataset: Sea Surface Temperature Integrated Climate Data Record (ICDR) from the Advanced Very High Resolution Radiometer (AVHRR), Level 3C (L3C), version 2.0", "abstract": "This dataset provides gridded Sea Surface Temperature data derived from the Advance Very High Resolution Radiometer (AVHRR) series of satellites. Data is available separately for the AVHRR instruments on NOAA-19, METOP-A and METOP-B.\r\n\r\nThis dataset is produced as an Intermediate Climate Data Record for the Copernicus Climate Change Service (C3S). V2.0 extends from 2017-2021.\r\n\r\nA historic Climate Data Record (CDR) has also been produced under the ESA Climate Change Initiative Sea Surface Temperature (CCI_sst). This is available as a separate dataset in the CEDA catalgoue and through the ESA CCI Open Data Portal." } }, { "ob_id": 797, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 39984, "uuid": "04f822f5cce4436ab88ac1f9a5109441", "short_code": "ob", "title": "Copernicus Climate Change Service Dataset: Sea Surface Temperature Integrated Climate Data Record (ICDR) from the SLSTR instrument on Sentinel 3, Level 3C (L3C), version 2.1", "abstract": "This dataset provides gridded Sea Surface Temperature data derived from the Sea and Land Surface Temperature Radiometer(SLSTR) on the Sentinel-3 series of satellites. \r\n\r\nThis dataset is produced as an Intermediate Climate Data Record for the Copernicus Climate Change Service (C3S). V2.1 extends from 2017-2022.\r\n\r\nA historic Climate Data Record (CDR) has also been produced under the ESA Climate Change Initiative Sea Surface Temperature (CCI_sst). This is available as a separate dataset in the CEDA catalogue and through the ESA CCI Open Data Portal." }, "objectObservation": { "ob_id": 39980, "uuid": "da2f3536e656437594db6c6de86df26f", "short_code": "ob", "title": "Copernicus Climate Change Service Dataset: Sea Surface Temperature Integrated Climate Data Record (ICDR) from the SLSTR instrument on Sentinel 3, Level 3C (L3C), version 2.0", "abstract": "This dataset provides gridded Sea Surface Temperature data derived from the Sea and Land Surface Temperature Radiometer(SLSTR) on the Sentinel-3 series of satellites. \r\n\r\nThis dataset is produced as an Intermediate Climate Data Record for the Copernicus Climate Change Service (C3S). V2.0 extends from 2017-2021.\r\n\r\nA historic Climate Data Record (CDR) has also been produced under the ESA Climate Change Initiative Sea Surface Temperature (CCI_sst). This is available as a separate dataset in the CEDA catalogue and through the ESA CCI Open Data Portal." } }, { "ob_id": 798, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 39989, "uuid": "edc03b1f5c4f42dcbe5906dd3b5fd592", "short_code": "ob", "title": "Copernicus Climate Change Service Dataset: L4 Sea Surface Temperature Analysis Integrated Climate Data Record (ICDR), version 2.1", "abstract": "This Sea Surface Temperature Level 4 Analysis Intermediate Climate Data Record (ICDR) provides a globally-complete daily analysis of sea surface temperature (SST) on a 0.05 degree regular latitude - longitude grid. It combines data from both the Advanced Very High Resolution Radiometer (AVHRR ) and Sea and Land Surface Temperature Radiometer (SLSTR) Intermediate Climate Data Records, using a data assimilation method to provide SSTs where there were no measurements. \r\n\r\nThis dataset was produced for the Copernicus Climate Change Service (C3S). V2.1 extends from 2017-2022.\r\n\r\nA historic Climate Data Record (CDR) has also been produced under the ESA Climate Change Initiative Sea Surface Temperature (CCI_sst). This is available as a separate dataset in the CEDA catalogue and through the ESA CCI Open Data Portal." }, "objectObservation": { "ob_id": 39986, "uuid": "5352ffa477fa489094a7c0a4b32ff677", "short_code": "ob", "title": "Copernicus Climate Change Service Dataset: L4 Sea Surface Temperature Analysis Integrated Climate Data Record (ICDR), version 2.0", "abstract": "This Sea Surface Temperature Level 4 Analysis Intermediate Climate Data Record (ICDR) provides a globally-complete daily analysis of sea surface temperature (SST) on a 0.05 degree regular latitude - longitude grid. It combines data from both the Advanced Very High Resolution Radiometer (AVHRR ) and Sea and Land Surface Temperature Radiometer (SLSTR) Intermediate Climate Data Records, using a data assimilation method to provide SSTs where there were no measurements. \r\n\r\nThis dataset was produced for the Copernicus Climate Change Service (C3S). V2.0 extends from 2017-2021.\r\n\r\nA historic Climate Data Record (CDR) has also been produced under the ESA Climate Change Initiative Sea Surface Temperature (CCI_sst). This is available as a separate dataset in the CEDA catalogue and through the ESA CCI Open Data Portal." } }, { "ob_id": 799, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 38002, "uuid": "c6b366dabf9b4536b5500e5f1f7a7235", "short_code": "ob", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 6.12 (v20220824)", "abstract": "Input Data for Figure 6.12 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 6.12 shows contribution to effective radiative forcing (ERF) and global mean surface air temperature (GSAT) change from component emissions between 1750 to 2019 based on CMIP6 models. \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\nSzopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, H. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers. 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. 817–922, doi:10.1017/9781009157896.008.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 2 subpanels, with data provided for both panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Contribution to effective radiative forcing (ERF) (a) and global mean surface air temperature (GSAT) change (b) from component emissions between 1750 to 2019 based on CMIP6 models\r\n\r\nERFs for the direct effect of well-mixed greenhouse gases (WMGHGs) are from the analytical formulae in section 7.3.2, H2O (strat) is from Table 7.8. ERFs for other components are multi-model means from Thornhill et al. (2021b) and are based on ESM simulations in which emissions of one species at a time are increased from 1850 to 2014 levels. The derived emissions-based ERFs are rescaled to match the concentration-based ERFs in Figure 7.6.\r\n\r\nError bars are 5–95% and for the ERF account for uncertainty in radiative efficiencies and multi-model error in the means. ERFs due to aerosol–radiation (ERFari) and cloud effects are calculated from separate radiation calls for clear-sky and aerosol-free conditions (Ghan, 2013; Thornhill et al., 2021b). \r\n\r\n‘Cloud’ includes cloud adjustments (semi-direct effect) and ERF from indirect aerosol-cloud to –0.22 W m–2 for ERFari and –0.84 W m–2 interactions (ERFaci). The aerosol components (SO2, organic carbon and black carbon) are scaled to sum to –0.22 W m–2 for ERFari and –0.84 W m–2 for ‘cloud’ (Section 7.3.3). \r\n\r\nFor GSAT estimates, time series (1750–2019) for the ERFs have been estimated by scaling with concentrations for WMGHGs and with historical emissions for SLCFs. The time variation of ERFaci for aerosols is from Chapter 7. The global mean temperature response is calculated from the ERF time series using an impulse response function (Cross-Chapter Box 7.1) with a climate feedback parameter of –1.31 W m–2 °C–1. \r\n\r\nContributions to ERF and GSAT change from contrails and light-absorbing particles on snow and ice are not represented, but their estimates can be seen on Figure 7.6 and 7.7, respectively. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 6.SM.3)\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nERFari stands for Effective Radiative Forcing of aerosol-radiation interactions.\r\nERFaci stands for Effective Radiative Forcing of aerosol-cloud interactions. \r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 6.12:\r\n \r\n - Data file: hodnebrog_tab3.csv: Radiative forcing for HFCs from Hodnebrog et al (2020)\r\n - Data file: recommended_irf_from_2xCO2_2021_02_25_222758.csv: Impulse response function (IRF) from AR6\r\n - Data file: table2_thornhill2020.csv: ERF from Thornhill et al (2021)\r\n - Data file: attribution_input.csv\r\n - Data file: attribution_input_sd.csv\r\n\r\nThe folder: 'LLGHG_history_AR6_v9_updated' - contains csv files for each sheet in excel file 'LLGHG_history_AR6_v9_updated.xlsx' which gives historical concentrations from AR6.\r\n\r\nThe folder CEDS_v2021-02-05_emissions (historical emissions of SLCFs from CEDS) contains the following file formats:\r\n\r\n${component}$_${region}$_CEDS_emissions_by${category}$_${type}$_2021_02_05.csv, with:\r\n\r\n- ${component}: BC, CH4, CO2, CO, N2O, NH3, NMVOC, NOx, OC, SO2\r\n- ${region}: blank, or 'global'\r\n- ${category}: sector, country, sector and country\r\n- ${type}: blank, or 'fuel'\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nPanels were plotted using Python and the code has been embedded in Jupyter notebooks for reproducibility - code is available in the GitHub repository linked in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3\r\n - Link to the GitHub repository containing the Jupyter notebooks used to run the code associated with this figure.\r\n - Link to the code for the figure, archived on Zenodo." }, "objectObservation": { "ob_id": 37887, "uuid": "8855e410adf547b4afd039a5b88487f4", "short_code": "ob", "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 6.12 (v20220815)", "abstract": "Data for Figure 6.12 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 6.12 shows contribution to effective radiative forcing (ERF) and global mean surface air temperature (GSAT) change from component emissions between 1750 to 2019 based on CMIP6 models. \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\nSzopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, H. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers. 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. 817–922, doi:10.1017/9781009157896.008.\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 2 subpanels, with data provided for both panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Contribution to effective radiative forcing (ERF) (a) and global mean surface air temperature (GSAT) change (b) from component emissions between 1750 to 2019 based on CMIP6 models\r\n\r\nERFs for the direct effect of well-mixed greenhouse gases (WMGHGs) are from the analytical formulae in section 7.3.2, H2O (strat) is from Table 7.8. ERFs for other components are multi-model means from Thornhill et al. (2021b) and are based on ESM simulations in which emissions of one species at a time are increased from 1850 to 2014 levels. The derived emissions-based ERFs are rescaled to match the concentration-based ERFs in Figure 7.6.\r\n\r\nError bars are 5–95% and for the ERF account for uncertainty in radiative efficiencies and multi-model error in the means. ERFs due to aerosol–radiation (ERFari) and cloud effects are calculated from separate radiation calls for clear-sky and aerosol-free conditions (Ghan, 2013; Thornhill et al., 2021b). \r\n\r\n‘Cloud’ includes cloud adjustments (semi-direct effect) and ERF from indirect aerosol-cloud to –0.22 W m–2 for ERFari and –0.84 W m–2 interactions (ERFaci). The aerosol components (SO2, organic carbon and black carbon) are scaled to sum to –0.22 W m–2 for ERFari and –0.84 W m–2 for ‘cloud’ (Section 7.3.3). \r\n\r\nFor GSAT estimates, time series (1750–2019) for the ERFs have been estimated by scaling with concentrations for WMGHGs and with historical emissions for SLCFs. The time variation of ERFaci for aerosols is from Chapter 7. The global mean temperature response is calculated from the ERF time series using an impulse response function (Cross-Chapter Box 7.1) with a climate feedback parameter of –1.31 W m–2 °C–1. \r\n\r\nContributions to ERF and GSAT change from contrails and light-absorbing particles on snow and ice are not represented, but their estimates can be seen on Figure 7.6 and 7.7, respectively. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 6.SM.3)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 6.12:\r\n \r\n - Data file: fig_em_based_ERF_GSAT_period_1750-2019_values_ERF.csv\r\n - Data file: fig_em_based_ERF_GSAT_period_1750-2019_values_ERF_uncertainty.csv\r\n - Data file: fig_em_based_ERF_GSAT_period_1750-2019_values_dT.csv\r\n - Data file: fig_em_based_ERF_GSAT_period_1750-2019_values_dT_uncertainty.csv\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nERFari stands for Effective Radiative Forcing of aerosol-radiation interactions.\r\nERFaci stands for Effective Radiative Forcing of aerosol-cloud interactions. \r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nPanels were plotted using Python and the code has been embedded in Jupyter notebooks for reproducibility - code is available in the GitHub repository linked in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 6)\r\n - Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3\r\n- Link to the GitHub repository containing the Jupyter notebooks used to run the code associated with this figure.\r\n- Link to the code for the figure, archived on Zenodo." } }, { "ob_id": 800, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40053, "uuid": "503edf9eb68040c4a439fed88b81c8c9", "short_code": "ob", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 9.22 (v20230206)", "abstract": "Data for Figure 9.22 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 9.22 shows simulated versus observed permafrost extent and volume change by warming level. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nFox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 2 subpanels, with data provided for both panels in one central directory.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- (a) Diagnosed Northern Hemisphere permafrost extent (area with perennially frozen ground at 15 m depth, or at the deepest model soil level if this is above 15 m) for 1979–1998, for available CMIP5 and CMIP6 models, from the first ensemble member of the historical coupled run, and for CMIP6 AMIP (atmosphere+land surface, prescribed ocean) and land-hist (land only, prescribed atmospheric forcing) runs. \r\n\r\n- (b) Simulated global permafrost volume change between the surface and 3 m depth as a function of the simulated global surface air temperature (GSAT) change, from the first ensemble members of a selection of scenarios, for available CMIP6 models. \r\n\r\nEstimates of current permafrost extents based on physical evidence and reanalyses are indicated as black symbols – triangle: Obu et al. (2018); star: Zhang et al. (1999); circle: central value and associated range from Gruber (2012). \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 9.SM.9)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 9.22\r\n \r\n- Data file: pf15m_CMIP5historical_NH_1979-1998.txt\r\n- Data file: pf15m_amip_NH_1979-1998.txt\r\n- Data file: pf15m_historical_NH_1979-1998.txt\r\n- Data file: pf15m_land-hist_NH_1979-1998.txt\r\n- Data file: pfv_ACCESS-CM2_historical_ssp126.nc\r\n- Data file: pfv_ACCESS-CM2_historical_ssp245.nc\r\n- Data file: pfv_ACCESS-CM2_historical_ssp370.nc\r\n- Data file: pfv_ACCESS-CM2_historical_ssp585.nc\r\n- Data file: pfv_ACCESS-ESM1-5_historical_ssp126.nc\r\n- Data file: pfv_ACCESS-ESM1-5_historical_ssp245.nc\r\n- Data file: pfv_ACCESS-ESM1-5_historical_ssp370.nc\r\n- Data file: pfv_ACCESS-ESM1-5_historical_ssp585.nc\r\n- Data file: pfv_BCC-CSM2-MR_historical_ssp126.nc\r\n- Data file: pfv_BCC-CSM2-MR_historical_ssp245.nc\r\n- Data file: pfv_BCC-CSM2-MR_historical_ssp370.nc\r\n- Data file: pfv_BCC-CSM2-MR_historical_ssp585.nc\r\n- Data file: pfv_CAMS-CSM1-0_historical_ssp126.nc\r\n- Data file: pfv_CAMS-CSM1-0_historical_ssp245.nc\r\n- Data file: pfv_CAMS-CSM1-0_historical_ssp370.nc\r\n- Data file: pfv_CAMS-CSM1-0_historical_ssp585.nc\r\n- Data file: pfv_CESM2-WACCM_historical_ssp126.nc\r\n- Data file: pfv_CESM2-WACCM_historical_ssp245.nc\r\n- Data file: pfv_CESM2-WACCM_historical_ssp370.nc\r\n- Data file: pfv_CESM2-WACCM_historical_ssp585.nc\r\n- Data file: pfv_CESM2_historical_ssp126.nc\r\n- Data file: pfv_CESM2_historical_ssp245.nc\r\n- Data file: pfv_CESM2_historical_ssp370.nc\r\n- Data file: pfv_CESM2_historical_ssp585.nc\r\n- Data file: pfv_CNRM-CM6-1-HR_historical_ssp126.nc\r\n- Data file: pfv_CNRM-CM6-1-HR_historical_ssp245.nc\r\n- Data file: pfv_CNRM-CM6-1-HR_historical_ssp370.nc\r\n- Data file: pfv_CNRM-CM6-1-HR_historical_ssp585.nc\r\n- Data file: pfv_CNRM-CM6-1_historical_ssp126.nc\r\n- Data file: pfv_CNRM-CM6-1_historical_ssp245.nc\r\n- Data file: pfv_CNRM-CM6-1_historical_ssp370.nc\r\n- Data file: pfv_CNRM-CM6-1_historical_ssp585.nc\r\n- Data file: pfv_CNRM-ESM2-1_historical_ssp126.nc\r\n- Data file: pfv_CNRM-ESM2-1_historical_ssp245.nc\r\n- Data file: pfv_CNRM-ESM2-1_historical_ssp370.nc\r\n- Data file: pfv_CNRM-ESM2-1_historical_ssp585.nc\r\n- Data file: pfv_CanESM5-CanOE_historical_ssp126.nc\r\n- Data file: pfv_CanESM5-CanOE_historical_ssp245.nc\r\n- Data file: pfv_CanESM5-CanOE_historical_ssp370.nc\r\n- Data file: pfv_CanESM5-CanOE_historical_ssp585.nc\r\n- Data file: pfv_CanESM5_historical_ssp126.nc\r\n- Data file: pfv_CanESM5_historical_ssp245.nc\r\n- Data file: pfv_CanESM5_historical_ssp370.nc\r\n- Data file: pfv_CanESM5_historical_ssp585.nc\r\n- Data file: pfv_EC-Earth3_historical_ssp126.nc\r\n- Data file: pfv_EC-Earth3_historical_ssp245.nc\r\n- Data file: pfv_EC-Earth3_historical_ssp370.nc\r\n- Data file: pfv_EC-Earth3_historical_ssp585.nc\r\n- Data file: pfv_FGOALS-g3_historical_ssp126.nc\r\n- Data file: pfv_FGOALS-g3_historical_ssp245.nc\r\n- Data file: pfv_FGOALS-g3_historical_ssp370.nc\r\n- Data file: pfv_FGOALS-g3_historical_ssp585.nc\r\n- Data file: pfv_GFDL-CM4_historical_ssp245.nc\r\n- Data file: pfv_GFDL-CM4_historical_ssp585.nc\r\n- Data file: pfv_GFDL-ESM4_historical_ssp126.nc\r\n- Data file: pfv_GFDL-ESM4_historical_ssp245.nc\r\n- Data file: pfv_GFDL-ESM4_historical_ssp370.nc\r\n- Data file: pfv_GFDL-ESM4_historical_ssp585.nc\r\n- Data file: pfv_GISS-E2-1-G_historical_ssp126.nc\r\n- Data file: pfv_GISS-E2-1-G_historical_ssp245.nc\r\n- Data file: pfv_GISS-E2-1-G_historical_ssp370.nc\r\n- Data file: pfv_GISS-E2-1-G_historical_ssp585.nc\r\n- Data file: pfv_HadGEM3-GC31-LL_historical_ssp126.nc\r\n- Data file: pfv_HadGEM3-GC31-LL_historical_ssp245.nc\r\n- Data file: pfv_HadGEM3-GC31-LL_historical_ssp585.nc\r\n- Data file: pfv_IPSL-CM6A-LR_historical_ssp126.nc\r\n- Data file: pfv_IPSL-CM6A-LR_historical_ssp245.nc\r\n- Data file: pfv_IPSL-CM6A-LR_historical_ssp370.nc\r\n- Data file: pfv_IPSL-CM6A-LR_historical_ssp585.nc\r\n- Data file: pfv_KACE-1-0-G_historical_ssp126.nc\r\n- Data file: pfv_KACE-1-0-G_historical_ssp245.nc\r\n- Data file: pfv_KACE-1-0-G_historical_ssp370.nc\r\n- Data file: pfv_KACE-1-0-G_historical_ssp585.nc\r\n- Data file: pfv_MIROC-ES2L_historical_ssp126.nc\r\n- Data file: pfv_MIROC-ES2L_historical_ssp245.nc\r\n- Data file: pfv_MIROC-ES2L_historical_ssp370.nc\r\n- Data file: pfv_MIROC-ES2L_historical_ssp585.nc\r\n- Data file: pfv_MIROC6_historical_ssp126.nc\r\n- Data file: pfv_MIROC6_historical_ssp245.nc\r\n- Data file: pfv_MIROC6_historical_ssp370.nc\r\n- Data file: pfv_MIROC6_historical_ssp585.nc\r\n- Data file: pfv_MPI-ESM1-2-HR_historical_ssp126.nc\r\n- Data file: pfv_MPI-ESM1-2-HR_historical_ssp245.nc\r\n- Data file: pfv_MPI-ESM1-2-HR_historical_ssp370.nc\r\n- Data file: pfv_MPI-ESM1-2-HR_historical_ssp585.nc\r\n- Data file: pfv_MPI-ESM1-2-LR_historical_ssp126.nc\r\n- Data file: pfv_MPI-ESM1-2-LR_historical_ssp245.nc\r\n- Data file: pfv_MPI-ESM1-2-LR_historical_ssp370.nc\r\n- Data file: pfv_MPI-ESM1-2-LR_historical_ssp585.nc\r\n- Data file: pfv_MRI-ESM2-0_historical_ssp126.nc\r\n- Data file: pfv_MRI-ESM2-0_historical_ssp245.nc\r\n- Data file: pfv_MRI-ESM2-0_historical_ssp370.nc\r\n- Data file: pfv_MRI-ESM2-0_historical_ssp585.nc\r\n- Data file: pfv_NorESM2-LM_historical_ssp126.nc\r\n- Data file: pfv_NorESM2-LM_historical_ssp245.nc\r\n- Data file: pfv_NorESM2-LM_historical_ssp370.nc\r\n- Data file: pfv_NorESM2-LM_historical_ssp585.nc\r\n- Data file: pfv_NorESM2-MM_historical_ssp126.nc\r\n- Data file: pfv_NorESM2-MM_historical_ssp245.nc\r\n- Data file: pfv_NorESM2-MM_historical_ssp370.nc\r\n- Data file: pfv_NorESM2-MM_historical_ssp585.nc\r\n- Data file: pfv_UKESM1-0-LL_historical_ssp126.nc\r\n- Data file: pfv_UKESM1-0-LL_historical_ssp245.nc\r\n- Data file: pfv_UKESM1-0-LL_historical_ssp370.nc\r\n- Data file: pfv_UKESM1-0-LL_historical_ssp585.nc\r\n\r\nIn the GitHub repository the filenames differ from that listed above, the final underscore is replaced with a '+'.\r\nFor example, ' pfv_ACCESS-CM2_historical_ssp126.nc' in the repository is called ' pfv_ACCESS-CM2_historical+ssp126.nc'\r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nAMIP is the Atmospheric Modelling Intercomparison Project.\r\nGSAT stands for Global Surface Air Temperature.\r\nACCESS-CM2 is the Australian Community Climate and Earth System Simulator coupled climate model.\r\nACCESS-ESM1-5 is the Australian Community Climate and Earth System Simulator Earth system model version designed to participate in CMIP6 simulations.\r\nBCC-CSM2-MR is one of the Beijing Climate Center Climate System Models designed for use in CMIP6 simulations.\r\nCAMS-CSM1-0 is the Chinese Academy of Meteorological Sciences Climate System Model version 1.\r\nCESM is the Community Earth System Model. \r\nCESM2-WACCM is the Community System Model - Whole Atmosphere Community Climate Model.\r\nCNRM-CM6-1 is the Centre National de Recherches Météorologiques Climate Model for CMIP6.\r\nCNRM-CM6-1-HR is the Centre National de Recherches Météorologiques Climate Model for CMIP6 - altered Horizontal Resolution.\r\nCNRM-ESM2-1 is the Centre National de Recherches Météorologiques Earth System Model, derived from CNRM-CM6-1.\r\nCanESM5 is the Canadian Earth System Model version 5.\r\nCanESM5-CanOE is the Canadian Earth System Model version 5 - Canadian Ocean Ecosystem.\r\nEC-Earth3 is the European Community Earth-system model version 3.\r\nFGOALS-g3 is the Flexible Global Ocean-Atmosphere-Land System Model, Grid-point Version 3\r\nGFDL-ESM4 is the Geophysical Fluid Dynamics Laboratory - Earth System Model version 4.\r\nGISS-E2-1-G is the Goddard Institute for Space Studies - chemistry-climate model version E2.1, using the GISS Ocean v1 (G01) model.\r\nHadGEM3-GC31-LL is the Met Offfice Hadley Centre Global Environment Model - Global Coupled configuration 3.1 - using an atmosphere/ocean resolution for historical simulation N96/ORCA1.\r\nIPSL-CM6A-LR is the Institut Pierre-Simon Laplace Climate Model for CMIP6 - Low Resolution.\r\nKACE-1-0-G is the Korean Advanced Community Earth system model. \r\nMIROC-ES2L is the Model for Interdisciplinary Research on Climate - Earth System version 2 for Long-term simulations.\r\nMIROC6 is the Model for Interdisciplinary Research on Climate - version 6.\r\nMPI-ESM1-2-HR is the Max Planck Institute Earth System Model - version 2 - altered Horizontal Resolution.\r\nMPI-ESM1-2-LR is the Max Planck Institute Earth System Model - version 2 - Low Resolution.\r\nMRI-ESM2-0 is the Meteorological Research Institute Earth System Model version 2.0.\r\nNorESM2-LM is the Norwegian Earth System Model version 2 - 2 degree resolution for atmosphere and land components, 1 degree resolution for ocean and sea-ice components.\r\nNorESM2-MM is the Norwegian Earth System Model version 2 - 1 degree resolution for all model components.\r\nUKESM1-0-LL is the United Kingdom Earth System Modelling project - version 1 - 2 degree resolution for all model components.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nThe panels were plotted using Python and shell scripts (BASH files) - code is available via the link in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n - Link to the data and code used to produce this figure and others in Chapter 9, archived on Zenodo.\r\n - Link to the code and output data for this figure, contained in a dedicated GitHub repository." }, "objectObservation": { "ob_id": 37736, "uuid": "80475295b32f4df6879ad7d2a23a88c1", "short_code": "ob", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 9.22 (v20220721)", "abstract": "Data for Figure 9.22 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 9.22 shows simulated versus observed permafrost extent and volume change by warming level. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nFox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 2 subpanels, with data provided for both panels in one central directory.