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1,077 results for “1981”
MAR-ERA5 European Alps (1981-2020)
<p>This deposit contains MAR simulations over the European Alps domain (7 kilometers resolution) forced by the ERA-5 reanalysis. The version of the MAR model used is v.3.10, model set-up is described in detail in Beaumet et al., 2021 (https://doi.org/10.1007/s10113-021-01830-x)<br> Contact person : Julien Beaumet (beaumetjulien@gmail.com), Martin Menegoz (martin.menegoz@univ-grenoble-alpes.fr)<br> The simulations cover the period covered by ERA5 reanalysis : 1981-2020 (1979-1980=Spin-up years)<br> Data are available at the daily frequency, with one variable (10 years of data) per file.<br> The available variables in this deposit are :<br> <strong>LWD: </strong>Surface downward longwave radiation, [W/m2]<br> <strong>LWU:</strong> Surface upward longwave radiation, [W/m2]<br> <strong>MB:</strong> Total snow water equivalent, [mm.We]<br> <strong>MBrr:</strong> Daily rainfall, [mm.We] (5)<br> <strong>MBsf:</strong> Daily snowfall, [mm.We] (5)</p> <p><strong>QQz: </strong> Near-surface specific humidity at constant height, [g/kg] (2)</p> <p><strong>SWD:</strong> Surface downward shortwave radiation, [W/m2]<br> <strong>SWU:</strong> Surface upward shortwave radiation, [W/m2]</p> <p><strong>TTmax:</strong> Near-surface maximum air temperature for the first model level above the surface (constant sigma), [C]<br> <strong>TTmin:</strong> Near-surface minimum air temperature for the first model level above the surface (constant sigma),[C]</p> <p><strong>TTz: </strong>Near-surface mean air temperature at constant-height, [C]<br> <strong>UUz:</strong> Near-surface zonal component of wind speed at constant height, [m/s]<br> <strong>VVz:</strong> Near surface Meridional component of wind speed at constant height, [m/s]<br> <strong>ZN3:</strong> Total snow height, [m]</p> <p>Other variables are available upon request (see email above).<br> AL : Surface albedo, [0-1]<br> CC: Cloud cover, [0-1]<br> CD: Low level Cloud cover, [0-1]<br> CM: Middle level Cloud cover, [0-1]<br> CU: High level Cloud cover, [0-1]<br> SP: Surface pressure, [hPa]<br> ST: Surface temperature, [C]<br> TT: Near-surface mean air temperature for the first three model level above the surface (constant sigma), [C] (1)<br> TTp: Constant pressure-level mean air temperature, [C] (4)<br> ZZ: Surface geopotential for the first three model level above the surface (constant sigma), [m]</p> <p>SHF: Surface sensible heat flux, [W/m2] LHF: Surface latent heat flux, [W/m2]</p> <p>* Latitude(LAT),longitude(LON)and surface elevation (SH) of each grid point can be read in MARgrid_EUl.nc file</p> <p>(1) For variable TT, ZZ model constant sigma level of 0.9997479<br> (2) For variables TTz, QQz constant height level at 2m<br> (3) For variables UUz, VVz constant height level at 10m<br> (4) Variables TTp, UUp, VVp available at pressure level : 925, 850, 800, 700, 600, 500, 200 hPa<br> (5) Snow height and snow water equivalent are available for three sectors which corresponds to three different vegetation type : The three vegetation type used can be readen in the file MARgrid_EUy.nc, with the variable VEG and their respectibe fraction for each grid point is given by the variable FRV. The third vegetation type (sector=3) mostly corresponds to bare soil or low crops by default, but sometimes its fraction=0, which gives unrealistic low values of snow height. In this case, using the max. value on the axis sector often gives the best results.<br> Legend of the vegetation type for the VEG variables : 0:NO_VEGETATION 1:CROPS_LOW 2:CROPS_MEDIUM 3:CROPS_HIGH 4:GRASS_LOW 5:GRASS_MEDIUM 6:GRASS_HIGH 7:BROADLEAF_LOW 8:BROADLEAF MEDIUM 9:BROADLEAF_HIGH 10:NEEDLELEAF_LOW 11:NEEDLELEAF MEDIUM 12:NEEDLELEAF_HIGH 13:City</p> <p> </p>
Adriatic Sea wind-wave climate years 1981-2010 and 2021-2050 (RCP4.5 and RCP8.5)
<p>Adriatic Sea mean annual 50th, 90th, 95th and 99th percentiles of the significant wave height (Hs) from WAVEWATCH III v6.07 (2 km) forced with 1-hour ERA5 wind fields statistically scaled to QQ-match COSMO-CLM fields (available at https://doi.org/10.5281/zenodo.6021380).</p> <p>Reference periods:</p> <p>1) Historical climate: years 1981-2010</p> <p>2) Future climate: years 2021-2050 (IPCC scenario RCP4.5 and RCP8.5)</p>
Adriatic Sea wind climate years 1981-2010 and 2021-2050 (RCP4.5 and RCP8.5)
<p>Adriatic Sea mean annual 50th, 90th, 95th and 99th percentiles of the sea surface wind speed (10-m height U10) from 1-hour ERA5 fields (25 km) statistically scaled to QQ-match COSMO-CLM fields (8 km).</p> <p>Reference periods:</p> <p>1) Historical climate: years 1981-2010</p> <p>2) Future climate: years 2021-2050 (IPCC scenario RCP4.5 and RCP8.5)</p>
Adriatic Sea wind and wave time series years 1981-2010 and 2021-2050 (RCP4.5 and RCP8.5)
