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87 results for “future projections”
Percent cover of under- and mid-story vegetation and seedling counts in the Future of Oak Forests project at Black Rock Forest, Cornwall, NY.
Black Rock Forest established a series of 12, 0.56 ha plots in 2005 to assess impacts of the loss of tree in the genus Quercus on the forest ecosystem (entitled the Future of Oak Forests experiment). Three trunk girdling treatments, with control plots were instituted in 2008. Each plot also contained an ~10m by ~15m deer exclosure to assess the impact of herbivory post-disturbance. In 2006 and 2008, before exclosures were erected, pre-treatment surveys were conducted in all unexclosed (n=120) quadrats. Surveys of all 240 understory quadrats were conducted annually in late summer (August to September) from 2009 to 2018 and then again in 2021. At each quadrat, trained observers identified all vascular plants to species and assigned each species a percent cover value. The percent cover of moss was also recorded but moss species were not identified. Counts of tree seedlings and some woody shrubs were also recorded in addition to percent cover values. Seedlings were considered saplings, and therefore not counted, once they reached 1.3 m tall (breast height).
Dataset for the paper "Historical model biases in monthly high temperature anomalies indicate under-projection of future temperature extremes"
<div> <div>This repository holds data and scripts related to the revision of the paper entitled: <span>"Historical model biases in monthly high temperature anomalies indicate under-projection of future temperature extremes" </span>by Lei Duan, Lyssa M. Freese, Govindasamy Bala, and Ken Caldeira. <span>The paper is currently submitted for peer review. </span>Any questions regarding the data and paper could be sent to the corresponding author: Lei Duan (leiduan@carnegiescience.edu). </div> </div>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the region of the Yucatán Peninsula
<p>The ensemble provides future projections of key marine variables under climate change for the region of the Yucatán Peninsula. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).<br> <br>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Baltic Sea, the Bay of Biscay and the Chilean coast, see “Related identifiers”.</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p> </p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Chilean coast
<p>The ensemble provides future projections of key marine variables under climate change for the Chilean coast. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and three different variables (potential temperature, dissolved oxygen, and pH) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p> <br>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Baltic Sea, the Bay of Biscay and the area around the Yucatán Peninsula, see “Related identifiers”.</p> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p><br><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Bay of Biscay
<p>The ensemble provides future projections of key marine variables under climate change for the Bay of Biscay region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Baltic Sea, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.</p> <p> </p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p><br><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the North Sea
<p>The ensemble provides future projections of key marine variables under climate change for the North Sea region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p> <br>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the Baltic Sea, the Bay of Biscay, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.</p> <p> </p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p><br><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Mediterranean Sea
<p>The ensemble provides future projections of key marine variables under climate change for the Mediterranean region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p> <br>Analogue datasets are provided in separate zenodo entries for the regions of the North Sea, the Baltic Sea, the Bay of Biscay, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.</p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p><br><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p> <p> </p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Baltic Sea
<p>The ensemble provides future projections of key marine variables under climate change for the Baltci Sea region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Bay of Biscay, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.</p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p><br><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p>
Soybean yield projections in Europe under historical (1981-2010) and future climate (2050-2059 and 2090-2099 for RCP4.5 and RCP8.5)