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- (a) Diagnosed Northern Hemisphere permafrost extent (area with perennially frozen ground at 15 m depth, or at the deepest model soil level if this is above 15 m) for 1979–1998, for available CMIP5 and CMIP6 models, from the first ensemble member of the historical coupled run, and for CMIP6 AMIP (atmosphere+land surface, prescribed ocean) and land-hist (land only, prescribed atmospheric forcing) runs. \r\n\r\n- (b) Simulated global permafrost volume change between the surface and 3 m depth as a function of the simulated global surface air temperature (GSAT) change, from the first ensemble members of a selection of scenarios, for available CMIP6 models. \r\n\r\nEstimates of current permafrost extents based on physical evidence and reanalyses are indicated as black symbols – triangle: Obu et al. (2018); star: Zhang et al. (1999); circle: central value and associated range from Gruber (2012). \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 9.SM.9)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 9.22\r\n \r\n- Data file: pf15m_CMIP5historical_NH_1979-1998.txt\r\n- Data file: pf15m_amip_NH_1979-1998.txt\r\n- Data file: pf15m_historical_NH_1979-1998.txt\r\n- Data file: pf15m_land-hist_NH_1979-1998.txt\r\n- Data file: pfv_ACCESS-CM2_historical_ssp126.nc\r\n- Data file: pfv_ACCESS-CM2_historical_ssp245.nc\r\n- Data file: pfv_ACCESS-CM2_historical_ssp370.nc\r\n- Data file: pfv_ACCESS-CM2_historical_ssp585.nc\r\n- Data file: pfv_ACCESS-ESM1-5_historical_ssp126.nc\r\n- Data file: pfv_ACCESS-ESM1-5_historical_ssp245.nc\r\n- Data file: pfv_ACCESS-ESM1-5_historical_ssp370.nc\r\n- Data file: pfv_ACCESS-ESM1-5_historical_ssp585.nc\r\n- Data file: pfv_BCC-CSM2-MR_historical_ssp126.nc\r\n- Data file: pfv_BCC-CSM2-MR_historical_ssp245.nc\r\n- Data file: pfv_BCC-CSM2-MR_historical_ssp370.nc\r\n- Data file: pfv_BCC-CSM2-MR_historical_ssp585.nc\r\n- Data file: pfv_CAMS-CSM1-0_historical_ssp126.nc\r\n- Data file: pfv_CAMS-CSM1-0_historical_ssp245.nc\r\n- Data file: pfv_CAMS-CSM1-0_historical_ssp370.nc\r\n- Data file: pfv_CAMS-CSM1-0_historical_ssp585.nc\r\n- Data file: pfv_CESM2-WACCM_historical_ssp126.nc\r\n- Data file: pfv_CESM2-WACCM_historical_ssp245.nc\r\n- Data file: pfv_CESM2-WACCM_historical_ssp370.nc\r\n- Data file: pfv_CESM2-WACCM_historical_ssp585.nc\r\n- Data file: pfv_CESM2_historical_ssp126.nc\r\n- Data file: pfv_CESM2_historical_ssp245.nc\r\n- Data file: pfv_CESM2_historical_ssp370.nc\r\n- Data file: pfv_CESM2_historical_ssp585.nc\r\n- Data file: pfv_CNRM-CM6-1-HR_historical_ssp126.nc\r\n- Data file: pfv_CNRM-CM6-1-HR_historical_ssp245.nc\r\n- Data file: pfv_CNRM-CM6-1-HR_historical_ssp370.nc\r\n- Data file: pfv_CNRM-CM6-1-HR_historical_ssp585.nc\r\n- Data file: pfv_CNRM-CM6-1_historical_ssp126.nc\r\n- Data file: pfv_CNRM-CM6-1_historical_ssp245.nc\r\n- Data file: pfv_CNRM-CM6-1_historical_ssp370.nc\r\n- Data file: pfv_CNRM-CM6-1_historical_ssp585.nc\r\n- Data file: pfv_CNRM-ESM2-1_historical_ssp126.nc\r\n- Data file: pfv_CNRM-ESM2-1_historical_ssp245.nc\r\n- Data file: pfv_CNRM-ESM2-1_historical_ssp370.nc\r\n- Data file: pfv_CNRM-ESM2-1_historical_ssp585.nc\r\n- Data file: pfv_CanESM5-CanOE_historical_ssp126.nc\r\n- Data file: pfv_CanESM5-CanOE_historical_ssp245.nc\r\n- Data file: pfv_CanESM5-CanOE_historical_ssp370.nc\r\n- Data file: pfv_CanESM5-CanOE_historical_ssp585.nc\r\n- Data file: pfv_CanESM5_historical_ssp126.nc\r\n- Data file: pfv_CanESM5_historical_ssp245.nc\r\n- Data file: pfv_CanESM5_historical_ssp370.nc\r\n- Data file: pfv_CanESM5_historical_ssp585.nc\r\n- Data file: pfv_EC-Earth3_historical_ssp126.nc\r\n- Data file: pfv_EC-Earth3_historical_ssp245.nc\r\n- Data file: pfv_EC-Earth3_historical_ssp370.nc\r\n- Data file: pfv_EC-Earth3_historical_ssp585.nc\r\n- Data file: pfv_FGOALS-g3_historical_ssp126.nc\r\n- Data file: pfv_FGOALS-g3_historical_ssp245.nc\r\n- Data file: pfv_FGOALS-g3_historical_ssp370.nc\r\n- Data file: pfv_FGOALS-g3_historical_ssp585.nc\r\n- Data file: pfv_GFDL-CM4_historical_ssp245.nc\r\n- Data file: pfv_GFDL-CM4_historical_ssp585.nc\r\n- Data file: pfv_GFDL-ESM4_historical_ssp126.nc\r\n- Data file: pfv_GFDL-ESM4_historical_ssp245.nc\r\n- Data file: pfv_GFDL-ESM4_historical_ssp370.nc\r\n- Data file: pfv_GFDL-ESM4_historical_ssp585.nc\r\n- Data file: pfv_GISS-E2-1-G_historical_ssp126.nc\r\n- Data file: pfv_GISS-E2-1-G_historical_ssp245.nc\r\n- Data file: pfv_GISS-E2-1-G_historical_ssp370.nc\r\n- Data file: pfv_GISS-E2-1-G_historical_ssp585.nc\r\n- Data file: pfv_HadGEM3-GC31-LL_historical_ssp126.nc\r\n- Data file: pfv_HadGEM3-GC31-LL_historical_ssp245.nc\r\n- Data file: pfv_HadGEM3-GC31-LL_historical_ssp585.nc\r\n- Data file: pfv_IPSL-CM6A-LR_historical_ssp126.nc\r\n- Data file: pfv_IPSL-CM6A-LR_historical_ssp245.nc\r\n- Data file: pfv_IPSL-CM6A-LR_historical_ssp370.nc\r\n- Data file: pfv_IPSL-CM6A-LR_historical_ssp585.nc\r\n- Data file: pfv_KACE-1-0-G_historical_ssp126.nc\r\n- Data file: pfv_KACE-1-0-G_historical_ssp245.nc\r\n- Data file: pfv_KACE-1-0-G_historical_ssp370.nc\r\n- Data file: pfv_KACE-1-0-G_historical_ssp585.nc\r\n- Data file: pfv_MIROC-ES2L_historical_ssp126.nc\r\n- Data file: pfv_MIROC-ES2L_historical_ssp245.nc\r\n- Data file: pfv_MIROC-ES2L_historical_ssp370.nc\r\n- Data file: pfv_MIROC-ES2L_historical_ssp585.nc\r\n- Data file: pfv_MIROC6_historical_ssp126.nc\r\n- Data file: pfv_MIROC6_historical_ssp245.nc\r\n- Data file: pfv_MIROC6_historical_ssp370.nc\r\n- Data file: pfv_MIROC6_historical_ssp585.nc\r\n- Data file: pfv_MPI-ESM1-2-HR_historical_ssp126.nc\r\n- Data file: pfv_MPI-ESM1-2-HR_historical_ssp245.nc\r\n- Data file: pfv_MPI-ESM1-2-HR_historical_ssp370.nc\r\n- Data file: pfv_MPI-ESM1-2-HR_historical_ssp585.nc\r\n- Data file: pfv_MPI-ESM1-2-LR_historical_ssp126.nc\r\n- Data file: pfv_MPI-ESM1-2-LR_historical_ssp245.nc\r\n- Data file: pfv_MPI-ESM1-2-LR_historical_ssp370.nc\r\n- Data file: pfv_MPI-ESM1-2-LR_historical_ssp585.nc\r\n- Data file: pfv_MRI-ESM2-0_historical_ssp126.nc\r\n- Data file: pfv_MRI-ESM2-0_historical_ssp245.nc\r\n- Data file: pfv_MRI-ESM2-0_historical_ssp370.nc\r\n- Data file: pfv_MRI-ESM2-0_historical_ssp585.nc\r\n- Data file: pfv_NorESM2-LM_historical_ssp126.nc\r\n- Data file: pfv_NorESM2-LM_historical_ssp245.nc\r\n- Data file: pfv_NorESM2-LM_historical_ssp370.nc\r\n- Data file: pfv_NorESM2-LM_historical_ssp585.nc\r\n- Data file: pfv_NorESM2-MM_historical_ssp126.nc\r\n- Data file: pfv_NorESM2-MM_historical_ssp245.nc\r\n- Data file: pfv_NorESM2-MM_historical_ssp370.nc\r\n- Data file: pfv_NorESM2-MM_historical_ssp585.nc\r\n- Data file: pfv_UKESM1-0-LL_historical_ssp126.nc\r\n- Data file: pfv_UKESM1-0-LL_historical_ssp245.nc\r\n- Data file: pfv_UKESM1-0-LL_historical_ssp370.nc\r\n- Data file: pfv_UKESM1-0-LL_historical_ssp585.nc\r\n\r\nIn the GitHub repository the filenames differ from that listed above, the final underscore is replaced with a '+'.\r\nFor example, ' pfv_ACCESS-CM2_historical_ssp126.nc' in the repository is called ' pfv_ACCESS-CM2_historical+ssp126.nc'\r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nAMIP is the Atmospheric Modelling Intercomparison Project.\r\nGSAT stands for Global Surface Air Temperature.\r\nACCESS-CM2 is the Australian Community Climate and Earth System Simulator coupled climate model.\r\nACCESS-ESM1-5 is the Australian Community Climate and Earth System Simulator Earth system model version designed to participate in CMIP6 simulations.\r\nBCC-CSM2-MR is one of the Beijing Climate Center Climate System Models designed for use in CMIP6 simulations.\r\nCAMS-CSM1-0 is the Chinese Academy of Meteorological Sciences Climate System Model version 1.\r\nCESM is the Community Earth System Model. \r\nCESM2-WACCM is the Community System Model - Whole Atmosphere Community Climate Model.\r\nCNRM-CM6-1 is the Centre National de Recherches Météorologiques Climate Model for CMIP6.\r\nCNRM-CM6-1-HR is the Centre National de Recherches Météorologiques Climate Model for CMIP6 - altered Horizontal Resolution.\r\nCNRM-ESM2-1 is the Centre National de Recherches Météorologiques Earth System Model, derived from CNRM-CM6-1.\r\nCanESM5 is the Canadian Earth System Model version 5.\r\nCanESM5-CanOE is the Canadian Earth System Model version 5 - Canadian Ocean Ecosystem.\r\nEC-Earth3 is the European Community Earth-system model version 3.\r\nFGOALS-g3 is the Flexible Global Ocean-Atmosphere-Land System Model, Grid-point Version 3\r\nGFDL-ESM4 is the Geophysical Fluid Dynamics Laboratory - Earth System Model version 4.\r\nGISS-E2-1-G is the Goddard Institute for Space Studies - chemistry-climate model version E2.1, using the GISS Ocean v1 (G01) model.\r\nHadGEM3-GC31-LL is the Met Offfice Hadley Centre Global Environment Model - Global Coupled configuration 3.1 - using an atmosphere/ocean resolution for historical simulation N96/ORCA1.\r\nIPSL-CM6A-LR is the Institut Pierre-Simon Laplace Climate Model for CMIP6 - Low Resolution.\r\nKACE-1-0-G is the Korean Advanced Community Earth system model. \r\nMIROC-ES2L is the Model for Interdisciplinary Research on Climate - Earth System version 2 for Long-term simulations.\r\nMIROC6 is the Model for Interdisciplinary Research on Climate - version 6.\r\nMPI-ESM1-2-HR is the Max Planck Institute Earth System Model - version 2 - altered Horizontal Resolution.\r\nMPI-ESM1-2-LR is the Max Planck Institute Earth System Model - version 2 - Low Resolution.\r\nMRI-ESM2-0 is the Meteorological Research Institute Earth System Model version 2.0.\r\nNorESM2-LM is the Norwegian Earth System Model version 2 - 2 degree resolution for atmosphere and land components, 1 degree resolution for ocean and sea-ice components.\r\nNorESM2-MM is the Norwegian Earth System Model version 2 - 1 degree resolution for all model components.\r\nUKESM1-0-LL is the United Kingdom Earth System Modelling project - version 1 - 2 degree resolution for all model components.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nThe panels were plotted using Python and shell scripts (BASH files) - code is available via the link in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n - Link to the data and code used to produce this figure and others in Chapter 9, archived on Zenodo.\r\n - Link to the code and output data for this figure, contained in a dedicated GitHub repository." } }, { "ob_id": 801, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 40089, "uuid": "c622adfeb4cc4ae181dc4cca82c2311c", "short_code": "ob", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Cross-Chapter Box 9.1, Figure 1 (v20230523)", "abstract": "Data for Cross-Chapter Box 9.1, Figure 1 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nCross-Chapter Box 9.1, Figure 1 shows observed and simulated regional probability ratio of marine heatwaves (MHWs) for the 1985-2014 period and for the end of the 21st century under two different greenhouse gas emissions 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 Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels with data provided for all panels in the main directory. \r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n Main assessment timeseries for GMSL change, OHC and ThSL. Timeseries are global integrals over the following vertical layers: 0-300 m; 0-700 m; 0-2000 m; 700-2000 m; > 2000 m; Full-depth.\r\n\r\n\r\nThis dataset are also used in the following figures:\r\na) AR6 FGD assessment timeseries GMSL satellite altimeter: Figure 2.28; \r\nb) AR6 FGD assessment timeseries GMSL tide gauge: Figure 2.28;\r\nc) AR6 FGD assessment timeseries OHC: Figure 3.26, Box 7.2, Figure 1; \r\n\r\nOther figures/tables: Figure 2.26, Table 2.7; Figure 3.26; Box 7.2 Figure 1, Table 9.5; Figure TS.8; Figure TS.13.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a: \r\n Data file: “AR6_FGD_assessment_timeseries_OHC.csv” => column 2 is used to plot the light blue shaded region, column 4 is used to plot the medium blue shaded region, column 6 is used to plot the dark blue shaded region in CCBox9.1 Figure 1 panel a). \r\n\r\n\r\nPanel b: \r\n Data file: “AR6_FGD_assessment_timseries_GMSL_satellite_altimeter.csv” => column 2 is used to plot the dashed black line in CCBox9.1 Figure 1 panel b)\r\n Data file: “AR6_FGD_assesssment_timeseries_GMSL_tide_gauge.csv” => column 2 is used to to plot the dashed black line in CCBox9.1 Figure 1 panel b)\r\n Data file: “AR6_FGD_assessment_timeseries_ThSL.csv” => column 2 is used to plot the light blue shaded region, column 4 is used to plot the medium blue shaded region, column 6 is used to plot the dark blue shaded region in CCBox9.1 Figure 1 panel b).\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to the code for the figure, archived on github repository for chapter 9.\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to Chapter 2 Figure 2.26 \r\n - Link to Chapter 2 Figure 2.28\r\n - Link to Chapter 3 Figure 3.26\r\n - Link to Chapter 7 Box 7.2, Figure 1\r\n - Link to Technical Summary Figure TS.13\r\n - Link to input data for Cross-Chapter Box 9.1, Figure 1" }, "objectObservation": { "ob_id": 39775, "uuid": "d54f2a1e4d2f42e68c10e2b11668dcd6", "short_code": "ob", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for CCB 9.1, Figure 1 (v20230310)", "abstract": "Input Data for CCB 9.1, Figure 1 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nCross Chapter Box 9.1, Figure 1 shows observed and simulated regional probability ratio of marine heatwaves (MHWs) for the 1985-2014 period and for the end of the 21st century under two different greenhouse gas emissions 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 Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels with input data provided for all panels in the main directory. \r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n Pre-processed annual timeseries of global ocean heat content change (ZJ) and global thermal expansion (mm) for the period 1870 to 2020. \r\n\r\n Timeseries are global integrals over the following vertical layers: 0-300 m; 0-700 m; 0-2000 m; 700-2000 m; > 2000 m; Full-depth.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a: global ocean heat content change (ZJ) for the layers 0-700 m, 700-2000 m and > 2000 m are represented by the blue shaded regions as indicated in the figure legend.\r\n Panel b: global thermal expansion (mm) for the layers 0-700 m, 700-2000 m and > 2000 m are represented by the blue shaded regions as indicated in the figure legend.\r\n\r\nFor files 'AR6_OHC_timeseries_MDP_2021-01-20_more_than_2000m.csv' and 'AR6_OHC_timeseries_MDP_2021-01-20_more_than_2000m_error.csv', 'more_than' has replaced '>' from the original filenames for archival. \r\n\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n These files contain the data used as input to the code used to generate the ensemble assessment time series of ocean heat content (OHC) change and global thermal expansion (ThSL) that were developed for the IPCC AR6 WG1 report.\r\n\r\n\r\nThe Python script used is called: compute_OHC_ThSL_ensemble_FGD_python3.py. The link to the code, archived on Zenodo, is provided in the Related Documents section of this catalogue record.\r\n\r\n\r\nThe actual file used as input data to the code is in a non proprietary format (AR6_GOHC_GThSL_timeseries_MDP_2021-01-20.mat) and is archived on Zenodo together with the code. The files archived here contain the same information but in a csv format.\r\n\r\n\r\nTo run the code, you will need to edit the paths for plotdir, savedir and datadir based on your local directory structure. \r\n\r\n\r\nOn running the code, the script creates two *.pickle files and corresponding *.csv files that contain the ensemble estimates of OHC and ThSL. It also generates four figure files that show the original input timeseries and the ensemble estimate, following the approach described by Palmer et al [2021].\r\n\r\n\r\nPre-processed individual ensemble member timeseries are available in *.csv format in the Supplementary Materials of Kuhlbrodt et al [in press]. Full citation: T. Kuhlbrodt, A. Voldoire, M.D. Palmer, O. Geoffroy and R.E. Killick “Historical ocean heat uptake in two pairs of CMIP6 models: global and regional perspectives”, Journal of Climate, in press.\r\n\r\nPlease note that for the files 'AR6_OHC_timeseries_MDP_2021-01-20_more_than_2000m.csv' and 'AR6_OHC_timeseries_MDP_2021-01-20_more_than_2000m_error.csv' CEDA staff were required to change the filenames in order to align with archive naming conventions. The original file names were 'AR6_OHC_timeseries_MDP_2021-01-20_>2000m.csv' and 'AR6_OHC_timeseries_MDP_2021-01-20_>2000m_error.csv'. \r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n - Link to the code for the figure, archived on Zenodo." } }, { "ob_id": 802, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 40016, "uuid": "bd375134bd8c4990a1e9eb6d199cc723", "short_code": "ob", "title": "Physical Marine Climate Projections for the North West European Shelf Seas: EnsStats", "abstract": "Ensemble statistics are calculated from the North West Shelf Perturbed Parameter Ensemble (NWSPPE) climatologies. The NWSPPE (https://catalogue.ceda.ac.uk/uuid/edf66239c70c426e9e9f19da1ac8ba87) provides climatological mean and climatological standard deviations for an early-century period (2000-2019) and a late-century period (2079-2098) for each of the 12 NWSPPE ensemble members, for the annual means, and for each month and season of the year. These have been processed into ensemble statistics, for each period (2000-2019, 2079-2098, and the difference 2079-2098minus2000-2019), and for each season and month and for annual means. For the early-century and late-century climatology periods, these ensemble statistics include the ensemble mean, ensemble variance, ensemble standard deviation and interannual variance. These describe the behaviour of the ensemble, including any present day ensemble spread. For the statistics of the difference between the periods (2079-2098minus2000-2019), we simply provide the difference between the ensemble statistics calculated in the near present and end of century period. To allow the user to simply use these data to provide projections, with the associated uncertainty, we also provide two additional statistics, the projected ensemble mean (projensmean) and the projected ensemble standard deviation (projensstd). We remove the early-century climatological mean from the late-century climatological mean, for each ensemble member of the NWEPPE to give an anomaly ensemble. We then calculate the resulting ensemble mean (projensmean) and its standard deviation (projensstd).\r\n\r\nNucleus for European Modelling of the Ocean (NEMO) has three model grids, the T, U and V grids, and we output variables in three files, respecting their native model grid. In practice, all our variables are on the T grid, apart from the Eastward and Northward components of the depth mean velocities (DMU, DMV), which are in the U and V grid files respectively. The T grid files have Sea Surface, Near Bed, and the Difference between the surface and bed Temperature and Salinity (SST, NBT, DFT, SSS, NBS, DFS), Potential Energy Anomaly (PEA), Mixed Layer Depth (MLD), the barotropic current magnitude interpolated onto the T grid (DMUV)." }, "objectObservation": { "ob_id": 40013, "uuid": "edf66239c70c426e9e9f19da1ac8ba87", "short_code": "ob", "title": "Physical Marine Climate Projections for the North West European Shelf Seas: NWSPPE", "abstract": "A Perturbed Physics Ensemble (PPE) of the Met Office Global Coupled model version 3.05 (HadGEM3-GC3.05) has been downscaled with the shelf seas climate version of the Nucleus for European Modelling of the Ocean (NEMO) Coastal Ocean model (CO9). Each of the 12 ensemble members have been downscaled as transient simulations (from 1990-2098) under RCP8.5 scenario, and we refer to the resultant downscaled PPE as the North West Shelf Perturbed Parameter Ensemble (NWSPPE). The HadGEM3-GC3.05 PPE was designed to span the range of uncertainty associated with model parameter uncertainty in the atmosphere of the driving global climate model. CO9 was run at a 7 km resolution, with 51 vertical levels using s-coordinates. This data collection includes 2D fields of monthly mean output for the full period, for each ensemble member, as well as pre-processed climatologies. Regional mean time series are also included for each ensemble member.\r\n\r\nNEMO has three model grids, the T, U and V grids, and we output variables in three files, respecting their native model grid. In practice, all our variables are on the T grid, apart from the Eastward and Northward components of the depth mean velocities (DMU, DMV), which are in the U and V grid files respectively. The T grid files have Sea Surface, Near Bed, and the Difference between the surface and bed Temperature and Salinity (SST, NBT, DFT, SSS, NBS, DFS), Potential Energy Anomaly (PEA), Mixed Layer Depth (MLD), the barotropic current magnitude interpolated onto the T grid (DMUV)." } }, { "ob_id": 803, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 38323, "uuid": "5d6b9c165edf4e69b624ddeb5d28f5ee", "short_code": "ob", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for FAQ 7.3, Figure 1 (v20220721)", "abstract": "Data for FAQ 7.3 Figure 1, from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFAQ 7.3 Figure 1 shows equilibrium climate sensitivity and future 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\nForster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, M. Watanabe, M. Wild, and H. Zhang, 2021: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. 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. 923–1054, doi:10.1017/9781009157896.009.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 2 subpanels, with data provided for both panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- (left) Equilibrium climate sensitivities for the current generation (CMIP6) climate models, and the previous (CMIP5) generation. The assessed range in this Report (AR6) is also shown. \r\n\r\n- (right) Climate projections of CMIP5, CMIP6 and AR6 for the very high-emissions scenarios RCP8.5, and SSP5-8.5, respectively. \r\n\r\nThe thick horizontal lines represent the multi-model average and the thin horizontal lines represent the results of individual models. The boxes represent the model ranges for CMIP5 and CMIP6 and the range assessed in AR6.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to FAQ 7.3 Figure 1.\r\n \r\n- Data file: ECS_Proj_CMIP5_CMIP6.csv \r\n \r\n\r\nCMIP5 is the fifth stage of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth stage of the Coupled Model Intercomparison Project.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory. Also listed on the 'master' GitHub page linked in the documentation of this catalogue record are external GitHub repositories and locations within the contributed directory where code for figures have been supplied by other authors. These are provided \"as-is\" and are not guaranteed to be reproducible within this environment. For external GitHub locations, check out the relevant repository READMEs.\r\n\r\nThe notebook used to plot this figure and the input data used in the code are linked in the 'Related Documents' section. The input data to this code is also archived at CEDA.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 7)\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7.\r\n - Link to the Jupyter notebook for plotting this figure from the Chapter 7 GitHub repository\r\n - Link to the code for the figure, archived on Zenodo" }, "objectObservation": { "ob_id": 37826, "uuid": "47586c6f52a9473ea0b1f909fb231bfc", "short_code": "ob", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for FAQ 7.3, Figure 1 (v20220721)", "abstract": "Input Data for FAQ 7.3 Figure 1, from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFAQ 7.3 Figure 1 shows equilibrium climate sensitivity and future 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\nForster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, M. Watanabe, M. Wild, and H. Zhang, 2021: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. 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. 923–1054, doi:10.1017/9781009157896.009.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 2 subpanels, with input data provided for both panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- (left) Equilibrium climate sensitivities for the current generation (CMIP6) climate models, and the previous (CMIP5) generation. The assessed range in this Report (AR6) is also shown. \r\n\r\n- (right) Climate projections of CMIP5, CMIP6 and AR6 for the very high-emissions scenarios RCP8.5, and SSP5-8.5, respectively. \r\n\r\nThe thick horizontal lines represent the multi-model average and the thin horizontal lines represent the results of individual models. The boxes represent the model ranges for CMIP5 and CMIP6 and the range assessed in AR6.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to FAQ 7.3 Figure 1.\r\n \r\n - Data file: ecs_for_faq.csv\r\n - Data file: tcr_for_faq.csv\r\n - Data files: CMIP5_means/dtas_\"+model+\".nc\r\n - Data files: CMIP5_means/tas_\"+model+\"_rcp85.nc\r\n - Data files: CMIP5_means/tas_\"+model+\"_piControl.nc\r\n - Data files: CMIP6_means/dtas_\"+model+\".nc\r\n - Data files: CMIP6_means/tas_\"+model+\"_ssp585.nc\r\n - Data files: CMIP6_means/tas_\"+model+\"_piControl.nc\r\n\r\nModels to be substituted in file names where +model+ exists:\r\n- ACCESS-CM2\r\n- ACCESS-ESM1-5\r\n- AWI-CM-1-1-MR\r\n- BCC-CSM2-MR\r\n- CAMS-CSM1-0\r\n- CESM2-WACCM\r\n- CESM2\r\n- CIESM\r\n- CMCC-CM2-SR5\r\n- CNRM-CM6-1-HR\r\n- CNRM-CM6-1\r\n- CNRM-ESM2-1\r\n- CanESM5\r\n- EC-Earth3-Veg\r\n- FGOALS-f3-L\r\n- FGOALS-g3\r\n- FIO-ESM-2-0\r\n- GISS-E2-1-G\r\n- HadGEM3-GC31-LL\r\n- HadGEM3-GC31-MM\r\n- IITM-ESM\r\n- INM-CM4-8\r\n- INM-CM5-0\r\n- IPSL-CM6A-LR\r\n- KACE-1-0-G\r\n- MCM-UA-1-0\r\n- MIROC-ES2L\r\n- MIROC6\r\n- MPI-ESM1-2-HR\r\n- MPI-ESM1-2-LR\r\n- MRI-ESM2-0\r\n- NESM3\r\n- NorESM2-LM\r\n- NorESM2-MM\r\n- TaiESM1\r\n- UKESM1-0-LL\r\nThis means the _ssp585.nc and _piControl.nc files have 36 versions each, for both CMIP5 and CMIP6 (a total of 144 netCDF files).\r\n\r\nThe above files are from the 'contributed' folder on the 'master' GitHub repository, rather than in data_input or data_output. \r\n\r\nCMIP5 is the fifth stage of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth stage of the Coupled Model Intercomparison Project.\r\nRCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100.\r\nSSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5.\r\nACCESS-CM2 is the Australian Community Climate and Earth System Simulator coupled climate model.\r\nACCESS-ESM1-5 is the Australian Community Climate and Earth System Simulator Earth system model version designed to participate in CMIP6 simulations.\r\nAWI-CM-1-1-MR is the Alfred Wegener Institute Climate Model version 1.1 - Medium Resolution, with locally-increased horizontal resolution over energetically active ocean areas.