<p>Wind and wave time series for 27 stations in the Adriatic Sea.</p> <p>Variables:</p> <p>- 10-m height wind speed (wnd) and wind direction (wnddir) from 1-hour ERA5 fields (25 km) statistically scaled to QQ-match COSMO-CLM fields (8 km)</p> <p>- significant wave height (hs) and peak wave period (tp) from WAVEWATCH III v6.07 (2 km) forced with the scaled ERA5 wind fields</p> <p>Reference periods:</p> <p>- Historical climate: years 1981-2010</p> <p>- Future climate: years 2021-2050 (IPCC scenario RCP4.5 and RCP8.5)</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>
Variability and drivers of the Oxygen Minimum Zone in the Tropical Pacific during 1981-2020
<p>This dataset contains model outputs simulated by a basin-scale model (OGCM-DEEC V1.4). It is supplementary of the paper "Variability and drivers of the Oxygen Minimum Zone in the Tropical Pacific during 1981-2020". Please let me know if you need any more information.</p>
GSDM-WBT: Global station-based daily maximum wet-bulb temperature data for 1981-2020
<p>The wet-bulb temperature integrates the temperature and humidity to comprehensively describe the thermal environment and the energy regulation of human bodies. Daily maximum wet-bulb temperature is an important indicator to be used for research on extreme humid heat. GSDM-WBT is a new dataset of global station-based daily maximum wet-bulb temperature, which was produced through calculating wet-bulb temperature, data quality control, infilling missing values and homogenisation based on the HadISD station-based observations and the NCEP-DOE reanalysis data. GSDM-WBT covers the complete daily series of 1834 stations around the world from 1981 to 2020. We provide the NetCDF files of GSDM-WBT for each station and one compressed file containing all data.</p>
The 0.1° stem area index dataset over Tibetan Plateau from 1981 to 2018
<p><strong>Description:</strong></p> <p>Based on the method of Zeng et al. (2002), we built the monthly stem area index (Ls) data product over the Tibetan Plateau (TP) at 0.1°×0.1° spatial resolution from 1981-09 to 2018-12 by using leaf area index data (LAI) from GLASS (Liang et al., 2021) and grass fractional cover data from Lawrence and Chase (2007). Here, We revised the method, considering that grass is completely green (no WGS) from May to August, the Ls,min is set to 0; in September when the grass begins to wither (Xiao et al., 2023), its Ls value is obtained by subtracting the Lgv in September from that in August; from October (when grass turns completely withered) to the April of next year, the Ls is calculated without adding of withered leaves, considering the small magnitude of LAI (<0.2) in non-growing season; the monthly remaining rate of withered leaves and stems (α) is obtained from the observed total area of leaf and stem data of Xiao et al. (2023), which actually represents the neutralization of monthly removal of dead leaves and the withering part, especially in October. The calculation of Ls starts from September, 1981. Based on the above, the Ls is calculated on the sub-grid scale, then it multiplies by the fractional vegetation cover of grass to obtain the stem area index on the grid scale.</p> <p><strong>Dataset Characteristics:</strong></p> <p>Spatial Coverage: 75º~105ºE, 25º~40ºN;</p> <p> Temporal Coverage: Sep. 1981-Dec. 2018;</p> <p> Spatial Resolution: 0.1º;</p> <p> Temporal Resolution: Monthly;</p> <p> Data Format: NetCDF.</p> <p><strong>Citation</strong><strong>:</strong></p> <p>Qi, Q., Yang, K., Li, H., Ai, L., Wang, C., Wu, T. (2024). Negative impacts of the withered grass stems on winter snow cover over the Tibetan Plateau. Agric. For. Meteorol., 352, 110053. https://doi.org/10.1016/j.agrformet.2024.110053</p> <p>If you have any questions, please contact <strong>Dr. Kai Yang (yangkai@lzu.edu.cn)</strong></p>
ERA5-Land selected indicators daily aggregates for Africa, 1981
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1981.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
Fine-scale anthropogenic nutrient input data for 8 watersheds along the Saint Lawrence River (1981, 2021)
<p>We quantified Net Anthropogenic Nitrogen and Phosphorus Inputs (NANI-NAPI) at two scales (the finest one, the municipality, and a coarser one, the county) for all municipalities and counties of 8 watersheds in Québec, Canada, for 1981 and 2021.</p> <p>The datasets here report 1) watershed NANI and NAPI values accounted from both scales for 1981 and 2021, 2) municipality-scale NANI values for each municipality in the 8 watersheds, and 3) certain components of the municipality-scale NANI for all municipalities in the Yamaska watershed.</p>
HCLIM-RK_1981_2010