<p><strong>General information</strong></p> <p>This dataset contains soybean yield projections in Europe under historical (1981-2010) and future climate with moderate (RCP 4.5) to intense (RCP 8.5) warming, up to the 2050s and 2090s time horizons. The data has been generated by <em>Guilpart et al. (2022) Data-driven projections suggest large opportunities to improve Europe's soybean self-sufficiency under climate change, Nature Food. </em>All details can be found in this paper. A brief summary is provided below.</p> <p><strong>Summary of soybean yield projections methodology</strong></p> <p>Yield projections have been performed using data-driven relationships between climate and soybean yield derived from machine-learning (Random Forest). The Random Forest model was trained using (i) the the global dataset of historical yields updated version (Iizumi et al. 2014a), which includes grid-wise soybean yields worldwide with the grid size of 1.125 degree over 1981-2010, and (ii) the global retrospective meteorological forcing dataset tailored for agricultural application (GRASP, Iizumi et al. 2014b), which covers the period 1961–2010 at the same spatial resolution as yield data, i.e. a grid size of 1.125 degree. Time-detrended soybean yield data was related (using Random Forest) to 35 climate variables defined at a monthly time step over the seven months of the soybean growing season, plus the fraction of irrigated area, i.e. a total of 36 variables. The 35 climate variables are monthly mean daily minimum and maximum temperatures (<em>Tmin</em> and <em>Tmax</em>, degree Celsius), monthly total precipitation (<em>rain</em>, mm month<sup>-1</sup>), monthly mean daily total solar radiation (<em>solar</em>, MJ m<sup>-2</sup> day<sup>-1</sup>), monthly mean air vapor pressure (VP, hPa). The fitted model showed high R² (higher than 0.9) and low RMSE (0.35 t ha<sup>-1</sup>) between observed and predicted yields based on cross-validation.</p> <p>Then, soybean yield projections under historical over whole Europe have been performed using the GRASP climate data, and yield projections under future climate have been performed using 16 climate change scenarios consisting of bias-corrected data of eight Global Circulation Models (GCM; GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC5, MIROC-ESM, MIROC-ESM-CHEM, MRI-CGCM3, and NorESM1-M, used in the Coupled Model Intercomparison phase 5 (CMIP5) and two Representative Concentration Pathways (RCPs; 4.5 and 8.5 W m<sup>-2</sup>). Soybean growing season used for projections is April to October. All projections assumed irrigated fraction equals to zero. Projections are shown only on agricultural area (cropland plus pasture), in the year 2000. Soybean yield is expressed in tons per hectare.</p> <p><strong>Files description</strong></p> <ul> <li><em>RF_soybean_historical_GRASP_median_1981_2010.nc</em> : random forest projections of soybean yield in Europe for the historical (1981-2010) period using GRASP climate data. This file contains the median yield (in tons per hectare) over 1981-2010.</li> <li><em>RF_soybean_rcp45_median_2050_2059.nc : </em>random forest projections of soybean yield in Europe for the 2050-2059 time period under RCP4.5. This file contains the median yield (in tons per hectare) over 2050-2059 and the 8 GCMs.</li> <li><em>RF_soybean_rcp45_median_2090_2099.nc : </em>random forest projections of soybean yield in Europe for the 2090-2099 time period under RCP4.5. This file contains the median yield (in tons per hectare) over 2090-2099 and the 8 GCMs.</li> <li><em>RF_soybean_rcp85_median_2050_2059.nc : </em>random forest projections of soybean yield in Europe for the 2050-2059 time period under RCP8.5. This file contains the median yield (in tons per hectare) over 2050-2059 and the 8 GCMs.</li> <li><em>RF_soybean_rcp85_median_2090_2099.nc : </em>random forest projections of soybean yield in Europe for the 2090-2099 time period under RCP8.5. This file contains the median yield (in tons per hectare) over 2090-2099 and the 8 GCMs.</li> </ul> <p><strong>References</strong></p> <p>Guilpart N. <em>et al.</em> (2022)<strong> </strong>Data-driven projections suggest large opportunities to improve Europe's soybean self-sufficiency under climate change, <em>Nature Food</em>.</p> <p>Iizumi T. <em>et al.</em> (2014a) Historical changes in global yields: Major cereal and legume crops from 1982 to 2006. <em>Glob. Ecol. Biogeogr.</em> 23, 346–357.</p> <p>Iizumi T. <em>et al</em>. (2014b). A meteorological forcing data set for global crop modeling: Development, evaluation, and intercomparison. <em>J. Geophys. Res. Atmos. Res.</em> 119, 363–384.</p>
Scripts and datas for "Climate-driven projections of future global wetlands extent"
