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        {
            "ob_id": 39496,
            "uuid": "5563afac2314436696df74653a68d73f",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/evoflood/data/Downscaled_CMIP6_Climate_Data",
            "numberOfFiles": 389,
            "volume": 23104948174732,
            "fileFormat": "NetCDF",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 38316,
                "uuid": "c107618f1db34801bb88a1e927b82317",
                "short_code": "ob",
                "title": "High-resolution daily global climate dataset of BCCAQ statistically downscaled CMIP6 models for the EVOFLOOD project",
                "abstract": "A novel statistical downscaling model, the Bias Correction Constructed Analogues with Quantile mapping reordering (BCCAQ), is used to downscale daily precipitation, air temperature, maximum and minimum temperature, wind speed, air pressure, and relative humidity from 18 global circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). This new high-resolution climate dataset will be useful in assessing future changes and variability in climate and in particular in driving richer impact assessment models.  The  data are available for the historical (1981-2014) and future (2015-2100) periods at 0.25 degree horizontal resolution and daily time step across three Shared Socioeconomic Pathways (SSP2-4.5, SSP5-3.4OS and SSP5-8.5).\r\n\r\n\r\nSSP2-4.5 is based on Shared Socioeconomic Pathway SSP2 with intermediate climate change mitigation and adaptation challenges and RCP2.6, 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\nSSP5-3.4OS is an overshoot scenario where emissions follow the SSP5-8.5 pathway until 2040 before dramatically declining."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39505,
            "uuid": "e2113459f5e9452b991600ac0fde4c94",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_06/ch6_fig25/v20221215",
            "numberOfFiles": 4,
            "volume": 7457,
            "fileFormat": "Data are CSV formatted",
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            "observation": {
                "ob_id": 39504,
                "uuid": "abb030f60cf848278fe519379a2aaac9",
                "short_code": "ob",
                "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 6.25 (v20221215)",
                "abstract": "Data for Figure 6.25 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 6.25 shows the effect of dedicated air pollution or climate policy on population-weighted PM2.5 (Fine particulate matter) concentrations (µg m-3) and share of population (%) exposed to different PM2.5 levels across 10 world regions.\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 Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 30 panels with data provided for all panels in one single file.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains PM2.5 (Fine particulate matter) concentrations (µg m–3) and share of population (%) exposed to different PM2.5 levels across selected world regions.\r\n \r\n - Percentage of population exposed to PM2.5 Exposure threshold <= 10 microgram per m^3, including dust + sea salt.\r\n - Percentage of population exposed to PM2.5 Exposure threshold > 10 microgram per m^3 and<= 35 microgram per m^3, including dust + sea salt.\r\n - Percentage of population exposed to PM2.5 Exposure threshold > 35 microgram per m^3, including dust + sea salt.\r\n - Population weighted mean PM2.5 concentration [mean microgram per m^3]\r\n Regions: North America, Europe, Southern Asia, Eastern Asia, South-East Asia and developing Pacific, Asia-Pacific developed, Africa, Middle East, Latin America and Caribbean, Eurasia\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n All panels:\r\n \r\n - Data file: Fig_6.25_plot_data.csv\r\n - rows 4 to 54: left panels\r\n - rows 55 to 105: central panels\r\n - rows 106 to 156: right panels\r\n - column 2: white\r\n - column 3: light grey\r\n - column 4: dark grey\r\n - column 5: orange line\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"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39511,
            "uuid": "39003aa907104e99a6d01beb535fe5e9",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_06/ch6_fig26/v20221215",
            "numberOfFiles": 4,
            "volume": 7577,
            "fileFormat": "Data are CSV formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39510,
                "uuid": "bf31afbbbafc49d39546aa78a2268f44",
                "short_code": "ob",
                "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 6.26 (v20221215)",
                "abstract": "Data for Figure 6.26 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 6.26 shows the effect of dedicated air pollution or climate policy on population-weighted ozone concentrations (SOMO0; ppb) and share of population (%) exposed to different ozone levels across 10  world regions.\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 30 panels with data provided for all panels in one single file.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains population-weighted ozone concentrations (SOMO0; ppb) and share of population (%) exposed to chosen ozone levels across across 10  world regions.\r\n \r\n - Percentage of population exposed to Ozone level exposure threshold <= 35 ppb, annual average daily maximum 8-hourly ozone concentration.\r\n - Percentage of population exposed to Ozone level exposure threshold > 35 ppb and<= 60 ppb, annual average daily maximum 8-hourly ozone concentration\r\n - Percentage of population exposed to Ozone level exposure threshold > 60 ppb, annual average daily maximum 8-hourly ozone concentration\r\n - Population weighted mean Ozone level concentration [mean ppb], annual average daily maximum 8-hourly ozone concentration.\r\n\r\n Regions: North America, Europe, Southern Asia, Eastern Asia, South-East Asia and developing Pacific, Asia-Pacific developed, Africa, Middle East, Latin America and Caribbean, Eurasia\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n All panels:\r\n \r\n - Data file: Fig_6.26_plot_data.csv\r\n - rows 4 to 54: left panels\r\n - rows 55 to 105: central panels\r\n - rows 106 to 156: right panels\r\n - column 2: white\r\n - column 3: light grey\r\n - column 4: dark grey\r\n - column 5: orange line\r\n\r\nppb stands for parts per billion.\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"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39514,
            "uuid": "30460cdab66d4e82803ea8c45b924a97",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/TS/BOX_ts7_fig1/v20221216",
            "numberOfFiles": 4,
            "volume": 8818,
            "fileFormat": "Data are CSV formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "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"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39517,
            "uuid": "92599e54b8e1401fbd0e6c7d397cd05a",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_02/ch2_fig04/v20221219",
            "numberOfFiles": 0,
            "volume": 0,
            "fileFormat": "Data are CSV formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": null,
            "onlineresource_set": []
        },
        {
            "ob_id": 39520,
            "uuid": "d7dc0e4388e3497c91da0cd61c8be419",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_02/ch2_fig04/v20221219",
            "numberOfFiles": 0,
            "volume": 0,
            "fileFormat": "Data are CSV formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": null,
            "onlineresource_set": []
        },
        {
            "ob_id": 39522,
            "uuid": "02ceb81a614f4287a978686983923ad4",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_02/ch2_fig04/v20221219",
            "numberOfFiles": 11,
            "volume": 503221,
            "fileFormat": "Data are CSV formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "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"
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                "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"
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                "abstract": "This dataset provides Sea Ice Concentration (SIC) for the polar regions, derived from the Nimbus-5 Electrical Scanning Microwave Radiometer (ESMR), which operated between 1972 and 1977. It is processed with an algorithm using the single channel ESMR data (19.35 GHz), and has been gridded at 25 km grid spacing. This is the first version of the product, v1.0.\r\n\r\nThis product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project."
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                "abstract": "Particulate Matter Distribution data measured at 7m above ground level by a Palas FIDAS 200 Instrument at Manchester Air Quality Site (MAQS) for the Integrated Research Observation System for Clean Air (OSCA) project.\r\nMeasurements include the abundance of mass concentration of PM1 ambient aerosol in air, mass concentration of PM2.5 ambient aerosol in air, mass concentration of PM10 ambient aerosol in Air, and the concentration of ambient aerosol particles."
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                "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."
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                "abstract": "Mass Concentration of the Nitrate, Chloride, Sulphate, Ammonium and Organic Component of Ambient Aerosol Particles in Air measured at 7 metres above ground level by an Aerosol Chemical Species Monitor (ACSM)  at the Manchester Air Quality Site (MAQS), from 2019 onwards, for the Integrated Research Observation System for Clean Air (OSCA) project."
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                "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."
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                "abstract": "This dataset covers a regional model of the North East Atlantic run for a historic reanalysis period (1979-2015) forced by ECMWF Re-Analysis winds (ERA-Interim, https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim) The data are produced with a spectral wave model; WaveWatchIII (Tolman, 1997) on a regular lat/lon grid with resolution around 1/12th degree. The netcdf data include high frequency (hourly) wave data of bulk parameters representing significant wave height, mean wave direction, and energy period. Partitioned wave data are also included for significant wave height, peak period, and direction. These data are presented in 3 partitions, with a fraction of wind sea specified for each. The data set is produced to benchmark the model before investigating future wave conditions of Europe under climate change. The simulations were run using funding from the European Union’s Seventh Programme for Research, Technological Development and Demonstration under Grant Agreement No: FP7-ENV-2013-Two-Stage-603396-RISES-AM-. This work used the ARCHER UK National Supercomputing Service (http://www.archer.ac.uk)"
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                "title": "RisesAM-NEA-clim: Hourly 1/12° regional simulations of the North East Atlantic Ocean sea surface waves (climatological forcing based on 1970-2100) for 2 climate futures",
                "abstract": "The climate projections for 2 possible futures (1970-2100) are forced by EC-Earth (https://ec-earth.org/) Regional models are driven by the same climate models, downscaled to 12km resolution, under EuroCordex programme (https://euro-cordex.net/) The data are produced with a spectral wave model; WaveWatchIII (Tolman, 1997) on a regular lat/lon grid with resolution around 1/12th degree. The netcdf data include high frequency (hourly) wave data of bulk parameters representing significant wave height, mean wave direction, and energy period. Partitioned wave data are also included for significant wave height, peak period, and direction. These data are presented in 3 partitions, with a fraction of wind sea specified for each. The data set is produced to investigate the future wave conditions of Europe under climate change. There are 2 different climate futures explored; RCP8.5 and RCP4.5 (regional concentration pathways). The two scenarios are driven by different Representative Concentration Pathways (RCPs). The RCPs, RCP4.5 and RCP8.5, are labelled after a possible range of radiative forcing values in the year 2100 relative to pre-industrial values (+4.5, and +8.5 W/m2, respectively). The simulations were run using funding from the European Union’s Seventh Programme for Research, Technological Development and Demonstration under Grant Agreement No: FP7-ENV-2013-Two-Stage-603396-RISES-AM-. This work used the ARCHER UK National Supercomputing Service (http://www.archer.ac.uk)"
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                "abstract": "This dataset covers the world ocean and is run for a historic reanalysis period (1979-2015) forced by ECMWF Re-Analysis winds and sea-ice (ERA-Interim, https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim) The data are produced with a spectral wave model; WaveWatchIII (Tolman, 1997) on a regular lat/lon grid with resolution is around 0.8 degrees. The netcdf data include high frequency (hourly) wave data of bulk parameters representing significant wave height, mean wave direction, and energy period. Partitioned wave data are also included for significant wave height, peak period, and direction. These data are presented in 3 partitions, with a fraction of wind sea specified for each. The data set is produced to benchmark the model before investigating future wave conditions under climate change. The simulations were run using funding from the European Union’s Seventh Programme for Research, Technological Development and Demonstration under Grant Agreement No: FP7-ENV-2013-Two-Stage-603396-RISES-AM-. This work used the ARCHER UK National Supercomputing Service (http://www.archer.ac.uk)"
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                "short_code": "ob",
                "title": "RisesAM-Global-clim: Hourly 1° global simulations of Ocean sea surface waves (climatological forcing based on 1970-2100) for 2 climate futures",
                "abstract": "This dataset covers the world ocean and it run for 2 climate futures. The climate projections (1970-2100) are forced by EC-Earth (https://ec-earth.org/) The data are produced with a spectral wave model; WaveWatchIII (Tolman, 1997) on a regular lat/lon grid with resolution is around 0.8 degrees. The netcdf data include high frequency (hourly) wave data of bulk parameters representing significant wave height, mean wave direction, and energy period. Partitioned wave data are also included for significant wave height, peak period, and direction. These data are presented in 3 partitions, with a fraction of wind sea specified for each. There are 2 different climate futures explored; RCP8.5 and RCP4.5 (regional concentration pathways). The two scenarios are driven by different Representative Concentration Pathways (RCPs). The RCPs, RCP4.5 and RCP8.5, are labelled after a possible range of radiative forcing values in the year 2100 relative to pre-industrial values (+4.5, and +8.5 W/m2, respectively). The data set is produced to benchmark the model before investigating future wave conditions under climate change. The simulations were run using funding from the European Union’s Seventh Programme for Research, Technological Development and Demonstration under Grant Agreement No: FP7-ENV-2013-Two-Stage-603396-RISES-AM-. This work used the ARCHER UK National Supercomputing Service (http://www.archer.ac.uk)"
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            "curationCategory": "",
            "dataPath": "/badc/glocaem/data/noramberd",
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            "fileFormat": "BADC CSV",
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        {
            "ob_id": 39576,
            "uuid": "136194eda77340448dde32855a454520",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/glocaem/data/ica/",
            "numberOfFiles": 1,
            "volume": 921,
            "fileFormat": "BADC-CSV",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39552,
                "uuid": "338774c1a9f34ce287f4cef9eb7bcdee",
                "short_code": "ob",
                "title": "GloCAEM: Atmospheric electricity measurements at Universidad Nacional San Luis Gonzaga, Ica, Peru",
                "abstract": "Global Coordination of Atmospheric Electricity Measurements (GloCAEM) project brought these experts together to make the first steps towards an effective global network for FW atmospheric electricity monitoring by holding workshops to discuss measurement practises and instrumentation, as well as establish recording and archiving procedures to archive electric field data in a standardised, easily accessible format, then by creating a central data repository. This project was funded in the UK under NERC grant NE/N013689/1.\r\n\r\nThis dataset contains measurements of atmospheric electricity and electric potential gradient made using a Boltek field meter at Universidad Nacional San Luis Gonzaga, Ica, Peru."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39603,
            "uuid": "a4dfbc22d92841af902d2b0caa6b389b",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/neodc/esacci/land_cover/data/pft/v2.0.8/",
            "numberOfFiles": 31,
            "volume": 99092861574,
            "fileFormat": "Data are in NetCDF format",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37340,
                "uuid": "26a0f46c95ee4c29b5c650b129aab788",
                "short_code": "ob",
                "title": "ESA Land Cover Climate Change Initiative (Land_Cover_cci):  Global Plant Functional Types (PFT) Dataset, v2.0.8",
                "abstract": "This dataset contains Global Plant Functional Types (PFT) data, from the ESA Medium Resolution Land Cover (MRLC) Climate Change Initiative project.   The data provides yearly data, and initially covers the time period from 1992 to 2020.   It is anticipated that the dataset will be updated annually going forward.\r\n\r\nThe PFT v2.0.8 global dataset has 14 layers, each describing the percentage cover (0-100%) of a plant functional type at a spatial resolution of 300 m: broadleaved evergreen trees, broadleaved deciduous trees, needleleaved evergreen trees, needleleaved deciduous trees, broadleaved evergreen shrubs, broadleaved deciduous shrubs, needleleaved evergreen shrubs, needleleaved deciduous shrubs, natural grasses, herbaceous cropland (i.e., managed grasses), built, water, bare areas, and snow and ice.\r\n\r\n\"Plant Functional Types” (PFTs) refer to globally representative and similarly behaving plant types. PFTs can be related to physiognomy and phenology, climate (which defines the geographical ranges in which a plant type can grow and reproduce under natural conditions, and physiological activity (e.g., C3/C4 photosynthetic pathways).\r\n\r\nAll terrestrial zones of the Earth between the parallels 90°N and 90°S are covered. The PFT dataset has a regular latitude-longitude grid with a grid spacing of 0.002777777777778°, corresponding to ~300 m at the equator and ~200 m in the midlatitudes.    The Coordinate Reference System used for the global land cover database is a geographic coordinate system (GCS) based on the World Geodetic System 84 (WGS84) reference ellipsoid.\r\n\r\nThe plant functional type (PFT) distribution was created by combining auxiliary data products with the CCI MRLC map series. The LC classification provides the broad characteristics of the 300 m pixel, including the expected vegetation form(s) (tree, shrub, grass) and/or abiotic land type(s) (water, bare area, snow and ice, built-up) in the pixel. For some classes, the class legend specifies an expected range for the fractional covers of the contributing PFTs and broadly differentiates between natural and cultivated vegetation. We used a quantitative, globally consistent method that fuses the 300-metre MRLC product with a suite of existing high-resolution datasets to develop spatially explicit annual maps of PFT fractional composition at 300 metres. The new PFT product exhibits intraclass spatial variability in PFT fractional cover at the 300-metre pixel level and is complementary to the MRLC maps since the derived PFT fractions maintain consistency with the original LC class legend. \r\n\r\nThis dataset was generated to reduce the cross-walking component of uncertainty by adding spatial variability to the PFT composition within a LC class. This work moved beyond fine-tuning the cross-walking approach for specific LC classes or regions and, instead, separately quantifies the PFT fractional composition for each 300 m pixel globally. The result is a dataset representing the cover fractions of 14 PFTs at 300 m for each year within the time range, consistent with the CCI MRLC LC maps for the corresponding year.\r\n\r\nThis study was carried out with the continued support of the European Space Agency Climate Change Initiative under the contract ESA/No.4000126564 Land_Cover_cci."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39607,
