Find research datasets worth reusing

opencc-by-4.0Feb 2022View details →

Soybean yield projections in Europe under historical (1981-2010) and future climate (2050-2059 and 2090-2099 for RCP4.5 and RCP8.5)

General information 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 Guilpart et al. (2022) Data-driven projections suggest large opportunities to improve Europe's soybean self-sufficiency under climate change, Nature Food. All details can be found in this paper. A brief summary is provided below. Summary of soybean yield projections methodology 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 (Tmin and Tmax, degree Celsius), monthly total precipitation (rain, mm month-1), monthly mean daily total solar radiation (solar, MJ m-2 day-1), monthly mean air vapor pressure (VP, hPa). The fitted model showed high R² (higher than 0.9) and low RMSE (0.35 t ha-1) between observed and predicted yields based on cross-validation. 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-2). 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. Files description <ul> <li>RF_soybean_historical_GRASP_median_1981_2010.nc : 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>RF_soybean_rcp45_median_2050_2059.nc : 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>RF_soybean_rcp45_median_2090_2099.nc : 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>RF_soybean_rcp85_median_2050_2059.nc : 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>RF_soybean_rcp85_median_2090_2099.nc : 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> References Guilpart N. et al. (2022) Data-driven projections suggest large opportunities to improve Europe's soybean self-sufficiency under climate change, Nature Food. Iizumi T. et al. (2014a) Historical changes in global yields: Major cereal and legume crops from 1982 to 2006. Glob. Ecol. Biogeogr. 23, 346–357. Iizumi T. et al. (2014b). A meteorological forcing data set for global crop modeling: Development, evaluation, and intercomparison. J. Geophys. Res. Atmos. Res. 119, 363–384.

opencc-by-4.0May 2024View details →

Scripts and datas for "Climate-driven projections of future global wetlands extent"

Computations scripts (1, 2), associated input dataset (3), and output datasets for wetland fractions (4, 5) used and presented in the study: L. Hardouin, B. Decharme, J. Colin, C. Delire: Climate-driven projections of future global wetlands extent. The calculation and input scripts include: 1_var_comput : 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. 2_TOPMODEL : 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. 3_alpha_and_beta : Calibrated alpha and beta parameters used to obtain the historical and projected wetland fractions with the calibrated version. The outputs datasets contain: 4_fwtl_model_period : 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. 5_not_calibrated_fwtl_model_period : 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.   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.

opencc-by-4.0Jul 2024View details →

Data: Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing

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". OGGM_area_projections.nc contains data for Figure 3. OGGM_volume_projections contains data for Figure 4. OGGM_MassLoss_SLR_projections_regions.nc contains data for Figure 5. OGGM_solid_ice_discharge_regions.nc contains data for Figure 6. OGGM_freshwater_runoff_magnitude_composition_timings_projections.nc & OGGM_freshwater_runoff_projections_regions.nc contain data for Figure 7. OGGM_PeakWaterYear_projections_regions.nc contains data for Figure 8.

opencc-by-4.0Mar 2023View details →

Dataset for "Future projections for the Antarctic ice sheet until the year 2300 with a climate-index method"

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>). Please see the README for details. V1.1: Run-specs header files for SICOPOLIS added. README updated. V1: Initial upload. * * * * * * * Users should cite the original publication when using all or parts of these data.

opencc-by-4.0Oct 2023View details →

Reanalysis and future wave climate projections of the wave climate of the Gulf of Riga 1993-2100

<h4>Data sets</h4>There are two data sets: (1) reanalysis (1993-2021) and (2) future projection (2015-2100).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: acronym, units) <ul><li>Mean wave direction: VMDR_WW, °</li><li>Spectral significant wave height: VHM0_WW, m</li><li>Spectral moment (0,1) of wave period or mean wave period: VTM01_WW, s</li><li>Eastward wave energy flux: WWEFu, W/m</li><li>Northward wave energy flux: WWEFv, W/m</li></ul> 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.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. <h4>Reanalysis</h4>Time period: 1993-2021, 29 years.The main characteristics of the input data and approach for the reanalysis run are as follows: <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>Future climate projection</h4>Time period: 2015-2100, 86 years.The main characteristics of the input data and approach for the future wave climate projections run are as follows: <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>References</h4>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).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>.Shore protection manual, Army Corps of Engineers, Coastal Engineering Research Center (CERC), (1984).

