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This file contains the complete dataset collected and underpinning the estimated carbon emissions data in the companion paper, in Microsoft Excel (XLSX) format. The workbook contains an index sheet with full details of each included worksheet. The file has been verified to open in Microsoft Excel (https://products.office.com/excel) and LibreOffice (https://www.libreoffice.org)

This ORDA repository location contains estimated contemporary and future surface relief, depression volume and surface lake volume maps of the Greenland Ice Sheet. The maps are produced for 3 time slices which each use different greenhouse gas emission scenarios (i.e. historical or Representative Concentration Pathways (RCP)):Two separate versions are produced for future time-slices:1. incorporating the effects of the changing surface mass balance2. incorporating the effects of both the changing surface mass balance and the changing ice sheet thickness and flow velocityThese datasets are the key outputs of our manuscript entitled: "Enhanced formation of surface lakes in the interior of the Greenland Ice Sheet after 2100 due to growing surface relief".The files are presented in GeoTiff (.tif) format.For further information please see "Readme.txt"

Replication data and materials for "Changing Prices in a Changing Climate: Electoral Competition and Fossil Fuel Taxation".

The data resource consists of half hourly time series of heat (latent and sensible) and trace gas (carbon dioxide and methane) fluxes obtained by eddy-covariance, gas concentrations and ancillary meteorological data (e.g. air temperature, relative humidity, pressure, photosynthetically active radiation, total incoming radiation, wind speed and direction). The data were collected at Guma Lagoon (18°57'53.01"S; 22°22'16.20"E), in the perennially flooded area of the Okavango Delta, Botswana, for the purpose of quantifying greenhouse gas fluxes over a Cyperus papyrus stand. The measurement period was 01/01/2018 to 31/12/2020. The instrumentation was installed the UK Centre for Ecology and Hydrology; monthly maintenance and data collection visits were effected by the Okavango Research Institute, University of Botswana. The research was funded through NERC grant reference NE/N015746/2 - The Global Methane Budget. Raw data from the eddy-covariance instrumentation were processed into half-hourly fluxes using the EddyPro software package version 7.0.6 and quality controlled by Dr Helfter. An automatic weather station recorded recorded air temperature, pressure, relative humidity, wind speed and wind direction, total solar radiation and photosynthetically active radiation (PAR). Data from both instruments were downloaded, processed and deposited into the EIDC as a comma separted value (.csv) file.

Large-scale reforestation can potentially bring both benefits and risks to the water cycle, which needs to be better quantified under future climates to inform reforestation decisions. We identified 477 water-insecure basins worldwide accounting for 44.6% (380.2 Mha) of the global reforestation potential. As many of these basins are in the Asia-Pacific, we used regional coupled land-climate modelling for the period 2041–2070 to reveal that reforestation increases evapotranspiration and precipitation for most water-insecure regions over the Asia-Pacific. This resulted in a statistically significant increase in water yield (p < 0.05) for the Loess Plateau-North China Plain, Yangtze Plain, Southeast China and Irrawaddy regions. Precipitation feedback was influenced by the degree of initial moisture limitation affecting soil moisture response and thus evapotranspiration, as well as precipitation advection from other reforested regions and moisture transport away from the local region. Reforestation also reduces the probability of extremely dry months in most of the water-insecure regions. However, some regions experience non-significant declines in net water yield due to heightened evapotranspiration outstripping increases in precipitation, or declines in soil moisture and advected precipitation. This dataset contains raw data outputs for Teo et al. (2022), Global Change Biology. Please see the published paper for further details on methods. For enquiries, please contact the corresponding authors: hcteo [at] u.nus.edu or lianpinkoh [at] nus.edu.sg.  Shapefiles can be opened with any GIS program such as ArcMap or QGIS. CSV files can be opened with any spreadsheet program such as Microsoft Excel or OpenOffice.

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.AerChemMIP.HAMMOZ-Consortium.MPI-ESM-1-2-HAM' 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 MPI-ESM1.2-HAM climate model, released in 2017, includes the following components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the ETH Zurich, Switzerland; Max Planck Institut fur Meteorologie, Germany; Forschungszentrum Julich, Germany; University of Oxford, UK; Finnish Meteorological Institute, Finland; Leibniz Institute for Tropospheric Research, Germany; Center for Climate Systems Modeling (C2SM) at ETH Zurich, Switzerland (HAMMOZ-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

Project: IPCC Data Distribution Centre : Supplementary data sets for the Sixth Assessment Report - For the Sixth Assessment Report of the IPCC (AR6) input/source and intermediate datasets underlying the AR6 were collected and long-term archived. This project compliments CMIP6 data subset and snapshot analyzed for the WGI AR6. Summary: This data set contains detailed elements the sea level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. In particular, it contains relative sea level projections that exclude the background term (representing primarily land subsidence or uplift). It includes probability distributions for all the workflows described in AR6 WGI 9.6.3.2. P-boxes derived from these distributions are available in the sister entry 'IPCC-DDC_AR6_Sup_PBox'. These data may be of use for users who want to substitute their own estimates of the background term. Regional projections can also be accessed through the NASA/IPCC Sea Level Projections Tool at https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool. See https://zenodo.org/communities/ipcc-ar6-sea-level-projections for additional related data sets.

Aim Despite an increasing number of studies highlighting the impacts of climate change on boreal species, the main factors that will drive changes in species assemblages remain ambiguous. We study how species community composition would change following anthropogenic and natural disturbances. We determine the main drivers of assemblage dissimilarity for bird and beetle communities. Location Côte-Nord, Québec, Canada. Methods We quantify two climate-induced pathways based on direct and indirect effects on species occurrence under different harvest management scenarios. The direct climate effects illustrate the impact of climate variables while the indirect effects are reflected through habitat-based climate change. We develop empirical models to predict the distribution of more than 100 species over the next century. We analyze the regional and the latitudinal species assemblage dissimilarity by decomposing it into 'balanced variation in species occupancy and occurrence' and 'occupancy and occurrence gradient'. Results Both pathways increased dissimilarity in species assemblage. At the regional scale, both effects have an impact on decreasing the number of winning species. Yet, responses are much larger in magnitude under mixed climate effects (a mixture of direct and indirect effects). Regional assemblage dissimilarity reached 0.77 and 0.69 under mixed effects versus 0.09 and 0.10 under indirect effects for beetles and birds, respectively, between RCP8.5 and baseline climate scenarios when considering harvesting. Latitudinally, assemblage dissimilarity increased following the climate conditions pattern. Main conclusions The two pathways are complementary and alter biodiversity, mainly caused by species turnover. Yet, responses are much larger in magnitude under mixed climate effects. Therefore, the inclusion of climatic variables considers aspects other than just those related to forest landscapes, such as life cycles of animal species. Moreover, we expect differences in occupancy between the two studied taxa. This could indicate the potential range of change in boreal species concerning novel environmental conditions.

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.ScenarioMIP.MOHC.UKESM1-0-LL.ssp245' 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 UKESM1.0-N96ORCA1 climate model, released in 2018, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), atmosChem: UKCA-StratTrop, land: JULES-ES-1.0, ocean: NEMO-HadGEM3-GO6.0 (eORCA1 tripolar primarily 1 deg with meridional refinement down to 1/3 degree in the tropics; 360 x 330 longitude/latitude; 75 levels; top grid cell 0-1 m), ocnBgchem: MEDUSA2, seaIce: CICE-HadGEM3-GSI8 (eORCA1 tripolar primarily 1 deg; 360 x 330 longitude/latitude). The model was run by the Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK (MOHC) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.