            "uuid": "08234196e70142a6b45c5617dc3a1f9e",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/deposited2023/CPDN_HadAM4",
            "numberOfFiles": 8401,
            "volume": 1698725532892,
            "fileFormat": "NetCDF",
            "storageStatus": "online",
            "storageLocation": "internal",
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            "observation": {
                "ob_id": 39606,
                "uuid": "9c41e3aa67024bbdad796290a861e968",
                "short_code": "ob",
                "title": "Large ensemble of global mean temperatures: 6-hourly HadAM4 model run data using the Climateprediction.net platform",
                "abstract": "Large ensembles of global temperature are provided for three climate scenarios: historical (2006-16), 1.5 C and 2.0 C above pre-industrial levels. Each scenario has 700 members (70 runs per year for 10-year periods) of 6-hourly mean temperatures at a resolution of 0.833 degrees x 0.556 degrees (longitude x latitude). The data was generated using the climateprediction.net (CPDN) climate simulation environment, to run the Met Office HadAM4 Atmosphere-only General Circulation Model (AGCM) from the UK Met Office Hadley Centre. Biases in simulated temperature were identified and corrected using quantile mapping with reference temperature data from ERA5 reanalysis. \r\n\r\nData were generated using the Met Office HadAM4 model at 6-hourly temporal resolution and 0.833 degrees x 0.556 degrees (longitude x latitude) over global domain. The data from each scenario is divided into 4 batches. Historic scenario (2006-2016): December-March data in Batch 889, April-May data in Batch 920, June-September data in Batch 901, October-November data in Batch 923. 1.5C scenario:  December-March data in Batch 891, April-May data in Batch 921, June-September data in Batch 902, October-November data in Batch 924. 2.0C scenario: December-March data in Batch 895, April-May data in Batch 922, June-September data in Batch 903, October-November data in Batch 925."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39632,
            "uuid": "976634fafd4544ec91da894c9e9d1a63",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/eprofile/data/daily_files/italy/aquila/aquila-university-vaisala-cl51_A",
            "numberOfFiles": 1084,
            "volume": 2814669751,
            "fileFormat": "Data are netCDF formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39633,
                "uuid": "f9525557d49b4363adccd8e68c0893de",
                "short_code": "ob",
                "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from University of L'Aquila's vaisala-cl51 instrument deployed at Aquila, Italy",
                "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from University of L'Aquila's vaisala-cl51 deployed at Aquila, Italy.\n\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\n\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20008-0-CEO.\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool.\n    \nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39637,
            "uuid": "37fa521b5c844984bf0af9844f333548",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/eprofile/data/daily_files/netherlands/borssele-alfa/knmi-lufft-chm15k_A",
            "numberOfFiles": 1101,
            "volume": 2934794363,
            "fileFormat": "Data are netCDF formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39638,
                "uuid": "f06e1510b56049559e87aa2f0db5fdcc",
                "short_code": "ob",
                "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from KNMI's lufft-chm15k instrument deployed at Borssele Alfa, Netherlands",
                "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Royal Netherlands Meteorological Institute (KNMI)'s lufft-chm15k deployed at Borssele Alfa, Netherlands.\n\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\n\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-528-0-06317.\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool.\n    \nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39643,
            "uuid": "090aa3d8f34c4ff99e531b5eb0384ae3",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_04/ch4_fig12/v20230203",
            "numberOfFiles": 4,
            "volume": 5727575,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39642,
                "uuid": "0078d944259049a4b1bc5947623f6e97",
                "short_code": "ob",
                "title": "Chapter 4 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 4.12 (v20230203)",
                "abstract": "Data for Figure 4.12 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 4.12 shows the projected near-term change of seasonal 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\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 (2021–2040) 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\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 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"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39646,
            "uuid": "e7b005a50157483f913a038f93500989",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_04/ch4_fig13/v20230203",
            "numberOfFiles": 4,
            "volume": 5723143,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39645,
                "uuid": "11d45679506d44fda224d65326edcdb4",
                "short_code": "ob",
                "title": "Chapter 4 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 4.13 (v20230203)",
                "abstract": "Data for Figure 4.13 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 4.13 shows the projected near-term change of seasonal mean precipitation.\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\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n CMIP6 multi-model mean projected change in precipitation (2021–2040) 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 pr 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\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 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"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39649,
            "uuid": "9f38cda3dbe6423b8f50fe8d8499ca8b",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_04/ch4_fig19/v20230203",
            "numberOfFiles": 4,
            "volume": 7092435,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "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"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39652,
            "uuid": "eb334edb2d6e47cfbab4f4dfa3743665",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_04/ch4_fig22/v20230203",
            "numberOfFiles": 5,
            "volume": 4197888,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39651,
                "uuid": "9527d9be07c243599f00af5ab945c7ed",
                "short_code": "ob",
                "title": "Chapter 4 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 4.22 (v20230203)",
                "abstract": "Data for Figure 4.22 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 4.22 shows the projected long-term change of annual and zonal mean atmospheric 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 panel a in the file \r\n named Data_shown_in_figure_panel_a.nc and for panel b in the file named Data_shown_in_figure_panel_b.nc.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n CMIP6 multi-model mean projected change in air temperature (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 Data file fig4_22a_ta.nc (panel a) includes the multi-model mean zonal mean temperature change as a function of latitude and pressure level for SSP1-2.6\r\n Data file fig4_22b_ta.nc (panel a) includes the multi-model mean zonal mean temperature change as a function of latitude and pressure level for SSP3-7.0\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 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"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39655,
            "uuid": "e4dd114fd04c47fb96fa4e121102a1cf",
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                "title": "Chapter 4 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 4.23 (v20230203)",
                "abstract": "Data for Figure 4.23 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 4.23 shows the projected long-term changes in seasonal mean relative humidity.\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\n\r\na) Global projected spatial patterns of multi-model mean change in DJF seasonal mean relative humidity in 2081-2100 relative to 1995-2014 in SSP1-2.6\r\n b) Global projected spatial patterns of multi-model mean change in DJF seasonal mean relative humidity in 2081-2100 relative to 1995-2014 in SSP3-7.0\r\n c) Global projected spatial patterns of multi-model mean change in JJA seasonal mean relative humidity in 2081-2100 relative to 1995-2014 in SSP1-2.6\r\n d) Global projected spatial patterns of multi-model mean change in JJA seasonal mean relative humidity 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 DJF and JJA seasonal mean relative humidity (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 hurs 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\nDJF stands for December, January, February.\r\nJJA stands for June, July, August.\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 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"
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                "abstract": "Met Office Unified Model single-site (Oden) output during MOCCHA. These model and observation data are used in McCusker et al. : Evaluating Arctic clouds modelled with the Unified Model and Integrated Forecasting System, Atmospheric Chemistry and Physics, 2023. \r\n\r\n Model data from the Met Office Unified Model are in directory 'um_model_data/'. Data are hourly data taken from grid box closest to ship location. Where the ship covers more than one grid box within an hour period, data are averaged from all grid boxes crossed. All data files are in a netCDF format, with one file per day. Rose suite ID: u-cc278. Model options include: \r\n Unified Model version - 11.3, \r\n CASIM microphysics + cloud scheme (i_cld_vn = 1). \r\n Double-moment cloud microphysics - droplet activation = Abdul-Razzak and Ghan (2000); ice nucleation = Cooper (1986). \r\n 3 modes of soluble aerosol, no insoluble aerosol. \r\n Accumulation mode soluble aerosol - num = 1.00e8 /m3, mass = 1.50e-9 kg/kg. \r\n Aitken and coarse modes = 0. \r\n No aerosol processing. \r\n Updated RHcrit profile used in Unified Model vn11.4. \r\n Uses sea ice options from the global model (alpham = 0.72, dtice = 2.0). \r\n U and V wind components interpolated on to common vertical grid. \r\n\r\n Model data from directory um_model_data/ are subsequently passed through the Cloudnet algorithm to produce calibrated model data that may be used for direct comparisons with observations. Cloudnet combines cloud radar, ceilometer, microwave radiometer, and radiosonde profiles averaged to a common grid at the cloud radar resolution to derive a set of retrieved cloud properties. The Cloudnet products are designed to be used for evaluation of weather forecast models as well as fundamental process studies of cloud. From a modelling perspective, Cloudnet converts liquid and ice mass mixing ratios to the respective cloud water contents for direct comparison with observations, as well as filtering ice water contents for values that would be unobservable by radar. Note that the latitude/longitude relevant for each date in question can be found in these Cloudnet files. \r\n\r\nIn directories:\r\n iwc-Z-T-metum-grid/ - data include ice water content and total ice water path for observations and model. \r\n lwc-scaled-metum-grid/ - data include cloud liquid water content and liquid water path for observations and model. \r\n cloud-fraction-metum-grid/ - data include cloud fractions by volume for observations and model."
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                "title": "Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA): Met Office Unified Model data and associated Cloudnet outputs (UM_RA2M_Cloudnet)",
                "abstract": "Met Office Unified Model single-site (Oden) output during MOCCHA. These model and observation data are used in McCusker et al.: Evaluating Arctic clouds modelled with the Unified Model and Integrated Forecasting System, Atmospheric Chemistry and Physics, 2023. \r\n\r\n Model data from the Met Office Unified Model are in directory 'um_model_data/'. Data are hourly data taken from grid box closest to ship location. Where the ship covers more than one grid box within an hour period, data are averaged from all grid boxes crossed. All data files are in a netCDF format, with one file per day. Rose suite ID: u-cc568. Model options include: \r\n Cloud microphysics: Smith (1990) but includes a cloud/precipitation microphysical scheme with prognostic ice (Wilson and Ballard, 1999), based on Rutledge and Hobbs (1983). \r\n Extended boundary layer diagnostic list. \r\n Updated revision of suite u-bg610. \r\n U and V wind components interpolated on to common vertical grid. \r\n\r\n Model data from um_model_data/ are subsequently passed through the Cloudnet algorithm to produce calibrated model data that may be used for direct comparisons with observations. Cloudnet combines cloud radar, ceilometer, microwave radiometer, and radiosonde profiles averaged to a common grid at the cloud radar resolution to derive a set of retrieved cloud properties. The Cloudnet products are designed to be used for evaluation of weather forecast models as well as fundamental process studies of cloud. From a modelling perspective, Cloudnet converts liquid and ice mass mixing ratios to the respective cloud water contents for direct comparison with observations, as well as filtering ice water contents for values that would be unobservable by radar. Note that the latitude/longitude relevant for each date in question can be found in these Cloudnet files. \r\nIn directories:\r\n iwc-Z-T-metum-grid/ - data include ice water content and total ice water path for observations and model. \r\n lwc-scaled-metum-grid/ - data include cloud liquid water content and liquid water path for observations and model. \r\n cloud-fraction-metum-grid/ - data include cloud fractions by volume for observations and model."
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                "abstract": "ECMWF Integrated Forecasting System (IFS) single-site (Oden) Cloudnet output during MOCCHA - data are used in McCusker et al. : Evaluating Arctic clouds modelled with the Unified Model and Integrated Forecasting System, Atmospheric Chemistry and Physics, 2023. \r\n\r\n IFS data are passed through the Cloudnet algorithm to produce calibrated model data that may be used for direct comparisons with observations. Cloudnet combines cloud radar, ceilometer, microwave radiometer, and radiosonde profiles averaged to a common grid at the cloud radar resolution to derive a set of retrieved cloud properties. The Cloudnet products are designed to be used for evaluation of weather forecast models as well as fundamental process studies of cloud. From a modelling perspective, Cloudnet converts liquid and ice mass mixing ratios to the respective cloud water contents for direct comparison with observations, as well as filtering ice water contents for values that would be unobservable by radar. Note that the latitude/longitude relevant for each date in question can be found in these Cloudnet files. \r\n\r\nIn directories:\r\n iwc-Z-T-ecmwf-grid/ - data include ice water content and total ice water path for observations and model. \r\n lwc-scaled-ecmwf-grid/ - data include cloud liquid water content and liquid water path for observations and model. \r\n cloud-fraction-ecmwf-grid/ - data include cloud fractions by volume for observations and model."
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                "short_code": "ob",
                "title": "Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA): Met Office Unified Model data and associated Cloudnet outputs (UM_RA2T_Cloudnet)",
                "abstract": "Met Office Unified Model single-site (Oden) output during MOCCHA. These model and observation data are used in McCusker et al.: Evaluating Arctic clouds modelled with the Unified Model and Integrated Forecasting System, Atmospheric Chemistry and Physics, 2023. \r\n\r\n Model data from the Met Office Unified Model are in directory 'um_model_data/'. Data are hourly data taken from grid box closest to ship location. Where the ship covers more than one grid box within an hour period, data are averaged from all grid boxes crossed. All data files are in a netCDF format, with one file per day. Rose suite ID: u-cc568. Model options include: \r\n Cloud microphysics: Both the global model and LAM use the PC2 (Wilson et al., 2008) cloud scheme (i_cld_vn = 2); specifically, the LAM uses the RA2T_CON configuration. \r\n Also set l_subgrid_qcl_mp to .true. to allow for turbulent production of mixed-phase cloud. \r\n Extended boundary layer diagnostic list. \r\n U and V wind components interpolated on to common vertical grid. \r\n\r\n Model data from directory um_model_data/ are subsequently passed through the Cloudnet algorithm to produce calibrated model data that may be used for direct comparisons with observations. Cloudnet combines cloud radar, ceilometer, microwave radiometer, and radiosonde profiles averaged to a common grid at the cloud radar resolution to derive a set of retrieved cloud properties. The Cloudnet products are designed to be used for evaluation of weather forecast models as well as fundamental process studies of cloud. From a modelling perspective, Cloudnet converts liquid and ice mass mixing ratios to the respective cloud water contents for direct comparison with observations, as well as filtering ice water contents for values that would be unobservable by radar. Note that the latitude/longitude relevant for each date in question can be found in these Cloudnet files. \r\nIn directories:\r\n iwc-Z-T-metum-grid/ - data include ice water content and total ice water path for observations and model. \r\n lwc-scaled-metum-grid/ - data include cloud liquid water content and liquid water path for observations and model. \r\n cloud-fraction-metum-grid/ - data include cloud fractions by volume for observations and model."