Fig. 2 Projected future changes for Dermacentor reticulatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus

Fig. 2 Projected future changes for Dermacentor reticulatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585. In dark blue: area projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction). In light blue: area projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e., stable absence). In orange: area projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable range). In red: area projected as unsuitable under current climatic conditions but suitable under future climatic conditions (i.e., potential new range). AUC = 0.8333 (average over 10 replicates using cross-validation, standard deviation = 0.001113603). Threshold to transform the logistic model output: 0.3816 (10% omission rate threshold). Maps were built using ESRI ArcGIS (Release 10.7, www.esri.com). Projection: Europe Albers Equal Area Conic

Fig. 2 Projected future changes for Dermacentor reticulatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus

Fig. 1 Projected future changes for Ixodes ricinus until 2081–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus

Fig. 1 Projected future changes for Ixodes ricinus until 2081–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585. In dark blue: area projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction). In light blue: area projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e., stable absence). In orange: area projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable range). In red:

Fig. 3 Projected future changes for Dermacentor marginatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus

Fig. 3 Projected future changes for Dermacentor marginatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585. In dark blue: area projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction). In light blue: area projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e., stable absence). In orange: area projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable range). In red: area projected as unsuitable under current climatic conditions but suitable under future climatic conditions (i.e., potential new range). AUC = 0.8229 (average over 10 replicates using cross-validation, standard deviation = 0.001121953). Threshold to transform the logistic model output: 0.4298 (10% omission rate threshold). Maps were built using ESRI ArcGIS (Release 10.7, www.esri.com). Projection: Europe Albers Equal Area Conic

opencc-by-4.0Aug 2023View details →

Reanalysis and future climate projections of the physical state of the Gulf of Riga 1993-2100

Data sets There are two data sets: (1) reanalysis (1993-2021) and (2) future projection (2015-2100). Future projection data set is split into 10 files. The dataset provides gridded monthly mean values of physical parameters in the Gulf of Riga, Baltic Sea. The variables of the dataset of the physical state of the Gulf of Riga are as follows (Long name: acronym, units) <ul> <li>Potential temperature: thetao, °C</li> <li>Sea water salinity: s, g/kg</li> <li>Eastward sea water velocity: ocu, m/s</li> <li>Northward sea water velocity: ocv, m/s</li> <li>Deviation of sea-level from the mean sea level: zos, m</li> <li>Sea ice area fraction siconc, m2/ m2</li> <li>Sea ice thickness: sithick, m</li> <li>Bathymetry: bathymetry, m (included only in reanalysis data set)</li> </ul> The grid size of the dataset is 15 (depth) x 203 (latitude) x 187 (longitude). The horizontal grid spacing is 0.5 nm; the vertical grid has 15 depth layers – 2 m deep surface layer and 4 m step for deeper layers. The time resolution of the dataset is monthly – the monthly mean value is provided in the 1st day of the month in time dimension. The original climatic calculations are based on the University of Latvia (UL) set-up of the Hiromb-BOOS model routinely implemented for the operational oceanography in the Baltic Sea and the Gulf of Riga in Latvia. Its parametrization is empirically suited for climatical reanalysis in the Gulf of Riga domain. The original output of the model run is hourly data series. Reanalysis Time period: 1993-2021 (29 years). The main characteristics of the input data and approach for the reanalysis run are as follows: <ul> <li>EMODNET2020 bathymetry.</li> <li>Initial conditions – bias corrected Copernicus Marine Service (CMS).</li> <li>Atmospheric forcing – ERA5 meteorology with improved cloudiness.</li> <li>Boundary conditions from CMS 1993-2018 reanalysis and CMS operational archive (2019-2021) with bias correction for waterlevel in CMS forecast.</li> <li>River inflow – 15 main rivers taken into consideration according to E-HYPE hydrological model data. E-HYPE discharge multiplied by 0.75.</li> <li>Tides: astronomic calculations.</li> </ul> Future climate projection Time period: 2015-2100 (86 yrs). The main characteristics of the input data and approach for the future climate projections run are as follows: <ul> <li>Emodnet2020 bathymetry.</li> <li>Initial conditions – bias corrected Copernicus Marine Service (CMS).</li> <li>Boundary conditions 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>River inflow – 15 main rivers taken into consideration according to E-HYPE climatological model (SMHI_RCA4_HadGEM2-ES_rcp45). E-HYPE discharge multiplied by 1.093.</li> <li>The past period data was used for the downscaling: <ul> <li>ERA5 reanalysis data was used for the downscaling of the atmospheric forcing time series of CMIP6 climate projection model,</li> <li>CMS reanalysis model data was used for the downscaling  of the sea state time series.</li> </ul> </li> <li>Downscaled variables: eastward and northward components of the near surface wind, air temperature, air pressure, water temperature, water salinity, sea level.</li> </ul>

dryad36/100

Data From: Conservation planning in an uncertain climate: identifying projects that remain valuable and feasible across future scenarios

