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7 results for “BIOCLIM”

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zenodo52/100

Decadal BIOCLIM estimates based on ISIMIP3b climatic forcing data for the European continent

This dataset contains BIOCLIM variables (plus huss, sfcwind, rsds) which have been prepared and calculated from the original ISIMIP3b bias-adjusted climate forcing data from 5 GCM models (obtained on 2023-08-07). For more information on the original data and its properties, please see the ISIMIP3b modelling protocol and here specifically the climate forcing section <a href="https://protocol.isimip.org/#/ISIMIP3b/31-forcing-data" target="_blank" rel="noopener">https://protocol.isimip.org/#/ISIMIP3b/31-forcing-data</a> and <a href="https://doi.org/10.5194/gmd-17-1-2024">Frieler et al. (2024)</a>. The original climate forcing data (global extent, daily temporal grain) were cropped to the European extent and spatial-temporally aggregated. Here 10 year (decadal) steps were chosen as target climatology. For each time slot (e.g. 10 years) and scenario (historical or ssps) the following 22 variables were calculated: bioclim01 = Annual Mean Temperature bioclim02 = Mean Diurnal Range (Mean of monthly (max temp - min temp)) bioclim03 = Isothermality (BIO2/BIO7) (×100) bioclim04 = Temperature Seasonality (standard deviation ×100) bioclim05 = Max Temperature of Warmest Month bioclim06 = Min Temperature of Coldest Month bioclim07 = Temperature Annual Range (BIO5-BIO6) bioclim08 = Mean Temperature of Wettest Quarter bioclim09 = Mean Temperature of Driest Quarter bioclim10 = Mean Temperature of Warmest Quarter bioclim11 = Mean Temperature of Coldest Quarter bioclim12 = Annual Precipitation bioclim13 = Precipitation of Wettest Month bioclim14 = Precipitation of Driest Month bioclim15 = Precipitation Seasonality (Coefficient of Variation) bioclim16 = Precipitation of Wettest Quarter bioclim17 = Precipitation of Driest Quarter bioclim18 = Precipitation of Warmest Quarter bioclim19 = Precipitation of Coldest Quarter huss = Average (arithmetric mean) specific humidity rsds = Average (arithmetric mean) Surface downwelling shortwave radiation sfcwind = Average near-surface wind speed (arithmetric mean) --- Data properties: <table> <tbody> <tr> <td>Shared Socioeconomic Pathways (SSP)</td> <td>SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5</td> </tr> <tr> <td>General circulation models (GCMs)</td> <td>GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL</td> </tr> <tr> <td>Spatial grain</td> <td>0.5 degree (~50km²)</td> </tr> <tr> <td>Geographic projection</td> <td>WGS 84</td> </tr> <tr> <td>Temporal grain</td> <td>10 year steps</td> </tr> <tr> <td>Spatial extent</td> <td>Continental Europe including Turkey (see screenshot)</td> </tr> <tr> <td>Temporal extent</td> <td>1850 to 2010 (Historical), 2010 - 2100 (Future)</td> </tr> <tr> <td>Number of variables</td> <td>22</td> </tr> </tbody> </table> All files are provided in netCDF (nc) format. The preprocessed datasets are provided as it and the author takes no responsibility for errors or misuse.

opencc-by-4.0Aug 2024View details →

zenodo44/100

Mean NDVI Values (1982-2018) and Future Predictions Using CHELSA Bioclim Variables for Türkiye