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                "ob_id": 38326,
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                "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 06.2",
                "abstract": "The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.\r\n\r\nThe v06.2 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.\r\n\r\nThe data set should be cited using the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001"
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                "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 06.2",
                "abstract": "The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. PASSIVE and ACTIVE products have also been created.\r\n\r\nThe v06.2 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.\r\n\r\nThe data set should be cited using the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001"
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                "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE product, Version 06.2",
                "abstract": "The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. ACTIVE and COMBINED products have also been created.\r\n\r\nThe v06.2 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.\r\n\r\nThe data set should be cited using the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001"
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                "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 07.1",
                "abstract": "The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.\r\n\r\nThe v07.1 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.\r\n\r\nThe data set should be cited using the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001"
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                "short_code": "ob",
                "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE product, Version 07.1",
                "abstract": "The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP satellite instruments. ACTIVE and COMBINED products have also been created.\r\n\r\nThe v07.1 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.\r\n\r\nThe data set should be cited using the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001"
            },
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            "observation": {
                "ob_id": 38333,
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                "short_code": "ob",
                "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 07.1",
                "abstract": "The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.\r\n\r\nThe v07.1 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.\r\n\r\nThe data set should be cited using the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39693,
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            "curationCategory": "",
            "dataPath": "/neodc/esacci/soil_moisture/data/daily_files/break_adjusted_COMBINED/v07.1",
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            "observation": {
                "ob_id": 38334,
                "uuid": "0ae6b18caf8a4aeba7359f11b8ad49ae",
                "short_code": "ob",
                "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Experimental Break-Adjusted COMBINED Product, Version 07.1",
                "abstract": "An experimental break-adjusted soil-moisture product has been generated by the ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci) project for their v07.1 data release. The product attempts to reduce breaks in the final CCI product by matching the statistics of the datasets between merging periods. At v07.1, the break-adjustment process (explained in Preimesberger et al. 2020) is applied only to the COMBINED product, using ERA5 soil moisture as a reference. The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.\r\n\r\nThe v07.1 COMBINED break-adjusted product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document and Preimesberger et al. 2020. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.\r\n\r\nThe data set should be cited using all of the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n3. Preimesberger, W., Scanlon, T., Su,  C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896."
            },
            "onlineresource_set": []
        },
        {
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            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/Meteo-France/CNRM-CM61/control",
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            "observation": {
                "ob_id": 39694,
                "uuid": "27e04957bfa24c6585c388f2cfe1f844",
                "short_code": "ob",
                "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): control data produced by the  CNRM-CM 6.1 model at Météo France",
                "abstract": "This dataset contains model data for SNAPSI experiment 'control' produced by the seasonal prediction research team at Météo-France. It is generated with the coupled climate model CNRM-CM 6.1. \r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe control experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. Following the initial date, the stratospheric zonal mean temperatures and zonal winds are nudged towards the time-evolving climatological state. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n-  Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n- Evaluation of CMIP6 DECK experiments with CNRM-CM6-1: Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A., Chevallier, M., Colin, J., Guérémy, J.-F., Michou, M., Moine, M.-P., Nabat, P., Roehrig, R., y Mélia, D. S., Séférian, R., Valcke, S., Beau, I., Belamari, S., Berthet, S., Cassou, C., Cattiaux, J., Deshayes, J., Douville, H., Ethé, C., Franchistéguy, L., Geoffroy, O., Lévy, C., Madec, G., Meurdesoif, Y., Msadek, R., Ribes, A., Sanchez-Gomez, E., Terray, L., and Waldman, R., J. Adv. Model Earth Sy., 11, 2177–2213,\r\nhttps://doi.org/10.1029/2019MS001683, 2019"
            },
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        },
        {
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            "oldDataPath": [],
            "observation": {
                "ob_id": 39697,
                "uuid": "bbe6dfaa0c9a4dfb8a0e7f131cc4d0b4",
                "short_code": "ob",
                "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): free data produced by the CNRM-CM 6.1 model at Météo France",
                "abstract": "This dataset contains model data for SNAPSI experiment 'free' produced by the seasonal prediction research team at Météo-France. It is generated with the coupled climate model CNRM-CM 6.1. \r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe free experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n- Evaluation of CMIP6 DECK experiments with CNRM-CM6-1: Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A., Chevallier, M., Colin, J., Guérémy, J.-F., Michou, M., Moine, M.-P., Nabat, P., Roehrig, R., y Mélia, D. S., Séférian, R., Valcke, S., Beau, I., Belamari, S., Berthet, S., Cassou, C., Cattiaux, J., Deshayes, J., Douville, H., Ethé, C., Franchistéguy, L., Geoffroy, O., Lévy, C., Madec, G., Meurdesoif, Y., Msadek, R., Ribes, A., Sanchez-Gomez, E., Terray, L., and Waldman, R., J. Adv. Model Earth Sy., 11, 2177–2213, https://doi.org/10.1029/2019MS001683, 2019"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39701,
            "uuid": "8998413d5e444248807fdfa5be8843e0",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/Meteo-France/CNRM-CM61/nudged",
            "numberOfFiles": 11401,
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            "fileFormat": "Data are Net-CDF formatted",
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            "oldDataPath": [],
            "observation": {
                "ob_id": 39698,
                "uuid": "8e60e41124c644688cd7e8650114dc10",
                "short_code": "ob",
                "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): nudged data produced by the CNRM-CM 6.1 model at Météo France",
                "abstract": "This dataset contains model data for SNAPSI experiment 'nudged' produced by the seasonal prediction research team at Météo-France. It is generated with the coupled climate model CNRM-CM 6.1. \r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe nudged experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. Following the initial date, the stratospheric zonal mean temperatures and zonal winds are nudged towards the observed time-evolving state. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n- Evaluation of CMIP6 DECK experiments with CNRM-CM6-1: Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A., Chevallier, M., Colin, J., Guérémy, J.-F., Michou, M., Moine, M.-P., Nabat, P., Roehrig, R., y Mélia, D. S., Séférian, R., Valcke, S., Beau, I., Belamari, S., Berthet, S., Cassou, C., Cattiaux, J., Deshayes, J., Douville, H., Ethé, C., Franchistéguy, L., Geoffroy, O., Lévy, C., Madec, G., Meurdesoif, Y., Msadek, R., Ribes, A., Sanchez-Gomez, E., Terray, L., and Waldman, R., J. Adv. Model Earth Sy., 11, 2177–2213, https://doi.org/10.1029/2019MS001683, 2019"
            },
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        {
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            "short_code": "result",
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            "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/Meteo-France/CNRM-CM61/nudged-full",
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            "observation": {
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                "uuid": "8b1801552b7e40b5acb3bd12d0b4203a",
                "short_code": "ob",
                "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): nudged-full data produced by the CNRM-CM 6.1 model at Météo France",
                "abstract": "This dataset contains model data for SNAPSI experiment 'nudged-full' produced by the seasonal prediction research team at Météo-France. It is generated with the coupled climate model CNRM-CM 6.1. \r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe nudged-full experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. Following the initial date, stratospheric temperatures and horizontal winds are nudged towards the observed time-evolving state. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n- Evaluation of CMIP6 DECK experiments with CNRM-CM6-1: Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A., Chevallier, M., Colin, J., Guérémy, J.-F., Michou, M., Moine, M.-P., Nabat, P., Roehrig, R., y Mélia, D. S., Séférian, R., Valcke, S., Beau, I., Belamari, S., Berthet, S., Cassou, C., Cattiaux, J., Deshayes, J., Douville, H., Ethé, C., Franchistéguy, L., Geoffroy, O., Lévy, C., Madec, G., Meurdesoif, Y., Msadek, R., Ribes, A., Sanchez-Gomez, E., Terray, L., and Waldman, R., J. Adv. Model Earth Sy., 11, 2177–2213, https://doi.org/10.1029/2019MS001683, 2019"
            },
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        {
            "ob_id": 39707,
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            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/neodc/esacci/soil_moisture/data/ancillary/v07.1",
            "numberOfFiles": 6,
            "volume": 1558698,
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            "observation": {
                "ob_id": 38330,
                "uuid": "56d43a34ec3e4a85b3f311470f58e664",
                "short_code": "ob",
                "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Ancillary data used for the ACTIVE, PASSIVE and COMBINED products, Version 07.1",
                "abstract": "These ancillary datasets were used in the production of the ACTIVE, PASSIVE and COMBINED soil moisture data products, created as part of the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. The set of ancillary datasets include datasets of Average Vegetation Optical Depth data from AMSR-E, Soil Porosity, Topographic Complexity and Wetland fraction, as well as a Land Mask. This version of the ancillary datasets were used in the production of the v07.1 Soil Moisture CCI data.\r\n\r\nThe ACTIVE, PASSIVE and COMBINED soil moisture products which these data were used to develop are fusions of scatterometer and radiometer soil moisture products, derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP satellite instruments. To access these products or for further details on them please see their dataset records. Additional reference documents and information relating to them can also be found on the CCI Soil Moisture project website.\r\n\r\nSoil moisture CCI data should be cited using the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001"
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                "ob_id": 39710,
                "uuid": "f154fc6376da4d188a5f460816f064d0",
                "short_code": "ob",
                "title": "EQUIPT4Risk: GEOS-Chem Chemical Transport Model Output",
                "abstract": "EQUIPT4Risk GEOS-Chem model simulations as part of EQUIPT4Risk WP1 (Model simulations of the concentration of pollutants over the UK with and without additional emissions associated with the new hydrocarbon sources). A nested-grid version of the GEOS-Chem chemistry transport model (v12.6.1) at 0.5°x0.625° horizontal resolution over the domain 15W-5E, 45N-65N, with additional emissions at two Shale extraction sites, was used to evaluate the impact of additional emissions of nitrogen oxides (NOx) on surface air quality over the UK for 2019. \r\n\r\nThis dataset includes 6 files corresponding to hourly GEOS-Chem model output of surface concentrations for either gaseous (“SpeciesConc”,) or aerosol (“AerosolMass”) species for the period 2019-01-01T00:00 to 2020-01-01T00:00.\r\n\r\nOutput from three model runs are provided:\r\n (1) “baseline”: standard model output, replacing default CEDS anthropogenic emissions over Europe with EMEP 2017 emissions \r\n(2) “PNR_fracking”: the same emissions as (1), with additional NOx (as NO) emissions of 29 tonnes per year at the Preston New Road (PNR, (53.787282,-2.951474)) fracking site. \r\n(3) “BGR_fracking”: the same emissions as (1), with additional NOx (as NO) emissions of 29 tonnes per year at the British Geological Survey Kirby Misperton (BGS, (54.204185,0.892848)) fracking site."
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            "onlineresource_set": []
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        {
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            "uuid": "ec4e6c8448b34e47833bd82762841171",
            "short_code": "result",
            "curationCategory": "A",
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            "observation": {
                "ob_id": 39715,
                "uuid": "c4031dd3227c4bceb6eae480d2a47a0c",
                "short_code": "ob",
                "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 6.7  (v20230210)",
                "abstract": "Input Data for Figure 6.7 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 6.7 shows distribution of PM2.5 composition mass concentration (in μg m-3) for the major PM2.5 aerosol components.\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 9 panels with input data provided for all panels in subdirectories named Asia, Europe, North_America, SPARTAN and stations.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains monthly averaged major PM2.5 aerosol component (sulphate, nitrate, ammonium, sodium, chloride, organic carbon and elemental carbon) measurements for the following regions:\r\n \r\n - Latin American and Caribbean\r\n - North America (rural)\r\n - North America (urban)\r\n - Europe\r\n - Eurasia\r\n - Eastern Asia\r\n - Southern Asia\r\n - South-East Asia and Developing Pacific\r\n - Asia-Pacific Developed\r\n\r\n\r\nAdditionally, input file for separating the world in individual regions following the IPCC Sixth Assessment Report Working Group III (AR6 WGIII) is provided as AR6WG3_Atlas_IntermedRegions_1.0x1.0.nc\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Instructions in how to relate the input data with the figure in the 'Notes on reproducing the figure from the provided data' field.\r\n\r\nPM2.5 stands for particulate matter in the air that are 2.5 micrometers or less in diameter.\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The data is used as input of the code (violin_regions.py and worldmap_AR6_nocoasts.py). \r\n\r\nviolin_regions.py: Python code that creates the regional violin subplots. The user needs to run the code for each subregion separately by activating the lines 6-17 accordingly.\r\n\r\nworldmap_AR6_nocoasts.py: Python code that creates the world map in the center of the figure, including the location of the observational sites used.\r\n\r\nEach of these scripts work independently. The nine individual plots produced by violin_regions.py can be collated with the map created by worldmap_AR6_nocoasts.py to produce the figure 6.7. A link to the code archived on Zenodo are 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 code archived on Zenodo"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39719,
            "uuid": "af8251a94ae449588f328e179982b50d",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_06/inputdata_ch6_fig08/v20230213",
            "numberOfFiles": 172,
            "volume": 1581196,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39718,
                "uuid": "dc11fff6fda04b1d82e317132b93a3bf",
                "short_code": "ob",
                "title": "Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 6.8 (v20230213)",
                "abstract": "Input Data for Figure 6.8 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 6.8 depicts the time evolution of changes in global mean aerosol optical depth (AOD) at 550 nm\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 List of data provided\r\n ---------------------------------------------------\r\n This dataset contains netcdf files of 3-dimensional (time,lat,lon) aerosol optical depth (od550aer) time-series from 1850 to 2014 from the historical simulations performed by 16 CMIP6 models. Multiple ensemble members are included for some models.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Instructions in how to relate the input data with the figure in the 'Notes on reproducing the figure from the provided data' field.\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n This data file should be used in conjunction with the code Fig6.8_trend_plot_AOD_rolling_10change_volcanic_mask.py \r\nThis code creates the AOD timeseries (1850-2014) using the ensemble mean of individual CMIP6 models output in AOD_trend.png and is linked 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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39722,
            "uuid": "a76e5f88c0ef48cfaa486b85c98119f3",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_04/ch4_fig24/v20230213",
            "numberOfFiles": 4,
            "volume": 5684527,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39721,
                "uuid": "5ed073b87dbc45d6a66d7c704caef01d",
                "short_code": "ob",
                "title": "Chapter 4 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 4.24 (v20230213)",
                "abstract": "Data for Figure 4.24 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 4.24 shows the projected long-term changes in seasonal mean precipitation.\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\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\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n a) Global projected spatial patterns of multi-model mean change in DJF seasonal mean precipitation in 2081-2100 relative to 1995-2014 in SSP1-2.6 b) Global projected spatial patterns of multi-model mean change in DJF seasonal mean precipitation in 2081-2100 relative to 1995-2014 in SSP3-7.0 c) Global projected spatial patterns of multi-model mean change in JJA seasonal mean precipitation in 2081-2100 relative to 1995-2014 in SSP1-2.6 d) Global projected spatial patterns of multi-model mean change in JJA seasonal mean precipitation in 2081-2100 relative to 1995-2014 in SSP3-7.0\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n The variable pr 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\n\r\nDJF stands for December, January, February.\r\nJJA stands for June, July, August.\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 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"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39728,
