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2022

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  • This dataset contains the Lakes Essential Climate Variable, which is comprised of processed satellite observations at the global scale, over the period 1992-2020, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website. This is version 2.0.2 of the dataset. The five thematic climate variables included in this dataset are: • Lake Water Level (LWL), derived from satellite altimetry, is fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change. • Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at set time-points, describes the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example. . • Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, is correlated with regional air temperatures and is informative about vertical mixing regimes, driving biogeochemical cycling and seasonality. • Lake Ice Cover (LIC), determined from optical observations, describes the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality. • Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, is a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions). Data generated in the Lakes_cci are derived from multiple satellite sensors including: TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel 2-3, Landsat OLI, ERS, MODIS Terra/Aqua and Metop. Detailed information about the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website and in Carrea, L., Crétaux, JF., Liu, X. et al. Satellite-derived multivariate world-wide lake physical variable timeseries for climate studies. Sci Data 10, 30 (2023). https://doi.org/10.1038/s41597-022-01889-z

  • Daily concatenated files of ceilometer cloud base height and aerosol profile data from Finnish Meteorological Institution (FMI)'s Vaisala CL31 deployed at Jyvaskyla Lentoasema Awos, Finland. These 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. The site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-246-0-137208. Details for this WIGOS station are presently unavailable in the Observing Systems Capability Analysis and Review (OSCAR) Tool. EUMETNET 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.