<p>1981–2010 monthly precipitation climatologies (i.e. the monthly total sums are averaged over the range of years 1981-2010) for the Norwegian mainland over a regular grid at 1 km of resolution. The gridded datasets are computed by an interpolation procedure (Kriging-based) combining the output from a numerical model (HCLIM-AROME) with the in-situ observations. The observations were retrieved from the MET Norway Climate daily database (KDVH) and they were integrated with the daily records contained in the European Climate Assessment and Dataset (ECA&D) for the surrounding countries.</p>
CLDF dataset derived from Kraft's "Chadic Wordlists" from 1981
<p>Cite the source of the dataset as:</p> <blockquote> <p>Kraft, Charles H. 1981. Chadic wordlists. Berlin: Dietrich Reimer.</p> </blockquote>
Global topsoil SOC stock from 1981 to 2018 estimated by combining process-based model and space-for-time digital soil mapping
<p>This dataset include the topsoil (0-30cm) soil organic carbon (SOC) stocks in mineral soils under major land classes (forest, grassland, shrub land, savannas, cropland, cropland/natural vegetation mosaic, and sparely vegetated land) from 1981 to 2018. The long-time series of SOC stocks were estimated by using a space-for-time digital soil mapping (DSMst) model where the RothC-simulated SOC stocks were incorporated as one of the dynamic covariates of the DSMst model.</p> <p>The detail information on the products were given below:</p> <p>Name: DSMst-RothC 5-km global topsoil SOC stock products</p> <p>Period: 1981-2018</p> <p>Spatial resolution: 0.041666667 degree</p> <p>Temporal resolution: 1 year</p> <p>CRS: geographic latitude/longitude (EPSG:4326 - WGS 84 – Geographic)</p> <p>Extent: -180°, -90°: 180°, 90°</p> <p>Data format: GeoTIFF</p> <p>Compression: LZW</p> <p>Data type: Float32</p> <p>Unit: t C ha<sup>-1</sup></p>
Heatwaves characterization derived from observations and climate projections to assess thermal behavior of 7 European city-hubs: Milano, Athens, Logroño, Cork, Gdynia, Lillestrøm and Amsterdam (1981-2100)
<p>This dataset includes the processing results used to create the interactive climate service <a href="https://thermal-assessment.urban.tecnalia.dev/">Thermal Assessment Tool</a>. It provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions and cities in Europe during episodes of extreme heat.</p> <p>A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010.</p> <p>To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the observations the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-gridded-observations-europe?tab=overview">e-OBS</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (<strong>RCP4.5</strong>) and very high (<strong>RCP8.5</strong>) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS).</p> <p>The database is organized in three datasets:</p> <p>Regional_eobs_thresholds_Reachout.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>tmax</strong>: daily maximum temperature threshold.</li> <li><strong>tmin</strong>: daily minimum temperature threshold.</li> </ul> <p>Historical_eobs_heatwaves_Reachout.csv: heatwaves of the historical period (1981-2021) for each region. The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>start</strong>: first date of the heatwave.</li> <li><strong>tmax</strong>: maximum temperature reached during the heatwave.</li> <li><strong>intensity</strong>: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li><strong>duration</strong>: duration of the heatwave.</li> </ul> <p>Future_and_baseline_eobs_heatwaves_Reachout.csv: ensemble future projections of heatwaves. The columns are:</p> <ul> <li><strong>hazard_level</strong>: it can be a warning, an alert or an alarm.</li> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>experiment</strong>: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li><strong>period</strong>: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li><strong>decade_frequency</strong>: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li><strong>decade_frequency_best</strong>: only applicable to the future. It determines the best projection among the models.</li> <li><strong>decade_frequency_worst</strong>: only applicable to the future. It determines the worst projection among the models.</li> <li><strong>year_days</strong>: average annual days.</li> <li><strong>year_tmax_intensity</strong>: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li><strong>year_tmin_intensity</strong>: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul>
Heatwaves characterization derived from reanalysis and climate projections to assess thermal behavior of regions in Europe (1981-2100)