            "uuid": "4300a0949b644a46a5bc8b47ba99b7f7",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_04/ch4_fig25/v20230213",
            "numberOfFiles": 4,
            "volume": 5607551,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39727,
                "uuid": "b1d79f8dea6244ea943d49040f0f9f6d",
                "short_code": "ob",
                "title": "Chapter 4 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 4.25 (v20230213)",
                "abstract": "Data for Figure 4.25 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 4.25 shows the projected long-term changes in seasonal mean sea level pressure.\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\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n a) Global projected spatial patterns of multi-model mean change in DJF seasonal mean sea level pressure in 2081-2100 relative to 1995-2014 in SSP1‑2.6\r\n b) Global projected spatial patterns of multi-model mean change in DJF seasonal mean sea level pressure in 2081-2100 relative to 1995-2014 in SSP3‑7.0\r\n c) Global projected spatial patterns of multi-model mean change in JJA seasonal mean sea level pressure in 2081-2100 relative to 1995-2014 in SSP1‑2.6\r\n d) Global projected spatial patterns of multi-model mean change in JJA seasonal mean sea level pressure in 2081-2100 relative to 1995-2014 in SSP3‑7.0\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n The variable psl 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\nDJF stands for December, January, February.\r\nJJA stands for June, July, August.\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 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"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39731,
            "uuid": "6f27ce84a0e44345ba1d459d7c56c281",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_04/ch4_fig41/v20230213",
            "numberOfFiles": 5,
            "volume": 2088562,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39730,
                "uuid": "e397fe6f20024295b095e2e3ca1e9f04",
                "short_code": "ob",
                "title": "Chapter 4 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 4.41 (v20230213)",
                "abstract": "Data for Figure 4.41 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 4.41 shows high-warming storylines for changes in annual mean 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 eight panels, with data provided for all panels in two files one including data for panels a, c, e, g and the other for panels b, d, f, h.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains temperature change patterns for the multi model means and different low-likelihood high warming storylines.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data file Data_shown_in_figure_panels_aceg.nc includes the temperature change pattern for the four panels as different variables called panelA, panelC, panelE and panel G, respectively.\r\n Data file Data_shown_in_figure_panels_bdfh.nc includes the temperature change pattern for the four panels as different variables called panelB, panelD, panelF and panel H, respectively.\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"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39734,
            "uuid": "3a1202c370e2454a97be4fb1e9737caa",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_04/ch4_fig42/v20230213",
            "numberOfFiles": 5,
            "volume": 1013786,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39733,
                "uuid": "e5e7afe5355a439e8d63be47ee7467c8",
                "short_code": "ob",
                "title": "Chapter 4 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 4.42 (v20230213)",
                "abstract": "Data for Figure 4.42 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 4.42 illustrates high-warming storylines for changes in annual mean precipitation.\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 seven panels, with data provided for panels a-c and e-f in two separate files.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains precipitation change patterns for the multi model means and different low-likelihood high warming storylines, and a low and high change storyline reflection the uncertainty due to model response and unforced internal variability at the grid point level.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data file Data_shown_in_figure_panels_abc.nc includes the precipitation change pattern for the three panels as different variables called panelA, panelB, and panelC, respectively.\r\n Data file Data_shown_in_figure_panels_ef.nc includes the precipitation change pattern for the two panels as different variables called panelE and panelF, respectively.\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"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39746,
            "uuid": "16b83b90d21a4f00b2bfc65e7502f45c",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/deposited2023/ACRG_monthly_methane_inversion/",
            "numberOfFiles": 3,
            "volume": 110001889,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39745,
                "uuid": "27273a9683be4c6991911c66535334ca",
                "short_code": "ob",
                "title": "UK and Europe Methane (CH4) surface flux inverse modelling estimates for 2015 - 2019",
                "abstract": "Monthly CH4 flux estimates for the UK and western Europe, derived from inverse modelling work by the Atmospheric Chemistry Research Group (ACRG) at the University of Bristol.\r\nPosterior flux estimates were derived from a Markov Chain Monte Carlo (MCMC) inversion process, using 4-hourly atmospheric mole fraction observations of CH4 from UK observation sites Mace Head (MHD), Tacolneston (TAC), Ridge Hill (RGL), Bilsdale (BSD) and Heathfield (HFD) and a priori estimate of CH4 emissions from the UK Greenhouse Gas (UKGHG) and Emissions Database for Global Atmospheric Research (EDGAR) models. Total CH4 flux estimates at a resolution of 0.23 degrees latitude x 0.35 degrees longitude over 10.7 to 79.3 degrees north and -97.9 to 39.7 degrees east are provided for 2015 - 2019."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39751,
            "uuid": "072f875f66c04fd7b0e7c043a96e93b3",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/osca/data/manchester/OSCA_MAQS_AE33_BC_UVPM",
            "numberOfFiles": 175,
            "volume": 536639586,
            "fileFormat": "NetCDF and BADC-CSV",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37941,
                "uuid": "03fd47b671b9482293ba122d3b7044da",
                "short_code": "ob",
                "title": "Black Carbon and Ultraviolet Particulate Matter Levels detected from a Magee Scientific Aethalometer Model AE33 Instrument at Manchester Air Quality Site, 2019 onwards",
                "abstract": "Use of Magee Scientific Aethalometer Model AE33 Instrument to measure the Black Carbon and Ultraviolet Particulate Matter Levels from the Particlate Matter in Air Data at Manchester Air Quality Site (MAQS) from 2019 onwards. Black Carbon measurements were obtained using electromagnetic wavelengths 370, 470, 520, 590, 660, 880, 950 nm."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39753,
            "uuid": "09848da8cafa43efb65e14922c601b53",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/TS/CSB_TS1_fig1/v20230203",
            "numberOfFiles": 15,
            "volume": 7306036,
            "fileFormat": "Data are netCDF, csv and txt formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "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."
            },
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            "observation": {
                "ob_id": 37815,
                "uuid": "5ef11ad195844a59b83393870a5860e1",
                "short_code": "ob",
                "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 7.8 (v20220721)",
                "abstract": "Data for Figure 7.8 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\nFigure 7.8 shows attributed global surface air temperature change (GSAT) from 1750 to 2019 produced using the two-layer emulator (Supplementary Material 7.SM.2), forced with ERF derived in this chapter (displayed in Figure 2.10) and climate response constrained to assessed ranges for key climate metrics described in Cross-Chapter Box 7.1.\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 1 panel, with data provided for this panel. A link to the code to plot the figure archived on Zenodo is provided in the Related Documents section of this catalogue record.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\nAttributed global surface air temperature change (GSAT) from 1750 to 2019 produced using the two-layer emulator (Supplementary Material 7.SM.2), forced with ERF derived in this chapter (displayed in Figure 2.10) and climate response constrained to assessed ranges for key climate metrics described in Cross-Chapter Box 7.1. The temperature contributions are expressed as follows:\r\n  - carbon dioxide (CO2)\r\n  - methane (CH4)\r\n  - nitrous oxide (N2O)\r\n  - other well-mixed greenhouse gases (WMGHGs)\r\n  - ozone (O3)\r\n  - aerosols\r\n  - other anthropogenic forcings\r\n  - total anthropogenic \r\n  - solar\r\n  - volcanic\r\n  - total forcing\r\n\r\nThe results shown are the medians from a 2237-member ensemble that encompasses uncertainty in forcing and climate response (year-2019 best estimates and uncertainties are shown in Figure 7.7 for several components). Shaded uncertainty bands show very likely (5–95%) ranges. \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 Figure 7.8:\r\n \r\n - Data file: fig7.8.csv\r\n\r\nERF stands for Effective Radiative Forcing. \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 of the Chapter 7 GitHub repository, which is linked in the 'Related Documents' section. Within the processing chain, every notebook is prefixed by a number. To reproduce all results in the chapter, the notebooks should be run in numerical order.\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 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 code for the figure, archived on Zenodo\r\n- Link to the Chapter 7 GitHub repository  \r\n- Link to the notebook for plotting figure"
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            "dataPath": "/badc/ar6_wg1/data/ch_09/ch9_fig30/v20220721",
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            "observation": {
                "ob_id": 37731,
                "uuid": "9374ee722fab464fb3ee8ea659b56546",
                "short_code": "ob",
                "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 9.30 (v20220712)",
                "abstract": "Data for Figure 9.30 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 9.30 shows global mean sea level (GMSL) commitment as a function of peak global surface air temperature. \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 10 subpanels, with data provided for all panels in one central directory in the GitHub repository linked in the documentation.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Global mean sea level (GMSL) commitment as a function of peak global surface air temperature from models (Clark et al., 2016; DeConto and Pollard, 2016; Garbe et al., 2020; Van Breedam et al., 2020) and paleo data on 2000-year (lower row) and 10,000 year (upper row) time scales. \r\n- Different contributors to GMSL rise (from left to right panels: total GMSL change, Antarctic Ice Sheet, Greenland Ice Sheet, global mean thermosteric sea level rise, and glaciers). \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 9.30\r\n \r\n - Data file: Fig9-30_data_Clark2016_UVic28_AIS_10000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic28_AIS_2000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic28_GIC_10000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic28_GIC_2000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic28_GMSL_10000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic28_GMSL_2000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic28_GMTE_10000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic28_GMTE_2000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic28_GrIS_10000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic28_GrIS_2000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic29_AIS_10000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic29_AIS_2000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic29_GIC_10000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic29_GIC_2000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic29_GMSL_10000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic29_GMSL_2000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic29_GMTE_10000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic29_GMTE_2000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic29_GrIS_10000y.nc\r\n - Data file: Fig9-30_data_Clark2016_UVic29_GrIS_2000y.nc\r\n - Data file: Fig9-30_data_DeConto2016_AIS_2000y.nc\r\n - Data file: Fig9-30_data_Garbe2020_AIS.nc\r\n - Data file: Fig9-30_data_Gregory2020_GrIS_10000y.nc\r\n - Data file: Fig9-30_data_Gregory2020_GrIS_2000y.nc\r\n - Data file: Fig9-30_data_VB2020_AIS_10000y.nc\r\n - Data file: Fig9-30_data_VB2020_AIS_2000y.nc\r\n - Data file: Fig9-30_data_VB2020_GIC_10000y.nc\r\n - Data file: Fig9-30_data_VB2020_GIC_2000y.nc\r\n - Data file: Fig9-30_data_VB2020_GMSL_10000y.nc\r\n - Data file: Fig9-30_data_VB2020_GMSL_2000y.nc\r\n - Data file: Fig9-30_data_VB2020_GMTE_10000y.nc\r\n - Data file: Fig9-30_data_VB2020_GMTE_2000y.nc\r\n - Data file: Fig9-30_data_VB2020_GrIS_10000y.nc\r\n - Data file: Fig9-30_data_VB2020_GrIS_2000y.nc\r\n\r\nGMSL stands for Global Mean Sea Level.\r\n\r\n---------------------------------------------------\r\nTemporal Range of Paleoclimate Data\r\n---------------------------------------------------\r\nThis dataset covers a paleoclimate timespan from 10,000 years ago to present.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nSLR commitments were plotted using standard matplotlib 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 (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 output data and plotting code for this figure, contained in a dedicated GitHub repository."
            },
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        {
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            "uuid": "49e8b6a08dc94bfa8862fc0f511bd4ee",
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            "dataPath": "/badc/ar6_wg1/data/ch_07/inputdata_ch7_fig05/v20230221",
            "numberOfFiles": 4,
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            "observation": {
                "ob_id": 39759,
                "uuid": "7b3d379fc1f040978df4806c6775a0df",
                "short_code": "ob",
                "title": "Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 7.5 (v20230221)",
                "abstract": "Input Data for Figure 7.5 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\nFigure 7.5 shows net aerosol effective radiative forcing (ERF) from different lines of evidence. \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 1 panel, with input data provided for this panel.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n\r\n- Net aerosol effective radiative forcing (ERF), in W m-2, from:\r\n   - AR5 assessment\r\n   - AR6 assessment comprising the following:\r\n       (Energy balance constraints [–2 to 0 W m–2 with no best estimate])\r\n       (Observational evidence from satellite retrievals of –1.4 [–2.2 to –0.6] W m–2)\r\n       (Combined model-based evidence of –1.25 [–2.1 to –0.4] W m–2)\r\n\r\nThe headline AR6 assessment of –1.3 [–2.0 to –0.6] W m–2 is highlighted in purple for 1750–2014 and compared to the AR5 assessment of –0.9  [–1.9 to –0.1] W m–2 for 1750–2011. The evidence comprising the AR6 assessment is shown below this (shown in brackets in the list of data provided). \r\n\r\nEstimates from individual CMIP5 (Zelinka et al., 2014) and CMIP6 (Smith et al., 2020b and Table 7.6) models are depicted by blue and red crosses respectively. \r\nFor each line of evidence the assessed best-estimate contributions from ERFari and ERFaci are shown with darker and paler shading respectively. \r\nThe observational assessment for ERFari is taken from the IRFari. \r\nUncertainty ranges are represented by black bars for the total aerosol ERF and depict very likely ranges. \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 Figure 7.5\r\n \r\n - Data file: table7.6.csv\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\nERFari stands for Effective Radiative Forcing of aerosol-radiation interaction.\r\nERFaci stands for Effective Radiative Forcing of aerosol-cloud interaction.\r\nIRFari stands for Instantaneous Radiative Forcing of aerosol-radiation interaction.\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 of the Chapter 7 GitHub repository. \r\nThe notebook to produce this figure uses Table 7.6 from the report chapter and data from Zelinka et al., 2014 written into the code.\r\nTo reproduce the figure from the input data provided here ('table7.6.csv'), you will need to edit the path in box 5 of 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 (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 code for the figure, archived on Zenodo,\r\n- Link to the notebook for plotting the figure from the Chapter 7 GitHub repository which also contains input data files"
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            "uuid": "5d54f6590b804eaf8fbc8c7e06215ecb",
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            "dataPath": "/badc/ukcp18/data/land-eurocordex/uk/country",
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            "observation": {
                "ob_id": 39542,
                "uuid": "84548d27aac94c32bf94c4d9e7fd5323",
                "short_code": "ob",
                "title": "EuroCORDEX-UK: Regional climate projections for the UK by Countries for 1980-2080",
                "abstract": "Regional climate model projections produced by the CoOrdinated Regional Downscaling EXperiment (CORDEX) and complementary to that produced by the UK Climate Projection 2018 (UKCP18) project. The data provides information on changes in climate for the UK until 2080, downscaled to a high resolution (12km), helping to inform adaptation to a changing climate. \r\n\r\nThe projections cover the UK for a 100 year period, 1981-2080, for a high emissions scenario, RCP8.5. Each projection provides an example of climate variability in a changing climate, which is consistent across climate variables at different times and spatial locations.\r\n\r\nThis dataset contains average values of indices for the countries of the UK.\r\n\r\nDataset development was funded under the UK Climate Resilience programme, which is supported by the UKRI Strategic Priorities Fund. The programme is co-delivered by the Met Office and NERC on behalf of UKRI partners AHRC, EPSRC, and ESRC."
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            "observation": {
                "ob_id": 39539,
                "uuid": "b109bd69e1af425aa0f661b01c40dc51",
                "short_code": "ob",
                "title": "EuroCORDEX-UK: Regional climate projections for the UK domain at 12 km Resolution for 1980-2080",
                "abstract": "Regional climate model projections produced by the CoOrdinated Regional Downscaling EXperiment (CORDEX) and complementary to that produced by the UK Climate Projection 2018 (UKCP18) project. The data provides information on changes in climate for the UK until 2080, downscaled to a high resolution (12 km), helping to inform adaptation to a changing climate. \r\n\r\nThe projections cover the UK for a 100 year period, 1981-2080, for a high emissions scenario, RCP8.5. Each projection provides an example of climate variability in a changing climate, which is consistent across climate variables at different times and spatial locations.\r\n\r\nThis dataset contains 12 km data for the UK on the OSGB (WGS84) grid.\r\n\r\nDataset development was funded under the UK Climate Resilience programme, which is supported by the UKRI Strategic Priorities Fund. The programme is co-delivered by the Met Office and NERC on behalf of UKRI partners AHRC, EPSRC, and ESRC."