\r\nBCC-CSM2-MR is the Beijing Climate Center Climate System Model version 2 - moderate vertical resolution.\r\nCAMS-CSM1-0 is the Chinese Academy of Meteorological Sciences Climate System Model version 1.\r\nCESM2-WACCM is the Community System Model version 2- Whole Atmosphere Community Climate Model.\r\nCESM2 is the Community Earth System Model version 2.\r\nCIESM is the Community Integrated Earth System Model.\r\nCMCC-CM2-SR5 is the Euro-Mediterranean Centre on Climate Change Coupled Climate Model version 2 - standard configuration.\r\nCNRM-CM6-1-HR is the Centre National de Recherches Météorologiques Climate Model for CMIP6 - altered Horizontal Resolution.\r\nCNRM-CM6-1 is the Centre National de Recherches Météorologiques Climate Model for CMIP6.\r\nCNRM-ESM2-1 is the Centre National de Recherches Météorologiques Earth System Model, derived from CNRM-CM6-1.\r\nCanESM5 is the Canadian Earth System Model version 5.\r\nEC-Earth3-Veg is the European Community Earth-system model version 3, with the Global Circulation Model (GCM) coupled to the dynamic vegetation model.\r\nFGOALS-f3-L is the Flexible Global Ocean-Atmosphere-Land System Model, Finite-volume version 3, low horizontal resolution. \r\nFGOALS-g3 is the Flexible Global Ocean-Atmosphere-Land System Model, Grid-point Version 3.\r\nFIO-ESM-2-0 is the First Institute of Oceanography Earth System Model version 2.0.\r\nGISS-E2-1-G is the Goddard Institute for Space Studies - chemistry-climate model version E2.1, using the GISS Ocean v1 (G01) model.\r\nHadGEM3-GC31-LL is the Met Offfice Hadley Centre Global Environment Model - Global Coupled configuration 3.1 - using an atmosphere/ocean resolution for historical simulation N96/ORCA1.\r\nHadGEM3-GC31-MM is the Met Offfice Hadley Centre Global Environment Model - Global Coupled configuration 3.1 - using an atmosphere/ocean resolution for historical simulation N216/ORCA025.\r\nIITM-ESM is the Indian Institute of Tropical Meteorology Earth System Model.\r\nINM-CM4-8 is the Institute for Numerical Mathematics Climate Model version 4.8. \r\nINM-CM5-0 is the Institute for Numerical Mathematics Climate Model version 5.0. \r\nIPSL-CM6A-LR is the Institut Pierre-Simon Laplace Climate Model for CMIP6 - Low Resolution.\r\nKACE-1-0-G is the Korean Advanced Community Earth system model. \r\nMCM-UA-1-0 is the Manabe Climate Climate - University of Arizona - version 1.0. \r\nMIROC-ES2L is the Model for Interdisciplinary Research on Climate - Earth System version 2 for Long-term simulations.\r\nMIROC6 is the Model for Interdisciplinary Research on Climate version 6.\r\nMPI-ESM1-2-HR is the Max Planck Institute Earth System Model - version 2 - altered Horizontal Resolution.\r\nMPI-ESM1-2-LR is the Max Planck Institute Earth System Model - version 2 - Low Resolution.\r\nMRI-ESM2-0 is the Meteorological Research Institute Earth System Model version 2.0.\r\nNESM3 is the Nanjing University of Information Science and Technology Earth System Model version 3.\r\nNorESM2-LM is the Norwegian Earth System Model version 2 - 2 degree resolution for atmosphere and land components, 1 degree resolution for ocean and sea-ice components.\r\nNorESM2-MM is the Norwegian Earth System Model version 2 - 1 degree resolution for all model components.\r\nTaiESM1 is theTaiwan Earth System Model version 1.\r\nUKESM1-0-LL is the The UK Earth System Model - version 1 - 2 degree resolution for all model components.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory. Also listed on the 'master' GitHub page linked in the documentation of this catalogue record are external GitHub repositories and locations within the contributed directory where code for figures have been supplied by other authors. These are provided \"as-is\" and are not guaranteed to be reproducible within this environment. For external GitHub locations, check out the relevant repository READMEs.\r\n\r\nThe notebook used to plot this figure is linked in the 'Related Documents' section. The output figure data is also archived at CEDA.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 7)\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7.\r\n - Link to the Jupyter notebook for plotting this figure from the Chapter 7 GitHub repository\r\n- Link to the code for the figure, archived on Zenodo." } }, { "ob_id": 804, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 38856, "uuid": "789ad030299342ea99534edfb62450d9", "short_code": "ob", "title": "Atlas of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure Atlas.2 (v20221104)", "abstract": "Data for Figure Atlas.2 from Atlas of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure Atlas.2 shows WGI reference regions used in the (a) AR5 and (b) AR6 reports.\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 citations:\r\nFor the report component from which the figure originates: \r\nGutiérrez, J.M., R.G. Jones, G.T. Narisma, L.M. Alves, M. Amjad, I.V. Gorodetskaya, M. Grose, N.A.B. Klutse, S. Krakovska, J. Li, D. Martínez-Castro, L.O. Mearns, S.H. Mernild, T. Ngo-Duc, B. van den Hurk, and J.-H. Yoon, 2021: Atlas. 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. 1927–2058, doi:10.1017/9781009157896.021\r\n\r\nIturbide, M. et al., 2021: Repository supporting the implementation of FAIR principles in the IPCC-WG1 Interactive Atlas. Zenodo. Retrieved from: http://doi.org/10.5281/zenodo.5171760\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels, with data provided for both panels in the master GitHub repository linked in the documentation.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nThis dataset contains the corner coordinates defining each reference region for the second panel of the figure, which contain coordinate information at a 0.44º resolution.\r\nThe repository directory 'reference-regions' contains data provided for the reference regions as polygons in different formats (CSV with coordinates, R data, shapefile and geojson) together with R and Python notebooks illustrating the use of these regions with worked examples.\r\n\r\nData for reference regions for AR5 can be found here: https://catalogue.ceda.ac.uk/uuid/a3b6d7f93e5c4ea986f3622eeee2b96f\r\n\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nCORDEX is The Coordinated Regional Downscaling Experiment from the WCRP.\r\nAR5 and AR6 refer to the 5th and 6th Annual Report of the IPCC.\r\nWGI stands for Working Group I\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures produced by the Jupyter Notebooks live inside the notebooks directory. The notebooks describe step by step the basic process followed to generate some key figures of the AR6 WGI Atlas and some products underpinning the Interactive Atlas, such as reference regions, global warming levels, aggregated datasets. They include comments and hints to extend the analysis, thus promoting reusability of the results. These notebooks are provided as guidance for practitioners, more user friendly than the code provided as scripts in the reproducibility folder. \r\n\r\nSome of the notebooks require access to large data volumes out of this repository. To speed up the execution of the notebook, in addition to the full code to access the data, we provide a data loading shortcut, by storing intermediate results in the auxiliary-material folder in this repository. To test other parameter settings, the full data access instructions should be followed, which can take long waiting times.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Atlas)\r\n - Link to the Supplementary Material for Atlas, which contains details on the input data used in Table Atlas.SM.15.\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to the necessary notebooks for reproducing the figure from GitHub.\r\n - Link to IPCC AR5 reference regions dataset" }, "objectObservation": { "ob_id": 27811, "uuid": "a3b6d7f93e5c4ea986f3622eeee2b96f", "short_code": "ob", "title": "IPCC AR5 reference regions", "abstract": "The boundaries of a set of regions which are defined in Chapter 14 of the Working Group 1 (WGI) contribution to the 2013 Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and in the AR5 Annex I Atlas of Global and Regional Climate Projections. The regions are used for the calculation of IPCC regional climate statistics.\r\n\r\nThe regions used to calculate regional climate statistics in AR5 are the 26 SREX regions defined by the IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (IPCC, 2012: also known as \"SREX\"). For AR5 an additional 7 regions containing the two polar regions, the Caribbean, Indian Ocean and Pacific Island States have been added. In total there are 33 region boundaries and of these, 4 (the Arctic, Antarctic, South Asia and South-East Asia) are used twice for land-only and sea-only analysis, giving a total of 37 IPCC analysis regions.\r\n\r\nEach of the 33 regions is provided with a name and a label. The label is set to the three letter code used in the SREX report for the 26 SREX regions. The 7 additional reference regions are also given three letter short names.\r\n\r\nThe 26 SREX regions are: Alaska/NW Canada (ALA), Eastern Canada/Greenland/Iceland (CGI), Western North America (WNA), Central North America (CNA), Eastern North America (ENA), Central America/Mexico (CAM), Amazon (AMZ), NE Brazil (NEB), West Coast South America (WSA), South- Eastern South America (SSA), Northern Europe (NEU), Central Europe (CEU), Southern Europe/the Mediterranean (MED), Sahara (SAH), Western Africa (WAF), Eastern Africa (EAF), Southern Africa (SAF), Northern Asia (NAS), Western Asia (WAS), Central Asia (CAS), Tibetan Plateau (TIB), Eastern Asia (EAS), Southern Asia (SAS), Southeast Asia (SEA), Northern Australia (NAS) and Southern Australia/New Zealand (SAU).\r\n\r\nThe non-SREX reference regions are: Antarctica (ANT), Arctic (ARC), Caribbean (CAR), Western Indian Ocean (WIO), Northern Tropical Pacific (NTP), Equatorial Tropical Pacific (ETP) and Southern Tropical Pacific (STP). \r\n\r\nThe region definitions have a subtlety regarding the treatment of land and sea areas which needs to be handled with care. The climate models use a range of methods for dealing with coastal boundaries. The archived data includes a field giving the proportion of each model grid cell which is land or sea. A model grid cell is considered land if more that 50% of the cell is land. The mean for a given region is then defined in terms of the grid points (which are the cell centres) which are within the specified reference boundaries. The spatial area covered by these grid cells will then differ from model to model. The reference boundaries thus provide a starting point for defining the regional means: the means are not a simple average of these areas. The distinction is not expected to be substantial, but anyone wanting to reproduce exactly the same numbers will need to follow all steps carefully." } }, { "ob_id": 805, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40085, "uuid": "4f3d4c97d9be45419679fc498e7f6501", "short_code": "ob", "title": "ATSR-2: Average Surface Temperature (AST) Product (AT2_AR__2P), v3.0.1", "abstract": "Along-Track Scanning Radiometer (ATSR) mission was funded jointly by the UK Department of Energy and Climate Change External Link (DECC) and the Australian Department of Innovation, Industry, Science and Research External Link (DIISR).\r\n\r\nThis dataset contains the Along-Track Scanning Radiometer on ESA ERS-2 satellite (ATSR-2) Average Surface Temperature (AST) Product. These data are the Level 2 spatially averaged geophysical product derived from Level 1B product and auxiliary data. This data is from the 3rd reprocessing and tagged v3.0.1\r\n\r\nThere are two types of averages provided: 10 arcminute cells and 30 arcminute cells. All cells are present regardless of the surface type. Hence, the sea (land) cells would also have the land (sea) records even though these would be empty. Cells containing coastlines will have both valid land and sea records; the land (sea) record only contains averages from the land (sea) pixels. The third reprocessing was done to implement the updated algorithms, processors, and auxiliary files." }, "objectObservation": { "ob_id": 8094, "uuid": "9347586615e04edf3f6d3b5f9b98b729", "short_code": "ob", "title": "ATSR-2: Average Surface Temperature (AST) Product (AT2_AR__2P), v2.1", "abstract": "Along-Track Scanning Radiometer (ATSR) mission was funded jointly by the UK Department of Energy and Climate Change External Link (DECC) and the Australian Department of Innovation, Industry, Science and Research External Link (DIISR).\r\n\r\nThis dataset contains the Along-Track Scanning Radiometer on ESA ERS-2 satellite (ATSR-2) Average Surface Temperature (AST) Product. These data are the Level 2 spatially averaged geophysical product derived from Level 1B product and auxiliary data.\r\n\r\nThere are two types of averages provided: 10 arcminute cells and 30 arcminute cells. All cells are present regardless of the surface type. Hence, the sea (land) cells would also have the land (sea) records even though these would be empty. Cells containing coastlines will have both valid land and sea records; the land (sea) record only contains averages from the land (sea) pixels. The third reprocessing was done to implement the updated algorithms, processors, and auxiliary files." } }, { "ob_id": 806, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 37208, "uuid": "59a7cd0dcd474f5f906ead4073a9be8b", "short_code": "ob", "title": "HadUK-Grid Climate Observations by UK countries, v1.1.0.0 (1836-2021)", "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 1836 to 2021, 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\nThe changes for v1.1.0.0 HadUK-Grid datasets are as follows:\r\n\r\n* The addition of data for calendar year 2021\r\n\r\n* The addition of 30 year averages for the new reference period 1991-2020\r\n\r\n* An update to 30 year averages for 1961-1990 and 1981-2010. This is an order of operation change. In this version 30 year averages have been calculated from the underlying monthly/seasonal/annual grids (grid-then-average) in previous version they were grids of interpolated station average (average-then-grid). This order of operation change results in small differences to the values, but provides improved consistency with the monthly/seasonal/annual series grids. However this order of operation change means that 1961-1990 averages are not included for sfcWind or snowlying variables due to the start date for these variables being 1969 and 1971 respectively.\r\n\r\n* A substantial new collection of monthly rainfall data have been added for the period before 1960. These data originate from the rainfall rescue project (Hawkins et al. 2022) and this source now accounts for 84% of pre-1960 monthly rainfall data, and the monthly rainfall series has been extended back to 1836.\r\n\r\nNet changes to the input station data used to generate this dataset:\r\n\r\n-Total of 122664065 observations\r\n\r\n-118464870 (96.5%) unchanged\r\n\r\n-4821 (0.004%) modified for this version\r\n\r\n-4194374 (3.4%) added in this version\r\n\r\n-5887 (0.005%) deleted from this version\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 output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." }, "objectObservation": { "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." } }, { "ob_id": 808, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40132, "uuid": "22df6602b5064b1686dda7e9455f86fc", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 60km grid over the UK, v1.2.0.ceda (1836-2022)", "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 1836 to 2022, 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\nThe changes for v1.2.0.ceda HadUK-Grid datasets are as follows:\r\n\r\n * Added data for calendar year 2022\r\n \r\n* Added newly digitised data for monthly sunshine 1910-1918\r\n\r\n * Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242\r\n\r\n * Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp-spatial-files\r\n\r\n\r\n * Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs\r\n\r\n * Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.\r\n\r\n * Updated ordering of regions within regional values files. Alphabetical ordering.\r\n\r\n * Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression\r\n\r\n* Net changes to the input station data used to generate this dataset:\r\n\r\n- Total of 125601744 observations\r\n\r\n- 122621050 (97.6%) unchanged\r\n\r\n- 26700 (0.02%) modified for this version\r\n\r\n- 2953994 (2.35%) added in this version\r\n\r\n- 16315 (0.01%) deleted from this version\r\n\r\n* Changes to monthly rainfall 1836-1960\r\n\r\n- Total of 4823973 observations\r\n\r\n- 3315657 (68.7%) unchanged\r\n\r\n- 21029 (0.4%) modified for this version\r\n\r\n- 1487287 (30.8%) added in this version\r\n\r\n- 11155 (0.2%) deleted from this version\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 output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." }, "objectObservation": { "ob_id": 37209, "uuid": "6f4ac352b19341eb8c5b26644845ac35", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 60km grid over the UK, v1.1.0.0 (1836-2021)", "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 1836 to 2021, 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\nThe changes for v1.1.0.0 HadUK-Grid datasets are as follows:\r\n\r\n* The addition of data for calendar year 2021\r\n\r\n* The addition of 30 year averages for the new reference period 1991-2020\r\n\r\n* An update to 30 year averages for 1961-1990 and 1981-2010. This is an order of operation change. In this version 30 year averages have been calculated from the underlying monthly/seasonal/annual grids (grid-then-average) in previous version they were grids of interpolated station average (average-then-grid). This order of operation change results in small differences to the values, but provides improved consistency with the monthly/seasonal/annual series grids. However this order of operation change means that 1961-1990 averages are not included for sfcWind or snowlying variables due to the start date for these variables being 1969 and 1971 respectively.\r\n\r\n* A substantial new collection of monthly rainfall data have been added for the period before 1960. These data originate from the rainfall rescue project (Hawkins et al. 2022) and this source now accounts for 84% of pre-1960 monthly rainfall data, and the monthly rainfall series has been extended back to 1836.\r\n\r\nNet changes to the input station data used to generate this dataset:\r\n\r\n-Total of 122664065 observations\r\n\r\n-118464870 (96.5%) unchanged\r\n\r\n-4821 (0.004%) modified for this version\r\n\r\n-4194374 (3.4%) added in this version\r\n\r\n-5887 (0.005%) deleted from this version\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 output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." } }, { "ob_id": 809, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40131, "uuid": "adf1a6cf830b4f5385c5d73609df8423", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 5km grid over the UK, v1.2.0.ceda (1836-2022)", "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 1836 to 2022, 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\nThe changes for v1.2.0.ceda HadUK-Grid datasets are as follows:\r\n\r\n * Added data for calendar year 2022\r\n \r\n* Added newly digitised data for monthly sunshine 1910-1918\r\n\r\n * Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242\r\n\r\n * Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp-spatial-files\r\n\r\n\r\n * Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs\r\n\r\n * Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.\r\n\r\n * Updated ordering of regions within regional values files. Alphabetical ordering.\r\n\r\n * Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression\r\n\r\n* Net changes to the input station data used to generate this dataset:\r\n\r\n- Total of 125601744 observations\r\n\r\n- 122621050 (97.6%) unchanged\r\n\r\n- 26700 (0.02%) modified for this version\r\n\r\n- 2953994 (2.35%) added in this version\r\n\r\n- 16315 (0.01%) deleted from this version\r\n\r\n* Changes to monthly rainfall 1836-1960\r\n\r\n- Total of 4823973 observations\r\n\r\n- 3315657 (68.7%) unchanged\r\n\r\n- 21029 (0.4%) modified for this version\r\n\r\n- 1487287 (30.8%) added in this version\r\n\r\n- 11155 (0.2%) deleted from this version\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 output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." }, "objectObservation": { "ob_id": 37213, "uuid": "aeb4ca481d634ec597831282c3baed32", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 5km grid over the UK, v1.1.0.0 (1836-2021)", "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 1836 to 2021, 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\nThe changes for v1.1.0.0 HadUK-Grid datasets are as follows:\r\n\r\n* The addition of data for calendar year 2021\r\n\r\n* The addition of 30 year averages for the new reference period 1991-2020\r\n\r\n* An update to 30 year averages for 1961-1990 and 1981-2010. This is an order of operation change. In this version 30 year averages have been calculated from the underlying monthly/seasonal/annual grids (grid-then-average) in previous version they were grids of interpolated station average (average-then-grid). This order of operation change results in small differences to the values, but provides improved consistency with the monthly/seasonal/annual series grids. However this order of operation change means that 1961-1990 averages are not included for sfcWind or snowlying variables due to the start date for these variables being 1969 and 1971 respectively.\r\n\r\n* A substantial new collection of monthly rainfall data have been added for the period before 1960. These data originate from the rainfall rescue project (Hawkins et al. 2022) and this source now accounts for 84% of pre-1960 monthly rainfall data, and the monthly rainfall series has been extended back to 1836.\r\n\r\nNet changes to the input station data used to generate this dataset:\r\n\r\n-Total of 122664065 observations\r\n\r\n-118464870 (96.5%) unchanged\r\n\r\n-4821 (0.004%) modified for this version\r\n\r\n-4194374 (3.4%) added in this version\r\n\r\n-5887 (0.005%) deleted from this version\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 output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." } }, { "ob_id": 810, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40130, "uuid": "0545f37fb7124df381d42468eb63c144", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 25km grid over the UK, v1.2.0.ceda (1836-2022)", "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 1836 to 2022, 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\nThe changes for v1.2.0.ceda HadUK-Grid datasets are as follows:\r\n\r\n * Added data for calendar year 2022\r\n \r\n* Added newly digitised data for monthly sunshine 1910-1918\r\n\r\n * Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242\r\n\r\n * Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp-spatial-files\r\n\r\n * Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs\r\n\r\n * Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.\r\n\r\n * Updated ordering of regions within regional values files. Alphabetical ordering.\r\n\r\n * Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression\r\n\r\n* Net changes to the input station data used to generate this dataset:\r\n\r\n- Total of 125601744 observations\r\n\r\n- 122621050 (97.6%) unchanged\r\n\r\n- 26700 (0.02%) modified for this version\r\n\r\n- 2953994 (2.35%) added in this version\r\n\r\n- 16315 (0.01%) deleted from this version\r\n\r\n* Changes to monthly rainfall 1836-1960\r\n\r\n- Total of 4823973 observations\r\n\r\n- 3315657 (68.7%) unchanged\r\n\r\n- 21029 (0.4%) modified for this version\r\n\r\n- 1487287 (30.8%) added in this version\r\n\r\n- 11155 (0.2%) deleted from this version\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 output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." }, "objectObservation": { "ob_id": 37210, "uuid": "e6866698e5bd46cfa726cd82a7971f9a", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 25km grid over the UK, v1.1.0.0 (1836-2021)", "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 1836 to 2021, 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\nThe changes for v1.1.0.0 HadUK-Grid datasets are as follows:\r\n\r\n* The addition of data for calendar year 2021\r\n\r\n* The addition of 30 year averages for the new reference period 1991-2020\r\n\r\n* An update to 30 year averages for 1961-1990 and 1981-2010. This is an order of operation change. In this version 30 year averages have been calculated from the underlying monthly/seasonal/annual grids (grid-then-average) in previous version they were grids of interpolated station average (average-then-grid). This order of operation change results in small differences to the values, but provides improved consistency with the monthly/seasonal/annual series grids. However this order of operation change means that 1961-1990 averages are not included for sfcWind or snowlying variables due to the start date for these variables being 1969 and 1971 respectively.\r\n\r\n* A substantial new collection of monthly rainfall data have been added for the period before 1960. These data originate from the rainfall rescue project (Hawkins et al. 2022) and this source now accounts for 84% of pre-1960 monthly rainfall data, and the monthly rainfall series has been extended back to 1836.