<p>This dataset provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions in Europe during episodes of extreme heat.</p> <p>A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010.</p> <p>To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the reanalysis the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview">ERA5-Land</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (<strong>RCP4.5</strong>) and very high (<strong>RCP8.5</strong>) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS).</p> <p>The database is organized in three datasets:</p> <p>Regional_era5land_thresholds_Europe.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li><strong>tmax</strong>: daily maximum temperature threshold.</li> <li><strong>tmin</strong>: daily minimum temperature threshold.</li> </ul> <p>Historical_era5land_heatwaves_Europe.csv: heatwaves of the historical period (1981-2021) for each region. The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li><strong>start</strong>: first date of the heatwave.</li> <li><strong>tmax</strong>: maximum temperature reached during the heatwave.</li> <li><strong>intensity</strong>: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li><strong>duration</strong>: duration of the heatwave.</li> </ul> <p>Future_and_baseline_era5land_heatwaves_Europe.csv: ensemble future projections of heatwaves. The columns are:</p> <ul> <li><strong>hazard_level</strong>: it can be a warning, an alert or an alarm.</li> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li><strong>experiment</strong>: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li><strong>period</strong>: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li><strong>decade_frequency</strong>: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li><strong>decade_frequency_best</strong>: only applicable to the future. It determines the best projection among the models.</li> <li><strong>decade_frequency_worst</strong>: only applicable to the future. It determines the worst projection among the models.</li> <li><strong>year_days</strong>: average annual days.</li> <li><strong>year_tmax_intensity</strong>: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li><strong>year_tmin_intensity</strong>: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul> <p> </p>
Heatwaves characterization derived from observations and climate projections to assess thermal behavior of regions in Europe (1981-2100)
<p>This dataset provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions in Europe during episodes of extreme heat.</p> <p>A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010.</p> <p>To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the observations the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-gridded-observations-europe?tab=overview">e-OBS</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (<strong>RCP4.5</strong>) and very high (<strong>RCP8.5</strong>) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS).</p> <p>The database is organized in three datasets:</p> <p>Regional_eobs_thresholds_Europe.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li><strong>tmax</strong>: daily maximum temperature threshold.</li> <li><strong>tmin</strong>: daily minimum temperature threshold.</li> </ul> <p>Historical_eobs_heatwaves_Europe.csv: heatwaves of the historical period (1981-2021) for each region. The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li><strong>start</strong>: first date of the heatwave.</li> <li><strong>tmax</strong>: maximum temperature reached during the heatwave.</li> <li><strong>intensity</strong>: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li><strong>duration</strong>: duration of the heatwave.</li> </ul> <p>Future_and_baseline_eobs_heatwaves_Europe.csv: ensemble future projections of heatwaves. The columns are:</p> <ul> <li><strong>hazard_level</strong>: it can be a warning, an alert or an alarm.</li> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li><strong>experiment</strong>: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li><strong>period</strong>: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li><strong>decade_frequency</strong>: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li><strong>decade_frequency_best</strong>: only applicable to the future. It determines the best projection among the models.</li> <li><strong>decade_frequency_worst</strong>: only applicable to the future. It determines the worst projection among the models.</li> <li><strong>year_days</strong>: average annual days.</li> <li><strong>year_tmax_intensity</strong>: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li><strong>year_tmin_intensity</strong>: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul> <p> </p>
Heatwaves characterization derived from reanalysis and climate projections to assess thermal behavior of 7 European city-hubs: Milano, Athens, Logroño, Cork, Gdynia, Lillestrøm and Amsterdam (1981-2100)