            },
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        {
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            "uuid": "c8cf59d3b64e4db3a7e08bf569aca512",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/ukcp18/data/land-eurocordex/uk/region",
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            "observation": {
                "ob_id": 39541,
                "uuid": "3da1c0a9458e48bfbc20622f4df7d15b",
                "short_code": "ob",
                "title": "EuroCORDEX-UK: Regional climate projections for the UK by Administrative Regions for 1980-2080",
                "abstract": "Regional climate model projections produced by the CoOrdinated Regional Downscaling EXperiment (CORDEX) and complementary to that produced by the UK Climate Projection 2018 (UKCP18) project. The data provides information on changes in climate for the UK until 2080, downscaled to a high resolution (12km), helping to inform adaptation to a changing climate. \r\n\r\nThe projections cover the UK for a 100 year period, 1981-2080, for a high emissions scenario, RCP8.5. Each projection provides an example of climate variability in a changing climate, which is consistent across climate variables at different times and spatial locations.\r\n\r\nThis dataset contains average values of indices for administrative regions of the UK.\r\n\r\nDataset development was funded under the UK Climate Resilience programme, which is supported by the UKRI Strategic Priorities Fund. The programme is co-delivered by the Met Office and NERC on behalf of UKRI partners AHRC, EPSRC, and ESRC."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39764,
            "uuid": "49c8258711b0405c922e0c37d31ba3e0",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/ukcp18/data/land-eurocordex/uk/river",
            "numberOfFiles": 14961,
            "volume": 7997581732,
            "fileFormat": "The data are in NetCDF format.",
            "storageStatus": "online",
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            "oldDataPath": [],
            "observation": {
                "ob_id": 39543,
                "uuid": "072fe59012d24ccaaa5b889a8b7a42b1",
                "short_code": "ob",
                "title": "EuroCORDEX-UK: Regional climate projections for the UK by River Basins for 1980-2080",
                "abstract": "Regional climate model projections produced by the CoOrdinated Regional Downscaling EXperiment (CORDEX) and complementary to that produced by the UK Climate Projection 2018 (UKCP18) project. The data provides information on changes in climate for the UK until 2080, downscaled to a high resolution (12 km), helping to inform adaptation to a changing climate. \r\n\r\nThe projections cover the UK for a 100 year period, 1981-2080, for a high emissions scenario, RCP8.5. Each projection provides an example of climate variability in a changing climate, which is consistent across climate variables at different times and spatial locations.\r\n\r\nThis dataset contains average values of indices for major UK river basins.\r\n\r\nDataset development was funded under the UK Climate Resilience programme, which is supported by the UKRI Strategic Priorities Fund. The programme is co-delivered by the Met Office and NERC on behalf of UKRI partners AHRC, EPSRC, and ESRC."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39766,
            "uuid": "85e5a897fc3845fb91488ee217ba23bd",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/neodc/comet/publications_data/Turkey_earthquake/v1.0/",
            "numberOfFiles": 74,
            "volume": 4982772539,
            "fileFormat": "These data are provided in GeoTIFF (.tif) or png format.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39765,
                "uuid": "df93e92a3adc46b9a5c4bd3a547cd242",
                "short_code": "ob",
                "title": "3D Displacements and Strain from the 2023 February Turkey Earthquakes, version 1",
                "abstract": "This data set contains strain, motion magnitude, 3D displacements, and surface slip distributions from the 2023 February Türkiye Earthquakes. COMET presents 0.001 degree (~100 m) resolution 3D displacement fields, with associated uncertainties, jointly inverted from 4 tracks of Sentinel-1 range and azimuth offsets and a set of north and east displacements from Sentinel-2 pixel tracking. From the 3D displacement fields, we calculate 0.001-degree resolution horizontal motion magnitudes, from which we extract surface slip distribution along the two faults ruptured during the Mw7.8 and Mw7.5 earthquakes. Further calculation of a 0.01-degree resolution strain field from the east and north displacement fields highlights the surface ruptures caused by the two earthquakes."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39770,
            "uuid": "dd21dacb09474cccb2197f4b3171bdc4",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/TS/inputdata_TS_12/v20230301",
            "numberOfFiles": 112,
            "volume": 1134270,
            "fileFormat": "Data are netCDF, csv and json formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39769,
                "uuid": "e046adc115b04395937e793c9f3dbcb1",
                "short_code": "ob",
                "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure TS.12 (v20230301)",
                "abstract": "Input data for Figure TS.12 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.12 shows land-related changes relative to the 1850-1900 as a function of global warming levels. \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 six panels, with input data provided for all panels in subdirectories named Extremes, ModelSnow/sncbin and WaterCyclePanels.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n - Relative frequency and intensity changes of 1-in-10- and 1-in-50-year extreme daily heat (TXx) in CMIP6 models (ScenarioMIP) with respect to 1850-1900. Medians and 5, 17, 83, 95 percentiles.\r\n - Relative frequency and intensity changes of 1-in-10- and 1-in-50-year extreme daily precipitation rates (Rx1day) in CMIP6 models (ScenarioMIP) with respect to 1850-1900. Medians and 5, 17, 83, 95 percentiles.\r\n - Relative frequency and intensity changes of 1-in-10-year drought events in CMIP6 models (ScenarioMIP) with respect to 1850-1900. Medians and 5, 17, 83, 95 percentiles.\r\n - Monthly NH snow cover extent changes (in %), dependent on the GWL (with respect to 1850-1900), for CMIP6 models (historical + ScenarioMIP), with respect to snow cover extent at 0°C GWL (1850-1900)\r\n - Average precipitable water (annual mean), precipitation rate (annual mean + interannual variability), and runoff (annual mean + interannual variability) over tropical land (|latitude|<30°), in the CMIP6 models that reach +5°C GWL in SSP5-8.5.\r\n - Average precipitable water (annual mean), precipitation rate (annual mean + interannual variability), and runoff (annual mean + interannual variability) over tropical land (|latitude|>30°), in the CMIP6 models that reach +5°C GWL in SSP5-8.5.\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel a:\r\n - Data file: Extremes/TXx_freq_change_10_year_event.csv; relates to the orange clock symbols and small orange 3-pronged symbols above and below.\r\n - Data file: Extremes/TXx_intens_change_10_year_event.csv; relates to the orange thermometer symbols and small orange 3-pronged symbols above and below.\r\n - Data file: Extremes/TXx_freq_change_50_year_event.csv; relates to the brown clock symbols and small orange 3-pronged symbols above and below.\r\n - Data file: Extremes/TXx_intens_change_50_year_event.csv; relates to the brown thermometer symbols and small orange 3-pronged symbols above and below.\r\n  \r\n Panel b:\r\n - Data file: Extremes/Rx1day _freq_change_10_year_event.csv; relates to the orange clock symbols and small orange 3-pronged symbols above and below.\r\n - Data file: Extremes/Rx1day _intens_change_10_year_event.csv; relates to the orange rain cloud symbols and small orange 3-pronged symbols above and below.\r\n - Data file: Extremes/Rx1day _freq_change_50_year_event.csv; relates to the brown clock symbols and small orange 3-pronged symbols above and below.\r\n - Data file: Extremes/Rx1day _intens_change_50_year_event.csv; relates to the brown rain cloud symbols and small orange 3-pronged symbols above and below.\r\n  \r\n Panel c:\r\n - Data file: Extremes/drought _freq_change_10_year_event.csv; relates to the orange clock symbols and small orange 3-pronged symbols above and below.\r\n - Data file: Extremes/drought _intens_change_10_year_event.csv; relates to the orange drop symbols and small orange 3-pronged symbols above and below.\r\n  \r\n Panel d:\r\n Data files: ModelSnow/sncbin/sncbin_{model}_historical_ssp{xyy}.nc. For each model and scenario, these files contain a table that gives the snow cover extent changes for each month of the year and for 0.2°C-wide temperature bins. The colours represent the 5 scenarios (see legend, standard IPCC scenario colour code). Each dot represents one GWL (0.2°C bins) for one model and one scenario. The linear multi-model regression lines are coloured dependent on the scenario they represent.\r\n\r\n  The filenames have been changed from the GitHub for archival with '_ssp' in the filename replacing the original '+ssp'.For example, file 'sncbin_BCC-CSM2-MR_historical_ssp126.nc' is named 'sncbin_BCC-CSM2-MR_historical+ssp126.nc' on GitHub.  This needs to be changed back when plotting with the code or amended in the code. \r\n\r\n Panel e:\r\n - Data files: WaterCyclePanels/Hydro_vars_change_20210201_derived_tropic.json. Multi-model mean precipitable water, precipitation, and runoff (annual mean and interannual variability (standard deviation)).\r\n Left part of the panel:\r\n * Black full line: Precipitable water, annual mean, multi-model mean\r\n * Brown full line: Runoff, annual mean, multi-model mean\r\n * Brown dashed line: Runoff, interannual variability, multi-model mean\r\n * Blue full line: Precipitation, annual mean, multi-model mean\r\n * Blue dashed line: Precipitation, interannual variability, multi-model mean\r\n Right part of the panel: 17-83% inter-model ranges for the +5°C GWL.\r\n\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nThis figure is plotted using python scripts which are archived on Zenodo at the link provided in the 'Related Documents' section of this catalogue record. A link to the GitHub repository for this figure is also provided. Please note that the filenames of the net-cdf files in ModelSnow/ have been changed from  '+ssp' to  '_ssp' which also needs to be amended in the code when running with these input files.\r\n\r\n Panel a:\r\n The Python script Extremes/TS2-Land-Extremes-202110.py reads the csv data sheets and plots the panel.\r\n\r\nPanel b:\r\n The Python script Extremes/TS2-Land-Extremes-202110.py reads the csv data sheets and plots the panel.\r\n\r\nPanel c:\r\n The Python script Extremes/TS2-Land-Extremes-202110.py reads the csv data sheets and plots the panel.\r\n\r\nPanel d:\r\n The Python script ModelSnow/AR6TS2-Snow.py reads the net-cdf data and plots the panel.\r\n\r\nPanel e:\r\n The Python script WaterCyclePanels/TS12-WaterCycle.py reads the json data sheets and plots the panel.\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 TS_Fig12 GitHub repository"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39772,
            "uuid": "da2a1d17467440f88e876092e7c5c330",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/ukcp18/data/marine-sim/ext-sea-lev-expl",
            "numberOfFiles": 4,
            "volume": 338311780,
            "fileFormat": "Data are in NetCDF format.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39773,
            "uuid": "4d8036681144409d9b6b563c5665cbff",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/ukcp18/data/marine-sim/ext-sea-lev-expl-shp",
            "numberOfFiles": 88,
            "volume": 53336444,
            "fileFormat": "The data are provided as zipped shapefiles.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39630,
                "uuid": "2b0261d03ae34e4abc8e32cb1a805887",
                "short_code": "ob",
                "title": "UKCP projected future extreme sea levels at approximately 2 km spacing around the UK coastline for 2020-2300, using exploratory extended time-mean sea level projections",
                "abstract": "The data are projected extreme sea levels at approximately 2 km spacing around the UK coastline, including England, Wales, Scotland, Northern Ireland, and Isle of Man. The data were produced by the Met Office, using estimates of present-day extreme sea levels at all open-coast Coastal Flood Boundary point locations, 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, identified in the shapefile as ‘t1’ to ‘t10000’. Confidence levels relating to the 5% and 95% lower and upper bounds of confidence are included, identified in the shapefile with the prefix ‘c1_’ and ‘c3_’ respectively, as well as the 70% confidence level identified in the shapefile with the prefix ‘c2_’. \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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39774,
            "uuid": "264d2152308a48aeb79f1be82d4eadaf",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/ukcp18/data/marine-sim/ext-sea-lev-shp",
            "numberOfFiles": 28,
            "volume": 16441733,
            "fileFormat": "The data are provided as zipped shapefiles.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39573,
                "uuid": "8b4c2455c27a4f0581e753be50ed94c7",
                "short_code": "ob",
                "title": "UKCP projected future extreme sea levels at approximately 2 km spacing around the UK coastline for 2020-2100, using 21st century time-mean sea level projections",
                "abstract": "The data are projected extreme sea levels at approximately 2 km spacing around the UK coastline, including England, Wales, Scotland, Northern Ireland, and Isle of Man. The data were produced by the Met Office, using estimates of present-day extreme sea levels at all open-coast Coastal Flood Boundary point locations, provided by the Environment Agency and projections of 21st Century 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, identified in the shapefile as ‘t1’ to ‘t10000’. Confidence levels relating to the 5% and 95% lower and upper bounds of confidence are included, identified in the shapefile with the prefix ‘c1_’ and ‘c3_’ respectively, as well as the 70% confidence level identified in the shapefile with the prefix ‘c2_’. \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 2100 and is available for each decade (i.e., 2020, 2030... 2100).\r\n\r\nFurther information on this dataset and UKCP18 can be found in the documentation section."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39776,
            "uuid": "25deb7d2836c4dabae7e398f102d8963",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ar6_wg1/data/ch_09/inputdata_ccb9_fig1/v20230310",
            "numberOfFiles": 15,
            "volume": 227677,
            "fileFormat": "Data are csv formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "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."
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                "short_code": "ob",
                "title": "WCRP CMIP6: Met Office Hadley Centre (MOHC) HadGEM3-GC31-MM model output for the \"dcppA-assim\" experiment",
                "abstract": "The World Climate Research Program (WCRP) Coupled Model Intercomparison Project, Phase 6 (CMIP6) data from the Met Office Hadley Centre (MOHC) HadGEM3-GC31-MM model output for the \"Assimilation run paralleling the historical simulation, which may be used to generate hindcast initial conditions\" (dcppA-assim) experiment. These are available at the following frequency: Omon. The runs included the ensemble members: r10i1p1f2, r1i1p1f2, r2i1p1f2, r3i1p1f2, r4i1p1f2, r5i1p1f2, r6i1p1f2, r7i1p1f2, r8i1p1f2 and r9i1p1f2.\n\nCMIP6 was a global climate model intercomparison project, coordinated by PCMDI (Program For Climate Model Diagnosis and Intercomparison) on behalf of the WCRP and provided input for the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report (AR6).\n\nThe official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record."
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                "short_code": "ob",
                "title": "GloCAEM: Atmospheric electricity measurements  at Kakioka Magnetic Observatory, Japan (low sensitivity)",
                "abstract": "This dataset contains measurements of atmospheric electricity and electric potential gradient made using a Boltek EFM 100 Instrument mounted at 0.6 m height on a metal pole and operated by the University of Shizuoka and Japan Meteorological Agency at Kakioka Magnetic Observatory,  Ishioka-shi, Ibaraki-ken, Japan\r\n\r\nThe provided data were calibrated using long-term observation of water dropper data (Nagamachi et al, Geosci. Data J., 2021).The low sensitivity instrument dataset provided through GLOCAEM have a resolution of 65.9 V/m and the measurement range is from -131.8 kV/m to +131.8 kV  /m. GPS-synchronized data with higher resolution (6.59 V/m) and with high sampling (10 Hz), all-sky camera images (10 min. sampling), animal camera images (motion detection) and are also available through on-demand request (kamogawa@u-shizuoka-ken.ac.jp).\r\n\r\nGlobal Coordination of Atmospheric Electricity Measurements (GloCAEM) project brought these experts together to make the first steps towards an effective global network for FW atmospheric electricity monitoring by holding workshops to discuss measurement practises and instrumentation, as well as establish recording and archiving procedures to archive electric field data in a standardised, easily accessible format, then by creating a central data repository. This project was funded in the UK under NERC grant NE/N013689/1."