\r\n\r\nNet changes to the input station data used to generate this dataset:\r\n\r\n-Total of 122664065 observations\r\n\r\n-118464870 (96.5%) unchanged\r\n\r\n-4821 (0.004%) modified for this version\r\n\r\n-4194374 (3.4%) added in this version\r\n\r\n-5887 (0.005%) deleted from this version\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 output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." } }, { "ob_id": 811, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40129, "uuid": "46f8c1377f8849eeb8570b8ac9b26d86", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 1km grid over the UK, v1.2.0.ceda (1836-2022)", "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 1836 to 2022, 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\nThe changes for v1.2.0.ceda HadUK-Grid datasets are as follows:\r\n\r\n * Added data for calendar year 2022\r\n \r\n* Added newly digitised data for monthly sunshine 1910-1918\r\n\r\n * Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242\r\n\r\n * Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp-spatial-files\r\n\r\n * Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs\r\n\r\n * Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.\r\n\r\n * Updated ordering of regions within regional values files. Alphabetical ordering.\r\n\r\n * Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression\r\n\r\n* Net changes to the input station data used to generate this dataset:\r\n\r\n- Total of 125601744 observations\r\n\r\n- 122621050 (97.6%) unchanged\r\n\r\n- 26700 (0.02%) modified for this version\r\n\r\n- 2953994 (2.35%) added in this version\r\n\r\n- 16315 (0.01%) deleted from this version\r\n\r\n* Changes to monthly rainfall 1836-1960\r\n\r\n- Total of 4823973 observations\r\n\r\n- 3315657 (68.7%) unchanged\r\n\r\n- 21029 (0.4%) modified for this version\r\n\r\n- 1487287 (30.8%) added in this version\r\n\r\n- 11155 (0.2%) deleted from this version\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 output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." }, "objectObservation": { "ob_id": 37212, "uuid": "bbca3267dc7d4219af484976734c9527", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 1km grid over the UK, v1.1.0.0 (1836-2021)", "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 1836 to 2021, 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\nThe changes for v1.1.0.0 HadUK-Grid datasets are as follows:\r\n\r\n* The addition of data for calendar year 2021\r\n\r\n* The addition of 30 year averages for the new reference period 1991-2020\r\n\r\n* An update to 30 year averages for 1961-1990 and 1981-2010. This is an order of operation change. In this version 30 year averages have been calculated from the underlying monthly/seasonal/annual grids (grid-then-average) in previous version they were grids of interpolated station average (average-then-grid). This order of operation change results in small differences to the values, but provides improved consistency with the monthly/seasonal/annual series grids. However this order of operation change means that 1961-1990 averages are not included for sfcWind or snowlying variables due to the start date for these variables being 1969 and 1971 respectively.\r\n\r\n* A substantial new collection of monthly rainfall data have been added for the period before 1960. These data originate from the rainfall rescue project (Hawkins et al. 2022) and this source now accounts for 84% of pre-1960 monthly rainfall data, and the monthly rainfall series has been extended back to 1836.\r\n\r\nNet changes to the input station data used to generate this dataset:\r\n\r\n-Total of 122664065 observations\r\n\r\n-118464870 (96.5%) unchanged\r\n\r\n-4821 (0.004%) modified for this version\r\n\r\n-4194374 (3.4%) added in this version\r\n\r\n-5887 (0.005%) deleted from this version\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 output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." } }, { "ob_id": 812, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40128, "uuid": "640d33e0cf99477990f7fee35a101850", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 12km grid over the UK, v1.2.0.ceda (1836-2022)", "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 1836 to 2022, 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\nThe changes for v1.2.0.ceda HadUK-Grid datasets are as follows:\r\n\r\n * Added data for calendar year 2022\r\n \r\n* Added newly digitised data for monthly sunshine 1910-1918\r\n\r\n * Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242\r\n\r\n * Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp- spatial-files\r\n\r\n * Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs\r\n\r\n * Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.\r\n\r\n * Updated ordering of regions within regional values files. Alphabetical ordering.\r\n\r\n * Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression\r\n\r\n* Net changes to the input station data used to generate this dataset:\r\n\r\n- Total of 125601744 observations\r\n\r\n- 122621050 (97.6%) unchanged\r\n\r\n- 26700 (0.02%) modified for this version\r\n\r\n- 2953994 (2.35%) added in this version\r\n\r\n- 16315 (0.01%) deleted from this version\r\n\r\n* Changes to monthly rainfall 1836-1960\r\n\r\n- Total of 4823973 observations\r\n\r\n- 3315657 (68.7%) unchanged\r\n\r\n- 21029 (0.4%) modified for this version\r\n\r\n- 1487287 (30.8%) added in this version\r\n\r\n- 11155 (0.2%) deleted from this version\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 output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." }, "objectObservation": { "ob_id": 37211, "uuid": "652cea3b8b4446f7bff73be0ce99ba0f", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 12km grid over the UK, v1.1.0.0 (1836-2021)", "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 1836 to 2021, 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\nThe changes for v1.1.0.0 HadUK-Grid datasets are as follows:\r\n\r\n* The addition of data for calendar year 2021\r\n\r\n* The addition of 30 year averages for the new reference period 1991-2020\r\n\r\n* An update to 30 year averages for 1961-1990 and 1981-2010. This is an order of operation change. In this version 30 year averages have been calculated from the underlying monthly/seasonal/annual grids (grid-then-average) in previous version they were grids of interpolated station average (average-then-grid). This order of operation change results in small differences to the values, but provides improved consistency with the monthly/seasonal/annual series grids. However this order of operation change means that 1961-1990 averages are not included for sfcWind or snowlying variables due to the start date for these variables being 1969 and 1971 respectively.\r\n\r\n* A substantial new collection of monthly rainfall data have been added for the period before 1960. These data originate from the rainfall rescue project (Hawkins et al. 2022) and this source now accounts for 84% of pre-1960 monthly rainfall data, and the monthly rainfall series has been extended back to 1836.\r\n\r\nNet changes to the input station data used to generate this dataset:\r\n\r\n-Total of 122664065 observations\r\n\r\n-118464870 (96.5%) unchanged\r\n\r\n-4821 (0.004%) modified for this version\r\n\r\n-4194374 (3.4%) added in this version\r\n\r\n-5887 (0.005%) deleted from this version\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 output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." } }, { "ob_id": 813, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40127, "uuid": "e6822428e4124c5986b689a37fda10bc", "short_code": "ob", "title": "HadUK-Grid Climate Observations by UK river basins, v1.2.0.ceda (1836-2022)", "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 1836 to 2022, 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\nThe changes for v1.2.0.ceda HadUK-Grid datasets are as follows:\r\n\r\n * Added data for calendar year 2022\r\n \r\n* Added newly digitised data for monthly sunshine 1910-1918\r\n\r\n * Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242\r\n\r\n * Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp-spatial-files\r\n\r\n * Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs\r\n\r\n * Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.\r\n\r\n * Updated ordering of regions within regional values files. Alphabetical ordering.\r\n\r\n * Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression\r\n\r\n* Net changes to the input station data used to generate this dataset:\r\n\r\n- Total of 125601744 observations\r\n\r\n- 122621050 (97.6%) unchanged\r\n\r\n- 26700 (0.02%) modified for this version\r\n\r\n- 2953994 (2.35%) added in this version\r\n\r\n- 16315 (0.01%) deleted from this version\r\n\r\n* Changes to monthly rainfall 1836-1960\r\n\r\n- Total of 4823973 observations\r\n\r\n- 3315657 (68.7%) unchanged\r\n\r\n- 21029 (0.4%) modified for this version\r\n\r\n- 1487287 (30.8%) added in this version\r\n\r\n- 11155 (0.2%) deleted from this version\r\n\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 output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." }, "objectObservation": { "ob_id": 37207, "uuid": "39b1337028d147d9b572ae352490bed0", "short_code": "ob", "title": "HadUK-Grid Climate Observations by UK river basins, v1.1.0.0 (1836-2021)", "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 1836 to 2021, 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\nThe changes for v1.1.0.0 HadUK-Grid datasets are as follows:\r\n\r\n* The addition of data for calendar year 2021\r\n\r\n* The addition of 30 year averages for the new reference period 1991-2020\r\n\r\n* An update to 30 year averages for 1961-1990 and 1981-2010. This is an order of operation change. In this version 30 year averages have been calculated from the underlying monthly/seasonal/annual grids (grid-then-average) in previous version they were grids of interpolated station average (average-then-grid). This order of operation change results in small differences to the values, but provides improved consistency with the monthly/seasonal/annual series grids. However this order of operation change means that 1961-1990 averages are not included for sfcWind or snowlying variables due to the start date for these variables being 1969 and 1971 respectively.\r\n\r\n* A substantial new collection of monthly rainfall data have been added for the period before 1960. These data originate from the rainfall rescue project (Hawkins et al. 2022) and this source now accounts for 84% of pre-1960 monthly rainfall data, and the monthly rainfall series has been extended back to 1836.\r\n\r\nNet changes to the input station data used to generate this dataset:\r\n\r\n-Total of 122664065 observations\r\n\r\n-118464870 (96.5%) unchanged\r\n\r\n-4821 (0.004%) modified for this version\r\n\r\n-4194374 (3.4%) added in this version\r\n\r\n-5887 (0.005%) deleted from this version\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 output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." } }, { "ob_id": 814, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40126, "uuid": "3d30627eee5a48be844c32723b7b6be8", "short_code": "ob", "title": "HadUK-Grid Climate Observations by UK countries, v1.2.0.ceda (1836-2022)", "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 1836 to 2022, 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\nThe changes for v1.2.0.ceda HadUK-Grid datasets are as follows:\r\n\r\n * Added data for calendar year 2022\r\n \r\n* Added newly digitised data for monthly sunshine 1910-1918\r\n\r\n * Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242\r\n\r\n * Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp-spatial-files\r\n\r\n * Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs\r\n\r\n * Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.\r\n\r\n * Updated ordering of regions within regional values files. Alphabetical ordering.\r\n\r\n * Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression\r\n\r\n* Net changes to the input station data used to generate this dataset:\r\n\r\n- Total of 125601744 observations\r\n\r\n- 122621050 (97.6%) unchanged\r\n\r\n- 26700 (0.02%) modified for this version\r\n\r\n- 2953994 (2.35%) added in this version\r\n\r\n- 16315 (0.01%) deleted from this version\r\n\r\n* Changes to monthly rainfall 1836-1960\r\n\r\n- Total of 4823973 observations\r\n\r\n- 3315657 (68.7%) unchanged\r\n\r\n- 21029 (0.4%) modified for this version\r\n\r\n- 1487287 (30.8%) added in this version\r\n\r\n- 11155 (0.2%) deleted from this version\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 output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." }, "objectObservation": { "ob_id": 37208, "uuid": "59a7cd0dcd474f5f906ead4073a9be8b", "short_code": "ob", "title": "HadUK-Grid Climate Observations by UK countries, v1.1.0.0 (1836-2021)", "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 1836 to 2021, 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\nThe changes for v1.1.0.0 HadUK-Grid datasets are as follows:\r\n\r\n* The addition of data for calendar year 2021\r\n\r\n* The addition of 30 year averages for the new reference period 1991-2020\r\n\r\n* An update to 30 year averages for 1961-1990 and 1981-2010. This is an order of operation change. In this version 30 year averages have been calculated from the underlying monthly/seasonal/annual grids (grid-then-average) in previous version they were grids of interpolated station average (average-then-grid). This order of operation change results in small differences to the values, but provides improved consistency with the monthly/seasonal/annual series grids. However this order of operation change means that 1961-1990 averages are not included for sfcWind or snowlying variables due to the start date for these variables being 1969 and 1971 respectively.\r\n\r\n* A substantial new collection of monthly rainfall data have been added for the period before 1960. These data originate from the rainfall rescue project (Hawkins et al. 2022) and this source now accounts for 84% of pre-1960 monthly rainfall data, and the monthly rainfall series has been extended back to 1836.\r\n\r\nNet changes to the input station data used to generate this dataset:\r\n\r\n-Total of 122664065 observations\r\n\r\n-118464870 (96.5%) unchanged\r\n\r\n-4821 (0.004%) modified for this version\r\n\r\n-4194374 (3.4%) added in this version\r\n\r\n-5887 (0.005%) deleted from this version\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 output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." } }, { "ob_id": 815, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40125, "uuid": "b39898e76ab7434a9a20a6dc4ab721f0", "short_code": "ob", "title": "HadUK-Grid Climate Observations by Administrative Regions over the UK, v1.2.0.ceda (1836-2022)", "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 1836 to 2022 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\nThe changes for v1.2.0.ceda HadUK-Grid datasets are as follows:\r\n\r\n * Added data for calendar year 2022\r\n \r\n* Added newly digitised data for monthly sunshine 1910-1918\r\n\r\n * Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242\r\n\r\n * Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp-spatial-files\r\n\r\n * Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs\r\n\r\n * Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.\r\n\r\n * Updated ordering of regions within regional values files. Alphabetical ordering.\r\n\r\n * Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression\r\n\r\n* Net changes to the input station data used to generate this dataset:\r\n\r\n- Total of 125601744 observations\r\n\r\n- 122621050 (97.6%) unchanged\r\n\r\n- 26700 (0.02%) modified for this version\r\n\r\n- 2953994 (2.35%) added in this version\r\n\r\n- 16315 (0.01%) deleted from this version\r\n\r\n* Changes to monthly rainfall 1836-1960\r\n\r\n- Total of 4823973 observations\r\n\r\n- 3315657 (68.7%) unchanged\r\n\r\n- 21029 (0.4%) modified for this version\r\n\r\n- 1487287 (30.8%) added in this version\r\n\r\n- 11155 (0.2%) deleted from this version\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 output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." }, "objectObservation": { "ob_id": 37214, "uuid": "7edd216fcf794b1f9a5889d496d50e54", "short_code": "ob", "title": "HadUK-Grid Climate Observations by Administrative Regions over the UK, v1.1.0.0 (1836-2021)", "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 1836 to 2021 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\nThe changes for v1.1.0.0 HadUK-Grid datasets are as follows:\r\n\r\n* The addition of data for calendar year 2021\r\n\r\n* The addition of 30 year averages for the new reference period 1991-2020\r\n\r\n* An update to 30 year averages for 1961-1990 and 1981-2010. This is an order of operation change. In this version 30 year averages have been calculated from the underlying monthly/seasonal/annual grids (grid-then-average) in previous version they were grids of interpolated station average (average-then-grid). This order of operation change results in small differences to the values, but provides improved consistency with the monthly/seasonal/annual series grids. However this order of operation change means that 1961-1990 averages are not included for sfcWind or snowlying variables due to the start date for these variables being 1969 and 1971 respectively.\r\n\r\n* A substantial new collection of monthly rainfall data have been added for the period before 1960. These data originate from the rainfall rescue project (Hawkins et al. 2022) and this source now accounts for 84% of pre-1960 monthly rainfall data, and the monthly rainfall series has been extended back to 1836.\r\n\r\nNet changes to the input station data used to generate this dataset:\r\n\r\n-Total of 122664065 observations\r\n\r\n-118464870 (96.5%) unchanged\r\n\r\n-4821 (0.004%) modified for this version\r\n\r\n-4194374 (3.4%) added in this version\r\n\r\n-5887 (0.005%) deleted from this version\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 output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." } }, { "ob_id": 816, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 37893, "uuid": "923b94820acd42a1888eaae24de328f8", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Box TS4, Figure 1 (v20220817)", "abstract": "Data for Box TS4 from Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nBox TS4, Figure 1 shows global mean sea level change on different time scales and under different 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\nArias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. 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. 33−144, doi:10.1017/9781009157896.002.\r\n\r\nWhen citing the SSP-based sea-level projections, please also include the following citation:\r\nGarner, G. G., T. Hermans, R. E. Kopp, A. B. A. Slangen, T. L. Edwards, A. Levermann, S. Nowikci, M. D. Palmer, C. Smith, B. Fox-Kemper, H. T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S. S. Drijfhout, T. L. Edwards, N. R. Golledge, M. Hemer, G. Krinner, A. Mix, D. Notz, S. Nowicki, I. S. Nurhati, L. Ruiz, J-B. Sallée, Y. Yu, L. Hua, T. Palmer, B. Pearson, 2021. IPCC AR6 Global Mean Sea-Level Rise Projections. Version 20210809. https://doi.org/10.5281/zenodo.5914710.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\nThe figure has three panels. Panel a shows global mean sea level (GMSL) change from 1900 to 2150, observed (1900–2018) and projected under the Shared Socioeconomic Pathway (SSP) scenarios (2000–2150). Panel b shows GMSL change on 100-, 2,000-, and 10,000-year time scales as a function of global surface temperature. Panel c shows timing of exceedance of different GMSL thresholds under different SSPs. \r\n\r\nFinal data is only available for panel c. \r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\nGlobal mean sea level change time-series from 1901-2150 for:\r\n- Observed global mean sea level change (1901-2018).\r\n- Projected global mean sea level change (2005-2150).\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Box TS4, Figure 1:\r\n\r\nSSP-based global mean sea level projections are archived as\r\n\r\nGarner, G. G., Hermans, T., Kopp, R. E., Slangen, A. B. A., Edwards, T. L., Levermann, A., Nowicki, S., Palmer, M. D., Smith, C., Fox-Kemper, B., Hewitt, H. T., Xiao, C., Aðalgeirsdóttir, G., Drijfhout, S. S., Edwards, T. L., Golledge, N. R., Hemer, M., Krinner, G., Mix, A., … Pearson, B. (2021). IPCC AR6 Sea Level Projections (Version 20210809) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5914710\r\n\r\nPanel c:\r\n\r\n- FigTS4-1c-milestone_ssp119_data.nc\r\n- FigTS4-1c-milestone_ssp126_data.nc\r\n- FigTS4-1c-milestone_ssp126_data.nc\r\n- FigTS4-1c-milestone_ssp126_data.nc\r\n- FigTS4-1c-milestone_ssp126_data.nc\r\n\r\nSee sections 9.6.3.2 and 9.6.3.3 for detailed information on the SSP-based global mean sea level projections and their production.\r\n\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nPanel c data were plotted using standard open-source R software - code is available via the link in the documentation.\r\n\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n- Link to the code for the figure, archived on Zenodo.\r\n- Link to the sea-level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report, archived on Zenodo." }, "objectObservation": { "ob_id": 37901, "uuid": "df7d665d7b7c4cadbd08558ea2e103c8", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Box TS4, Figure 1 (v20220817)", "abstract": "Input Data for Box TS4 from Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nBox TS4, Figure 1 shows global mean sea level change on different time scales and under different scenarios.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nArias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. 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. 33−144, doi:10.1017/9781009157896.002.\r\n\r\nWhen citing the SSP-based sea-level projections, please also include the following citation:\r\nGarner, G. G., T. Hermans, R. E. Kopp, A. B. A. Slangen, T. L. Edwards, A. Levermann, S. Nowikci, M. D. Palmer, C. Smith, B. Fox-Kemper, H. T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S. S. Drijfhout, T. L. Edwards, N. R. Golledge, M. Hemer, G. Krinner, A. Mix, D. Notz, S. Nowicki, I. S. Nurhati, L. Ruiz, J-B. Sallée, Y. Yu, L. Hua, T. Palmer, B. Pearson, 2021. IPCC AR6 Global Mean Sea-Level Rise Projections. Version 20210809. https://doi.org/10.5281/zenodo.5914710.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\nThe figure has three panels. Panel a shows global mean sea level (GMSL) change from 1900 to 2150, observed (1900–2018) and projected under the Shared Socioeconomic Pathway (SSP) scenarios (2000–2150). Panel b shows GMSL change on 100-, 2,000-, and 10,000-year time scales as a function of global surface temperature. Panel c shows timing of exceedance of different GMSL thresholds under different SSPs. \r\n\r\nInput data is only available for panel a and c. \r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\nGlobal mean sea level change time-series from 1901-2150 for:\r\n- Observed global mean sea level change (1901-2018).\r\n- Projected global mean sea level change (2005-2150).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Box TS4, Figure 1:\r\n\r\nSSP-based global mean sea level projections are archived as\r\n\r\nGarner, G. G., Hermans, T., Kopp, R. E., Slangen, A. B. A., Edwards, T. L., Levermann, A., Nowicki, S., Palmer, M. D., Smith, C., Fox-Kemper, B., Hewitt, H. T., Xiao, C., Aðalgeirsdóttir, G., Drijfhout, S. S., Edwards, T. L., Golledge, N. R., Hemer, M., Krinner, G., Mix, A., … Pearson, B. (2021). IPCC AR6 Sea Level Projections (Version 20210809) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5914710\r\n\r\nPanel a:\r\n\r\nConsensus observational GMSL curve: gmsl_altimeter+TG_ensemble_28012021.mat\r\nThis file is not provided but a link to the Chapter 9 GitHub repository which contains this file is provided.\r\n\r\nSSP-based GMSL projections through 2100, medium confidence:\r\n- pbox1e_total_ssp119_globalsl_figuredata.nc\r\n- pbox1e_total_ssp126_globalsl_figuredata.nc\r\n- pbox1e_total_ssp245_globalsl_figuredata.nc\r\n- pbox1e_total_ssp370_globalsl_figuredata.nc\r\n- pbox1e_total_ssp585_globalsl_figuredata.nc\r\n\r\nSSP-based GMSL projections after 2100, medium confidence:\r\n- pbox1f_total_ssp119_globalsl_figuredata.nc\r\n- pbox1f_total_ssp126_globalsl_figuredata.nc\r\n- pbox1f_total_ssp245_globalsl_figuredata.nc\r\n- pbox1f_total_ssp370_globalsl_figuredata.nc\r\n- pbox1f_total_ssp585_globalsl_figuredata.nc\r\n\r\nSSP-based GMSL projections through 2100, low confidence:\r\n- pbox2e_total_ssp126_globalsl_figuredata.nc\r\n- pbox2e_total_ssp585_globalsl_figuredata.nc\r\n\r\nSSP-based GMSL projections after 2100, low confidence:\r\n- pbox2f_total_ssp126_globalsl_figuredata.nc\r\n- pbox2f_total_ssp245_globalsl_figuredata.nc\r\n- pbox2f_total_ssp585_globalsl_figuredata.nc\r\n\r\nPanel c:\r\n\r\nThreshold exceedance timing under different SSPs, medium confidence, with parametric emulator for Antarctic ice sheet:\r\n- wf_1f_ssp119_milestone_figuredata.nc\r\n- wf_1f_ssp126_milestone_figuredata.nc\r\n- wf_1f_ssp245_milestone_figuredata.nc\r\n- wf_1f_ssp370_milestone_figuredata.nc\r\n- wf_1f_ssp585_milestone_figuredata.nc\r\n\r\nThreshold exceedance timing under different SSPs, medium confidence, with LARMIP-2 emulator for Antarctic ice sheet:\r\n- wf_2f_ssp119_milestone_figuredata.nc\r\n- wf_2f_ssp126_milestone_figuredata.nc\r\n- wf_2f_ssp245_milestone_figuredata.nc\r\n- wf_2f_ssp370_milestone_figuredata.nc\r\n- wf_2f_ssp585_milestone_figuredata.nc\r\n\r\nThreshold exceedance timing under different SSPs, low confidence, with DeConto et al. 2021-based Antarctic ice sheet projections incorporating Marine Ice Cliff Instability:\r\n- wf_3f_ssp126_milestone_figuredata.nc\r\n- wf_3f_ssp585_milestone_figuredata.nc\r\n\r\nThreshold exceedance timing under different SSPs, low confidence, with Bamber et al. 2019-based structured expert judgement ice sheet projections:\r\n- wf_4_ssp126_milestone_figuredata.nc\r\n- wf_4_ssp585_milestone_figuredata.nc\r\n\r\nSee sections 9.6.3.2 and 9.6.3.3 for detailed information on the SSP-based global mean sea level projections and their production.