<p>Computations scripts (1, 2), associated input dataset (3), and output datasets for wetland fractions (4, 5) used and presented in the study:</p> <p><em><strong>L. Hardouin, B. Decharme, J. Colin, C. Delire: </strong>Climate-driven projections of future global wetlands extent.</em></p> <p>The calculation and input scripts include:<br><em>1_var_comput </em>: Calculation of the main variables used to diagnose wetlands: depth of the "active" layer d_wtl, liquid water content w_l, ice content and maximum content in the layer d_wtl.</p> <p><em>2_TOPMODEL </em>: The scripts used to diagnose the wetland fraction and to calibrate the models using the TOPMODEL approach. In this folder, the mean, maximum, minimum, standard deviation and skewness datasets of the topographic indices at the grid-cell level are also included.</p> <p><em>3_alpha_and_beta </em>: Calibrated alpha and beta parameters used to obtain the historical and projected wetland fractions with the calibrated version.</p> <p>The outputs datasets contain:</p> <p><em>4_fwtl_model_period </em>: The fraction of wetlands computed from each model in the calibrated version, for the historical period and the 4 SSPs scenarios presented in the submitted work.</p> <p><em>5_not_calibrated_fwtl_model_period </em>: The fraction of wetlands computed from each model in the uncalibrated version with alpha=0.65, for the historical period and the 4 SSPs scenarios, where only the historical period is used in the submitted work.</p> <p> </p> <p>Additional data not created by the authors are needed to reproduce the study (see the Open research section in the submitted article). Feel free to contact the authors (lucas.hardouin@meteo.fr) for any help or questions.</p>
Data: Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing
<p>The dataset contains supporting data for the paper submitted to The Cryosphere "Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing".<br><br>OGGM_area_projections.nc contains data for Figure 3.<br>OGGM_volume_projections contains data for Figure 4.</p> <p>OGGM_MassLoss_SLR_projections_regions.nc contains data for Figure 5.</p> <p>OGGM_solid_ice_discharge_regions.nc contains data for Figure 6.</p> <p>OGGM_freshwater_runoff_magnitude_composition_timings_projections.nc & OGGM_freshwater_runoff_projections_regions.nc contain data for Figure 7.</p> <p>OGGM_PeakWaterYear_projections_regions.nc contains data for Figure 8.</p>
INTEND D 2.1 Transport projects & future technologies synopses database
<p>This excel file provides the database that contains all the project synopses that were carried out in the INTEND D 2.1 Transport projects & future technologies handbook deliverable. The reviews are divided into transport modes and contain the technology themes that were identified and brief summaries of what each project that was reviewed had researched. The database contains a total of 354 transport projects that have carried out hard technology research, predominantly funded under FP7 (2010-2014), all H2020 projects that have been funded as well as other international projects.</p>
Projection of potential future tree cover persistence for 2029 based on the global model
<p>Tree cover persistence projection results for 2029 based on the global model under a business-as-usual scenario.</p>
Projection of potential future tree cover persistence for 2029 based on the six regional models
<p>Tree cover persistence projection results for 2029 based on the six regional models under a business-as-usual scenario.</p>
Ensemble projections (+ uncertainties) of contemporary (2012-2031) and future (2081-2100) mean annual plankton/phytoplankton/zooplankton species diversity (and species turn-over in time) for the global surface open ocean.
<p><em><strong>Gridded spatial fields (raster objects) containing the species distribution models (SDMs) projections of mean annual plankton total plankton, phytoplankton and zooplankton species diversity from Benedetti et al. (2021). </strong></em></p> <p>The present .grd file ('rasterStack' object in R) contain the fields of mean annual surface plankton/phytoplankton/zooplankton species diversity for the contemporary (2012-2031) and future (2081-2100) conditions of the global open ocean (i.e., data underlying those maps in Figure 1 and Figure 3 of Benedetti et al., 2021). Layers quantifying the uncertainty (i.e., the variablity across models projections estimated through the standard deviation) in ensemble projections were also added (i.e., data underlying the maps in Supplementary Figure 4). See the Methods section of Benedetti et al. (2021) for a full description of the methodology and the ensemble SDMs forecasting framework. The raster layers follow the 1°x1° cell grid of the World Ocean Atlas (https://www.ncei.noaa.gov/).