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Wheat Biomass for Kherson and Poltava regions in Ukraine The dataset contains Dry Above Ground Biomass (DAM) estimates over the Kherson and Poltava regions in Ukraine for years 2020,2021 and 2022. - Processing:The processing is done using the AgriCarbon-EOv1.5 processing chain, using the TREX processing centre at CNES France.The input remote sensing data are L2A Sentinel-2 surface reflectances provided by the MAJA processing chain based on the Copernicus Sentinel-2 L1C data.The Landcover maps are provided using ML Deep learning based on the Copernicus L2A data.The daily weather data is extracted from ERA5Land products (C3S). -Geophysical variable:Dry Above ground biomass of winter wheat in g/m2. - Extents: * DAM estimates over the Copernicus Sentinel-2 tile 36TWT cover the Kherson region.* DAM estimates over the Copernicus Sentinel-2 tile 36UVA cover the Poltava region. - Spatial resolution:10m resolution estimlates over wheat plots identified in the landcover map. - Temporal coverage:Estimates are provided at the end of the wheat cycle for cycles:* The year 2020 correspond to cycle: 2019-2020* The year 2021 corresponds to cycle : 2020-2021* The year 2022 corresponds to cycle : 2021-2022 - Projection: EPSG:32636 - File content: Each Raster file has 2 bands containing respectively: * band1: mean value of DAM in g/m2. * band2: standard deviation of DAM in g/m2. - List of maps:* Dry_aboveground_biomass_2020_T36TWT_Kherson_Ukraine.tif* Dry_aboveground_biomass_2020_T36UVA_Poltava_Ukraine.tif* Dry_aboveground_biomass_2021_T36TWT_Kherson_Ukraine.tif* Dry_aboveground_biomass_2021_T36UVA_Poltava_Ukraine.tif* Dry_aboveground_biomass_2022_T36TWT_Kherson_Ukraine.tif* Dry_aboveground_biomass_2022_T36UVA_Poltava_Ukraine.tif

The Global Yield Gap Atlas project (GYGA - http://yieldgap.org ) has undertaken a yield gap assessment following the protocol recommended by van Ittersum et. al. (van Ittersum et. al., 2013). This datafile holds the results for rainfed rice.

The atmosphere concentration of CO2 is steadily increasing and causing climate change. To achieve the Paris 1.5 or 2 oC target, negative emissions technologies must be deployed in addition to reducing carbon emissions. The ocean is a large carbon sink but the potential of marine primary producers to contribute to carbon neutrality remains unclear. Here we review the alterations to carbon capture and sequestration of marine primary producers (including traditional ‘blue carbon’ plants, microalgae, and macroalgae) in the Anthropocene, and, for the first time, assess and compare the potential of various marine primary producers to carbon neutrality and climate change mitigation via biogeoengineering approaches. The contributions of marine primary producers to carbon sequestration have been decreasing in the Anthropocene due to the decrease in biomass driven by direct anthropogenic activities and climate change. The potential of blue carbon plants (mangroves, saltmarshes, and seagrasses) is limited by the available areas for their revegetation. Microalgae appear to have a large potential due to their ubiquity but how to enhance their carbon sequestration efficiency is very complex and uncertain. On the other hand, macroalgae can play an essential role in mitigating climate change through extensive offshore cultivation due to higher carbon sequestration capacity and substantial available areas. This approach seems both technically and economically feasible due to the development of offshore aquaculture and a well-established market for macroalgal products. Synthesis and applications: This paper provides new insights and suggests promising directions for utilizing marine primary producers to achieve the Paris temperature target. We propose that macroalgae cultivation can play an essential role in attaining carbon neutrality and climate change mitigation, although its ecological impacts need to be assessed further. To calculate the parameters presented in Table 1, the relevant keywords "mangroves, salt marshes, macroalgae, microalgae, global area, net primary productivity, CO2 sequestration" were searched through the ISI Web of Science and Google Scholar in July 2021. Recent data published after 2010 were collected and used since area and productivity of plants change with decade. For data with limited availability, such as net primary productivity (NPP) of seagrasses and global area and NPP of wild macroalgae, data collection was extended back to 1980. Total NPP and CO2 sequestration for mangroves, salt marshes, seagrasses and wild macroalgae were obtained by the multiplication of area and NPP/CO2 sequestration density and subjected to error propagation analysis. Data were expressed as means ± standard error.

Geoven (http://www.geoven.fr) is a geothermal power-plant project led by Fonroche Géothermie (now Arverne). The project is implemented on the site of the Rhenan Ecoparc at Vendenheim, North of Strasbourg. The future geothermal power-plant was expected to produce 6 MW of electrical energy and 40 MW of thermal energy. To this end, two wells were used to draw the hot water and reinject it at more than four thousand meters deep.

The Aneto Glacier, is the largest glacier in the Pyrenees. Its shrinkage and wastage have been continuous in recent decades, and there are signs of accelerated melting in recent years. In this study, changes in the surface and ice thickness of the Aneto Glacier from 1981 to 2022 are investigated using historical aerial imagery, airborne LiDAR point clouds, and UAV imagery. A GPR survey conducted in 2020, combined with data from photogrammetric analyses, allowed us to reconstruct the current ice thickness and also the existing ice distribution in 1981 and 2011. Over the last 41 years, the total glaciated area has shrunk by 64.7% and the ice thickness has decreased, on average, by 30.5 m. The mean remaining ice thickness in autumn 2022 was 11.9 m, as against the mean thicknesses of 32.9 m, 19.2 m reconstructed for 1981 and 2011 and 15.0 m observed in 2020 respectively. The results demonstrate the critical situation of the glacier, with an imminent segmentation into two smaller ice bodies and no evidence of an accumulation zone. We also found that the occurrence of an extremely hot and dry year, as observed in the 2021–2022 season, leads to a drastic degradation of the glacier, posing a high risk to the persistence of the Aneto Glacier, a situation that could extend to the rest of the Pyrenean glaciers in a relatively short time.