Conservation actors face the challenge of allocating limited resources despite uncertainty about future climate. A key goal is to minimize the potential for negative outcomes under future scenarios. Thus, we address a global conservation challenge: how to allocate conservation investments given high uncertainty about future climate conditions. To that end, we present a method for identifying projects that remain valuable and feasible across climate scenarios and apply our framework to freshwater biodiversity conservation in the South-Central USA. We combine data from a recent high-resolution hydrologic planning tool and species distribution models to estimate the conservation feasibility and biodiversity value of river reaches below 38 major reservoirs in the Red River basin.We find that only 13% of sites have high conservation priority across all future climate scenarios and that spatial patterns of conservation priority largely reflect patterns of water availability and fish biodiversity.

opencc-zeroOct 2020View details →

opencc-by-4.0Dec 2020View details →

Dataset for Bukovsky et al. (2021): "SSP-Based Land Use Change Scenarios: A Critical Uncertainty in Future Regional Climate Change Projections"

This dataset contains derived data and model data necessary for reproducing the results found in "SSP-Based Land Use Change Scenarios: A Critical Uncertainty in Future Regional Climate Change Projections" by Melissa S. Bukovsky, Jing Gao, Linda O. Mearns, and Brian C. O'Neill. This dataset contains data not otherwise available in other public archives, as noted in Bukovsky et al. (2021, Earth's Future; preprint available at https://doi.org/10.1002/essoar.10504141.2). That is, this dataset contains data from the land-use change simulations that are not part of NA-CORDEX (na-cordex.org), but which are complementary to those published in the NA-CORDEX archive.

opencc-by-4.0Apr 2024View details →

Calibrated and uncalibrated projection data from the paper "Assessing observational constraints on future European climate in an out-of-sample framework"

Individual uncalibrated and calibrated projections for each of the five methods (A-E) in the following folders: MethodA_proj/ MethodB_proj/ MethodC_proj/ MethodD_proj/ MethodE_proj/   Also included are the out-of-sample data from the "pseudo-observations" (taken from CMIP6 models) used for the verification (see paper for full details): FUTUREverif/

opencc-by-4.0Jan 2022View details →

Future Projection of Solar Energy Over China Based on Multi-Regional Climate Model Simulations

Data for article "Future Projection of Solar Energy Over China Based on Multi-Regional Climate Model Simulations"

opencc-by-4.0Aug 2022View details →

Data from "Projections of leaf turgor loss point shifts under future climate change scenarios" (Tordoni et al. 2022 Global Change Biology)

The dataset includes four sheets representing the average turgor loss point (tlp) values at grid cell level (tlp_data) and the climatic variables and related climate change scenarios derived from the three models used in this study (HadGEM2-ES-RACMO22E, EC-EARTH_RACMO22E, EC-EARTH_CCLM4-8-17, respectively). The sheet "tlp_data" reports the cell ID (OGU) and the average tlp values for each taxonomic group considered in this study (gymnosperms, angiosperms, herbaceous and woody angiosperms).  Each of the other three sheets reports the cell ID (OGU), coordinates of the cell centroid (Long, Lat) and a set of six climatic variables: 95th percentiles of average temperature (BIO1.95, °C), temperature seasonality (BIO4, °C), annual consecutive frost days where temperature was ≤ 0 °C (CFD.ann, n° days), annual consecutive dry days where precipitation was < 1 mm (CDD.ann, n° days), 5th percentiles of cumulate annual precipitation (BIO12.5, mm), and precipitation seasonality (BIO15, %). For each model, "hist" refers to historical data encompassing the period 1970-2005, whereas "RCP2.6" and "RCP8.5" reports the average value of future projections for the period 2080-2100 in two representative concentration pathway (RCP) scenarios (RCP2.6 and RCP8.5).