</p> <p>In short, we empirically modelled the monthly and mean annual diversity patterns stemming from the distribution of 860 plankton species (336 phytoplankton, 524 zooplankton) spanning 13 phyla, 71 orders and 324 genera through an ensemble approach based on SDMs. The considered species cover a wide range of traits and functions, representing 10 major plankton functional groups (PFGs; three phytoplankton and seven zooplankton groups). We compiled the species occurrence records from various data sources (available here: https://zenodo.org/record/5101349#.YO7Dqm469lM) and aggregated them onto a monthly-resolved 1°x1° grid, excluding observations from regions where the seafloor is shallower than 200 m. We matched these binned open ocean records with observation-based climatologies of environmental predictors (temperature, dissolved oxygen concentration, solar irradiance, macronutrients concentration, chlorophyll a concentration) that reflect the climatic and biogeochemical conditions of the surface open ocean. Four types of SDMs (generalized linear models, generalized additive models, artificial neural networks, and random forests) were fitted to model the species’ current environmental habitat suitability patterns. For each SDMs, we used four alternative pools of predictors. Assuming niche conservatism, we projected each of the 16 resulting species-level habitat suitability models into the future using outputs from five ESMs belonging to the Coupled Model Intercomparison Project 5 (CMIP5) that were forced by the Representative Concentration Pathway 8.5 (RCP8.5) scenario of high greenhouse gas concentrations. To this end, we first computed the modelled monthly climatologies of the selected predictors for the 2012-2031 and 2081-2100 periods, and derive the future monthly anomalies from the differences between these two time periods. These anomalies were added to the observation-based monthly climatologies (i.e., those used to train the SDMs) to estimate the future environmental conditions of the ocean, and projected the SDMs in these future conditions. Finally, we estimated the mean annual present and future alpha diversity (species richness; SR) and beta diversity (species turnover through time) patterns for both trophic levels, for each cell, from the ensemble of SDMs. SR ensembles are estimated as the sum of all species’ habitat suitability patterns averaged across all 80 possible combinations (i.e., "ensemble members") of SDMs (n = 4), ESMs (n = 5) and predictor pools (n = 4). To assess the uncertainties of our diversity projections based on the ensemble members, we compute the interquartile range of the 80 ensemble members SR projections. We calculate species turnover as the change in mean annual species composition between present and future time based on Jaccard’s dissimilarity index and by decomposing this total turnover into the true species turnover (ST, also known as species replacement) and the nestedness (SR change) components. Numerous tests are conducted to ensure the robustness of the results with regard to the spatially and temporally highly uneven sampling effort as well as with regard to the relative role of different predictors.</p> <p><strong>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862923. This output reflects only the author’s view, and the European Union cannot be held responsible for any use that may be made of the information contained therein.</strong></p>
Future Projections for Posidonia oceanica and Zostera marina in Europe
<p>Future projections of seagrass biomass for <em>Posidonia oceanica</em> and <em>Zostera marina </em>in Europe.</p> <p>Supporting data for T4.1 of Horizon 2020 project FutureMARES.</p> <p>The file naming convention is {species}_{cmip6_model}_{ssp}, where <em>species</em> identifies whether the model run is for <em>P. oceanica</em> or <em>Z. marina</em>, <em>cmip6_model</em> names the CMIP6 model uses to drive the projections, and <em>ssp</em> identifies which of SSP126, SSP245 and SSP585 were used.</p> <p>Projections cover the year 1995-2099.</p> <p> </p>
Assessment of current and future invasive plants in protected dune habitats of the Atlantic coastal region for the LIFE DUNIAS project (LIFE20 NAT/BE/001442)
<p>This .csv file contains the raw data from the risk screening supplementing the LIFE DUNIAS horizon scan for (invasive) alien species in protected habitats of Atlantic coastal dune ecosystems (<a href="https://doi.org/10.21436/inbor.86703335">Adriaens et al. 2022</a>). We gladly refer to the annexes and methods section in this report for more explanation about the fields and their contained values.</p> <p>The file contains the following fields:</p> <p><em>TaxonName</em>: original taxonomic name of the considered alien species</p> <p><em>WorkName</em>: taxonomic name of the considered alien species after lumping of subspecies, closely related species of a complex, functionally similar species of the same genus (see chapter 3.1)</p> <p><em>hab_xxxx</em> (1110, 1130, 1140, 1210, 1230, 1310, 1320, 1330, 2110, 2120, 2130, 2140, 21A0, 2150, 2190, 2160, 2170, 2180): susceptibility of habitat for the alien species (4-digit code refering to the Annex I habitat under the Habitats Directive) </p> <p><em>occ_XX</em> (BE, FR, IE, NL, ES, UK, DK, DE, PT, ALL): occupancy of the alien species in different countries of the Atlantic European region (as the number of 10km<sup>2</sup> squares per country). Country codes: BE = Belgium, FR = France, IE = Ireland, NL = Netherlands, ES = Spain, UK = United Kingdom, DK = Denmark, DE = Germany, PT = Portugal, ALL = total for all countries.</p> <p><em>scor_XXX_xxxx</em>: score of the assessment per criterium (INT = introduction, EST = establishment, SPR = spread, IMP = ecological impact, ALL = overall score) and per habitat group (salt = salties, sand = sandies, shru = shrubbies) conf_<em>XXX_xxxx</em>: confidence on the scores of the assessment per criterium (INT = introduction, EST = establishment, SPR = spread, IMP = ecological impact, ALL = overall score) and per habitat group (salt = salties, sand = sandies, shru = shrubbies)</p> <p><em>scor_ALL_MAX</em>: maximum ecological impact score of the alien taxon across all habitats</p>