<p>This dataset includes the processing results used to create the interactive climate service <a href="https://thermal-assessment.urban.tecnalia.dev/">Thermal Assessment Tool</a>. It provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions and cities in Europe during episodes of extreme heat.</p> <p>A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010.</p> <p>To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the reanalysis the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview">ERA5-Land</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (<strong>RCP4.5</strong>) and very high (<strong>RCP8.5</strong>) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS).</p> <p>The database is organized in three datasets:</p> <p>Regional_era5land_thresholds_Reachout.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>tmax</strong>: daily maximum temperature threshold.</li> <li><strong>tmin</strong>: daily minimum temperature threshold.</li> </ul> <p>Historical_era5land_heatwaves_Reachout.csv: heatwaves of the historical period (1981-2021) for each region. The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>start</strong>: first date of the heatwave.</li> <li><strong>tmax</strong>: maximum temperature reached during the heatwave.</li> <li><strong>intensity</strong>: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li><strong>duration</strong>: duration of the heatwave.</li> </ul> <p>Future_and_baseline_era5land_heatwaves_Reachout.csv: ensemble future projections of heatwaves. The columns are:</p> <ul> <li><strong>hazard_level</strong>: it can be a warning, an alert or an alarm.</li> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>experiment</strong>: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li><strong>period</strong>: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li><strong>decade_frequency</strong>: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li><strong>decade_frequency_best</strong>: only applicable to the future. It determines the best projection among the models.</li> <li><strong>decade_frequency_worst</strong>: only applicable to the future. It determines the worst projection among the models.</li> <li><strong>year_days</strong>: average annual days.</li> <li><strong>year_tmax_intensity</strong>: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li><strong>year_tmin_intensity</strong>: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul>
ERA5-Land selected indicators daily aggregates for the Latin America region, 1981
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 1980.</p><p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p><p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p><p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
North Temperate Lakes LTER: Crayfish Abundance 1981 - current (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/3/29. The abstract below was extracted from the Level 0 data package and is included for context: Crayfish data include crayfish catch in cylindrical minnow traps baited with beef liver and occasional occurrence in other gear used to sample fish. Traps are placed at fyke net locations in nine study lakes (Allequash, Big Muskellunge, Crystal, Sparkling, Trout, Mendota, Monona, Wingra and Fish). Crayfish traps have been eliminated as gear in the Madison area lakes (Mendota, Monona, Wingra, and Fish) after 2003. Individuals are identified to species and counted. In Trout and Sparkling Lake more detailed surveys have been conducted during the summer on an ad hoc basis to track distribution and abundance of the invading species Orconectes rusticus. Additional data sets consist of pre-LTER sets (initiated in late June 1972) gathered by Capelli (Ph.D. dissertation) and Lorman (Ph.D. dissertation). Most of pre-LTER data is detailed distribution in Trout Lake, and community composition in other area lakes. Sampling Frequency: annually Number of sites: 9 Note that 2020 data does not exist due to insufficient sampling.
North Temperate Lakes LTER: Benthic Macroinvertebrates 1981 - current (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/11/35. The abstract below was extracted from the Level 0 data package and is included for context: Macroinvertebrates are collected from selected shoreline and deep water locations in the seven primary lakes (Allequash, Big Muskellunge, Crystal, Sparkling, and Trout lakes, and unnamed lakes 27-02 [Crystal Bog], and 12-15 [Trout Bog]) in the Trout Lake area using modified Hester-Dendy samplers. Samplers are placed at fyke net and gill net locations in August and retrieved 3-4 weeks later. Macroinvertebrates are preserved in ethanol. This dataset contains counts of various groups of macroinvertebrates identified from specific samples. The majority of the identifications are at the genus level. The data table "Benthic Macroinvertebrate Codes" identifies the taxonomic group represented by each group code. Taxonomic references: Ecology and Classification of North American Freshwater Invertebrates, Edited by James H Thorp and Alan P Covich, Academic Press, Inc, 1991; Aquatic Insects of Wisconsin, William L Hilsenhoff, Natural History Museums Council, University of Wisconsin-Madison (1995). Sampling Frequency: annually Number of sites: 7
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