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nPanel a data were plotted using standard matplotlib software and a Linux shell script, - code is available via the link in the documentation. The code requires the input data provided here and the additional gmsl_altimeter+TG_ensemble_28012021.mat file from Figure 9.27. The link to this file from the Chapter 9 GitHub is provided. \r\n\r\nPanel c data were plotted using standard open-source R software - code is available via the link in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n- Link to the code for the figure, archived on Zenodo.\r\n- Link to the sea-level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report, archived on Zenodo.\r\n - Link to figure data in matlab format on github" } }, { "ob_id": 818, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40160, "uuid": "4371686200b444ffa6abf675334fd932", "short_code": "ob", "title": "ATSR-2: Gridded Surface Temperature (GST) Product (AT2_NR__2P), v3.0.1", "abstract": "Along-Track Scanning Radiometer (ATSR) mission was funded jointly by the UK Department of Energy and Climate Change External Link (DECC) and the Australian Department of Innovation, Industry, Science and Research External Link (DIISR).\r\n\r\nThis dataset contains the Along-Track Scanning Radiometer on ESA ERS-2 satellite (ATSR-2) Gridded Surface Temperature (GST) Product. These data are the Level 2 full spatial resolution (approximately 1 km by 1 km) geophysical product derived from Level 1B product and auxiliary data. The contents of the pixel fields, which are a mixture of Top of Atmosphere (TOA) and surface brightness temperature/radiance, are switch-able depending on the surface type. The third reprocessing was done to implement updated algorithms, processors, and auxiliary files." }, "objectObservation": { "ob_id": 8113, "uuid": "36473df8dd5b82309c06539a57210698", "short_code": "ob", "title": "ATSR-2: Gridded Surface Temperature (GST) Product (AT2_NR__2P), v2.1", "abstract": "Along-Track Scanning Radiometer (ATSR) mission was funded jointly by the UK Department of Energy and Climate Change External Link (DECC) and the Australian Department of Innovation, Industry, Science and Research External Link (DIISR).\r\n\r\nThis dataset contains the Along-Track Scanning Radiometer on ESA ERS-2 satellite (ATSR-2) Gridded Surface Temperature (GST) Product. These data are the Level 2 full spatial resolution (approximately 1 km by 1 km) geophysical product derived from Level 1B product and auxiliary data. The contents of the pixel fields, which are a mixture of Top of Atmosphere (TOA) and surface brightness temperature/radiance, are switch-able depending on the surface type. The third reprocessing was done to implement updated algorithms, processors, and auxiliary files." } }, { "ob_id": 819, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 8113, "uuid": "36473df8dd5b82309c06539a57210698", "short_code": "ob", "title": "ATSR-2: Gridded Surface Temperature (GST) Product (AT2_NR__2P), v2.1", "abstract": "Along-Track Scanning Radiometer (ATSR) mission was funded jointly by the UK Department of Energy and Climate Change External Link (DECC) and the Australian Department of Innovation, Industry, Science and Research External Link (DIISR).\r\n\r\nThis dataset contains the Along-Track Scanning Radiometer on ESA ERS-2 satellite (ATSR-2) Gridded Surface Temperature (GST) Product. These data are the Level 2 full spatial resolution (approximately 1 km by 1 km) geophysical product derived from Level 1B product and auxiliary data. The contents of the pixel fields, which are a mixture of Top of Atmosphere (TOA) and surface brightness temperature/radiance, are switch-able depending on the surface type. The third reprocessing was done to implement updated algorithms, processors, and auxiliary files." }, "objectObservation": { "ob_id": 40160, "uuid": "4371686200b444ffa6abf675334fd932", "short_code": "ob", "title": "ATSR-2: Gridded Surface Temperature (GST) Product (AT2_NR__2P), v3.0.1", "abstract": "Along-Track Scanning Radiometer (ATSR) mission was funded jointly by the UK Department of Energy and Climate Change External Link (DECC) and the Australian Department of Innovation, Industry, Science and Research External Link (DIISR).\r\n\r\nThis dataset contains the Along-Track Scanning Radiometer on ESA ERS-2 satellite (ATSR-2) Gridded Surface Temperature (GST) Product. These data are the Level 2 full spatial resolution (approximately 1 km by 1 km) geophysical product derived from Level 1B product and auxiliary data. The contents of the pixel fields, which are a mixture of Top of Atmosphere (TOA) and surface brightness temperature/radiance, are switch-able depending on the surface type. The third reprocessing was done to implement updated algorithms, processors, and auxiliary files." } }, { "ob_id": 821, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40166, "uuid": "8956cf9e31334914ab4991796f0f645a", "short_code": "ob", "title": "HadISDH.land: gridded global monthly land surface humidity data version 4.5.1.2022f", "abstract": "This is the HadISDH.land 4.5.1.2022f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.land is a near-global gridded monthly mean land surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from weather stations. The observations have been quality controlled and homogenised. Uncertainty estimates for observation issues and gridbox sampling are provided (see data quality statement section below). The data are provided by the Met Office Hadley Centre and this version spans 1/1/1973 to 31/12/2022. \r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD).\r\n\r\nThis version extends the previous version to the end of 2022. Users are advised to read the update document in the Docs section for full details on all changes from the previous release.\r\n\r\nAs in previous years, the annual scrape of NOAAs Integrated Surface Dataset for HadISD.3.3.0.2022f, which is the basis of HadISDH.land, has pulled through some historical changes to stations. This, and the additional year of data, results in small changes to station selection. The homogeneity adjustments differ slightly due to sensitivity to the addition and loss of stations, historical changes to stations previously included and the additional 12 months of data.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link\r\nto the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E.,\r\nJones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and\r\ntemperature record for climate monitoring, Clim. Past, 10, 1983-2006,\r\ndoi:10.5194/cp-10-1983-2014, 2014.\r\n\r\nDunn, R. J. H., et al. 2016: Expanding HadISD: quality-controlled, sub-daily station\r\ndata from 1931, Geoscientific Instrumentation, Methods and Data Systems, 5, 473-491.\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent\r\nDevelopments and Partnerships. Bulletin of the American Meteorological Society, 92,\r\n704-708, doi:10.1175/2011BAMS3015.1\r\n\r\nWe strongly recommend that you read these papers before making use of the data, more\r\ndetail on the dataset can be found in an earlier publication:\r\n\r\nWillett, K. M., Williams Jr., C. N., Dunn, R. J. H., Thorne, P. W., Bell, S., de\r\nPodesta, M., Jones, P. D., and Parker D. E., 2013: HadISDH: An updated land surface\r\nspecific humidity product for climate monitoring. Climate of the Past, 9, 657-677,\r\ndoi:10.5194/cp-9-657-2013." }, "objectObservation": { "ob_id": 37289, "uuid": "062942e96a6e4567b2bc47045be910a7", "short_code": "ob", "title": "HadISDH.land: gridded global monthly land surface humidity data version 4.4.0.2021f", "abstract": "This is the HadISDH.land 4.4.0.2021f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.land is a near-global gridded monthly mean land surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from weather stations. The observations have been quality controlled and homogenised. Uncertainty estimates for observation issues and gridbox sampling are provided (see data quality statement section below). The data are provided by the Met Office Hadley Centre and this version spans 1/1/1973 to 31/12/2021. \r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD).\r\n\r\nThis version extends the previous version to the end of 2021. Users are advised to read the update document in the Docs section for full details on all changes from the previous release.\r\n\r\nAs in previous years, the annual scrape of NOAAs Integrated Surface Dataset for HadISD.3.1.2.202101p, which is the basis of HadISDH.land, has pulled through some historical changes to stations. This, and the additional year of data, results in small changes to station selection. The homogeneity adjustments differ slightly due to sensitivity to the addition and loss of stations, historical changes to stations previously included and the additional 12 months of data.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link\r\nto the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E.,\r\nJones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and\r\ntemperature record for climate monitoring, Clim. Past, 10, 1983-2006,\r\ndoi:10.5194/cp-10-1983-2014, 2014.\r\n\r\nDunn, R. J. H., et al. 2016: Expanding HadISD: quality-controlled, sub-daily station\r\ndata from 1931, Geoscientific Instrumentation, Methods and Data Systems, 5, 473-491.\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent\r\nDevelopments and Partnerships. Bulletin of the American Meteorological Society, 92,\r\n704-708, doi:10.1175/2011BAMS3015.1\r\n\r\nWe strongly recommend that you read these papers before making use of the data, more\r\ndetail on the dataset can be found in an earlier publication:\r\n\r\nWillett, K. M., Williams Jr., C. N., Dunn, R. J. H., Thorne, P. W., Bell, S., de\r\nPodesta, M., Jones, P. D., and Parker D. E., 2013: HadISDH: An updated land surface\r\nspecific humidity product for climate monitoring. Climate of the Past, 9, 657-677,\r\ndoi:10.5194/cp-9-657-2013." } }, { "ob_id": 822, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40271, "uuid": "aed8e269513f446fb1b5d2512bb387ad", "short_code": "ob", "title": "CRU JRA v2.4: A forcings dataset of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data; Jan.1901 - Dec.2022.", "abstract": "The CRU JRA V2.4 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 degree latitude x 0.5 degree longitude grid, the grid is near global but excludes Antarctica (this is the 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 2022.\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 4.07 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\nThis version of CRUJRA, v2.4 (1901-2022) is, where possible, adjusted to align with CRU TS monthly means or totals. A consequence of this is that, if CRU TS changes, then CRUJRA changes.\r\n\r\nFor this version, and version 4.07 of CRU TS, the CLD (cloud cover, %) variable is now actualised (converted from gridded anomalies) using the original CLD climatology and not the revised climatology introduced last year. This change/reversion is summarised here: https://crudata.uea.ac.uk/cru/data/hrg/cru_cl_1.1/Read_Me_CRU_CL_CLD_Reversion.txt\r\n\r\nSince CLD is used to align DSWRF, CRUJRA DSWRF will now be 'closer to' version 2.2 and earlier and should be used in preference to v2.3.\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" }, "objectObservation": { "ob_id": 38218, "uuid": "38715b12b22043118a208acd61771917", "short_code": "ob", "title": "CRU JRA v2.3: A forcings dataset of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data; Jan.1901 - Dec.2021.", "abstract": "The CRU JRA V2.3 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 2021.\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 4.06 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" } }, { "ob_id": 823, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 40095, "uuid": "3659eca2afe54ab9ae437bf25fec1c2e", "short_code": "ob", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.26 (v20230523)", "abstract": "Data for Figure 2.26 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 2.26 shows changes in ocean heat content (OHC).\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 2.26:\r\n\r\n- AR6 FGD assessment timeseries OHC\r\n\r\nThese data files are from data for Cross-Chapter Box 9.1, Figure 1. The link to this dataset is provided in the Related Documents section of this catalogue record.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n- Link to Cross-Chapter Box 9.1, Figure 1" }, "objectObservation": { "ob_id": 40089, "uuid": "c622adfeb4cc4ae181dc4cca82c2311c", "short_code": "ob", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Cross-Chapter Box 9.1, Figure 1 (v20230523)", "abstract": "Data for Cross-Chapter Box 9.1, Figure 1 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nCross-Chapter Box 9.1, Figure 1 shows observed and simulated regional probability ratio of marine heatwaves (MHWs) for the 1985-2014 period and for the end of the 21st century under two different greenhouse gas emissions 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 Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels with data provided for all panels in the main directory. \r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n Main assessment timeseries for GMSL change, OHC and ThSL. Timeseries are global integrals over the following vertical layers: 0-300 m; 0-700 m; 0-2000 m; 700-2000 m; > 2000 m; Full-depth.\r\n\r\n\r\nThis dataset are also used in the following figures:\r\na) AR6 FGD assessment timeseries GMSL satellite altimeter: Figure 2.28; \r\nb) AR6 FGD assessment timeseries GMSL tide gauge: Figure 2.28;\r\nc) AR6 FGD assessment timeseries OHC: Figure 3.26, Box 7.2, Figure 1; \r\n\r\nOther figures/tables: Figure 2.26, Table 2.7; Figure 3.26; Box 7.2 Figure 1, Table 9.5; Figure TS.8; Figure TS.13.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a: \r\n Data file: “AR6_FGD_assessment_timeseries_OHC.csv” => column 2 is used to plot the light blue shaded region, column 4 is used to plot the medium blue shaded region, column 6 is used to plot the dark blue shaded region in CCBox9.1 Figure 1 panel a). \r\n\r\n\r\nPanel b: \r\n Data file: “AR6_FGD_assessment_timseries_GMSL_satellite_altimeter.csv” => column 2 is used to plot the dashed black line in CCBox9.1 Figure 1 panel b)\r\n Data file: “AR6_FGD_assesssment_timeseries_GMSL_tide_gauge.csv” => column 2 is used to to plot the dashed black line in CCBox9.1 Figure 1 panel b)\r\n Data file: “AR6_FGD_assessment_timeseries_ThSL.csv” => column 2 is used to plot the light blue shaded region, column 4 is used to plot the medium blue shaded region, column 6 is used to plot the dark blue shaded region in CCBox9.1 Figure 1 panel b).\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to the code for the figure, archived on github repository for chapter 9.\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to Chapter 2 Figure 2.26 \r\n - Link to Chapter 2 Figure 2.28\r\n - Link to Chapter 3 Figure 3.26\r\n - Link to Chapter 7 Box 7.2, Figure 1\r\n - Link to Technical Summary Figure TS.13\r\n - Link to input data for Cross-Chapter Box 9.1, Figure 1" } }, { "ob_id": 824, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 40093, "uuid": "12ce1a305f7649bc85a9b81e782da0c9", "short_code": "ob", "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.28 (v20230523)", "abstract": "Data for Figure 2.28 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 2.28 shows changes in global mean sea level.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 2.28:\r\n\r\n a) AR6 FGD assessment timeseries GMSL satellite altimeter \r\n b) AR6 FGD assessment timeseries GMSL tide gauge\r\n\r\nThese data files can be found from data for Cross-Chapter Box 9.1, Figure 1. The link to this dataset is provided in the Related Documents section of this catalogue record.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n- Link to Cross-Chapter Box 9.1, Figure 1" }, "objectObservation": { "ob_id": 40089, "uuid": "c622adfeb4cc4ae181dc4cca82c2311c", "short_code": "ob", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Cross-Chapter Box 9.1, Figure 1 (v20230523)", "abstract": "Data for Cross-Chapter Box 9.1, Figure 1 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nCross-Chapter Box 9.1, Figure 1 shows observed and simulated regional probability ratio of marine heatwaves (MHWs) for the 1985-2014 period and for the end of the 21st century under two different greenhouse gas emissions 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 Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels with data provided for all panels in the main directory. \r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n Main assessment timeseries for GMSL change, OHC and ThSL. Timeseries are global integrals over the following vertical layers: 0-300 m; 0-700 m; 0-2000 m; 700-2000 m; > 2000 m; Full-depth.\r\n\r\n\r\nThis dataset are also used in the following figures:\r\na) AR6 FGD assessment timeseries GMSL satellite altimeter: Figure 2.28; \r\nb) AR6 FGD assessment timeseries GMSL tide gauge: Figure 2.28;\r\nc) AR6 FGD assessment timeseries OHC: Figure 3.26, Box 7.2, Figure 1; \r\n\r\nOther figures/tables: Figure 2.26, Table 2.7; Figure 3.26; Box 7.2 Figure 1, Table 9.5; Figure TS.8; Figure TS.13.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a: \r\n Data file: “AR6_FGD_assessment_timeseries_OHC.csv” => column 2 is used to plot the light blue shaded region, column 4 is used to plot the medium blue shaded region, column 6 is used to plot the dark blue shaded region in CCBox9.1 Figure 1 panel a). \r\n\r\n\r\nPanel b: \r\n Data file: “AR6_FGD_assessment_timseries_GMSL_satellite_altimeter.csv” => column 2 is used to plot the dashed black line in CCBox9.1 Figure 1 panel b)\r\n Data file: “AR6_FGD_assesssment_timeseries_GMSL_tide_gauge.csv” => column 2 is used to to plot the dashed black line in CCBox9.1 Figure 1 panel b)\r\n Data file: “AR6_FGD_assessment_timeseries_ThSL.csv” => column 2 is used to plot the light blue shaded region, column 4 is used to plot the medium blue shaded region, column 6 is used to plot the dark blue shaded region in CCBox9.1 Figure 1 panel b).\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to the code for the figure, archived on github repository for chapter 9.\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to Chapter 2 Figure 2.26 \r\n - Link to Chapter 2 Figure 2.28\r\n - Link to Chapter 3 Figure 3.26\r\n - Link to Chapter 7 Box 7.2, Figure 1\r\n - Link to Technical Summary Figure TS.13\r\n - Link to input data for Cross-Chapter Box 9.1, Figure 1" } }, { "ob_id": 827, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 37892, "uuid": "568fb4b2e6464a50a30c7140bb88a497", "short_code": "ob", "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Box 7.2, Figure 1. (v20220817)", "abstract": "Input Data for Box 7.2, Figure 1 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nBox 7.2, Figure 1 shows estimates of the net cumulative energy change (ZJ = 1021 Joules) for the period 1971–2018 associated with: (a) observations of changes in the Global Energy Inventory (b) Integrated Radiative Forcing; (c) Integrated Radiative Response. \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\nForster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, M. Watanabe, M. Wild, and H. Zhang, 2021: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. 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. 923–1054, doi:10.1017/9781009157896.009.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 6 subpanels, with input data provided for panels a-f.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Estimates of the net cumulative energy change (ZJ = 1021 Joules) for the period 1971–2018 associated with: \r\n(a) observations of changes in the Global Energy Inventory \r\n(b) Integrated Radiative Forcing; \r\n(c) Integrated Radiative Response.\r\n\r\nBlack dotted lines indicate the central estimate with likely and very likely ranges as indicated in the legend. The grey dotted lines indicate the energy change associated with an estimated pre-industrial Earth energy imbalance of 0.2 W m–2 (a), and an illustration of an assumed pattern effect of –0.5 W m–2 °C–1 (c). \r\n\r\nBackground grey lines indicate equivalent heating rates in W m–2 per unit area of Earth’s surface. \r\nPanels (d) and (e) show the breakdown of components, as indicated in the legend, for the global energy inventory and integrated radiative forcing, respectively. Panel (f) shows the global energy budget assessed for the period 1971–2018, that is, the consistency between the change in the global energy inventory relative to pre-industrial and the implied energy change from integrated radiative forcing plus integrated radiative response under a number of different assumptions, as indicated in the legend, including assumptions of correlated and uncorrelated uncertainties in forcing plus response. \r\n\r\nShading represents the very likely range for observed energy change relative to pre-industrial levels and likely range for all other quantities. \r\nForcing and response time series are expressed relative to a baseline period of 1850–1900. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 7.SM.14).\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Box 7.2, Figure 1:\r\n \r\n - Data file: AR6_ERF_1750-2019.csv\r\n - Data file: AR6_energy_GMSL_timeseries_FGD_1971to2018_IMBIEupdate.csv\r\n - Data file: AR6_energy_GMSL_timeseries_FGD_1971to2018_corrigendum.csv\r\n - Data file: Box7.2_ERF_ZJ_percentiles_FGD_1971to2018.csv\r\n - Data file: Box7.2_Response_ZJ_percentiles_FGD_1971to2018.csv\r\n - Data file: Box7.2_ERFResp_uncorrelated_ZJ_percentiles_FGD_1971to2018.csv\r\n - Data file: Box7.2_ERFResp_correlated_ZJ_percentiles_FGD_1971to2018.csv\r\n\r\nData files are converted to csv from pickle format for archival. A link to the original files on GitHub is provided in the 'Related Documents' section.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory. Also listed on the 'master' GitHub page linked in the documentation of this catalogue record are external GitHub repositories and locations within the contributed directory where code for figures have been supplied by other authors. These are provided \"as-is\" and are not guaranteed to be reproducible within this environment. For external GitHub locations, check out the relevant repository READMEs.\r\n\r\nThe data provided here is converted from pickle files which are used in the plotting script. The link to the original pickle files on GitHub is provided. To reproduce the figure from the input data provided, you will need to edit the filepaths within the notebook based on your local directory structure.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 7)\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7.\r\n - Link to the notebook to plot the figure on the Chapter 7 GitHub repository\r\n - Link to the original pickle files on GitHub\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to Cross-Chapter Box 9.1, Figure 1" }, "objectObservation": { "ob_id": 40089, "uuid": "c622adfeb4cc4ae181dc4cca82c2311c", "short_code": "ob", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Cross-Chapter Box 9.1, Figure 1 (v20230523)", "abstract": "Data for Cross-Chapter Box 9.1, Figure 1 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nCross-Chapter Box 9.1, Figure 1 shows observed and simulated regional probability ratio of marine heatwaves (MHWs) for the 1985-2014 period and for the end of the 21st century under two different greenhouse gas emissions 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 Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels with data provided for all panels in the main directory. \r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n Main assessment timeseries for GMSL change, OHC and ThSL. Timeseries are global integrals over the following vertical layers: 0-300 m; 0-700 m; 0-2000 m; 700-2000 m; > 2000 m; Full-depth.\r\n\r\n\r\nThis dataset are also used in the following figures:\r\na) AR6 FGD assessment timeseries GMSL satellite altimeter: Figure 2.28; \r\nb) AR6 FGD assessment timeseries GMSL tide gauge: Figure 2.28;\r\nc) AR6 FGD assessment timeseries OHC: Figure 3.26, Box 7.2, Figure 1; \r\n\r\nOther figures/tables: Figure 2.26, Table 2.7; Figure 3.26; Box 7.2 Figure 1, Table 9.5; Figure TS.8; Figure TS.13.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a: \r\n Data file: “AR6_FGD_assessment_timeseries_OHC.csv” => column 2 is used to plot the light blue shaded region, column 4 is used to plot the medium blue shaded region, column 6 is used to plot the dark blue shaded region in CCBox9.1 Figure 1 panel a). \r\n\r\n\r\nPanel b: \r\n Data file: “AR6_FGD_assessment_timseries_GMSL_satellite_altimeter.csv” => column 2 is used to plot the dashed black line in CCBox9.1 Figure 1 panel b)\r\n Data file: “AR6_FGD_assesssment_timeseries_GMSL_tide_gauge.csv” => column 2 is used to to plot the dashed black line in CCBox9.1 Figure 1 panel b)\r\n Data file: “AR6_FGD_assessment_timeseries_ThSL.csv” => column 2 is used to plot the light blue shaded region, column 4 is used to plot the medium blue shaded region, column 6 is used to plot the dark blue shaded region in CCBox9.1 Figure 1 panel b).