• Energy Research
• 13. Climate action close
• 1. No poverty close
• US close
• CN close
• GB close
• AU close
• CA close

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.CAS.FGOALS-f3-L' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The FGOALS-f3-L climate model, released in 2017, includes the following components: atmos: FAMIL2.2 (Cubed-sphere, c96; 360 x 180 longitude/latitude; 32 levels; top level 2.16 hPa), land: CLM4.0, ocean: LICOM3.0 (LICOM3.0, tripolar primarily 1deg; 360 x 218 longitude/latitude; 30 levels; top grid cell 0-10 m), seaIce: CICE4.0. The model was run by the Chinese Academy of Sciences, Beijing 100029, China (CAS) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.C4MIP.E3SM-Project.E3SM-1-1' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The E3SM 1.1 (Energy Exascale Earth System Model) climate model, released in 2019, includes the following components: aerosol: MAM4 with resuspension, marine organics, and secondary organics (same grid as atmos), atmos: EAM (v1.1, cubed sphere spectral-element grid; 5400 elements with p=3; 1 deg average grid spacing; 90 x 90 x 6 longitude/latitude/cubeface; 72 levels; top level 0.1 hPa), atmosChem: Troposphere specified oxidants for aerosols. Stratosphere linearized interactive ozone (LINOZ v2) (same grid as atmos), land: ELM (v1.1, same grid as atmos; active biogeochemistry using the Converging Trophic Cascade plant and soil carbon and nutrient mechanisms to represent carbon, nitrogen and phosphorus cycles), MOSART (v1.1, 0.5 degree latitude/longitude grid), ocean: MPAS-Ocean (v6.0, oEC60to30 unstructured SVTs mesh with 235160 cells and 714274 edges, variable resolution 60 km to 30 km; 60 levels; top grid cell 0-10 m), ocnBgchem: BEC (Biogeochemical Elemental Cycling model, NPZD-type with C/N/P/Fe/Si/O; same grid as ocean), seaIce: MPAS-Seaice (v6.0; same grid as ocean). The model was run by the LLNL (Lawrence Livermore National Laboratory, Livermore, CA 94550, USA); ANL (Argonne National Laboratory, Argonne, IL 60439, USA); BNL (Brookhaven National Laboratory, Upton, NY 11973, USA); LANL (Los Alamos National Laboratory, Los Alamos, NM 87545, USA); LBNL (Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA); ORNL (Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA); PNNL (Pacific Northwest National Laboratory, Richland, WA 99352, USA); SNL (Sandia National Laboratories, Albuquerque, NM 87185, USA). Mailing address: LLNL Climate Program, c/o David C. Bader, Principal Investigator, L-103, 7000 East Avenue, Livermore, CA 94550, USA (E3SM-Project) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.

Outputs from the Network Innovation Allowance project "Control REACT" (workstream 2), sponsored by National Grid Electricity System Operator (NGESO). This deposit contains underlying data used in this project. The R code (Rmarkdown) and html renders of these workbooks are available in a separate deposit linked below. See description there for further details. In order to run the R scripts, data and code must be arranged in the directory structure given in "Directory Structure.pdf". Wind, solar and net-demand data are derived from raw data made available by Elexon and Solar Sheffield via public APIs. See respective websites for details, our processed (aggregated and cleaned) versions of this data are shared here under a CC-BY license. Weather forecast data are derived from historic operational forecasts from the ECMWF HRES model and are shared under a CC-BY licence. For details on how these were processed please see references. {"references": ["J. Browell and M. Fasiolo, \"Probabilistic Forecasting of regional net-load with conditional extremes and gridded NWP\", IEEE Transactions on Smart Grid, vol. 12, no, 6, pp. 5011-5019, 2021", "C. Gilbert \"Topics in high dimensional energy forecasting\", J. Browell & D. McMillan, degree supervisors; Centre for Doctoral Training in Wind and Marine Energy Systems; Department of Electronic and Electrical Engineering Thesis [PhD] 2021"]}

This is a set of aggregated data tables that underly the key figures in the SERL stats report "Smart Energy Research Lab: Energy use in GB domestic buildings 2021" (Volume 1). The report describes domestic gas and electricity energy use in Great Britain in 2021 based on data from the Smart Energy Research Lab (SERL) Observatory, which consists of smart meter and contextual data from approximately 13,000 homes that are broadly representative of the GB population in terms of region and Index of Multiple Deprivation (IMD) quintile. The report shows how residential energy use in GB varies over time (monthly over the year and half-hourly over the course of the day), with occupant characteristics (number of occupants, tenure), property characteristics (age, size, form, and Energy Performance Certificate (EPC)), by type of heating system, presence of solar panels and of electric vehicles, and by weather, region and IMD quintile.