  • Data for Figure 10.19 from Chapter 10 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 10.19 shows changes in the Indian summer monsoon in the historical and future periods. --------------------------------------------------- How to cite this dataset --------------------------------------------------- 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: Doblas-Reyes, F.J., A.A. Sörensson, M. Almazroui, A. Dosio, W.J. Gutowski, R. Haarsma, R. Hamdi, B. Hewitson, W.-T. Kwon, B.L. Lamptey, D. Maraun, T.S. Stephenson, I. Takayabu, L. Terray, A. Turner, and Z. Zuo, 2021: Linking Global to Regional Climate 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. 1363–1512, doi:10.1017/9781009157896.012. --------------------------------------------------- Figure subpanels --------------------------------------------------- The figure has 6 subpanels. Data for all subpanels is provided. --------------------------------------------------- List of data provided --------------------------------------------------- The dataset contains: APHRODITE station density for June-September (JJAS) 1956 Precipitation June-September (JJAS): - Model mean bias 1985-2010 - Observed and modelled trends: CRU TS 1950-2000, CMIP6 hist-GHG & hist-aer 1950-2000, and CMIP6 SSP5-8.5 2015-2100 trends - Observed and model relative anomalies over 1950-2100 with respect to 1995-2014 averages over central India (lon: 76°E-87°E, lat: 20°N-28°N) - Modelled change until 2081‒2100 with respect to 1995-2014 averages over central India (lon: 76°E-87°E, lat: 20°N-28°N) - Trends in relative precipitation anomalies (baseline 1995-2014) over past (1950-2000) and future (2015-2100) period over central India (lon: 76°E-87°E, lat: 20°N-28°N). - Trend difference between the 3 MPI-ESM runs with the lowest and the 3 MPI-ESM runs with the highest trend --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- Panel (a): APHRODITE station density for JJAS 1956: - Data file: Fig_10_19_panel-a_mapplot_APHRODITE_stationdensity_single_mean.nc Panel (b): CMIP6 mean precipitation bias June-September mean 1985-2010 mean with respect to CRU TS: - Data file: Fig_10_19_panel-b_mapplot_pr_cmip6_bias_pr_cmip6_maps_past_bias_MultiModelMean_bias.nc Panel (c): OLS linear precipitation for June-September mean trend of CRU TS 1950-2000 (top left), CMIP6 hist-GHG (bottom left) & hist-aer (bottom right) 1950-2000, and CMIP6 SSP5-8.5 2015-2100 (top right): - Data files: Fig_10_19_panel-c_mapplot_pr_cmip6_mean_trend_future_pr_cmip6_maps_trend_future_MultiModelMean_trend.nc, Fig_10_19_panel-c_mapplot_pr_histaer_mean_trend_past_pr_aer_maps_trend_past_MultiModelMean_trend.nc, Fig_10_19_panel-c_mapplot_pr_histghg_mean_trend_past_pr_ghg_maps_trend_past_MultiModelMean_trend.nc, Fig_10_19_panel-c_mapplot_pr_obs_mean_trend_past_CRU_single_trend.nc; Panel (d): Observed and model relative precipitation June-September mean anomalies over 1950-2100 in respect to 1995-2014 averages over central India (lon: 76°E-87°E, lat: 20°N-28°N) (CRU TS (brown), GPCC (dark blue), REGEN (green), APHRO-MA (light brown), IITM all-India rainfall (light blue), CMIP6 hist all-forcings sample corresponding to DAMIP experiments (pink), CMIP6 hist-aer (grey), hist-GHG (light blue) CMIP6 historical/SSP5-8.5 (dark red) and CMIP5 historical/RCP8.5 (dark blue) and Modelled change until 2081‒2100 in respect to 1995-2014 averages over central India (CMIP6 SSP5-8.5 (dark red) and CMIP5 historical/RCP8.5 (dark blue)): - Data files: Fig_10_19_panel-d_timeseries.csv, Fig_10_19_panel-d_boxplot.csv Panel (e): OLS linear trends in relative precipitation June-September mean anomalies (baseline 1995-2014) over past (1950-2000) and future (2015-2100) period over central India (lon: 76°E-87°E, lat: 20°N-28°N) of observations (GPCC, CRU TS, REGEN and APRHO-MA: black crosses) and models (individual members of CMIP5 historical-RCP8.5 (blue), CMIP6 historical-SSP5-8.5 (dark red), CMIP6 hist all-forcings sample corresponding to DAMIP experiments (pink circles), CMIP6 hist-GHG (blue triangles), CMIP6 hist-aer (grey triangles)), and box-and-whisker plots for the SMILEs: MIROC6, CSIRO-Mk3-6-0, MPI-ESM, d4PDF (grey shading): - Data file: Fig_10_19_panel-e_trends.csv Panel (f): June-September mean 2016-2045 OLS linear trend difference in precipitation between the 3 MPI-ESM runs with the lowest and the 3 MPI-ESM runs with the highest trend: - Data file: Fig_10_19_panel-f_mapplot_pr_mpige_mean_trend_future_spread_single_trend-difference-min3-max3.nc Acronyms: CMIP - Coupled Model Intercomparison Project, APHRODITE - ASIAN PRECIPITATION - HIGHLY-RESOLVED OBSERVATIONAL DATA INTEGRATION TOWARDS EVALUATION OF WATER RESOURCES, CRU TS- Climatic Research Unit Time Series, GHG - Greenhouse gas, IITM - Indian Institute of Technology Madras, RCP - Representative Concentration Pathway, DAIMP - Detection and Attribution Model Intercomparison Project, SSP - Shared Socioeconomic Pathways, GPCC - GLOBAL PRECIPITATION CLIMATOLOGY CENTRE, REGEN - Rainfall Estimates on a Gridded Network, S MILEs -single model initial-condition large ensembles, d4PDF - Database for Policy Decision-Making for Future Climate Change, MIROC - Model for Interdisciplinary Research on Climate, MPI - Max-Planck-Institut für Meteorologie, ESM - Earth System Model, Cordex – Coordinated Regional Climate Downscaling Experiment, OLS - ordinary least squares regression. --------------------------------------------------- Notes on reproducing the figure from the provided data --------------------------------------------------- The code for ESMValTool is provided. --------------------------------------------------- Sources of additional information --------------------------------------------------- The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the figure on the IPCC AR6 website - Link to the report component containing the figure (Chapter 10) - Link to the Supplementary Material for Chapter 10, which contains details on the input data used in Table 10.SM.11 - Link to the code for the figure, archived on Zenodo.

  • The BGS Debris Flow Susceptibility Model for Great Britain v6.1 is a 1:50 000 scale raster dataset of Great Britain providing 50 m ground resolution information on the potential of the ground, at a given location, to form a debris flow. It is based on a combination of geological, hydrogeological and geomorphological data inputs and is primarily concerned with potential ground stability related to natural (rather than man-made) geological conditions and slopes. The dataset is designed for those interested specifically in debris flow susceptibility at a regional or national planning scale such as those involved in construction or maintenance of infrastructure networks (road or rail or utilities), or other asset managers such as for property (including developers and home owners), loss adjusters, surveyors or local government. The dataset builds on research BGS has conducted over the past 15 years investigating debris flows. The model was designed to identify potential source-areas for debris flows rather than locate where material may be deposited following a long-run-out failure i.e. the track and flow of debris. This work focuses on natural geological and geomorphological controls that are likely to influence the initiation of debris flows. It therefore, does not consider the influence of land use or land cover factors.