\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to the code for the figure, archived on github repository for chapter 9.\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to Chapter 2 Figure 2.26 \r\n - Link to Chapter 2 Figure 2.28\r\n - Link to Chapter 3 Figure 3.26\r\n - Link to Chapter 7 Box 7.2, Figure 1\r\n - Link to Technical Summary Figure TS.13\r\n - Link to input data for Cross-Chapter Box 9.1, Figure 1" } }, { "ob_id": 828, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 38908, "uuid": "f3b6afe197d24d7eb58ed2364ac0f18e", "short_code": "ob", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure TS.13 v20221111", "abstract": "Input data for Figure TS.13 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure TS.13 shows estimates of the net cumulative energy change for the period 1971–2018 associated with observations of changes in the Global Energy Inventory, Integrated Radiative Forcing and Integrated Radiative Response.\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\nArias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. 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. 33−144, doi:10.1017/9781009157896.002.\r\n\r\n---------------------------------------------------\r\nFigure subpanels\r\n---------------------------------------------------\r\nThe figure has 6 subpanels, with input data provided for panels a-f.\r\n\r\n---------------------------------------------------\r\nList of data provided\r\n---------------------------------------------------\r\nThis dataset contains:\r\n\r\n- Estimates of the net cumulative energy change (ZJ = 1021 Joules) for the period 1971–2018 associated with:\r\n(a) observations of changes in the Global Energy Inventory\r\n(b) Integrated Radiative Forcing;\r\n(c) Integrated Radiative Response.\r\n\r\nBlack dotted lines indicate the central estimate with likely and very likely ranges as indicated in the legend. The grey dotted lines indicate the energy change associated with an estimated pre-industrial Earth energy imbalance of 0.2 W m–2 (a), and an illustration of an assumed pattern effect of –0.5 W m–2 °C–1 (c).\r\n\r\nBackground grey lines indicate equivalent heating rates in W m–2 per unit area of Earth’s surface.\r\nPanels (d) and (e) show the breakdown of components, as indicated in the legend, for the global energy inventory and integrated radiative forcing, respectively. Panel (f) shows the global energy budget assessed for the period 1971–2018, that is, the consistency between the change in the global energy inventory relative to pre-industrial and the implied energy change from integrated radiative forcing plus integrated radiative response under a number of different assumptions, as indicated in the legend, including assumptions of correlated and uncorrelated uncertainties in forcing plus response.\r\n\r\nShading represents the very likely range for observed energy change relative to pre-industrial levels and likely range for all other quantities.\r\nForcing and response time series are expressed relative to a baseline period of 1850–1900.\r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 7.SM.14).\r\n\r\n---------------------------------------------------\r\nData provided in relation to figure\r\n---------------------------------------------------\r\nData provided in relation to Figure TS.13:\r\n\r\n- Data file: AR6_ERF_1750-2019.csv\r\n- Data file: AR6_energy_GMSL_timeseries_FGD_1971to2018_corrigendum.csv\r\n- Data file: Box7.2_ERF_ZJ_percentiles_FGD_1971to2018.csv\r\n- Data file: Box7.2_Response_ZJ_percentiles_FGD_1971to2018.csv\r\n- Data file: Box7.2_ERFResp_uncorrelated_ZJ_percentiles_FGD_1971to2018.csv\r\n- Data file: Box7.2_ERFResp_correlated_ZJ_percentiles_FGD_1971to2018.csv\r\n\r\nData files are converted to csv from pickle format for archival. A link to the original files on GitHub is provided in the 'Related Documents' section.\r\n\r\n---------------------------------------------------\r\nNotes on reproducing the figure from the provided data\r\n---------------------------------------------------\r\nData and figures are produced by the Jupyter Notebooks that live inside the notebooks directory. Also listed on the 'master' GitHub page linked in the documentation of this catalogue record are external GitHub repositories and locations within the contributed directory where code for figures have been supplied by other authors. These are provided \"as-is\" and are not guaranteed to be reproducible within this environment. For external GitHub locations, check out the relevant repository READMEs.\r\n\r\nThe data provided here is converted from pickle files which are used in the plotting script. The link to the original pickle files on GitHub is provided. To reproduce the figure from the input data provided, you will need to edit the filepaths within the notebook based on your local directory structure.\r\n\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Technical Summary)\r\n - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7.\r\n - Link to the notebook to plot the figure on the Chapter 7 GitHub repository\r\n - Link to the original pickle files on GitHub\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to Cross-Chapter Box 9.1, Figure 1" }, "objectObservation": { "ob_id": 40089, "uuid": "c622adfeb4cc4ae181dc4cca82c2311c", "short_code": "ob", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Cross-Chapter Box 9.1, Figure 1 (v20230523)", "abstract": "Data for Cross-Chapter Box 9.1, Figure 1 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nCross-Chapter Box 9.1, Figure 1 shows observed and simulated regional probability ratio of marine heatwaves (MHWs) for the 1985-2014 period and for the end of the 21st century under two different greenhouse gas emissions 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 Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels with data provided for all panels in the main directory. \r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n Main assessment timeseries for GMSL change, OHC and ThSL. Timeseries are global integrals over the following vertical layers: 0-300 m; 0-700 m; 0-2000 m; 700-2000 m; > 2000 m; Full-depth.\r\n\r\n\r\nThis dataset are also used in the following figures:\r\na) AR6 FGD assessment timeseries GMSL satellite altimeter: Figure 2.28; \r\nb) AR6 FGD assessment timeseries GMSL tide gauge: Figure 2.28;\r\nc) AR6 FGD assessment timeseries OHC: Figure 3.26, Box 7.2, Figure 1; \r\n\r\nOther figures/tables: Figure 2.26, Table 2.7; Figure 3.26; Box 7.2 Figure 1, Table 9.5; Figure TS.8; Figure TS.13.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a: \r\n Data file: “AR6_FGD_assessment_timeseries_OHC.csv” => column 2 is used to plot the light blue shaded region, column 4 is used to plot the medium blue shaded region, column 6 is used to plot the dark blue shaded region in CCBox9.1 Figure 1 panel a). \r\n\r\n\r\nPanel b: \r\n Data file: “AR6_FGD_assessment_timseries_GMSL_satellite_altimeter.csv” => column 2 is used to plot the dashed black line in CCBox9.1 Figure 1 panel b)\r\n Data file: “AR6_FGD_assesssment_timeseries_GMSL_tide_gauge.csv” => column 2 is used to to plot the dashed black line in CCBox9.1 Figure 1 panel b)\r\n Data file: “AR6_FGD_assessment_timeseries_ThSL.csv” => column 2 is used to plot the light blue shaded region, column 4 is used to plot the medium blue shaded region, column 6 is used to plot the dark blue shaded region in CCBox9.1 Figure 1 panel b).\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n - Link to the code for the figure, archived on Zenodo.\r\n - Link to the code for the figure, archived on github repository for chapter 9.\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to Chapter 2 Figure 2.26 \r\n - Link to Chapter 2 Figure 2.28\r\n - Link to Chapter 3 Figure 3.26\r\n - Link to Chapter 7 Box 7.2, Figure 1\r\n - Link to Technical Summary Figure TS.13\r\n - Link to input data for Cross-Chapter Box 9.1, Figure 1" } }, { "ob_id": 829, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 39803, "uuid": "90049a6555d1480bb5ce9637051dede8", "short_code": "ob", "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): New network of virtual altimetry stations for measuring sea level along the world coastlines from 2002 to 2019, v2.2", "abstract": "This dataset contains a 17-year-long (January 2002 to December 2019 ), high-resolution (20 Hz), along-track sea level dataset in coastal zones of: Northeast Atlantic, Mediterranean Sea, whole African continent, North Indian Ocean, Southeast Asia, Australia and North and South America. Up to now, satellite altimetry has provided global gridded sea level time series up to 10-15 km from the coast only, preventing the estimation of how sea level changes very close to the coast on interannual to decadal time scales. \r\n\r\nThis dataset has been derived from a new version of the ESA SL_cci+ dataset of coastal sea level anomalies which is based on the reprocessing of raw radar altimetry waveforms from the Jason-1, Jason-2 and Jason-3 satellite missions to derive satellite-sea surface ranges as close as possible to the coast (a process called ‘retracking’) and optimization of the geophysical corrections applied to the range measurements to produce sea level time series.\r\n\r\nThis large amount of coastal sea level estimates has been further analysed to produce the present dataset: a total of 756 altimetry-based virtual coastal stations have been selected and sea level anomalies time series together with associated coastal sea level trends have been computed over the study time span. \r\n\r\nThe main objective of this dataset is to analyze the sea level trends close to the coast and compare them with the sea level trends observed in the open ocean and to determine the causes of the potential differences.\r\n\r\nThe product has been developed within the sea level project of the extension phase of the European Space Agency (ESA) Climate Change Initiative (SL_cci+). See 'The Climate Change Coastal Sea Level Team (2020). Sea level anomalies and associated trends estimated from altimetry from 2002 to 2018 at selected coastal sites. Scientific Data (Nature), in press'.\r\n\r\nThis dataset is v2.2 of the data and is a copy of the v2.2 data published on the SEANOE (SEA scieNtific Open data Edition) website (https://doi.org/10.17882/74354#98856) \r\n\r\nThe dataset should be cited as: \tCazenave Anny, Gouzenes Yvan, Birol Florence, Legér Fabien, Passaro Marcello, Calafat Francisco M, Shaw Andrew, Niño Fernando, Legeais Jean François, Oelsmann Julius, Benveniste Jérôme (2022). New network of virtual altimetry stations for measuring sea level along the world coastlines. SEANOE. https://doi.org/10.17882/74354\r\n\r\nIn addition,it would be appreciated that the following work(s) be cited too, when using this dataset in a publication :\r\n\r\n - Cazenave Anny, Gouzenes Yvan, Birol Florence, Leger Fabien, Passaro Marcello, Calafat Francisco M., Shaw Andrew, Nino Fernando, Legeais Jean François, Oelsmann Julius, Restano Marco, Benveniste Jérôme (2022). Sea level along the world’s coastlines can be measured by a network of virtual altimetry stations. Communications Earth & Environment, 3 (1). https://doi.org/10.1038/s43247-022-00448-z\r\n\r\n - Benveniste Jérôme, Birol Florence, Calafat Francisco, Cazenave Anny, Dieng Habib, Gouzenes Yvan, Legeais Jean François, Léger Fabien, Niño Fernando, Passaro Marcello, Schwatke Christian, Shaw Andrew (2020). Coastal sea level anomalies and associated trends from Jason satellite altimetry over 2002–2018. Scientific Data, 7 (1). https://doi.org/10.1038/s41597-020-00694-w" }, "objectObservation": { "ob_id": 31825, "uuid": "a386504aa8ae492f9f2af04c109346e9", "short_code": "ob", "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): A database of coastal sea level anomalies and associated trends from Jason satellite altimetry from 2002 to 2018", "abstract": "This dataset contains 17-year-long (June 2002 to May 2018 ), high-resolution (20 Hz), along-track sea level dataset in coastal zones of six regions: Mediterranean Sea, Northeast Atlantic, West Africa, North Indian Ocean, Southeast Asia and Australia. Up to now, satellite altimetry has provided global gridded sea level time series up to 10-15 km from the coast only, preventing the estimation of how sea level changes very close to the coast on interannual to decadal time scales. \r\n\r\nThis dataset has been derived from the ESA SL_cci+ v1.1 dataset of coastal sea level anomalies (also available in the catalogue, DOI:10.5270/esa-sl_cci-xtrack_ales_sla-200206_201805-v1.1-202005), which is based on the reprocessing of raw radar altimetry waveforms from the Jason-1, Jason-2 and Jason-3 satellite missions to derive satellite-sea surface ranges as close as possible to the coast (a process called ‘retracking’) and optimization of the geophysical corrections applied to the range measurements to produce sea level time series. This large amount of coastal sea level estimates has been further analysed to produce the present dataset: it consists in a selection of 429 portions of satellite tracks crossing land for which valid sea level time series are provided at monthly interval together with the associated sea level trends over the 17-year time span at each along-track 20-Hz point, from 20 km offshore to the coast.\r\n\r\nThe main objective of this dataset is to analyze the sea level trends close to the coast and compare them with the sea level trends observed in the open ocean and to determine the causes of the potential differences.\r\n\r\nThe product has been developed within the sea level project of the extension phase of the European Space Agency (ESA) Climate Change Initiative (SL_cci+). See 'The Climate Change Coastal Sea Level Team (2020). Sea level anomalies and associated trends estimated from altimetry from 2002 to 2018 at selected coastal sites. Scientific Data (Nature), in press'.\r\n\r\nThis dataset has a DOI: https://doi.org/10.17882/74354" } }, { "ob_id": 831, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40618, "uuid": "755bc61b5524498db67f9468a92d8cfc", "short_code": "ob", "title": "HadISDH.marine: gridded global monthly ocean surface humidity data version 1.4.1.2022f", "abstract": "This is the HadISDH.marine 1.4.1.2022f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.marine is a near-global gridded monthly mean marine surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from ships. The observations have been quality controlled and bias-adjusted. Uncertainty estimates for observation issues and gridbox sampling are provided (see data quality statement section below). The data are provided by the Met Office Hadley Centre and this version spans 1/1/1973 to 31/12/2022.\r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD).\r\n\r\nThis version extends the previous version to the end of 2022. Users are advised to read the update document in the Docs section for full details on all changes from the previous release.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link\r\nto the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K. M., Dunn, R. J. H., Kennedy, J. J. and Berry, D. I., 2020: Development of\r\nthe HadISDH marine humidity climate monitoring dataset. Earth System Sciences Data,\r\n12, 2853-2880, https://doi.org/10.5194/essd-12-2853-2020\r\n\r\nFreeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C., Angel, W. E.,\r\nBerry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W., Ji, Z., Lawrimore, J.,\r\nRayner, N. A., Rosenhagen, G. and Smith, S. R., ICOADS Release 3.0: A major update to\r\nthe historical marine climate record. International Journal of Climatology.\r\ndoi:10.1002/joc.4775." }, "objectObservation": { "ob_id": 37290, "uuid": "54d3408edd9e41bca226924754619812", "short_code": "ob", "title": "HadISDH.marine: gridded global monthly ocean surface humidity data version 1.3.0.2021f", "abstract": "This is the HadISDH.marine 1.3.0.2021f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.marine is a near-global gridded monthly mean marine surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from ships. The observations have been quality controlled and bias-adjusted. Uncertainty estimates for observation issues and gridbox sampling are provided (see data quality statement section below). The data are provided by the Met Office Hadley Centre and this version spans 1/1/1973 to 31/12/2021.\r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD).\r\n\r\nThis version extends the previous version to the end of 2021. Users are advised to read the update document in the Docs section for full details on all changes from the previous release.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link\r\nto the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K. M., Dunn, R. J. H., Kennedy, J. J. and Berry, D. I., 2020: Development of\r\nthe HadISDH marine humidity climate monitoring dataset. Earth System Sciences Data,\r\n12, 2853-2880, https://doi.org/10.5194/essd-12-2853-2020\r\n\r\nFreeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C., Angel, W. E.,\r\nBerry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W., Ji, Z., Lawrimore, J.,\r\nRayner, N. A., Rosenhagen, G. and Smith, S. R., ICOADS Release 3.0: A major update to\r\nthe historical marine climate record. International Journal of Climatology.\r\ndoi:10.1002/joc.4775." } }, { "ob_id": 832, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40621, "uuid": "c3c1526fba8f4a5382d2f9fb86966d82", "short_code": "ob", "title": "HadISDH.blend: gridded global monthly land and ocean surface humidity data version 1.4.1.2022f", "abstract": "This is the HadISDH.blend 1.4.1.2022f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.blend is a near-global gridded monthly mean surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from ships and weather stations. The observations have been quality controlled and homogenised / bias adjusted. Uncertainty estimates for observation issues and gridbox sampling are provided (see data quality statement section below). These data are provided by the Met Office Hadley Centre. This version spans 1/1/1973 to 31/12/2022.\r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD).\r\n\r\nThis version extends the previous version to the end of 2022. It combines the latest version of HadISDH.land and HadISDH.marine. and therefore their respective update notes. Users are advised to read the update documents in the Docs section for full details.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link\r\nto the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K. M., Dunn, R. J. H., Kennedy, J. J. and Berry, D. I., 2020: Development of\r\nthe HadISDH marine humidity climate monitoring dataset. Earth System Sciences Data,\r\n12, 2853-2880, https://doi.org/10.5194/essd-12-2853-2020\r\n\r\nFreeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C., Angel, W. E.,\r\nBerry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W., Ji, Z., Lawrimore, J.,\r\nRayner, N. A., Rosenhagen, G. and Smith, S. R., ICOADS Release 3.0: A major update to\r\nthe historical marine climate record. International Journal of Climatology.\r\ndoi:10.1002/joc.4775.\r\n\r\nWillett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E.,\r\nJones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and\r\ntemperature record for climate monitoring, Clim. Past, 10, 1983-2006,\r\ndoi:10.5194/cp-10-1983-2014, 2014.\r\n\r\nDunn, R. J. H., et al. 2016: Expanding HadISD: quality-controlled, sub-daily station\r\ndata from 1931, Geoscientific Instrumentation, Methods and Data Systems, 5, 473-491.\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent\r\nDevelopments and Partnerships. Bulletin of the American Meteorological Society, 92,\r\n704-708, doi:10.1175/2011BAMS3015.1\r\n\r\nWe strongly recommend that you read these papers before making use of the data, more\r\ndetail on the dataset can be found in an earlier publication:\r\n\r\nWillett, K. M., Williams Jr., C. N., Dunn, R. J. H., Thorne, P. W., Bell, S., de\r\nPodesta, M., Jones, P. D., and Parker D. E., 2013: HadISDH: An updated land surface\r\nspecific humidity product for climate monitoring. Climate of the Past, 9, 657-677,\r\ndoi:10.5194/cp-9-657-2013." }, "objectObservation": { "ob_id": 37291, "uuid": "563cb665bc6e43f99b355a9bb8134317", "short_code": "ob", "title": "HadISDH.blend: gridded global monthly land and ocean surface humidity data version 1.3.0.2021f", "abstract": "This is the HadISDH.blend 1.3.0.2021f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.blend is a near-global gridded monthly mean surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from ships and weather stations. The observations have been quality controlled and homogenised / bias adjusted. Uncertainty estimates for observation issues and gridbox sampling are provided (see data quality statement section below). These data are provided by the Met Office Hadley Centre. This version spans 1/1/1973 to 31/12/2021.\r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD).\r\n\r\nThis version extends the previous version to the end of 2021. It combines the latest version of HadISDH.land and HadISDH.marine. and therefore their respective update notes. Users are advised to read the update documents in the Docs section for full details.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link\r\nto the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K. M., Dunn, R. J. H., Kennedy, J. J. and Berry, D. I., 2020: Development of\r\nthe HadISDH marine humidity climate monitoring dataset. Earth System Sciences Data,\r\n12, 2853-2880, https://doi.org/10.5194/essd-12-2853-2020\r\n\r\nFreeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C., Angel, W. E.,\r\nBerry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W., Ji, Z., Lawrimore, J.,\r\nRayner, N. A., Rosenhagen, G. and Smith, S. R., ICOADS Release 3.0: A major update to\r\nthe historical marine climate record. International Journal of Climatology.\r\ndoi:10.1002/joc.4775.\r\n\r\nWillett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E.,\r\nJones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and\r\ntemperature record for climate monitoring, Clim. Past, 10, 1983-2006,\r\ndoi:10.5194/cp-10-1983-2014, 2014.\r\n\r\nDunn, R. J. H., et al. 2016: Expanding HadISD: quality-controlled, sub-daily station\r\ndata from 1931, Geoscientific Instrumentation, Methods and Data Systems, 5, 473-491.\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent\r\nDevelopments and Partnerships. Bulletin of the American Meteorological Society, 92,\r\n704-708, doi:10.1175/2011BAMS3015.1\r\n\r\nWe strongly recommend that you read these papers before making use of the data, more\r\ndetail on the dataset can be found in an earlier publication:\r\n\r\nWillett, K. M., Williams Jr., C. N., Dunn, R. J. H., Thorne, P. W., Bell, S., de\r\nPodesta, M., Jones, P. D., and Parker D. E., 2013: HadISDH: An updated land surface\r\nspecific humidity product for climate monitoring. Climate of the Past, 9, 657-677,\r\ndoi:10.5194/cp-9-657-2013." } }, { "ob_id": 833, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40655, "uuid": "c9663d0c525f4b0698f1ec4beae3688e", "short_code": "ob", "title": "MIDAS Open: UK hourly weather observation data, v202308", "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 2022.\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. These include the addition of data for calendar year 2022.\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." }, "objectObservation": { "ob_id": 38070, "uuid": "6180fb7ed76a442eb1b8f3f152fd08d7", "short_code": "ob", "title": "MIDAS Open: UK hourly weather observation data, v202207", "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 2021.\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. These include the addition of data for calendar year 2021, and additional historical data for Sheffield (South Yorkshire, 1882-1935).\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." } }, { "ob_id": 834, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40652, "uuid": "1ce37461affc43bbbd78beaaacf5911d", "short_code": "ob", "title": "MIDAS Open: UK daily weather observation data, v202308", "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 2022. 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. These include the addition of data for calendar year 2022.\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." }, "objectObservation": { "ob_id": 38069, "uuid": "4b44cec2f9a846f39d5007983b7eaaab", "short_code": "ob", "title": "MIDAS Open: UK daily weather observation data, v202207", "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 2021. 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. These include the addition of data for calendar year 2021, and additional historical data for Sheffield (South Yorkshire, 1898-1935).\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." } }, { "ob_id": 835, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40651, "uuid": "220b9b8ffbed43fcbbd323e739118f6c", "short_code": "ob", "title": "MIDAS Open: UK daily temperature data, v202308", "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 2022. 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. These include the addition of data for calendar year 2022.\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." }, "objectObservation": { "ob_id": 38067, "uuid": "8bcf6925cddc4681b96f94d424537b9e", "short_code": "ob", "title": "MIDAS Open: UK daily temperature data, v202207", "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 2021. 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. These include the addition of data for calendar year 2021.