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                "title": "GloCAEM: Atmospheric electricity measurements  at Kakioka Magnetic Observatory, Japan (high sensitivity)",
                "abstract": "This dataset contains measurements of atmospheric electricity and electric potential gradient made using a Boltek EFM 100 Instrument mounted at 0.6 m height on a metal pole and operated by the University of Shizuoka and Japan Meteorological Agency at Kakioka Magnetic Observatory, Ishioka-shi, Ibaraki-ken, Japan\r\n\r\nThe provided data was calibrated using long-term observation of water dropper data (Nagamachi et al, Geosci. Data J., 2021). The resolution of the high sensitivity data provided through GLOCAEM is 1.9 V/m and the measurement range is from -3.8 kV/m to +3.8 kV/m. GPS-synchronized data with higher resolution (0.19 V/m) and with high sampling (10 Hz), all-sky camera images (10 min. sampling), animal camera images (motion detection) and are also available through on-demand request (kamogawa@u-shizuoka-ken.ac.jp).\r\n\r\nGlobal Coordination of Atmospheric Electricity Measurements (GloCAEM) project brought these experts together to make the first steps towards an effective global network for FW atmospheric electricity monitoring by holding workshops to discuss measurement practises and instrumentation, as well as establish recording and archiving procedures to archive electric field data in a standardised, easily accessible format, then by creating a central data repository. This project was funded in the UK under NERC grant NE/N013689/1."
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                "title": "ESA Snow Climate Change Initiative (Snow_cci): Fractional Snow Cover in CryoClim, v1.0",
                "abstract": "This dataset contains the CryoClim Daily Snow Cover Fraction (snow on ground) product, produced by the Snow project of the ESA Climate Change Initiative programme.\r\n\r\nFractional snow cover (FSC) on the ground indicates the area of snow observed from space on land surfaces, in forested areas compensated for the effect of trees hiding the ground surface snow cover under the forest canopy. The FSC is given in percentage (%) per grid cell. \r\n\r\nThe global snow_cci CryoClim fractional snow cover (FSC) product is available at 0.05° grid size (about 5 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included. \r\n\r\nThe CryoClim FSC time series provides daily products for the period 1982 – 2019. \r\n\r\nThe CryoClim FSC product is based on a multi-sensor time-series fusion algorithm combining observations by optical and passive microwave radiometer (PMR) data. The product combines an historical record of AVHRR sensor data with PMR data from the SMMR, SSM/I and SSMIS sensors. \r\n\r\nThe overall aim of the CryoClim FSC climate data record is to provide one of the longest snow cover extent time series available with global coverage and without hindrance from clouds and polar night. This has been achieved by utilising the best features of optical and passive microwave radiometer observations of snow using a sensor-fusion algorithm generating a consistent time series of global FSC products (Solberg et al. 2014, 2015; Rudjord et al. 2015). \r\n\r\nThe snow_cci project has advanced the original CryoClim binary product to an FSC product. The thematic variable represents snow on the ground (SCFG). \r\n\r\nAVHRR sensors aboard the satellites NOAA-7, -9, -11, -14, -16, -18, -19 have been used as the optical data source, and SMMR, SSM/I and SSMIS sensors aboard the Nimbus-7, DMSP F8, DMSP F10, DMSP F11, DMSP F13, DMSP F14, DMSP F15, DMSP F16, DMSP F17 and DMSP F18 satellites, respectively, have been used as PMR data source. To have the best possible input data quality, we have used fundamental climate data records (FCDRs) developed by EUMETSAT CM SAF for AVHRR (Karlson et al. 2020) and PMR (Fenning et al. 2017).\r\n\r\nThe optical algorithm component processes all available swaths from AVHRR GAC. The calculations are based on a Bayesian approach using a set of signatures (instrument channel combinations) and statistical coefficients. For each pixel of the swath, the probabilities for the surface classes snow, bare ground and cloud are estimated. The statistical coefficients are based on pre-knowledge of the typical behaviour of the surface classes in the different parts of the electromagnetic spectrum.\r\n\r\nThe algorithm for PMR is also based on a Bayesian estimation approach. For SSM/I and SSMIS four snow classes were defined to model the snow surface state. For SMMR two classes were considered. The algorithm estimates the probability for each snow class given the PMR measurements. Land cover data are included to improve the performance of the Bayesian algorithm. This made it possible to construct a Bayesian estimator for each land cover regime. \r\n\r\nThe multi-sensor multi-temporal fusion algorithm (Rudjord et al. 2015; Solberg et al. 2017) is based on a hidden Markov model (HMM) simulating the snow states based on observations with PMR and optical sensors. The basic idea is to simulate the states the snow surface goes through during the snow season with a state model. The states are not directly observable, but the remote sensing observations give data describing the snow conditions, which are related to the snow states. The HMM solution represents not only a multi-sensor model but also a multi-temporal model. The sequence of states over time is conditioned to follow certain optimisation criteria.\r\n\r\nThe advancement from binary to fractional snow cover carried out by snow_cci has followed two main paths: First, we introduced more HMM states to be able to classify the snow cover into 10% FSC intervals. However, introducing 100 primary states to obtain 1% FSC intervals would not give a stable model. For obtaining higher precision, we have interpolated between HMM states using a secondary Viterbi sequence. The two probabilities are used as weights to estimate the FSC.\r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the FSC product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.\r\n\r\nThe FSC product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nThe Norwegian Computing Center (Norsk Regnesentral, NR) is together with the Norwegian Meteorological Institute (MET Norway) responsible for the FSC product development and generation from satellite data. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation.\r\n\r\nFor the whole time series, there are 27 days with neither optical nor PMR retrieval. These are individual days and not series of days in a row. The multi-sensor time-series algorithm handles this by making a best estimate of snow cover, based on days both prior to and following after the lack of data. This will not reduce the quality of the snow maps much for days without data as long as they are just individual days.\r\nThe algorithm estimating the uncertainty associated with the FSC maps needs observations of covariates from the same day as the time stamp of the FSC product. These covariates are partly based on data from PMR sensors. Hence, estimates of uncertainty could not be produced for days lacking PMR acquisitions. Most days lacking PMR are in the period 1982-1988 (53 days), and there are only two cases after that (in 2008)."
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                "ob_id": 33169,
                "uuid": "e7e31b86b2644e0da69090bc37360c97",
                "short_code": "ob",
                "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction – snow on ground (SCFG) from AATSR (2002 – 2012), version 1.0",
                "abstract": "This dataset contains Daily Snow Cover Fraction (snow on ground) from AATSR, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the transparency (“transmissivity”) of the forest canopy. The SCFG is given in percentage (%) per grid cell. \r\n\r\nThe global SCFG product is available at 0.01° grid size (about 1 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included. \r\n\r\nThe SCFG time series provides daily products for the period 2002 – 2012. \r\n\r\nThe SCFG product is based on Advanced Along-Track Scanning Radiometer (AATSR) data aboard the Envisat satellite. \r\n\r\nThe retrieval method of the snow_cci SCFG product from AATSR data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. \r\n\r\nImprovements to the GlobSnow algorithm implemented for snow_cci version 1 include the usage of a global forest transmissivity map developed and created within snow_cci based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny 2019). The forest transmissivity map provides the local transparency of the forest canopy and is applied or estimating the fractional snow cover on the ground.\r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the SCFG product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.\r\n\r\nThe SCFG product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nThe Norwegian Computing Center (Norsk Regnesentral, NR) is responsible for the SCFG product development and generation from AATSR data. The Remote Sensing Research Group of the University of Bern supported the development. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation.\r\n\r\nThere are a few days without any AATSR acquisitions in the years 2002, 2003, 2004, 2006, 2008, 2010 and 2012."
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                "uuid": "0aeba0c203c2447b9553a78f99d3a276",
                "short_code": "ob",
                "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction – snow on ground (SCFG) from ATSR-2 (1995 – 2003), version 1.0",
                "abstract": "This dataset contains Daily Snow Cover Fraction (snow on ground) from ATSR-2, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the transparency (“transmissivity”) of the forest canopy. The SCFG is given in percentage (%) per grid cell. \r\n\r\nThe global SCFG product is available at 0.01° grid size (about 1 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included.\r\n \r\nThe SCFG time series provides daily products for the period 1995 – 2003. \r\n\r\nThe SCFG product is based on Along-Track Scanning Radiometer 2 (ATSR-2) data aboard the ERS-2 satellite. \r\n\r\nThe retrieval method of the snow_cci SCFG product from ATSR-2 data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. \r\n\r\nImprovements to the GlobSnow algorithm implemented for snow_cci version 1 include the usage of a global forest transmissivity map developed and created within snow_cci based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny 2019). The forest transmissivity map provides the local transparency of the forest canopy and is applied or estimating the fractional snow cover on the ground.\r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the SCFG product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.\r\n\r\nThe SCFG product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nThe Norwegian Computing Center (Norsk Regnesentral, NR) is responsible for the SCFG product development and generation from ATSR-2 data. The Remote Sensing Research Group of the University of Bern supported the development. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation.\r\n\r\nThere are a few days without any ATSR-2 acquisitions in the years 1995, 1996, 1999, 2000, 2001, 2002 and 2003."
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                "short_code": "ob",
                "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction – viewable snow (SCFV) from AATSR (2002 – 2012), version 1.0",
                "abstract": "This dataset contains Daily Snow Cover Fraction of viewable snow from AATSR, produced by the Snow project of the ESA Climate Change Initiative programme.  \r\n\r\nSnow cover fraction viewable (SCFV) indicates the area of snow viewable from space over all land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. \r\n\r\nThe global SCFV product is available at 0.01° grid size (about 1 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included. \r\nThe SCFV time series provides daily products for the period 2002 – 2012. \r\n\r\nThe SCFV product is based on Advanced Along-Track Scanning Radiometer (AATSR) data aboard the Envisat satellite. \r\n\r\nThe retrieval method of the snow_cci SCFV product from AATSR data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include adaptation of the retrieval method for mapping in forested areas the SCFV.\r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the SCFV product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.\r\n\r\nThe SCFV product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nThe Norwegian Computing Center (Norsk Regnesentral, NR) is responsible for the SCFV product development and generation from AATSR data. The Remote Sensing Research Group of the University of Bern supported the development. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation.\r\n\r\nThere are a few days without any AATSR acquisitions in the years 2002, 2003, 2004, 2006, 2008, 2010 and 2012."
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                "short_code": "ob",
                "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction – viewable snow (SCFV) from ATSR-2 (1995 – 2003), version 1.0",
                "abstract": "This dataset contains Daily Snow Cover Fraction of viewable snow from ATSR-2, produced by the Snow project of the ESA Climate Change Initiative programme.  \r\n\r\nSnow cover fraction viewable (SCFV) indicates the area of snow viewable from space over all land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel.\r\n\r\nThe global SCFV product is available at 0.01° grid size (about 1 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included. \r\n\r\nThe SCFV time series provides daily products for the period 1995 – 2003. \r\n\r\nThe SCFV product is based on Along-Track Scanning Radiometer 2 (ATSR-2) data aboard the ERS-2 satellite. \r\n\r\nThe retrieval method of the snow_cci SCFV product from ATSR-2 data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include adaptation of the retrieval method for mapping in forested areas the SCFV. \r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the SCFV product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.\r\n\r\nThe SCFV product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nThe Norwegian Computing Center (Norsk Regnesentral, NR) is responsible for the SCFV product development and generation from ATSR-2 data. The Remote Sensing Research Group of the University of Bern supported the development. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation.\r\n\r\nThere are a few days without any ATSR-2 acquisitions in the years 1995, 1996, 1999, 2000, 2001, 2002 and 2003."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39799,
            "uuid": "abfe6312e9cc45289872e5629db277c8",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/deposited2023/GETQUOCS",
            "numberOfFiles": 103,
            "volume": 526363658858,
            "fileFormat": "NetCDF",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39802,
                "uuid": "98cda325efc54da0aacca1d658e4a54a",
                "short_code": "ob",
                "title": "Global Ensemble of Temperatures with Quantified Uncertainties in Observations, Coverage and Spatial modeling (GETQUOCS) from 1850-2018",
                "abstract": "Instrumental global temperature records are derived from the network of in situ measurements of land and sea surface temperatures. This observational evidence is seen as being fundamental to climate science. Therefore, the accuracy of these measurements is of prime importance for the analysis of temperature variability. There are spatial gaps in the distribution of instrumental temperature measurements across the globe. This lack of spatial coverage introduces coverage error. An approximate Bayesian computation based multi-resolution lattice kriging is developed and used to quantify the coverage errors through the variance of the spatial process at multiple spatial scales. It critically accounts for the uncertainties in the parameters of this advanced spatial statistics model itself, thereby providing, for the first time, a full description of both the spatial coverage uncertainties along with the uncertainties in the modeling of these spatial gaps. These coverage errors are combined with the existing estimates of uncertainties due to observational issues at each station location. It results in an ensemble of 100,000 monthly temperatures fields over the entire globe that samples the combination of coverage, parametric and observational uncertainties from 1850 to 2018 on a 5° by 5° grid. \r\n  \r\n The 100,000 equally-plausible ensemble members are stored in a series of separate netcdf files each containing 1000 realisations. \r\n  \r\n Additionally, there is 100-realisation subsample that provides an estimate of the uncertainty in the full ensemble. This has been created using conditional Latin hypercube sampling across 25 key regions of the globe. It many cases it would be sufficient to analyse just this 100-member subsample, for example to compute a likely range in a quantity. It is recommended that full 100,000 member ensemble is only investigated in those situations where the precise shape of the uncertainty distribution is required. NetCDF files at both the annual and monthly resolution are provided for this subsample."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39815,
            "uuid": "25c559cb75284483b5d0312f882ec680",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/deposited2023/UKCA_volcanic_eruptions_QBO",
            "numberOfFiles": 30,
            "volume": 40538150779,
            "fileFormat": "Data are netCDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39814,
                "uuid": "5f7206e5854246a9ae7498305d620590",
                "short_code": "ob",
                "title": "UM-UKCA model data for study investigating the QBO response to large tropical eruptions",
                "abstract": "This dataset contains summary 36-month data of the global mean volcanic stratospheric aerosol optical depth, temperature, sulfate aerosol mass concentration and wind velocities necessary for analysing the quasi-biennial oscillation (QBO). The data are from 8 model simulations of volcanic eruptions that have different sulfur dioxide emissions, eruption initiation date/season and QBO phase at the time of eruption. Three control ensembles were conducted for each initial condition. The simulations are from the Unified Model coupled with the United Kingdom Chemistry and Aerosol Scheme (UM-UKCA) Vn.11.2 and were conducted at a global resolution of 1.875 ° x 1.25°.\r\n\r\nVariables for calculating the QBO momentum budget (w*, flux divergence) are contained in the files ending ‘_momentum.nc’.\r\n\r\nThere are files for each initial condition named '{SEASON}_{SO2_INJECTION_MAGNITUDE}_{QBO_PHASE}.nc'.\r\n\r\nThere are 12 control files to give three ensemble members for each initial condition. These files are named ‘{SEASON}_{QBO_PHASE}_control_{no.}.nc’ for example: ‘July_wQBO_control_2.nc’."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39818,