This dataset contains mean Normalized Difference Vegetation Index (NDVI) values from 1982 to 2018 and their future predictions based on CHELSA bioclimatic variables, specifically for the region of Türkiye. The data is provided in .asc format and includes both historical and projected NDVI values under different climate scenarios. <h4>Contents:</h4> <ul> <li>Historical NDVI Data (1982-2018): Mean NDVI values derived from remote sensing data.</li> <li>Future NDVI Predictions: NDVI projections for the periods 2011-2040, 2041-2070, and 2071-2100 under three Shared Socioeconomic Pathways (SSPs): SSP1-2.6, SSP3-7.0, and SSP5-8.5.</li> </ul> <h4>Methodology:</h4> <ol> <li>Model Training: <ul> <li>A Random Forest Regressor was used to model the relationship between NDVI and the selected bioclim variables.</li> <li>The model achieved an R² of 0.9341, Mean Absolute Error of 0.0275, and Root Mean Squared Error of 0.0499.</li> </ul> </li> <li>Future Predictions: <ul> <li>Future NDVI values were predicted using the trained model and future CHELSA bioclim projections.</li> <li>Predictions were made for three future periods (2011-2040, 2041-2070, 2071-2100) under three SSPs (SSP1-2.6, SSP3-7.0, SSP5-8.5).</li> </ul> </li> </ol> <h4>Data Specifications:</h4> <ul> <li>Extent: Covers the geographical area of Türkiye and adjacents.</li> </ul> <h4>Sources:</h4> <ul> <li>NDVI Data: <ul> <li>Ma, Z., Dong, C., Lin, K., Yan, Y., Luo, J., Jiang, D., & Chen, X. (2022). A Global 250-m Downscaled NDVI Product from 1982 to 2018. Remote Sensing, 14(15), 3639.</li> </ul> </li> <li>CHELSA Bioclim Data: <ul> <li>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017). Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122" target="_new" rel="noreferrer">https://doi.org/10.1038/sdata.2017.122</a></li> <li>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4" target="_new" rel="noreferrer">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a></li> </ul> </li> </ul>

opencc-by-4.0May 2024View details →

zenodo44/100

High-resolution BIOCLIM and ENVIREM grids for Europe in consecutive 100-year bins spanning the last 21,000 years

Here, I provide a dataset of gridded climatic variables at a spatial resolution of 30 arc-seconds for 210 consecutive 100-year bins spanning the period from 21,000 to 0 BP. The dataset includes 19 bioclimatic and 16 ENVIREM variables (described by Title & Bemmels, 2018) commonly used in species distribution modelling. It covers the European continent and adjacent regions within the following boundaries: 32.5°W–70°E and 32.5°N–82.5°N. For each 100-year bin, bioclimatic and ENVIREM variables were calculated based on the downscaled and debiased monthly temperature and precipitation simulations of the Community Climate System Model version 3 (CCSM3; Collins et al., 2006) as provided by the PaleoView software (Fordham et al., 2017). The downscaling procedure was based on the delta-change method (Ramirez Villejas & Jarvis, 2010). As a baseline climatic data, I used monthly temperature and precipitation grids from the CHELSA database for 1940–1989 (Karger et al., 2017). A detailed description of the dataset, including the downscaling method applied, can be found in the Technical specification attached to this dataset.

opencc-by-4.0Jul 2021View details →

dryad36/100

Data from: Mediterranean bioclimate zones of the world for the understanding of fire regimes

Aim: To quantify the role of soil fertility in the spatial variability of fire activity and to identify the mechanisms that drive this variability. Location: The five Mediterranean-type climate regions of the world. Time period: 2002 – present. Major taxa studied: Terrestrial plants. Methods: We compiled remotely sensed data on fire activity, climate, net primary productivity, and chemical soil properties for bioclimatically homogeneous zones within the five Mediterranean-type climate regions of the world. Putative direct and indirect effects of the environmental variables on fire activity were evaluated through structural equation modelling. Results: Fire activity increased with net primary productivity, as expected for ecosystems with fuel-limited fire regimes. Soil acidity and the concentration of exchangeable aluminium also increased fire activity, supporting the idea that low fertility promotes plant characteristics that favour fire initiation and spread. Main conclusions: Our research supports a positive relationship between wildfires and low soil fertility in Mediterranean-type climate regions across the globe. Therefore, soil fertility should be incorporated into models predicting future fires in a warming world.

opencc-zeroFeb 2024View details →

dryad36/100

Data from: Mediterranean bioclimate zones of the world for the understanding of fire regimes

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publicFeb 2024View details →

zenodo28/100

BioClim Austria: Gridded climate indicators for 1961-1990 and 1991-2020 at 250m resolution