\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." } }, { "ob_id": 836, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40650, "uuid": "3f3809143a224c84962f52757d668f77", "short_code": "ob", "title": "MIDAS Open: UK daily rainfall data, v202308", "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 2022. 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 (202207) 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. These include the addition of data for calendar year 2022.\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." }, "objectObservation": { "ob_id": 38071, "uuid": "15deeb29cdcd4524b07560e5aad45ded", "short_code": "ob", "title": "MIDAS Open: UK daily rainfall data, v202207", "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 2021. 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 (202107) 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. These include the addition of data for calendar year 2021, and additional historical data for Colmonell (Ayrshire, 1924-1960), Camps Reservoir (Lanarkshire, 1934-1960), and Greenock (Renfrewshire, 1910-1960).\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." } }, { "ob_id": 837, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40649, "uuid": "c21639861fb54623a749e502ebac74ed", "short_code": "ob", "title": "MIDAS Open: UK hourly rainfall data, v202308", "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 2022.\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." }, "objectObservation": { "ob_id": 38072, "uuid": "64f5d7be890a4ac08cb2b4e78eb5fcc1", "short_code": "ob", "title": "MIDAS Open: UK hourly rainfall data, v202207", "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 2021.\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." } }, { "ob_id": 838, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40653, "uuid": "85596b72ff024837a64bf22a8d1a72be", "short_code": "ob", "title": "MIDAS Open: UK soil temperature data, v202308", "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 2022.\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. These include the addition of data for calendar year 2022.\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." }, "objectObservation": { "ob_id": 38073, "uuid": "4ecbf3fa1b084c5a9080248433275124", "short_code": "ob", "title": "MIDAS Open: UK soil temperature data, v202207", "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 2021.\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. These include the addition of data for calendar year 2021.\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." } }, { "ob_id": 839, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40654, "uuid": "87eb67c08f5c4518a3723d0ca2d9b4b1", "short_code": "ob", "title": "MIDAS Open: UK hourly solar radiation data, v202308", "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\nFor details about the different measurements made and the limited number of sites making them please see the MIDAS Solar Irradiance table linked to in the online resources section of 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. These include the addition of data for calendar year 2022.\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 2022.\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." }, "objectObservation": { "ob_id": 38074, "uuid": "e3a7f3336ff8464f9ae6534a8e8676e5", "short_code": "ob", "title": "MIDAS Open: UK hourly solar radiation data, v202207", "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\nFor details about the different measurements made and the limited number of sites making them please see the MIDAS Solar Irradiance table linked to in the online resources section of 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. These include the addition of data for calendar year 2021.\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 2021.\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." } }, { "ob_id": 840, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40656, "uuid": "68920a29caf44f21be6371d9f87f578b", "short_code": "ob", "title": "MIDAS Open: UK mean wind data, v202308", "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 2022.\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. These include the addition of data for calendar year 2022.\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." }, "objectObservation": { "ob_id": 38068, "uuid": "fa83484e57854d6fbde16ff945ff6dc0", "short_code": "ob", "title": "MIDAS Open: UK mean wind data, v202207", "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 2021.\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. These include the addition of data for calendar year 2021.\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." } }, { "ob_id": 841, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40619, "uuid": "dc11996a68c446abb342e917efdaac30", "short_code": "ob", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Climatology Climate Data Record, version 2.2", "abstract": "This v2.2 SST_cci Climatology Data Record (CDR) consists of daily climatological mean sea surface temperature on a global 0.05 degree latitude-longitude grid, derived from the SST CCI analysis data for the period 1982 to 2010 (29 years). This climatology includes the post-hoc dust corrections from Merchant and Embury (2020) https://doi.org/10.3390/rs12162554.\r\n\r\nThe changes from climatology v2.1 are:\r\n* Inclusion of post-hoc dust corrections from Merchant and Embury (2020) reduces biases in affected regions (tropical Atlantic Ocean and the Mediterranean, Red, and Arabian Seas).\r\n* Improved compliance with CF Conventions.\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ . \r\n\r\nWhen citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. (2019) Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223. http://doi.org/10.1038/s41597-019-0236-x" }, "objectObservation": { "ob_id": 27534, "uuid": "83e51cf29821434ea14db56c564946d5", "short_code": "ob", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Climatology Climate Data Record, version 2.1", "abstract": "This v2.1 SST_cci Climatology Data Record (CDR) consists of Level 4 daily climatology files gridded on a 0.05 degree grid. \r\n\r\nThe dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ . \r\n\r\nWhen citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x" } }, { "ob_id": 842, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40841, "uuid": "86d4b9195b40469e920cb56044adb265", "short_code": "ob", "title": "MOSAiC: Wind profiles from Galion G4000 Lidar Wind Profiler - Version 3", "abstract": "Wind profiles from a Galion G4000 Doppler lidar for the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) project, derived from conical scans at 30 degree and 50 degree beam elevation angles.\r\n\r\nThe University of Leeds participation in the project- MOSAiC Boundary Layer -was funded by the Natural Environment Research Council (NERC, grant: NE/S002472/1) and involved instrumentation from the Atmospheric Measurement and Observations Facility of the UK's National Centre for Atmospheric Science (NCAS AMOF). This was a year-long project on the German icebreaker Polarstern to study Arctic climate focused on measurements of atmospheric boundary layer dynamics and turbulent structure. The Galion wind profiler provides high resolution (~15m vertical and 5 minute temporal) measurements of wind profiles. Data are only available where sufficient particles are available to backscatter the laser light - in the clean arctic environment, this requires cloud or precipitation.\r\n\r\nThis is version 3 of this dataset which corrects an error in the implementation of the correction\r\nof the lidar azimuth when the scanning head slipped at very low temperatures." }, "objectObservation": { "ob_id": 38257, "uuid": "cc32622e68ad40bcac14fc6cd69ae4b7", "short_code": "ob", "title": "MOSAiC: Wind profiles from Galion G4000 Lidar Wind Profiler - Version 2", "abstract": "Wind profiles from a Galion G4000 Doppler lidar for the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) project, derived from conical scans at 30 degree and 50 degree beam elevation angles.\r\n\r\nThe University of Leeds participation in the project- MOSAiC Boundary Layer -was funded by the Natural Environment Research Council (NERC, grant: NE/S002472/1) and involved instrumentation from the Atmospheric Measurement and Observations Facility of the UK's National Centre for Atmospheric Science (NCAS AMOF). This was a year-long project on the German icebreaker Polarstern to study Arctic climate focused on measurements of atmospheric boundary layer dynamics and turbulent structure. The Galion wind profiler provides high resolution (~15m vertical and 5 minute temporal) measurements of wind profiles. Data are only available where sufficient particles are available to backscatter the laser light - in the clean arctic environment, this requires cloud or precipitation.\r\n\r\nThis is version 2 of this dataset." } }, { "ob_id": 843, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40300, "uuid": "5fda109ab71947b6b7724077bf7eb753", "short_code": "ob", "title": "CRU TS4.07: Climatic Research Unit (CRU) Time-Series (TS) version 4.07 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2022)", "abstract": "The gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.07 data are month-by-month variations in climate over the period 1901-2022, 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 TS4.07 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 2022.\r\n\r\nThe CRU TS4.07 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 TS4.07 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." }, "objectObservation": { "ob_id": 38103, "uuid": "e0b4e1e56c1c4460b796073a31366980", "short_code": "ob", "title": "CRU TS4.06: Climatic Research Unit (CRU) Time-Series (TS) version 4.06 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2021)", "abstract": "The gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.06 data are month-by-month variations in climate over the period 1901-2021, 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 TS4.06 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 2021.\r\n\r\nThe CRU TS4.06 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 TS4.06 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." } }, { "ob_id": 844, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 40991, "uuid": "d1c61edc4f554ee09ad370f6b52f82ce", "short_code": "ob", "title": "DCMEX: cloud images from the NCAS Camera 12 from the New Mexico field campaign 2022", "abstract": "This dataset contains cloud images from the NCAS Camera 12, one of two identical cameras (designated as ncas-cam-11 and ncas-cam-12), captured at various sites around the Magdalena Mountains, New Mexico, USA, as part of the Deep Convective Microphysics Experiment (DCMEX). DCMEX examined the formation and development of clouds over mountains during July and August 2022.\r\n\r\nThese cameras were designed to take simultaneous images of the same object while placed a distance apart to create a stereo image, but this was not always possible; on some days only one camera was used or the two cameras were deployed in separate locations.\r\n\r\nThe images from this camera were taken during the duration of the DCMEX campaign of clouds from a range of sites. These are accompanied by similar images from a sibling camera (see connected dataset). Where the two cameras were operated at the same site they were synchronised in terms of camera settings (exposure, etc) and camera pointing directions to facilitate the onward use of images as stereoscopic imagery. For those latter instances files have been marked with stereo-a or stereo-b within the filename to denote where the images form the left of right image for such images. Other images do not contain these additional filename fields to denote when the cameras were used in stand-along mode. Note, due to the nature of coordinating images between the two cameras one was designated as the primary camera from which the settings were then conveyed to the secondary camera by the coordinating software. As a result exact image synchronisation wasn't possible and thus the secondary camera image may have a timestamp that is a second or so later." }, "objectObservation": { "ob_id": 39197, "uuid": "b839ae53abf94e23b0f61560349ccda1", "short_code": "ob", "title": "DCMEX: cloud images from the NCAS Camera 11 from the New Mexico field campaign 2022", "abstract": "This dataset contains cloud images from the NCAS Camera 11, one of two identical cameras (designated as ncas-cam-11 and ncas-cam-12) captured at various sites around the Magdalena Mountains, New Mexico, USA, as part of the Deep Convective Microphysics Experiment (DCMEX). DCMEX examined the formation and development of clouds over mountains during July and August 2022.\r\n\r\nThese cameras were designed to take simultaneous images of the same object while placed a distance apart to create a stereo image, but this was not always possible; on some days only one camera was used or the two cameras were deployed in separate locations.\r\n\r\nThe images from this camera were taken during the duration of the DCMEX campaign of clouds from a range of sites. These are accompanied by similar images from a sibling camera (see connected dataset). Where the two cameras were operated at the same site they were synchronised in terms of camera settings (exposure, etc) and camera pointing directions to facilitate the onward use of images as stereoscopic imagery. For those latter instances files have been marked with stereo-a or stereo-b within the filename to denote where the images form the left of right image for such images. Other images do not contain these additional filename fields to denote when the cameras were used in stand-along mode. Note, due to the nature of coordinating images between the two cameras one was designated as the primary camera from which the settings were then conveyed to the secondary camera by the coordinating software. As a result exact image synchronisation wasn't possible and thus the secondary camera image may have a timestamp that is a second or so later." } }, { "ob_id": 845, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 40991, "uuid": "d1c61edc4f554ee09ad370f6b52f82ce", "short_code": "ob", "title": "DCMEX: cloud images from the NCAS Camera 12 from the New Mexico field campaign 2022", "abstract": "This dataset contains cloud images from the NCAS Camera 12, one of two identical cameras (designated as ncas-cam-11 and ncas-cam-12), captured at various sites around the Magdalena Mountains, New Mexico, USA, as part of the Deep Convective Microphysics Experiment (DCMEX). DCMEX examined the formation and development of clouds over mountains during July and August 2022.\r\n\r\nThese cameras were designed to take simultaneous images of the same object while placed a distance apart to create a stereo image, but this was not always possible; on some days only one camera was used or the two cameras were deployed in separate locations.\r\n\r\nThe images from this camera were taken during the duration of the DCMEX campaign of clouds from a range of sites. These are accompanied by similar images from a sibling camera (see connected dataset). Where the two cameras were operated at the same site they were synchronised in terms of camera settings (exposure, etc) and camera pointing directions to facilitate the onward use of images as stereoscopic imagery. For those latter instances files have been marked with stereo-a or stereo-b within the filename to denote where the images form the left of right image for such images. Other images do not contain these additional filename fields to denote when the cameras were used in stand-along mode. Note, due to the nature of coordinating images between the two cameras one was designated as the primary camera from which the settings were then conveyed to the secondary camera by the coordinating software. As a result exact image synchronisation wasn't possible and thus the secondary camera image may have a timestamp that is a second or so later." }, "objectObservation": { "ob_id": 39197, "uuid": "b839ae53abf94e23b0f61560349ccda1", "short_code": "ob", "title": "DCMEX: cloud images from the NCAS Camera 11 from the New Mexico field campaign 2022", "abstract": "This dataset contains cloud images from the NCAS Camera 11, one of two identical cameras (designated as ncas-cam-11 and ncas-cam-12) captured at various sites around the Magdalena Mountains, New Mexico, USA, as part of the Deep Convective Microphysics Experiment (DCMEX). DCMEX examined the formation and development of clouds over mountains during July and August 2022.\r\n\r\nThese cameras were designed to take simultaneous images of the same object while placed a distance apart to create a stereo image, but this was not always possible; on some days only one camera was used or the two cameras were deployed in separate locations.\r\n\r\nThe images from this camera were taken during the duration of the DCMEX campaign of clouds from a range of sites. These are accompanied by similar images from a sibling camera (see connected dataset). Where the two cameras were operated at the same site they were synchronised in terms of camera settings (exposure, etc) and camera pointing directions to facilitate the onward use of images as stereoscopic imagery. For those latter instances files have been marked with stereo-a or stereo-b within the filename to denote where the images form the left of right image for such images. Other images do not contain these additional filename fields to denote when the cameras were used in stand-along mode. Note, due to the nature of coordinating images between the two cameras one was designated as the primary camera from which the settings were then conveyed to the secondary camera by the coordinating software. As a result exact image synchronisation wasn't possible and thus the secondary camera image may have a timestamp that is a second or so later." } }, { "ob_id": 846, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 39197, "uuid": "b839ae53abf94e23b0f61560349ccda1", "short_code": "ob", "title": "DCMEX: cloud images from the NCAS Camera 11 from the New Mexico field campaign 2022", "abstract": "This dataset contains cloud images from the NCAS Camera 11, one of two identical cameras (designated as ncas-cam-11 and ncas-cam-12) captured at various sites around the Magdalena Mountains, New Mexico, USA, as part of the Deep Convective Microphysics Experiment (DCMEX). DCMEX examined the formation and development of clouds over mountains during July and August 2022.\r\n\r\nThese cameras were designed to take simultaneous images of the same object while placed a distance apart to create a stereo image, but this was not always possible; on some days only one camera was used or the two cameras were deployed in separate locations.\r\n\r\nThe images from this camera were taken during the duration of the DCMEX campaign of clouds from a range of sites. These are accompanied by similar images from a sibling camera (see connected dataset). Where the two cameras were operated at the same site they were synchronised in terms of camera settings (exposure, etc) and camera pointing directions to facilitate the onward use of images as stereoscopic imagery. For those latter instances files have been marked with stereo-a or stereo-b within the filename to denote where the images form the left of right image for such images. Other images do not contain these additional filename fields to denote when the cameras were used in stand-along mode. Note, due to the nature of coordinating images between the two cameras one was designated as the primary camera from which the settings were then conveyed to the secondary camera by the coordinating software. As a result exact image synchronisation wasn't possible and thus the secondary camera image may have a timestamp that is a second or so later." }, "objectObservation": { "ob_id": 40991, "uuid": "d1c61edc4f554ee09ad370f6b52f82ce", "short_code": "ob", "title": "DCMEX: cloud images from the NCAS Camera 12 from the New Mexico field campaign 2022", "abstract": "This dataset contains cloud images from the NCAS Camera 12, one of two identical cameras (designated as ncas-cam-11 and ncas-cam-12), captured at various sites around the Magdalena Mountains, New Mexico, USA, as part of the Deep Convective Microphysics Experiment (DCMEX). DCMEX examined the formation and development of clouds over mountains during July and August 2022.\r\n\r\nThese cameras were designed to take simultaneous images of the same object while placed a distance apart to create a stereo image, but this was not always possible; on some days only one camera was used or the two cameras were deployed in separate locations.\r\n\r\nThe images from this camera were taken during the duration of the DCMEX campaign of clouds from a range of sites. These are accompanied by similar images from a sibling camera (see connected dataset). Where the two cameras were operated at the same site they were synchronised in terms of camera settings (exposure, etc) and camera pointing directions to facilitate the onward use of images as stereoscopic imagery. For those latter instances files have been marked with stereo-a or stereo-b within the filename to denote where the images form the left of right image for such images. Other images do not contain these additional filename fields to denote when the cameras were used in stand-along mode. Note, due to the nature of coordinating images between the two cameras one was designated as the primary camera from which the settings were then conveyed to the secondary camera by the coordinating software. As a result exact image synchronisation wasn't possible and thus the secondary camera image may have a timestamp that is a second or so later." } }, { "ob_id": 847, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 39197, "uuid": "b839ae53abf94e23b0f61560349ccda1", "short_code": "ob", "title": "DCMEX: cloud images from the NCAS Camera 11 from the New Mexico field campaign 2022", "abstract": "This dataset contains cloud images from the NCAS Camera 11, one of two identical cameras (designated as ncas-cam-11 and ncas-cam-12) captured at various sites around the Magdalena Mountains, New Mexico, USA, as part of the Deep Convective Microphysics Experiment (DCMEX). DCMEX examined the formation and development of clouds over mountains during July and August 2022.\r\n\r\nThese cameras were designed to take simultaneous images of the same object while placed a distance apart to create a stereo image, but this was not always possible; on some days only one camera was used or the two cameras were deployed in separate locations.\r\n\r\nThe images from this camera were taken during the duration of the DCMEX campaign of clouds from a range of sites. These are accompanied by similar images from a sibling camera (see connected dataset). Where the two cameras were operated at the same site they were synchronised in terms of camera settings (exposure, etc) and camera pointing directions to facilitate the onward use of images as stereoscopic imagery. For those latter instances files have been marked with stereo-a or stereo-b within the filename to denote where the images form the left of right image for such images. Other images do not contain these additional filename fields to denote when the cameras were used in stand-along mode. Note, due to the nature of coordinating images between the two cameras one was designated as the primary camera from which the settings were then conveyed to the secondary camera by the coordinating software. As a result exact image synchronisation wasn't possible and thus the secondary camera image may have a timestamp that is a second or so later." }, "objectObservation": { "ob_id": 40991, "uuid": "d1c61edc4f554ee09ad370f6b52f82ce", "short_code": "ob", "title": "DCMEX: cloud images from the NCAS Camera 12 from the New Mexico field campaign 2022", "abstract": "This dataset contains cloud images from the NCAS Camera 12, one of two identical cameras (designated as ncas-cam-11 and ncas-cam-12), captured at various sites around the Magdalena Mountains, New Mexico, USA, as part of the Deep Convective Microphysics Experiment (DCMEX). DCMEX examined the formation and development of clouds over mountains during July and August 2022.\r\n\r\nThese cameras were designed to take simultaneous images of the same object while placed a distance apart to create a stereo image, but this was not always possible; on some days only one camera was used or the two cameras were deployed in separate locations.