            "uuid": "ec78019f9b0f4dad8fade7b080ed2054",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/neodc/esacci/ozone/data/limb_profiles/l3/megridop/monthly_zonal_mean/v0001",
            "numberOfFiles": 2,
            "volume": 755882923,
            "fileFormat": "Data are in NetCDF format.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 33001,
                "uuid": "e0329fa69dfa49d0903e132dca1c4890",
                "short_code": "ob",
                "title": "ESA Ozone Climate Change Initiative (Ozone_cci): MErged GRIdded Dataset of Ozone Profiles (MEGRIDOP), v0001",
                "abstract": "This dataset comprises the MErged GRIdded Dataset of Ozone Profiles (MEGRIDOP) in the stratosphere with a resolved longitudinal structure, which is derived from data by six limb and occultation satellite instruments: GOMOS, SCIAMACHY and MIPAS on Envisat, OSIRIS on Odin, OMPS on Suomi-NPP, and MLS on Aura. The merged dataset was generated as a contribution to the European Space Agency Climate Change Initiative Ozone project (Ozone_cci).  The period of this merged time series of ozone profiles is from late 2001 until the end of 2022.\r\n\r\nThe monthly mean gridded ozone profiles and deseasonalised anomalies are provided in the altitude range from 10 to 50 km in bins of 10 degree latitude x 20 degree longitude.  \r\n\r\nFor more details please see the associated readme file and Sofieva, V. F., Szeląg, M., Tamminen, J., Kyrölä, E., Degenstein, D., Roth, C., Zawada, D., Rozanov, A., Arosio, C., Burrows, J. P., Weber, M., Laeng, A., Stiller, G. P., von Clarmann, T., Froidevaux, L., Livesey, N., van Roozendael, M. and Retscher, C.: Measurement report: regional trends of stratospheric ozone evaluated using the MErged GRIdded Dataset of Ozone Profiles (MEGRIDOP), Atmos. Chem. Phys., 21(9), 6707–6720, doi:10.5194/acp-21-6707-2021, 2021"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39830,
            "uuid": "1b11112f9b9340e6a57ca31e19bfa6c3",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/deposited2023/oxaria_2020_2022/",
            "numberOfFiles": 11,
            "volume": 32989121339,
            "fileFormat": "Data are CSV formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39829,
                "uuid": "7a2a80d01dd645b2b14efb5647835c78",
                "short_code": "ob",
                "title": "Sensor based ambient air concentration data for nitrogen dioxide and particles in Oxford, measured by the OxAria project 2020 to 2022.",
                "abstract": "This dataset contains raw and processed levels of nitrogen dioxide (NO2), particles (PM10 & PM2.5) in ambient air in Oxford, UK. These are derived from low cost sensor units located ...... The raw data is at 10-second intervals and the processed data is at 15-minute and 1-hour resolutions. The raw data is available in JSON format 2020 to 2022 and the processed data is available in CSV format Oct 2020 to Oct 2021. These data were collected for the OxAria project.\r\n\r\nThe Oxaria project is a Natural Environmental Research Council funded collaboration between the University of Birmingham and University of Oxford, supported by public and commercial partners. The project has applied advanced technological and environmental health expertise to understand the air and noise impacts of COVID-19 across Oxford City. See also https://oxaria.org.uk/ \r\n\r\nFor Project record: - The application of high-resolution sensing technology in this context offers potential to measure air pollution at an unprecedented scale and scope, providing a more comprehensive picture of air pollution across Oxford than has previously been possible. Data obtained before, during and after relevant COVID-19 restrictions have been used to understand impacts upon road traffic, air and noise pollution\r\nlevels and to assess implications for healthy life expectancy and therefore human health. This information will be used to provide an evidence-base for air quality policy within local authorities, public agencies and national Government."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39832,
            "uuid": "d6992215ffad4954920ce0689f4cc2ce",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/neodc/esacci/ozone/data/limb_profiles/l3/merged_sage_cci_omps/monthly_zonal_mean/v0002",
            "numberOfFiles": 2,
            "volume": 58123579,
            "fileFormat": "Data are in NetCDF format.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 33000,
                "uuid": "8220babd91534bb3b83b87141c4ccc14",
                "short_code": "ob",
                "title": "ESA Ozone Climate Change Initiative (Ozone_cci): Merged SAGE II, Ozone_cci and OMPS-LP dataset of ozone profiles, v0002",
                "abstract": "The merged SAGE-CCI-OMPS+ dataset of ozone profiles is created using the data from several satellite instruments: SAGE II on ERBS; GOMOS, SCIAMACHY and MIPAS on Envisat; OSIRIS on Odin; ACE-FTS on SCISAT; OMPS on Suomi-NPP;  POAM III on SPOT 4 and SAGE III on ISS. The merged dataset is created in the framework of European Space Agency Climate Change Initiative (Ozone_cci) with the aim of analyzing stratospheric ozone trends. For the merged dataset, we used the latest versions of the original ozone datasets. The long-term SAGE-CCI-OMPS+ dataset is created by computation and merging of deseasonalized anomalies from individual instruments. The detailed description of the dataset can be found in (Sofieva et al., 2017) and (Sofieva et al., 2023).\r\n\r\nThe merged SAGE-CCI-OMPS+ dataset consists of deseasonalized anomalies of ozone and ozone concentrations in 10 degree latitude bands from 90S to 90N and from 10 to 50 km in steps of 1 km covering the period from October 1984 to December 2021."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39838,
            "uuid": "849586bfc24f41aa9fd9d26a52537aef",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/neodc/esacci/land_surface_temperature/data/MTSAT_JAMI/L3C/v1.00/monthly",
            "numberOfFiles": 601,
            "volume": 9254379736,
            "fileFormat": "Data are in NetCDF format",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 38238,
                "uuid": "56742f742ea14edf872c3eca99f4a61a",
                "short_code": "ob",
                "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci):  Monthly Multi-Functional Transport Satellite (MTSAT) level 3C (L3C) product (2009-2015), version 1.00",
                "abstract": "This dataset contains monthly averaged land surface temperatures (LST) and their uncertainty estimates from the Japanese Advanced Meteorological Imager (JAMI) onboard the Multi-Functional Transport Satellite series (MTSAT1 and 2, also known as Himiwari-6 and 7). The original surface temperatures are generated every 3 hours and in this L3C product are monthly averaged at each time step and distributed on a regular latitude-longitude grid with a resolution of 0.05ºx0.05º. The coverage is limited to land surfaces within the MTSAT disk, which encompasses Australia and part of Asia.\r\n\r\nThe LSTs in this dataset are estimated from infrared measurements using a single channel algorithm, and, therefore, are only available under clear-sky conditions. The quality of single channel algorithms is generally lower than dual channel ones, and users are advised to read the respective Validation Report for more information on the expected quality of these LST estimates.\r\n\r\nThe dataset was produced by the Portuguese Institute for Sea and Atmosphere (IPMA) as part of the ESA Land Surface Temperature Climate Change Initiative. 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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39839,
            "uuid": "fdc5d97e281e4c4aad4e87c26580c946",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/neodc/esacci/land_surface_temperature/data/MTSAT_JAMI/L3U/v1.00/",
            "numberOfFiles": 17037,
            "volume": 225779044268,
            "fileFormat": "Data are in NetCDF format",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37377,
                "uuid": "d215ebefe04546088a14dfc7ffc0643f",
                "short_code": "ob",
                "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multi-Functional Transport Satellite (MTSAT) level 3U (L3U) product (2009-2015), version 1.00",
                "abstract": "This dataset contains land surface temperatures (LST) and their uncertainty estimates from the Japanese Advanced Meteorological Imager (JAMI) onboard the Multi-Functional Transport Satellite series (MTSAT1 and 2, also known as Himiwari-6 and 7). The surface temperatures are generated every 3 hours and distributed on a regular latitude-longitude grid with a resolution of 0.05ºx0.05º. The coverage is limited to land surfaces within the MTSAT disk, which encompasses Australia and part of Asia.\r\n\r\nThe LSTs in this dataset are estimated from infrared measurements using a single channel algorithm, and, therefore, are only available under clear-sky conditions. The quality of single channel algorithms is generally lower than dual channel ones, and users are advised to read the respective Validation Report for more information on the expected quality of these LST estimates.\r\n\r\nThe dataset was produced by the Portuguese Institute for Sea and Atmosphere (IPMA) as part of the ESA Land Surface Temperature Climate Change Initiative. 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."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39845,
            "uuid": "284af4b50cf343a1b39d6e2727e8bcb0",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/bodc/LSB230075",
            "numberOfFiles": 698,
            "volume": 2735789971244,
            "fileFormat": "Data are CF-Compliant NetCDF formatted data files",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39844,
                "uuid": "295ee0ee59ee43439de38f83bb818b4f",
                "short_code": "ob",
                "title": "HighResCoralStress: Observed and statistically downscaled CMIP6 projections of daily sea surface temperature (SST) at 0.01°/1 km spatial resolution for the global coral reef area from 1985 to 2100.",
                "abstract": "The HighResCoralStress dataset comprises high spatial resolution (0.01°/1 km) daily sea surface temperature (SST) for the global coral reef area for past (1985-2019) and future (2020-2100) time periods. There are 12 coral reef regions  based on those described by McWilliam et al. (2018). They vary in their functional redundancy and so indicate susceptibility to ecological changes with climate change.  Statistically downscaled Coupled Model Intercomparison Project 6 (CMIP6) projections of daily SST are provided for 420,334 1 km coral reef pixels in 12 coral reef regions for four shared socio-economic pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) for the time period (1985-2100). \r\nThere are separate downscaled CMIP6 files for each region and SSP. The 1 km observed dataset used in the statistical downscaling is also provided for the period (1985-2019). CMIP6 projections of SST were statistically downscaled from their native model resolution (25-100 km) to 1 km spatial resolution using asynchronous linear regression. Full details of the statistical downscaling methodology and source datasets used in this project can be found in Dixon et al. (2022). \r\nCoral reefs globally are threatened by rising ocean temperatures and increasing frequency and severity of thermal stress events. High resolution sea surface temperature (SST) projections are a useful tool in climate vulnerability assessments, ecological modelling, spatial planning and conservation decision making for coral reefs around the world. \r\nAll source datasets used in the generation of HighResCoralStress are openly available and details can be found in Dixon et al. (2022). The authors request that you refer to this article before using the datasets, please refer to online resources on this record for links. Thermal stress metrics calculated using HighResCoralStress can be found at the following link: https://highrescoralstress.org/. The coordinates for the regional boundaries can be found in the NetCDF file attributes.\r\nThis work was undertaken by researchers from the University of Leeds, Texas Tech University and James Cook University funded by the Natural Environment Research Council (NERC) project DTP Spheres (NE/L002574/1)."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39850,
            "uuid": "ea3152009e6743d0906f34a2f2accf12",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/neodc/hyperdrone/data/2021/HyperDrone_20210722",
            "numberOfFiles": 103,
            "volume": 275068519154,
            "fileFormat": "ENVI BIL and BADC-CSV Format",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39819,
                "uuid": "e0f6a223220d463ea7a5d2a7520000cf",
                "short_code": "ob",
                "title": "HyperDrone Flight 20210722 - hyperspectral in situ radiometry and hyperspectral imagery  at different altitudes for plastics detection",
                "abstract": "Airborne remote-sensed hyperspectral in-situ radiometry data and hyperspectral imagery collected by the NERC Field Spectroscopy Facility (FSF) Headwall Co-aligned VNIR and SWIR imager (450-2500 nm) with LiDAR instruments mounted on a drone platform. \r\nThese hyperspectral data collected over a sandy and rocky shore have associated uncertainty estimations that will be used to develop of radiometric proxies for plastics detection and assess future mission requirements. This dataset was collected on 22nd July 2021 from Oban airport's shore using a range of different plastic targets"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39852,
            "uuid": "27517f5ca1f5447baac2baddcda9f629",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/eprofile/data/daily_files/croatia/ogulin/dhmz-lufft-chm15k_A",
            "numberOfFiles": 1096,
            "volume": 2904352155,
            "fileFormat": "Data are netCDF formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39853,
                "uuid": "9ef2331d1a6341f79773440c5ba25fb2",
                "short_code": "ob",
                "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from DMHZ's lufft-chm15k instrument deployed at Ogulin, Croatia",
                "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Croatian Meteorological and Hydrological Service (DMHZ)'s lufft-chm15k deployed at Ogulin, Croatia.\n\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\n\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-14328.\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool.\n    \nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39856,
            "uuid": "dbc334d97e19415ea3979703fb266ffd",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/eprofile/data/daily_files/croatia/osijek/dhmz-lufft-chm15k_A",
            "numberOfFiles": 1071,
            "volume": 2831384744,
            "fileFormat": "Data are netCDF formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39857,
                "uuid": "9ee48040310846bc85666c58a5bd7934",
                "short_code": "ob",
                "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from DMHZ's lufft-chm15k instrument deployed at Osijek, Croatia",
                "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Croatian Meteorological and Hydrological Service (DMHZ)'s lufft-chm15k deployed at Osijek, Croatia.\n\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\n\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-14280.\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool.\n    \nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39860,
            "uuid": "a846287e77df42feb64219c94168fe8b",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/eprofile/data/daily_files/croatia/varazdin/dhmz-lufft-chm15k_A",
            "numberOfFiles": 1096,
            "volume": 2900476722,
            "fileFormat": "Data are netCDF formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39861,
                "uuid": "bbd5204ace084772b30d1762ce922500",
                "short_code": "ob",
                "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from DMHZ's lufft-chm15k instrument deployed at Varazdin, Croatia",
                "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Croatian Meteorological and Hydrological Service (DMHZ)'s lufft-chm15k deployed at Varazdin, Croatia.\n\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\n\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-14246.\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool.\n    \nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39864,
            "uuid": "17afc6d2ceb545a491b747b78d88ac61",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/eprofile/data/daily_files/cyprus/cao-amx/cao-amx-vaisala-cl51_A",
            "numberOfFiles": 1072,
            "volume": 2706675851,
            "fileFormat": "Data are netCDF formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39865,
                "uuid": "0b180cb9cd7d4186ac483d967a3d9af5",
                "short_code": "ob",
                "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from The Cyprus Institute's Vaisala-CL51 instrument deployed at the Cyprus Atmospheric Observatory, Ayia Marina (CAO-AMX), Cyprus",
                "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from The Cyprus Institute's Vaisala-cl51 deployed at the Cyprus Atmospheric Observatory, Ayia Marina (CAO-AMX), Cyprus.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20008-0-CYP.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool.\r\n    \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities."
            },
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        {
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            "uuid": "fcf2713207b648dd8d7b866a85f0067b",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/neodc/hyperdrone/data/2020/HyperDrone_20200929",
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            "observation": {
                "ob_id": 39827,
                "uuid": "2485214239134768820ffb50fb5513bc",
                "short_code": "ob",
                "title": "HyperDrone Flight 20200929 - hyperspectral in situ radiometry and hyperspectral imagery at different altitudes for plastics detection",
                "abstract": "Airborne remote-sensed hyperspectral in-situ radiometry data and hyperspectral imagery collected by the NERC Field Spectroscopy Facility (FSF) Headwall Co-aligned VNIR and SWIR imager (450-2500 nm) with LiDAR instruments mounted on a drone platform. \r\nThese hyperspectral data collected over a sandy and rocky shore have associated uncertainty estimations that will be used to develop of radiometric proxies for plastics detection and assess future mission requirements. \r\nThis dataset was collected on 29th September 2020 at Tyninghame beach, East Lothian, Scotland using a range of different plastic targets."