This data set contains the essential files used as input for the analysis, intermediate files produced during the analysis, and the key output fields. The code of the analysis is available here: https://github.com/VUB-HYDR/2021_Thiery_etal_Science Input fields: - isimip.zip: Postprocessed ISIMIP2b simulation output. This data set is very similar to the data presented in Lange et al. (2020 Earth's Future) but includes selected additional impact models and scenarios (notably RCP8.5). This data set also includes the gridded population data. - GMT_50pc_manualoutput_4pathways.xlsx: Global mean temperature anomaly trajectories from the IPCC SR15 - wcde_data.xlsx: postprocessed cohort size data originally obtained from the Wittgenstein Centre Human Capital Data Explorer. - WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx: Postprocessed life expectancy data originally obtained from the UNited Nations World Population Programme Intermediate files *only use if you're interested in reproducing the results*: - workspaces.zip: Postprocessed ISIMIP2b simulation output. These matlab workspaces contain data on land area annually exposed to extreme events which is stored in a format designed to speed up the analysis. - mw_isimip.mat: ISIMIP2 simulations metadata (e.g. model, gcm and rcp name per simulation) - mw_countries.mat: information on the countries used in the analysis (e.g. border polygon coordinates) - mw_exposure.mat: age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic.mat: pre-industrial control age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic_coldwaves.mat: pre-industrial control age-dependent exposure to coldwaves computed from the ISIMIP and population data Output of the analysis: - mw_output.mat: Matlab workspace containing all variables produced during the analysis presented in thepaper. Use this file if you wish to look up certain numbers or want to use the study results for further analysis.

This dataset contains two metrics for climate change exposure using downscaled climate projections with the SRES A2 emissions scenario (Tabor and Williams, 2007).The metrics represent dissimilarity measurements of the squared Euclidean distance between seasonal (June–August and December–February) temperature and precipitation variables in the 20th century climate and mid-21st century climate. (1) disappearing climate risk - measure of dissimilarity between a pixel’s late 20th century climate and its closest matching pixel in the global set of 21st-century climates (2) novel climate risk - measure of dissimilarity between a pixel’s future climate and its closest matching pixel in the global set of late 20th-century climates. The data are in arcASCII format. All data are in units of standard Euclidean distance and multiplied by 1000. This is the original data. To scale the data similar to Tabor et al. (2018), remove outliers above the 99th percentile distribution before rescaling from 0-1. Unprojected number of columns 2160 number of rows 857 Lower Left X Center -179.917 Lower Left Y Center -59.084 Cell size 0.166667 decimal degrees (10 minutes or ~17 km) {"references": ["Tabor, K. et al. (2018). Tropical Protected Areas Under Increasing Threats from Climate Change and Deforestation: https://doi.org/10.3390/land7030090", "Tabor and Williams (2010). Globally downscaled climate projections for assessing the conservation impacts of climate change. https://doi.org/10.1890/09-0173.1", "Williams, J.W. et al. (2007). Projected distributions of novel and disappearing climates by 20100 AD. https://doi.org/10.1073/pnas.0606292104"]} Support for this project was provided by Conservation International, the Land Tenure Center at the University of Wisconsin, the Center for Climatic Research at the University of Wisconsin, and the Environment Program at the University of Wisconsin–Madison. This research has been funded in part by the Walton Family Foundation, the Gordon and Betty Moore Foundation, and a gift from Betty and Gordon Moore.

Forest aboveground carbon density (ACD) for the main eight Hawaiian Islands in 2015-2016. The data are in 30 meter resolution format with the units of Mg C per hectare. The file is a standard GeoTIFF. Use of these data requires citation of this dataset plus citation of the source study as follows: Asner, G.P., S. Sousan, D.E. Knapp, P.C. Selmants, R.E. Martin, R.F. Hughes, and C.P. Giardina. 2016. Rapid forest carbon assessments of oceanic islands: a case study of the Hawaiian archipelago. Carbon Balance and Management 11, doi:10.1186/s13021-015-0043-4

Methane emissions from aquatic and terrestrial ecosystems play a crucial role in global warming, which is particularly affecting high-latitude ecosystems. As major contributors to methane emissions in natural environments, the microbial communities involved in methane production and oxidation deserve a special attention. Microbial diversity and activity are expected to be strongly affected by the already observed (and further predicted) temperature increase in high-latitude ecosystems, eventually resulting in disrupted feedback methane emissions. The METHANOBASE project has been designed to investigate the intricate relations between microbial diversity and methane emissions in Arctic, Subarctic and Subantarctic ecosystems, under natural (baseline) conditions and in response to simulated temperature increments. We report here a small subunit ribosomal RNA (16S rRNA) analysis of lake, peatland and mineral soil ecosystems.