Dataset for "Future projections for the Antarctic ice sheet until the year 2300 with a climate-index method"
<p>Dataset for the paper "Future projections for the Antarctic ice sheet until the year 2300 with a climate-index method" (Journal of Glaciology, <a href="https://doi.org/10.1017/jog.2023.41">doi: 10.1017/jog.2023.41</a>).</p> <p>Please see the README for details.</p> <p>V1.1: Run-specs header files for SICOPOLIS added. README updated.<br>V1: Initial upload.</p> <p>* * * * * * *</p> <p>Users should cite the original publication when using all or parts of these data.</p>
Simulated spatially explicit dataset (300 m) on future forest cover changes in Southeast Asia projected under the baseline shared socioeconomic pathways
<p>This is a simulated spatially explicit dataset on future forest cover changes in Southeast Asia projected under the baseline shared socioeconomic pathways. It includes six raster maps at a spatial resolution of 300 m: (1) 2015 baseline forest and non-forest map; (2) SSP1 2050 projected net forest gain map; (3) SSP2 2050 projected net forest gain map; (4) SSP3 2050 projected net forest loss map; (5) SSP4 2050 projected net forest gain map; and SSP5 2050 projected net forest loss map. This dataset is the result of a study published in Nature Communications (2019) (https://doi.org/10.1038/s41467-019-09646-4).</p>
Reanalysis and future wave climate projections of the wave climate of the Gulf of Riga 1993-2100
<h4><strong>Data sets</strong></h4><p>There are two data sets: (1) reanalysis (1993-2021) and (2) future projection (2015-2100).</p><p>The dataset provides gridded monthly mean values of the parameters of the wind waves in the Gulf of Riga, Baltic Sea. The variables of the dataset of the wave field state of the Gulf of Riga are as follows (Long name: <i>acronym</i>, <i>units</i>) </p><ul><li>Mean wave direction: <i>VMDR_WW, </i>°</li><li>Spectral significant wave height: <i>VHM0_WW, m</i></li><li>Spectral moment (0,1) of wave period or mean wave period: <i>VTM01_WW, s</i></li><li>Eastward wave energy flux: <i>WWEFu, W/m</i></li><li>Northward wave energy flux: <i>WWEFv, W/m</i></li></ul><p> </p><p>The grid size of the dataset is 101 (latitude) x 93 (longitude). The horizontal grid spacing is 1 nm. The time resolution of the dataset is monthly – the monthly mean value is provided in the 1st day of the month in the time dimension.</p><p>The original climatic calculations are based on the University of Latvia (UL) set-up of the SWAN model for the Gulf of Riga. The original output of the model run is hourly data series. </p><h4><strong>Reanalysis</strong></h4><p>Time period: 1993-2021, 29 years.</p><p>The main characteristics of the input data and approach for the reanalysis run are as follows: </p><ul><li>EMODNET2020 bathymetry.</li><li>Atmospheric forcing (eastward and northward components of the near surface wind) – ERA5 meteorology.</li><li>Ice conditions – LU HBM, see Frishfelds et. al. 2023.</li><li>Boundary conditions – Baltic Sea Wave Hindcast.</li></ul><h4><strong>Future climate projection</strong></h4><p>Time period: 2015-2100, 86 years.</p><p>The main characteristics of the input data and approach for the future wave climate projections run are as follows: </p><ul><li>Emodnet2020 bathymetry.</li><li>Atmospheric forcing (eastward and northward components of the near surface wind) from downscaled CMIP6 climate projection model NorESM2-MM_ssp585_r1i1p1f1 (search string – project:'CMIP6', source_id:'NorESM2-MM', experiment_id:'ssp585', variant_label:'r1i1p1f1').</li><li>Ice conditions – LU HBM, see Frishfelds et. al. 2023. </li><li>Boundary conditions – fetch model according to Shore protection manual, 1984.</li></ul><h4><strong>References</strong></h4><p>Frishfelds, V., Cepīte-Frišfelde, D., Timuhins, A., Bethers, U., Sennikovs, J., Reanalysis and future climate projections of the physical state of the Gulf of Riga 1993-2100, Zenodo, <a href="https://zenodo.org/doi/10.5281/zenodo.8248942">10.5281/zenodo.8248942</a>, (2023).</p><p>Baltic Sea Wave Hindcast. E.U. Copernicus Marine Service Information (CMEMS). Marine Data Store (MDS). doi: <a href="https://doi.org/10.48670/moi-00014">https://doi.org/10.48670/moi-00014</a>.</p><p>Shore protection manual, Army Corps of Engineers, Coastal Engineering Research Center (CERC), (1984).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.