  • A geographic information system (GIS) containing geo-data for the energy transition across continental Africa created by extracting data from open sources into a series of shapefiles and rasters containing information on culture, geology, geothermal and geophysical data. This data is stored in the World Geodetic System (WGS) 1984 Geographic Projection System.

  • The World Climate Research Program (WCRP) Coupled Model Intercomparison Project, Phase 6 (CMIP6) data from the Canadian Centre for Climate Modelling and Analysis (CCCma) CanESM5 model output for the "control plus perturbative surface fluxes of momentum and freshwater into ocean, the latter around the coast of Antarctica only" (faf-antwater-stress) experiment. These are available at the following frequencies: Amon, Lmon, Omon and fx. The runs included the ensemble member: r1i1p2f1. CMIP6 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). The official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record.

  • The World Climate Research Program (WCRP) Coupled Model Intercomparison Project, Phase 6 (CMIP6) data from the the MIROC team MIROC-ES2L model output for the "idealized equatorial volcanic eruption emitting 56.2 Tg SO2" (volc-long-eq) experiment. These are available at the following frequencies: Amon, Lmon and Omon. The runs included the ensemble members: r1i1p1f2 and r2i1p1f2. CMIP6 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). The official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record. The the MIROC team team consisted of the following agencies: Atmosphere and Ocean Research Institute (AORI), Centre for Climate System Research - National Institute for Environmental Studies (CCSR-NIES) and Atmosphere and Ocean Research Institute (AORI).

  • PRIMAVERA Project data from the European Centre for Medium-Range Weather Forecasts (ECMWF) ECMWF-IFS-LR model output for the "coupled control with fixed 1950's forcing (HighResMIP equivalent of pre-industrial control)" (control-1950) experiment. These are available at the following frequencies: Prim6hrPt, PrimOday, PrimOmon, PrimSIday and Primday. The runs included the ensemble member: r1i1p1f1. PRIMAVERA was a European Union Horizon2020 (grant agreement 641727) project.

  • This dataset contains raw beaching data computed by marine debris simulations (run using OceanParcels) for a range of physical scenarios (surface currents from GLORYS12V1 (https://doi.org/10.3389/feart.2021.698876), Stokes drift from WAVERYS (https://doi.org/10.1007/s10236-020-01433-w), and surface winds from ERA5 (https://doi.org/10.1002/qj.3803)), as described in the accompanying manuscript. Through postprocessing, debris ‘connectivity’ matrices can be computed, providing predictions for the main terrestrial and marine source regions of plastic debris accumulating at remote islands in the western Indian Ocean. These simulations include beaching and sinking processes, and a set of example matrices is provided here (https://doi.org/10.5287/bodleian:DEdqwXZQw). However, these matrices can be recomputed for different sinking and beaching rates using the scripts archived here (https://doi.org/10.5281/zenodo.7351695), or see here (https://github.com/nvogtvincent/WIO_Marine_Debris/) for the live version with documentation. These predictions will be useful for environmental practitioners in the western Indian Ocean to assess source regions for marine debris accumulating at islands of interest, and when this debris is likely to beach. The data were produced as part of the Marine Dispersal and Retention in the Western Indian Ocean project funded by the Natural Environment Research Council (NERC) grant NE/S007474/1. See linked online references on this record for cited items given above.

  • PRIMAVERA Project data from the European Centre for Medium-Range Weather Forecasts (ECMWF) ECMWF-IFS-MR model output for the "coupled control with fixed 1950's forcing (HighResMIP equivalent of pre-industrial control)" (control-1950) experiment. These are available at the following frequencies: Prim6hrPt, PrimOday, PrimOmon, PrimSIday and Primday. The runs included the ensemble member: r1i1p1f1. PRIMAVERA was a European Union Horizon2020 (grant agreement 641727) project.