            },
            "onlineresource_set": []
        },
        {
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            "uuid": "f979365d1c7140528a390804bf944f7f",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/eprofile/data/daily_files/czech-republic/praha/chmi-vaisala-cl31_A",
            "numberOfFiles": 23,
            "volume": 48470039,
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            "observation": {
                "ob_id": 39873,
                "uuid": "77ed3c65425d44b2ac469ad388261e69",
                "short_code": "ob",
                "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from CHMI's vaisala-cl31 instrument deployed at Praha, Czech Republic",
                "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Czech Hydrometeorological Institute (CHMI)'s vaisala-cl31 deployed at Praha, Czech Republic.\n\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\n\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-11518.\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool.\n    \nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities."
            },
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        },
        {
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            "uuid": "543de666aa464522b160b4d1e2063bc5",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/eprofile/data/daily_files/france/issy-heliport/meteofrance-vaisala-cl31_A",
            "numberOfFiles": 1095,
            "volume": 1495559751,
            "fileFormat": "Data are netCDF formatted.",
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            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39877,
                "uuid": "5b97cea480ed428eaf1809394821d94c",
                "short_code": "ob",
                "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from Météo-France's vaisala-cl31 instrument deployed at Issy Heliport, France",
                "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Météo-France's vaisala-cl31 deployed at Issy Heliport, France.\n\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\n\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-250-0-92040002.\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool.\n    \nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39880,
            "uuid": "c9a35dd295c448069a894e78d78bdf3f",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/eprofile/data/daily_files/italy/monte-cimone/cnr-isac-lufft-chm15k_A",
            "numberOfFiles": 382,
            "volume": 968259039,
            "fileFormat": "Data are netCDF formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39881,
                "uuid": "594dcfb3de2c48a4b4ba3e83672edf35",
                "short_code": "ob",
                "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from CNR-ISAC's lufft-chm15k instrument deployed at Monte Cimone, Italy",
                "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Institute of Atmospheric Sciences and Climate (CNR-ISAC)'s lufft-chm15k deployed at Monte Cimone, Italy.\n\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\n\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20008-0-CMN.\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool.\n    \nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39884,
            "uuid": "e0d03d93b10a4a98ab122790add0a50c",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/eprofile/data/daily_files/switzerland/bern-belpmoos/meteoswiss-vaisala-cl31_A",
            "numberOfFiles": 1125,
            "volume": 1554753368,
            "fileFormat": "Data are netCDF formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39885,
                "uuid": "9e63b5f806524340aea5c5deadbcd30c",
                "short_code": "ob",
                "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from MeteoSwiss's vaisala-cl31 instrument deployed at Bern Belpmoos, Switzerland",
                "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from MeteoSwiss's vaisala-cl31 deployed at Bern Belpmoos, Switzerland.\n\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\n\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06630.\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool.\n    \nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39888,
            "uuid": "61bac70f6d8140a391d4ec4cdebe1d96",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/eprofile/data/daily_files/uk/maqs/man-lufft-chm15k_A",
            "numberOfFiles": 236,
            "volume": 303723574,
            "fileFormat": "Data are netCDF formatted.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39889,
                "uuid": "856b788f5e934fa1bf8d1793fdcb3f56",
                "short_code": "ob",
                "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from University of Manchester's lufft-chm15k instrument deployed at the Manchester Air Quality Supersite (MAQS), UK",
                "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from University of Manchester's lufft-chm15k deployed at Manchester Air Quality Supersite (MAQS), UK.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20008-0-MCR.\r\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool.\r\n    \r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39900,
            "uuid": "cdc9ed0ae7ae4b0a97fb3829beedeeac",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/osca/data/iop-2-winter/",
            "numberOfFiles": 5,
            "volume": 202383136,
            "fileFormat": "BADC-CSV",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39898,
                "uuid": "5c1742753edb40a78cb7d0c2aa003ab3",
                "short_code": "ob",
                "title": "OSCA IOP-2 Winter:  Roadside site gas data (January-February 2022)",
                "abstract": "Measurements of a range of atmospheric gases made during the Integrated Research Observation System for Clean Air (OSCA) Intensive Operations Project 2 (IOP) in Winter (January-February) 2022 at the Manchester Air Quality Site (MAQS) and the Upper Brook Street, Manchester, roadside site.\r\n\r\nThis dataset include measurements of Soil NOx using the Lancaster University Dynamic Soil Chambers  connected to a Teledyne N500 NOx analyser at the MAQS site,  and carbon dioxide (CO2), Nitrous oxides (NOx) and Volatile Organic Compounds (VOCs) at the roadside site in Manchester"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39901,
            "uuid": "8d7d3f34e42d452ab96e806d99ff5593",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/ccmi/data/post-cmip6/ccmi-2022/NIWA/NIWA-UKCA2/senD2-ssp126",
            "numberOfFiles": 322,
            "volume": 148106994989,
            "fileFormat": "Files are Net-CDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37359,
                "uuid": "9ffb90316a73409380c5708803e3d4de",
                "short_code": "ob",
                "title": "CCMI-2022: senD2-ssp126 data produced by the NIWA-UKCA2 model at NIWA",
                "abstract": "This dataset contains model data for CCMI-2022 experiment senD2-ssp126 produced by the NIWA-UKCA2 chemistry-climate model run by the modelling team at NIWA (National Institute of Water and Atmospheric Research) in New Zealand.\r\n\r\nExperiment senD2-ssp126 is a future projection with specified forcings largely following the same specifications as for the SSP1-2.6 scenario of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Ozone Depleting Substances (ODSs) are specified by the WMO(2018) baseline scenario.\r\n\r\nThe CCMI-2022 Chemistry-climate model initiative is a set of model experiments focused on the stratosphere, with the goals of providing updated projections towards the future evolution of the ozone layer and improving our understanding of chemistry-climate interactions from models.\r\n\r\nSSP1-2.6 is a Shared Socio-economic Pathway scenario that follows socio-economic storyline SSP1 with low climate change mitigation and adaptation challenges, and climate forcing pathway RCP2.6 which leads to a radiative forcing of 2.6 Wm-2 by the year 2100.\r\n\r\nWMO-2018 refers to the Scientific Assessment of Ozone Depletion: 2018.\r\n\r\nMPI-ESM1-2-LR is a Max Planck Institute Earth System Model with a 1.5 degree resolution in the ocean.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n-  Review of the global models used within phase 1 of the Chemistry-Climate Model Initiative (CCMI)\r\n-  WMO (World Meteorological Organization), Scientific Assessment of Ozone Depletion: 2018, Global Ozone Research and Monitoring Project – Report No. 58, 588 pp., Geneva, Switzerland, 2018.\r\n-  A new set of Chemistry-Climate Model Initiative (CCMI) Community Simulations to Update the Assessment of Models and Support Upcoming Ozone Assessment Activities, David Plummer and Tatsuya Nagashima and Simone Tilmes and Alex Archibald and Gabriel Chiodo and Suvarna Fadnavis and Hella Garny and Beatrice Josse and Joowan Kim and Jean-Francois Lamarque and Olaf Morgenstern and Lee Murray and Clara Orbe and Amos Tai and Martyn Chipperfield and Bernd Funke and Martin Juckes and Doug Kinnison and Markus Kunze and Beiping Luo and Katja Matthes and Paul A. Newman and Charlotte Pascoe and Thomas Peter (2021), SPARC Newsletter, volume 57, pp 22-30"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39902,
            "uuid": "9e337728af774d7ea303171aa749efb7",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/osca/data/iop-1-summer/",
            "numberOfFiles": 6,
            "volume": 333093080,
            "fileFormat": "BADC-CSV",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39892,
                "uuid": "5bdabb4cba5d43e6a577f72ae03ca25b",
                "short_code": "ob",
                "title": "OSCA IOP-1 Summer:  Roadside site gas data (June-July 2021)",
                "abstract": "Measurements of a range of atmospheric gases made during the Integrated Research Observation System for Clean Air (OSCA) Intensive Operations Project 1 (IOP) in Summer (June-July) 2021 at the Manchester Air Quality Site (MAQS) and the Upper Brook Street, Manchester, roadside site.\r\n\r\nThis dataset include measurements of Soil NOx using the Lancaster University Dynamic Soil Chambers  connected to a Teledyne N500 NOx analyser, hydrogen chloride using the Tunable IR Laser Direct Absorption Spectrometer (TILDAS) at the MAQS site,  and carbon dioxide (CO2), Nitrous oxides (NOx) and Volatile Organic Compounds (VOCs) at the roadside site in Manchester"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39903,
            "uuid": "80ad7390780d472d85756549a09d8665",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/ccmi/data/post-cmip6/ccmi-2022/NIWA/NIWA-UKCA2/senD2-sai",
            "numberOfFiles": 322,
            "volume": 130760323283,
            "fileFormat": "Files are Net-CDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37358,
                "uuid": "1fcf16f9d33348589d4e8431083470d0",
                "short_code": "ob",
                "title": "CCMI-2022: senD2-sai data produced by the NIWA-UKCA2 model at NIWA",
                "abstract": "This dataset contains model data for CCMI-2022 experiment senD2-sai produced by the NIWA-UKCA2 chemistry-climate model run by the modelling team at NIWA (National Institute of Water and Atmospheric Research) in New Zealand.\r\n\r\nThe senD2-sai simulation is based on the refD2 experiment but with a modified specified stratospheric aerosol distribution reflecting increased stratospheric aerosol amounts from stratospheric aerosol injection (SAI). Sea ice and sea surface temperatures (SSTs) are specified to follow a repeating annual cycle taken from those used by the same model for their refD2 experiment over 2020 - 2030, the period when SAI is assumed to have been initiated.\r\n\r\nThe refD2 experiment is the baseline projection for updated projections of ozone recovery. Specified forcings largely following the same specifications as for the SSP2-4.5 scenario of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), with the exception of the near-surface mixing ratio of Ozone Depleting Substances which follow the baseline projection from WMO (2018).\r\n\r\nThe CCMI-2022 Chemistry-climate model initiative is a set of model experiments focused on the stratosphere, with the goals of providing updated projections towards the future evolution of the ozone layer and improving our understanding of chemistry-climate interactions from models.\r\n\r\nSSP2-4.5 is a Shared Socio-economic Pathway scenario that follows socio-economic storyline SSP2 with intermediate mitigation and adaptation challenges and climate forcing pathway RCP4.5 which leads to a radiative forcing of 4.5 Wm-2 by the year 2100.\r\nWMO-2018 refers to the Scientific Assessment of Ozone Depletion: 2018.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n-  Review of the global models used within phase 1 of the Chemistry-Climate Model Initiative (CCMI)\r\n-  A new set of Chemistry-Climate Model Initiative (CCMI) Community Simulations to Update the Assessment of Models and Support Upcoming Ozone Assessment Activities, David Plummer and Tatsuya Nagashima and Simone Tilmes and Alex Archibald and Gabriel Chiodo and Suvarna Fadnavis and Hella Garny and Beatrice Josse and Joowan Kim and Jean-Francois Lamarque and Olaf Morgenstern and Lee Murray and Clara Orbe and Amos Tai and Martyn Chipperfield and Bernd Funke and Martin Juckes and Doug Kinnison and Markus Kunze and Beiping Luo and Katja Matthes and Paul A. Newman and Charlotte Pascoe and Thomas Peter (2021), SPARC Newsletter, volume 57, pp 22-30"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39904,
            "uuid": "4fa81c1fafcb4692afada5eded1173da",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/ccmi/data/post-cmip6/ccmi-2022/NIWA/NIWA-UKCA2/senD2-ssp370",
            "numberOfFiles": 322,
            "volume": 148098065485,
            "fileFormat": "Files are Net-CDF formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 37360,
                "uuid": "98851c33e7fe481c8179b2c0fe70af77",
                "short_code": "ob",
                "title": "CCMI-2022: senD2-ssp370 data produced by the NIWA-UKCA2 model at NIWA",
                "abstract": "This dataset contains model data for CCMI-2022 experiment senD2-ssp370 produced by the NIWA-UKCA2 chemistry-climate model run by the modelling team at NIWA (National Institute of Water and Atmospheric Research) in New Zealand.\r\n\r\nExperiment senD2-ssp370 is a future projection with specified forcings largely following the same specifications as for the SSP3-7.0 scenario of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Ozone Depleting Substances (ODSs) are specified by the WMO(2018) baseline scenario.\r\n\r\nThe CCMI-2022 Chemistry-climate model initiative is a set of model experiments focused on the stratosphere, with the goals of providing updated projections towards the future evolution of the ozone layer and improving our understanding of chemistry-climate interactions from models.\r\n\r\nSSP3-7.0 is a Shared Socio-economic Pathway scenario that follows socio-economic storyline SSP3 with high climate change mitigation and adaptation challenges, and climate forcing pathway RCP7.0 which leads to a radiative forcing of 7.0 Wm-2 by the year 2100.\r\n\r\nWMO-2018 refers to the Scientific Assessment of Ozone Depletion: 2018\r\n\r\nMPI-ESM1-2-LR is a Max Planck Institute Earth System Model with a 1.5 degree resolution in the ocean.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n-  Review of the global models used within phase 1 of the Chemistry-Climate Model Initiative (CCMI)\r\n-  WMO (World Meteorological Organization), Scientific Assessment of Ozone Depletion: 2018, Global Ozone Research and Monitoring Project – Report No. 58, 588 pp., Geneva, Switzerland, 2018.\r\n-  A new set of Chemistry-Climate Model Initiative (CCMI) Community Simulations to Update the Assessment of Models and Support Upcoming Ozone Assessment Activities, David Plummer and Tatsuya Nagashima and Simone Tilmes and Alex Archibald and Gabriel Chiodo and Suvarna Fadnavis and Hella Garny and Beatrice Josse and Joowan Kim and Jean-Francois Lamarque and Olaf Morgenstern and Lee Murray and Clara Orbe and Amos Tai and Martyn Chipperfield and Bernd Funke and Martin Juckes and Doug Kinnison and Markus Kunze and Beiping Luo and Katja Matthes and Paul A. Newman and Charlotte Pascoe and Thomas Peter (2021), SPARC Newsletter, volume 57, pp 22-30"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 39910,
            "uuid": "6c7c79d3aee747b2b99628f24d5d0e71",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/deposited2023/moasa_clean_air_data/M326",
            "numberOfFiles": 38,
            "volume": 58590813,
            "fileFormat": "NetCDF format",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39909,
                "uuid": "899d7de10af547f4a1b19c3e70882e5a",
                "short_code": "ob",
                "title": "MOASA flight M326: airborne observations from the Clean Air project over London, UK",
                "abstract": "In-situ airborne observations collected during flight M326 on 11 April 2022 by the Clean Air instrument suite on board the Met Office Atmospheric Survey Aircraft (MOASA) for the MOASA Clean Air project. \r\n\r\nThis dataset contains in meteorological and situ atmospheric composition measurements  (gaseous nitrogen dioxide, ozone, sulphur dioxide and fine mode (PM2.5) aerosol) over London, UK, in a city survey flight pattern as part of the Strategic Priorities Fund (SPF) Clean Air Programme Intense Observational Period. Quicklook plots are also included.\r\n\r\nFull flight track and other MOASA flights can be seen via the CEDA Flight Finder tool linked to on this record."
            },
            "onlineresource_set": []
        }
    ]
}