Data type: Experimental measurements, correlations and Van't Hoff plot. Date format: .opj. Origin of the data: Experimental pressure composition isotherm measurements. Data generated by a home-made Sieverts’ type apparatus from CNRS, ICMPE, Thiais, France. Software needed to plot the data: Origin.

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.CAMS.CAMS-CSM1-0.ssp119' 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 CAMS-CSM 1.0 climate model, released in 2016, includes the following components: atmos: ECHAM5_CAMS (T106; 320 x 160 longitude/latitude; 31 levels; top level 10 mb), land: CoLM 1.0, ocean: MOM4 (tripolar; 360 x 200 longitude/latitude, primarily 1deg latitude/longitude, down to 1/3deg within 30deg of the equatorial tropics; 50 levels; top grid cell 0-10 m), seaIce: SIS 1.0. The model was run by the Chinese Academy of Meteorological Sciences, Beijing 100081, China (CAMS) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

The Arctic is one of Earth’s regions most susceptible to climate change. However, the in-situ long-term observations used for climate research are relatively sparse in the Arctic Ocean, and the simulations from current climate models exhibit remarkable biases in the Arctic. Here we present an Arctic Ocean dynamical downscaling dataset based on a high-resolution ice-ocean coupled model FESOM and a climate model FIO-ESM. The dataset includes 115-year (1900–2014) historical simulations and two 86-year future scenario simulations (2015–2100) under scenarios SSP245 and SSP585. The historical results demonstrate that the root mean square errors of temperature and salinity in the dynamical downscaling dataset are much smaller than those from CMIP6 (the Coupled Model Intercomparison Project phase 6) climate models. The common biases, such as the too deep and too thick Atlantic layer in climate models, are reduced significantly by dynamical downscaling. This dataset serves as a crucial long-term data source for climate change assessments and scientific research in the Arctic Ocean, providing valuable information for the scientific community. The Arctic is one of Earth’s regions most susceptible to climate change. However, the in-situ long-term observations used for climate research are relatively sparse in the Arctic Ocean, and the simulations from current climate models exhibit remarkable biases in the Arctic. Here we present an Arctic Ocean dynamical downscaling dataset based on a high-resolution ice-ocean coupled model FESOM and a climate model FIO-ESM. The dataset includes 115-year (1900–2014) historical simulations and two 86-year future scenario simulations (2015–2100) under scenarios SSP245 and SSP585. The historical results demonstrate that the root mean square errors of temperature and salinity in the dynamical downscaling dataset are much smaller than those from CMIP6 (the Coupled Model Intercomparison Project phase 6) climate models. The common biases, such as the too deep and too thick Atlantic layer in climate models, are reduced significantly by dynamical downscaling. This dataset serves as a crucial long-term data source for climate change assessments and scientific research in the Arctic Ocean, providing valuable information for the scientific community.

# Data on: Projecting spatiotemporal dynamics of severe fever with thrombocytopenia syndrome in the mainland of China [https://doi.org/10.5061/dryad.vdncjsz1z](https://doi.org/10.5061/dryad.vdncjsz1z) This dataset is the data used in the paper of Global change biology entitled "Projecting spatiotemporal dynamics of severe fever with thrombocytopenia syndrome in the mainland of China". We use an integrated multi-model, multi-scenario framework to assess the impact of global climate change on SFTS disease in the mainland of China. ## Description of the data and file structure The predicted annual incidence of national SFTS cases with or without human population reduction under four RCPs under different climate change scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) in the 2030s, 2050s, and 2080s. The value represents the annual incidence, and the unit is 105/year. The Dataset-1 file includes the predicted annual incidence of national SFTS cases with a fixed future human population under different climate change scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) in the 2030s, 2050s, and 2080s. The Dataset-2 file includes the predicted annual incidence of national SFTS cases in the 2030s, 2050s, and 2080s with human population reduction (SSP2) under four RCPs. ## Sharing/Access information Data was derived from the following sources: * https://doi.org/10.1111/gcb.16969 This dataset is the data used in the paper of Global change biology entitled "Projecting spatiotemporal dynamics of severe fever with thrombocytopenia syndrome in the mainland of China". We use an integrated multi-model, multi-scenario framework to assess the impact of global climate change on SFTS disease in the mainland of China. The SFTS incidence in three time periods (2030-2039, 2050-2059, 2080-2089) is predicted to be increased as compared to the 2010s in the context of various RCPs. The projected spatiotemporal dynamics of SFTS will be heterogeneous across provinces. Notably, we predict possible outbreaks in Xinjiang and Yunnan in the future, where only sporadic cases have been reported previously. These findings highlight the need for population awareness of SFTS in endemic regions, and enhanced monitoring in potential risk areas.  See the Materials and methods section in the original paper. The code used in the statistical analyses are present in the paper and/or the Supplementary Materials.