\r\n\r\nThe images from this camera were taken during the duration of the DCMEX campaign of clouds from a range of sites. These are accompanied by similar images from a sibling camera (see connected dataset). Where the two cameras were operated at the same site they were synchronised in terms of camera settings (exposure, etc) and camera pointing directions to facilitate the onward use of images as stereoscopic imagery. For those latter instances files have been marked with stereo-a or stereo-b within the filename to denote where the images form the left of right image for such images. Other images do not contain these additional filename fields to denote when the cameras were used in stand-along mode. Note, due to the nature of coordinating images between the two cameras one was designated as the primary camera from which the settings were then conveyed to the secondary camera by the coordinating software. As a result exact image synchronisation wasn't possible and thus the secondary camera image may have a timestamp that is a second or so later." } }, { "ob_id": 848, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 41180, "uuid": "bc5b7568ef2a467fa97642910eb45aa7", "short_code": "ob", "title": "Atmospheric trace gas observations from the UK Deriving Emissions linked to Climate Change (DECC) Network and associated data - Version 23.08", "abstract": "This version 23.08 dataset consists of atmospheric trace gas observations made as part of the UK Deriving Emissions linked to Climate Change (DECC) Network. It includes core DECC Network measurements, funded by the UK Government Department for Energy Security and Net Zero (TRN: 54\r\n88/11/2021) and through the National Measurement System at the National Physical Laboratory, supplemented by observations funded through other associated projects. The core DECC network\r\n consists of five sites in the UK and Ireland measuring greenhouse and ozone-depleting gases. The four UK-based sites (Ridge Hill, Herefordshire; Tacolneston, Norfolk; Bilsdale, North Yorkshire; and Heathfield, East Sussex) sample air from elevated inlets on tall telecommunications towers. Mace Head, situated on the west coast of Ireland, samples from an inlet 10 metres above ground level and is ideally situated to intercept baseline air from the North Atlantic Ocean. The measurement site at Weybourne, Norfolk, funded by the National Centre for Atmospheric Science (NCAS) and operated by the University of East Anglia, is also affiliated with the network. Mace Head and Weybourne data are archived separately - see links in documentation. Data from the UK DECC network are used to\r\n assess atmospheric trends and quantify UK emissions, and feed into other international research programs, including the Integrated Carbon Observation System (ICOS) and Advanced Global Atmospheric Gases Experiment (AGAGE) networks." }, "objectObservation": { "ob_id": 27793, "uuid": "df502fe4715c4177ab5e4e367a99316b", "short_code": "ob", "title": "Deriving Emissions related to Climate Change Network: CO2, CH4, N2O, SF6 and CO measurements from Heathfield Tall Tower, East Sussex", "abstract": "Measurements of carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), sulfur hexafluoride (SF6) and carbon monoxide (CO) have been taken at Heathfield tall tower as part of the UK-DECC (Deriving Emissions related to Climate Change) Network. \r\n\r\nHeathfield (HFD) is a semi-rural UK site located 19 km south of Royal Tunbridge Wells (population ~118,000), in East Sussex, UK. This station is affiliated to the UK DECC (Deriving Emissions related to Climate Change) Network, and is operated by the National Physics Laboratory (NPL). CO2, CH4 and CO are measured at a height of 50 m and 100 m above ground level. Due to the sites location, far from strong sources of local pollution, measurements from this site will be used to calculate emission maps of trace gas species in the UK in combination with other measurement stations in the UK and Ireland.\r\n\r\nDue to the sites location, far from strong sources of local pollution, measurements from this site are used to calculate emission maps of trace gas species in the UK in combination with other measurement stations in the UK (Bilsdale, Ridge Hill and Tacolneston) and Ireland (Mace Head).\r\n\r\nThis work was funded by Business Energy and Industrial Strategy (BEIS) contracts TRN1028/06/2015 and TRN1537/06/2018 to the University of Bristol and through the National Measurement System at the National Physical Laboratory." } }, { "ob_id": 849, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 41180, "uuid": "bc5b7568ef2a467fa97642910eb45aa7", "short_code": "ob", "title": "Atmospheric trace gas observations from the UK Deriving Emissions linked to Climate Change (DECC) Network and associated data - Version 23.08", "abstract": "This version 23.08 dataset consists of atmospheric trace gas observations made as part of the UK Deriving Emissions linked to Climate Change (DECC) Network. It includes core DECC Network measurements, funded by the UK Government Department for Energy Security and Net Zero (TRN: 54\r\n88/11/2021) and through the National Measurement System at the National Physical Laboratory, supplemented by observations funded through other associated projects. The core DECC network\r\n consists of five sites in the UK and Ireland measuring greenhouse and ozone-depleting gases. The four UK-based sites (Ridge Hill, Herefordshire; Tacolneston, Norfolk; Bilsdale, North Yorkshire; and Heathfield, East Sussex) sample air from elevated inlets on tall telecommunications towers. Mace Head, situated on the west coast of Ireland, samples from an inlet 10 metres above ground level and is ideally situated to intercept baseline air from the North Atlantic Ocean. The measurement site at Weybourne, Norfolk, funded by the National Centre for Atmospheric Science (NCAS) and operated by the University of East Anglia, is also affiliated with the network. Mace Head and Weybourne data are archived separately - see links in documentation. Data from the UK DECC network are used to\r\n assess atmospheric trends and quantify UK emissions, and feed into other international research programs, including the Integrated Carbon Observation System (ICOS) and Advanced Global Atmospheric Gases Experiment (AGAGE) networks." }, "objectObservation": { "ob_id": 27563, "uuid": "d2090552c8fe4c16a2fd7d616adc2d9f", "short_code": "ob", "title": "Deriving Emissions related to Climate Change Network: CO2, CH4, N2O, SF6 and CO measurements from Bilsdale Tall Tower, North York Moors National Park", "abstract": "High frequency measurements of carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), sulfur hexafluoride (SF6) and carbon monoxide (CO) made at Bilsdale Tall Tower, North York Moors National Park were made for the UK-DECC (Deriving Emissions related to Climate Change) Network. \r\n\r\nBilsdale (BSD) tall tower is in a remote area of the North York Moors National Park and is the first monitoring site in the northeast region of England. The closest large conurbations are York and Middlesbrough, located 30 miles south and 16 miles northeast, respectively. The tower is on a high plateau overlooking green valleys used mainly for livestock (sheep and cattle). Between 2014-01-01 and 2017-03-17, air samples are taken from a line sampling 108 m above ground level. From 2017-03-17 onwards, air was sampled from 248 m above ground level. Due to the sites location, far from strong sources of local pollution, measurements from this site will be used to calculate emission maps of trace gas species in the UK in combination with other measurement stations in the UK (Ridge Hill, Tacolneston and Heathfield) and Ireland (Mace Head).\r\n\r\n\r\nThis work was funded by Business Energy and Industrial Strategy (BEIS) contracts TRN1028/06/2015 and TRN1537/06/2018 to the University of Bristol." } }, { "ob_id": 850, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 41180, "uuid": "bc5b7568ef2a467fa97642910eb45aa7", "short_code": "ob", "title": "Atmospheric trace gas observations from the UK Deriving Emissions linked to Climate Change (DECC) Network and associated data - Version 23.08", "abstract": "This version 23.08 dataset consists of atmospheric trace gas observations made as part of the UK Deriving Emissions linked to Climate Change (DECC) Network. It includes core DECC Network measurements, funded by the UK Government Department for Energy Security and Net Zero (TRN: 54\r\n88/11/2021) and through the National Measurement System at the National Physical Laboratory, supplemented by observations funded through other associated projects. The core DECC network\r\n consists of five sites in the UK and Ireland measuring greenhouse and ozone-depleting gases. The four UK-based sites (Ridge Hill, Herefordshire; Tacolneston, Norfolk; Bilsdale, North Yorkshire; and Heathfield, East Sussex) sample air from elevated inlets on tall telecommunications towers. Mace Head, situated on the west coast of Ireland, samples from an inlet 10 metres above ground level and is ideally situated to intercept baseline air from the North Atlantic Ocean. The measurement site at Weybourne, Norfolk, funded by the National Centre for Atmospheric Science (NCAS) and operated by the University of East Anglia, is also affiliated with the network. Mace Head and Weybourne data are archived separately - see links in documentation. Data from the UK DECC network are used to\r\n assess atmospheric trends and quantify UK emissions, and feed into other international research programs, including the Integrated Carbon Observation System (ICOS) and Advanced Global Atmospheric Gases Experiment (AGAGE) networks." }, "objectObservation": { "ob_id": 27559, "uuid": "4370dacd17544eb781aa1e51cc4dc633", "short_code": "ob", "title": "Deriving Emissions related to Climate Change Network: CO2, CH4, N2O, and SF6 measurements from Ridge Hill Tall Tower, Herefordshire", "abstract": "High frequency measurements of carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and sulfur hexafluoride (SF6) have been taken at Ridge Hill tall tower as part of the UK DECC (Deriving Emissions linked to Climate Change) Network. \r\n\r\nRidge Hill (RGL) is a rural UK site located 13 km south-east of Hereford (population ~55,000), and 30 km south-west of Worcester (population ~94,000), in Herefordshire, UK. Measurements of SF6 and N2O have been taken using gas chromatography with an electron capture detector (GC-ECD), sampling from a height of 90 m above ground level. Measurements are also taken from inlet heights of 45 m and 90 m above ground level using laser-based techniques for CO2 and CH4. Due to the location of the site, far from strong sources of local pollution, measurements from this site are used to calculate emission maps of trace gas species in the UK in combination with other measurement stations in the UK (Bilsdale, Tacolneston and Heathfield) and Ireland (Mace Head).\r\n\r\nThis work was funded by Business Energy and Industrial Strategy (BEIS) contracts TRN1028/06/2015 and TRN1537/06/2018 to the University of Bristol." } }, { "ob_id": 851, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 41180, "uuid": "bc5b7568ef2a467fa97642910eb45aa7", "short_code": "ob", "title": "Atmospheric trace gas observations from the UK Deriving Emissions linked to Climate Change (DECC) Network and associated data - Version 23.08", "abstract": "This version 23.08 dataset consists of atmospheric trace gas observations made as part of the UK Deriving Emissions linked to Climate Change (DECC) Network. It includes core DECC Network measurements, funded by the UK Government Department for Energy Security and Net Zero (TRN: 54\r\n88/11/2021) and through the National Measurement System at the National Physical Laboratory, supplemented by observations funded through other associated projects. The core DECC network\r\n consists of five sites in the UK and Ireland measuring greenhouse and ozone-depleting gases. The four UK-based sites (Ridge Hill, Herefordshire; Tacolneston, Norfolk; Bilsdale, North Yorkshire; and Heathfield, East Sussex) sample air from elevated inlets on tall telecommunications towers. Mace Head, situated on the west coast of Ireland, samples from an inlet 10 metres above ground level and is ideally situated to intercept baseline air from the North Atlantic Ocean. The measurement site at Weybourne, Norfolk, funded by the National Centre for Atmospheric Science (NCAS) and operated by the University of East Anglia, is also affiliated with the network. Mace Head and Weybourne data are archived separately - see links in documentation. Data from the UK DECC network are used to\r\n assess atmospheric trends and quantify UK emissions, and feed into other international research programs, including the Integrated Carbon Observation System (ICOS) and Advanced Global Atmospheric Gases Experiment (AGAGE) networks." }, "objectObservation": { "ob_id": 27558, "uuid": "ae483e02e5c345c59c2b72ac46574103", "short_code": "ob", "title": "Deriving Emissions related to Climate Change Network: CO2, CH4, N2O, SF6, CO and halocarbon measurements from Tacolneston Tall Tower, Norfolk", "abstract": "Measurements of carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), sulfur hexafluoride (SF6), carbon monoxide (CO) and a suite of halocarbons and other trace gases have been taken at Tacolneston tall tower as part of the UK DECC (Deriving Emissions linked to Climate Change) Network. \r\n\r\nTacolneston (TAC) is a rural UK site located on the in the east of England, 16 km south-west of Norwich (population ~200,000), and 32 km east of Thetford (population ~20,000), in Norfolk, UK.\r\n\r\nThree gas chromatography instruments measured atmospheric N2O, SF6, CO, H2 and other trace gas species from an inlet positioned at a height of 100 m above ground level between 2012-01-01 and 2017-03-09. The inlet height was then changed to 185 m above ground level. Two instruments (GC-RGA and GC-ECD) were decommissioned on 2018-03-13. The remaining two continue to operate. Two laser-based instruments have been used to measure CO2, CH4, N2O and CO from inlet heights of 54 m, 100 m, and 185 m above ground level. Due to the location of the site, far from strong sources of local pollution, measurements from this site can be used to calculate emission maps of trace gas species in the UK in combination with other measurement stations in the UK (Bilsdale, Ridge Hill and Heathfield) and Ireland (Mace Head).\r\n\r\nThis work was funded by Business Energy and Industrial Strategy (BEIS) contracts TRN1028/06/2015 and TRN1537/06/2018 to the University of Bristol." } }, { "ob_id": 852, "relationType": "Continues", "subjectObservation": { "ob_id": 40870, "uuid": "1d554ff41c104491ac3661c6f6f52aab", "short_code": "ob", "title": "Aerial LiDAR data from French Guiana, Paracou, November 2019", "abstract": "This dataset contains Aerial LiDAR (also known as airborne laser scanning, ALS) data in .las format collected over tropical forests in Paracou in French Guiana in 2019. The data were collected by Altoa using a BN2 aircraft flying at approximately 900 m altitude at a speed of approximately 180 km/hr. Trajectory files in txt format giving detailed flight data are included with the archived dataset. The LiDAR instrume was a RIEGL LMS-Q780 and used a minimum pulse density of 15 points/sqm. The lateral overlap between two flight lines was 80% with a scan angle of +/- 30 degrees. The data coordinate reference system used with the data files is epsg 2972 more details of this and of the Paracou site can be found in the documentation section." }, "objectObservation": { "ob_id": 40872, "uuid": "7bdc5bfc06264802be34f918597150e8", "short_code": "ob", "title": "Aerial LiDAR data from French Guiana, Nouragues, November 2019", "abstract": "This dataset contains Aerial LiDAR (also known as airborne laser scanning, ALS) data in .las format collected over tropical forests in Nouragues in French Guiana in 2019. The data were collected by Altoa using a BN2 aircraft flying at approximately 900 m altitude at a speed of approximately 180 km/hr. Trajectory files in txt format giving detailed flight data are included with the archived dataset. The LiDAR instrument was RIEGL LMS-Q780 and used a minimum pulse density of 15 points/sqm. The lateral overlap between two flight lines was 80%. with a Scan angle of +/- 30 degrees. The data coordinate reference system used with the data files is epsg 2972 more details of this and of the Nouragues site can be found in the documentation section." } }, { "ob_id": 853, "relationType": "Continues", "subjectObservation": { "ob_id": 40872, "uuid": "7bdc5bfc06264802be34f918597150e8", "short_code": "ob", "title": "Aerial LiDAR data from French Guiana, Nouragues, November 2019", "abstract": "This dataset contains Aerial LiDAR (also known as airborne laser scanning, ALS) data in .las format collected over tropical forests in Nouragues in French Guiana in 2019. The data were collected by Altoa using a BN2 aircraft flying at approximately 900 m altitude at a speed of approximately 180 km/hr. Trajectory files in txt format giving detailed flight data are included with the archived dataset. The LiDAR instrument was RIEGL LMS-Q780 and used a minimum pulse density of 15 points/sqm. The lateral overlap between two flight lines was 80%. with a Scan angle of +/- 30 degrees. The data coordinate reference system used with the data files is epsg 2972 more details of this and of the Nouragues site can be found in the documentation section." }, "objectObservation": { "ob_id": 40870, "uuid": "1d554ff41c104491ac3661c6f6f52aab", "short_code": "ob", "title": "Aerial LiDAR data from French Guiana, Paracou, November 2019", "abstract": "This dataset contains Aerial LiDAR (also known as airborne laser scanning, ALS) data in .las format collected over tropical forests in Paracou in French Guiana in 2019. The data were collected by Altoa using a BN2 aircraft flying at approximately 900 m altitude at a speed of approximately 180 km/hr. Trajectory files in txt format giving detailed flight data are included with the archived dataset. The LiDAR instrume was a RIEGL LMS-Q780 and used a minimum pulse density of 15 points/sqm. The lateral overlap between two flight lines was 80% with a scan angle of +/- 30 degrees. The data coordinate reference system used with the data files is epsg 2972 more details of this and of the Paracou site can be found in the documentation section." } }, { "ob_id": 854, "relationType": "Continues", "subjectObservation": { "ob_id": 37160, "uuid": "fd841b4cf6d0403f99f1f9a9252fc0e2", "short_code": "ob", "title": "Druim a'Starraig C-band rain radar dual polar products", "abstract": "Dual-polar products from the Met Office's Druim a'Starraig C-band rain radar, Isle of Lewis, Scotland. Data from this site includes augmented ldr (linear depolarisation ratio) and zdr (differential reflectivity) scan data (both long and short pulse) available from August 2018 at present. The radar is a C-band (5.3 cm wavelength) radar and data are received by the Nimrod system at 5 minute intervals." }, "objectObservation": { "ob_id": 12178, "uuid": "b81325887fae44f8bfaff281598cbcb1", "short_code": "ob", "title": "Druima a'Starraig C-band rain radar single polar products", "abstract": "Single-polar products from the Met Office's Druim a'Starraig C-band rain radar, Isle of Lewis, Scotland. Data include reflectivity (from April 2012), CPA (from October 2011) and CPAPR (from September 2012). Dual-polar products from this site are available from 2018. The radar is a C-band (5.3 cm wavelength) radar and data are received by the Nimrod system at 5 minute intervals." } }, { "ob_id": 855, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 41268, "uuid": "6285564c34a246fc9ba5ce053d85e5e7", "short_code": "ob", "title": "ACSIS: Merged airborne chemistry data from instruments on board the FAAM aircraft", "abstract": "A collection of collated chemistry measurements made onboard the FAAM BAE-146 aircraft for The North Atlantic Climate System Integrated Study: ACSIS project. These data were collected by core and non-core instruments and compiled into a merged dataset. \r\n\r\nFlights included are from the measurement campaigns: ACSIS1 - February 2017, ACSIS2 - October 2017, ACSIS3 - May 2018, ACSIS4 - February 2019, ACSIS5 - August 2019, ACSIS6 - February 2020 and ACSIS7 - May 2022. Parameters include Carbon Dioxide (CO2), Methane (CH4), Nitric oxide (NO), Nitric dioxide (NO2), Total Organic Aerosol, Sulphate Aerosol, Nitrate Aerosol, Ammonium Aerosol, Chloride Aerosol, Urea (CH4N2O), Hydrogen cyanide (HCN), Nitryl chloride (ClNO2), and photolysis rates." }, "objectObservation": { "ob_id": 24736, "uuid": "85023a32f2ed467695275ba90bf3dee9", "short_code": "ob", "title": "FAAM B996 ACSIS Transit flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS project." } }, { "ob_id": 856, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 41268, "uuid": "6285564c34a246fc9ba5ce053d85e5e7", "short_code": "ob", "title": "ACSIS: Merged airborne chemistry data from instruments on board the FAAM aircraft", "abstract": "A collection of collated chemistry measurements made onboard the FAAM BAE-146 aircraft for The North Atlantic Climate System Integrated Study: ACSIS project. These data were collected by core and non-core instruments and compiled into a merged dataset. \r\n\r\nFlights included are from the measurement campaigns: ACSIS1 - February 2017, ACSIS2 - October 2017, ACSIS3 - May 2018, ACSIS4 - February 2019, ACSIS5 - August 2019, ACSIS6 - February 2020 and ACSIS7 - May 2022. Parameters include Carbon Dioxide (CO2), Methane (CH4), Nitric oxide (NO), Nitric dioxide (NO2), Total Organic Aerosol, Sulphate Aerosol, Nitrate Aerosol, Ammonium Aerosol, Chloride Aerosol, Urea (CH4N2O), Hydrogen cyanide (HCN), Nitryl chloride (ClNO2), and photolysis rates." }, "objectObservation": { "ob_id": 24741, "uuid": "1072c94a044646f5b0f5b6168f78e8d8", "short_code": "ob", "title": "FAAM B997 ACSIS Transit flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS project." } }, { "ob_id": 857, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 41268, "uuid": "6285564c34a246fc9ba5ce053d85e5e7", "short_code": "ob", "title": "ACSIS: Merged airborne chemistry data from instruments on board the FAAM aircraft", "abstract": "A collection of collated chemistry measurements made onboard the FAAM BAE-146 aircraft for The North Atlantic Climate System Integrated Study: ACSIS project. These data were collected by core and non-core instruments and compiled into a merged dataset. \r\n\r\nFlights included are from the measurement campaigns: ACSIS1 - February 2017, ACSIS2 - October 2017, ACSIS3 - May 2018, ACSIS4 - February 2019, ACSIS5 - August 2019, ACSIS6 - February 2020 and ACSIS7 - May 2022. Parameters include Carbon Dioxide (CO2), Methane (CH4), Nitric oxide (NO), Nitric dioxide (NO2), Total Organic Aerosol, Sulphate Aerosol, Nitrate Aerosol, Ammonium Aerosol, Chloride Aerosol, Urea (CH4N2O), Hydrogen cyanide (HCN), Nitryl chloride (ClNO2), and photolysis rates." }, "objectObservation": { "ob_id": 24745, "uuid": "27a478b5cc35406e84dd630b0f2fd1cc", "short_code": "ob", "title": "FAAM B998 ACSIS flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS project." } }, { "ob_id": 858, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 41268, "uuid": "6285564c34a246fc9ba5ce053d85e5e7", "short_code": "ob", "title": "ACSIS: Merged airborne chemistry data from instruments on board the FAAM aircraft", "abstract": "A collection of collated chemistry measurements made onboard the FAAM BAE-146 aircraft for The North Atlantic Climate System Integrated Study: ACSIS project. These data were collected by core and non-core instruments and compiled into a merged dataset. \r\n\r\nFlights included are from the measurement campaigns: ACSIS1 - February 2017, ACSIS2 - October 2017, ACSIS3 - May 2018, ACSIS4 - February 2019, ACSIS5 - August 2019, ACSIS6 - February 2020 and ACSIS7 - May 2022. Parameters include Carbon Dioxide (CO2), Methane (CH4), Nitric oxide (NO), Nitric dioxide (NO2), Total Organic Aerosol, Sulphate Aerosol, Nitrate Aerosol, Ammonium Aerosol, Chloride Aerosol, Urea (CH4N2O), Hydrogen cyanide (HCN), Nitryl chloride (ClNO2), and photolysis rates." }, "objectObservation": { "ob_id": 24749, "uuid": "d60cd1eb5883409d9f5ccffc70701120", "short_code": "ob", "title": "FAAM B999 ACSIS flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS project." } }, { "ob_id": 859, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 41268, "uuid": "6285564c34a246fc9ba5ce053d85e5e7", "short_code": "ob", "title": "ACSIS: Merged airborne chemistry data from instruments on board the FAAM aircraft", "abstract": "A collection of collated chemistry measurements made onboard the FAAM BAE-146 aircraft for The North Atlantic Climate System Integrated Study: ACSIS project. These data were collected by core and non-core instruments and compiled into a merged dataset. \r\n\r\nFlights included are from the measurement campaigns: ACSIS1 - February 2017, ACSIS2 - October 2017, ACSIS3 - May 2018, ACSIS4 - February 2019, ACSIS5 - August 2019, ACSIS6 - February 2020 and ACSIS7 - May 2022. Parameters include Carbon Dioxide (CO2), Methane (CH4), Nitric oxide (NO), Nitric dioxide (NO2), Total Organic Aerosol, Sulphate Aerosol, Nitrate Aerosol, Ammonium Aerosol, Chloride Aerosol, Urea (CH4N2O), Hydrogen cyanide (HCN), Nitryl chloride (ClNO2), and photolysis rates." }, "objectObservation": { "ob_id": 24753, "uuid": "81902dc06146452e98a7db0d2b876855", "short_code": "ob", "title": "FAAM C001 ACSIS flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS project." } }, { "ob_id": 860, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 41268, "uuid": "6285564c34a246fc9ba5ce053d85e5e7", "short_code": "ob", "title": "ACSIS: Merged airborne chemistry data from instruments on board the FAAM aircraft", "abstract": "A collection of collated chemistry measurements made onboard the FAAM BAE-146 aircraft for The North Atlantic Climate System Integrated Study: ACSIS project. These data were collected by core and non-core instruments and compiled into a merged dataset. \r\n\r\nFlights included are from the measurement campaigns: ACSIS1 - February 2017, ACSIS2 - October 2017, ACSIS3 - May 2018, ACSIS4 - February 2019, ACSIS5 - August 2019, ACSIS6 - February 2020 and ACSIS7 - May 2022. Parameters include Carbon Dioxide (CO2), Methane (CH4), Nitric oxide (NO), Nitric dioxide (NO2), Total Organic Aerosol, Sulphate Aerosol, Nitrate Aerosol, Ammonium Aerosol, Chloride Aerosol, Urea (CH4N2O), Hydrogen cyanide (HCN), Nitryl chloride (ClNO2), and photolysis rates." }, "objectObservation": { "ob_id": 24757, "uuid": "1d75255b335b497684c7e30506119715", "short_code": "ob", "title": "FAAM C002 ACSIS flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS project." } } ] }