<h2>Overview</h2> This gridded meteorological data set consists of climatologies (climate indicators) on a 30-year average basis for Austria and covers two historical periods with a high spatial resolution of 250m. The two 30-year periods provided for the observations allow the analysis of the climate change that has already occurred. The selection of climate indicators is optimized for the needs of ecological models.  Resolution: 250x250m Projection: EPSG 31287 Extent: Austria Periods: 1961-1990 and 1991-2020 Format: GeoTIFF Data sources: Station data and derived products List of climatologies (climate indicators)  <table> <tbody> <tr> <td> # </td> <td> Short name </td> <td> Name </td> <td> Description </td> <td> Unit </td> <td> Yearly (Y),  monthly (M) or growing season (GS) </td> </tr> <tr> <td> 1 </td> <td> tasmin </td> <td> Average daily minimum temperature </td> <td> Arithmetic mean </td> <td> °C </td> <td> Y, M </td> </tr> <tr> <td> 2 </td> <td> tasmax </td> <td> Average daily maximum temperature </td> <td> Arithmetic mean </td> <td> °C </td> <td> Y, M </td> </tr> <tr> <td> 3 </td> <td> tas </td> <td> Average temperature </td> <td> Arithmetic mean </td> <td> °C </td> <td> Y, M </td> </tr> <tr> <td> 4 </td> <td> tas_warmest_month </td> <td> Average temperature mean in the warmest month </td> <td> Calculation of the mean temperature over the climate period for all months and then selection of the highest value for the warmest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 5 </td> <td> tas_coldest_month </td> <td> Average temperature mean in the coldest month </td> <td> Calculation of the mean temperature over the climate period for all months and then selection of the lowest value for the coldest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 6 </td> <td> tasmin_coldest_month </td> <td> Average temperature minimum in the coldest month </td> <td> Calculation of the mean minimum temperature over the climate period for all months and then selection of the lowest value for the coldest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 7 </td> <td> tasmax_warmest_month </td> <td> Average temperature maximum in the warmest month </td> <td> Calculation of the mean maximum temperature over the climate period for all months and then selection of the highest value for the warmest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 8 </td> <td> thermal_continentality </td> <td> Average annual amplitude of monthly mean temperature (Thermal continentality) </td> <td> Climatological monthly mean temperature in the warmest month minus climatological monthly mean temperature in the coldest month (04_tas* minus 05_tas*).  </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 9 </td> <td> FD </td> <td> Average number of frost days per year </td> <td> Defined by 0°C daily minimum temperature at 2m height. </td> <td> days </td> <td> Y </td> </tr> <tr> <td> 10 </td> <td> GSL </td> <td> Average length of the growing season </td> <td> The growing season is the duration in days of the longest continuous period of days with an average temperature of at least 5°C. However, an earlier or later period of such warm days is included in the growing season if it lasts longer than the sum of all intervening cooler days </td> <td> days </td> <td> Y </td> </tr> <tr> <td> 11 </td> <td> tas </td> <td> Average temperature in the growing season </td> <td> Average temperature in the growing season defined as in climate indicator 10 (GSL). If a year does not have a growing season, this value is not defined.  </td> <td> °C </td> <td> GS </td> </tr> <tr> <td> 12 </td> <td> GDD </td> <td> Average Growing Degree Days per year above 5°C </td> <td> Σ(Tmean – 5°C) per year.  </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 13 </td> <td> FD_first </td> <td> Average date of the first frost occurrence </td> <td> Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate </td> <td> day of year </td> <td> Y </td> </tr> <tr> <td> 14 </td> <td> FD_last </td> <td> Average date of the last frost occurrence </td> <td> Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate </td> <td> day of year </td> <td> Y </td> </tr> <tr> <td> 15 </td> <td> HD35 </td> <td> Average number of extremely hot days above 35°C per year </td> <td> Defined by 35°C daily maximum temperature at 2m height. </td> <td> days </td> <td> Y </td> </tr> <tr> <td> 16 </td> <td> FD_10 </td> <td> Average number of days with hard frost below -10°C per year </td> <td> Defined by -10°C daily minimum temperature at 2m height. </td> <td> days </td> <td> Y </td> </tr> <tr> <td> 20 </td> <td> GLO_hori </td> <td> Average sum of global radiation on horizontal surface </td> <td> Also called irradiance or shortwave incoming radiation, taking cloud cover into account. </td> <td> kWh </td> <td> Y, M </td> </tr> <tr> <td> 21 </td> <td> GLO_real </td> <td> Average sum of global radiation on the real surface </td> <td> Also called irradiance or shortwave incoming radiation, taking cloud cover into account. </td> <td> kWh </td> <td> Y, M </td> </tr> <tr> <td> 31 </td> <td> vpd </td> <td> Average water vapor pressure deficit </td> <td> Calculated from daily dew point temperature and temperature.  </td> <td> hPa </td> <td> Y, M </td> </tr> <tr> <td> 31 </td> <td> hurs </td> <td> Average relative humidity </td> <td> Calculated from daily dew point temperature and temperature.  </td> <td> % </td> <td> Y, M </td> </tr> <tr> <td> 32 </td> <td> vpd </td> <td> Average water vapor pressure deficit </td> <td> Calculated from daily dew point temperature and temperature.  </td> <td> hPa </td> <td> GS </td> </tr> <tr> <td> 32 </td> <td> hurs </td> <td> Average relative humidity </td> <td> Calculated from daily dew point temperature and temperature.  </td> <td> % </td> <td> GS </td> </tr> <tr> <td> 33 </td> <td> pr </td> <td> Average precipitation sum </td> <td>   </td> <td> mm </td> <td> Y, M </td> </tr> <tr> <td> 34 </td> <td> hygric_continentality </td> <td> Average hygric continentality according to Gams </td> <td> Defined as arctan of (elevation/annual_precip). Gams, H. (1931). Die klimatische Begrenzung von Pflanzenarealen und die Verteilung der hygrischen Kontinentalität in den Alpen.  </td> <td> ° </td> <td> Y </td> </tr> <tr> <td> 35 </td> <td> pr1mm </td> <td> Average number of days with precipitation  </td> <td> Daily precipitation of at least 1mm.  </td> <td> days </td> <td> Y </td> </tr> <tr> <td> 36 </td> <td> pr1mm </td> <td> Average number of days with precipitation in the growing season </td> <td> Daily precipitation of at least 1mm.  </td> <td> days </td> <td> GS </td> </tr> <tr> <td> 37 </td> <td> DP_3days, DP_5days, DP_7days </td> <td> Average number of days in dry periods in summer half-year </td> <td> Number of days in periods of at least 3, 5, or 7 days with a daily precipitation total of less than 1mm. Summer half-year: April to September </td> <td> days </td> <td> Y </td> </tr> <tr> <td> 39 </td> <td> ET0 </td> <td> Average annual potential evapotranspiration </td> <td> Calculation according to FAO Penman-Monteith: fao.org/3/X0490E/x0490e08.htm </td> <td> mm </td> <td> Y, M </td> </tr> <tr> <td> 40 </td> <td> WBAL </td> <td> Average climatic water balance </td> <td> Precipitation minus potential evapotranspiration </td> <td> mm </td> <td> Y, M </td> </tr> <tr> <td> 41 </td> <td> WBAL </td> <td> Average climatic water balance in the growing season </td> <td> Precipitation minus potential evapotranspiration in the growing season </td> <td> mm </td> <td> GS </td> </tr> </tbody> </table>   <h2>Data sources</h2> The climate indicators were calculated using daily data from different sources: <ul> <li>Temperature: SPARTACUS v2.1 (Gridded data set, 1x1km, https://doi.org/10.1007/s00704-015-1411-4 )</li> <li>Precipitation: SPARTACUS v2.1 (Gridded data set, 1x1km, https://doi.org/10.1007/s00704-017-2093-x )</li> <li>Radiation: APOLIS SHORT(Gridded data set, 100x100m, 2006-2020, https://adsabs.harvard.edu/abs/2012EGUGA..14.9705O) APOLIS LONG (Gridded data set, 100x100m, 1981-2016) SPARTACUS v2.1 (Gridded data set, 1x1km, daily sunshine duration, 1961-2020).</li> <li>Wind: Daily station data (https://doi.org/10.60669/gs6w-jd70)</li> <li>Humidity: Daily station data  (https://doi.org/10.60669/gs6w-jd70)</li> <li>Digital elevation model: © Kooperation Länder, Bund (BEV, BML), 2022</li> </ul>

opencc-by-4.0Apr 2024View details →

dryad20/100

Beef cattle value analysis towards bioclimate data

Open the record for dataset details and reuse information.

publicOct 2017View details →

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