Species Distribution Toolkit

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CHELSA2 ​

CHELSA (Climatologies at high resolution for the earth’s land surface areas) is a very high resolution (30 arc sec, ~1km) global downscaled climate data set currently hosted by the Swiss Federal Institute for Forest, Snow and Landscape Research WSL. It is built to provide free access to high resolution climate data for research and application, and is constantly updated and refined.

Citations

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.

Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth’s land surface areas. EnviDat.

For more information about this provider: https://chelsa-climate.org/

AverageTemperature ​

julia
using SpeciesDistributionToolkit
layer = SDMLayer(RasterData(CHELSA2, AverageTemperature))

Average temperature within each grid cell, usually represented in degrees, and usually provided as part of a dataset giving daily, weekly, or monthly temporal resolution.

For more information about this dataset: https://chelsa-climate.org/

Keyword argument month

This dataset can be accessed monthly, using the month keyword argument. You can list the available months using SimpleSDMDatasets.months(RasterData{CHELSA2, AverageTemperature}).

Projections for SSP126

Note that the future scenarios support the same keyword arguments as the contemporary data.

Models: GFDL_ESM4, IPSL_CM6A_LR, MPI_ESM1_2_HR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2011) => Year(2040), Year(2041) => Year(2070) and Year(2071) => Year(2100)

Projections for SSP370

Note that the future scenarios support the same keyword arguments as the contemporary data.

Models: GFDL_ESM4, IPSL_CM6A_LR, MPI_ESM1_2_HR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2011) => Year(2040), Year(2041) => Year(2070) and Year(2071) => Year(2100)

Projections for SSP585

Note that the future scenarios support the same keyword arguments as the contemporary data.

Models: GFDL_ESM4, IPSL_CM6A_LR, MPI_ESM1_2_HR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2011) => Year(2040), Year(2041) => Year(2070) and Year(2071) => Year(2100)

BioClim ​

julia
using SpeciesDistributionToolkit
layer = SDMLayer(RasterData(CHELSA2, BioClim))

The BioClim variables are derived from monthly data about precipitation and temperature, and convey information about annual variation, as well as extreme values for specific quarters. These variables are usually thought to represent limiting environmental conditions.

For more information about this dataset: https://chelsa-climate.org/

Keyword argument layer
Layer codeDescription
BIO8Mean Temperature of Wettest Quarter
BIO14Precipitation of Driest Month
BIO16Precipitation of Wettest Quarter
BIO18Precipitation of Warmest Quarter
BIO19Precipitation of Coldest Quarter
BIO10Mean Temperature of Warmest Quarter
BIO12Annual Precipitation
BIO13Precipitation of Wettest Month
BIO2Mean Diurnal Range (Mean of monthly (max temp - min temp))
BIO11Mean Temperature of Coldest Quarter
BIO6Min Temperature of Coldest Month
BIO4Temperature Seasonality (standard deviation ×100)
BIO17Precipitation of Driest Quarter
BIO7Temperature Annual Range (BIO5-BIO6)
BIO1Annual Mean Temperature
BIO5Max Temperature of Warmest Month
BIO9Mean Temperature of Driest Quarter
BIO3Isothermality (BIO2/BIO7) (×100)
BIO15Precipitation Seasonality (Coefficient of Variation)
Projections for SSP126

Note that the future scenarios support the same keyword arguments as the contemporary data.

Models: GFDL_ESM4, IPSL_CM6A_LR, MPI_ESM1_2_HR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2011) => Year(2040), Year(2041) => Year(2070) and Year(2071) => Year(2100)

Projections for SSP370

Note that the future scenarios support the same keyword arguments as the contemporary data.

Models: GFDL_ESM4, IPSL_CM6A_LR, MPI_ESM1_2_HR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2011) => Year(2040), Year(2041) => Year(2070) and Year(2071) => Year(2100)

Projections for SSP585

Note that the future scenarios support the same keyword arguments as the contemporary data.

Models: GFDL_ESM4, IPSL_CM6A_LR, MPI_ESM1_2_HR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2011) => Year(2040), Year(2041) => Year(2070) and Year(2071) => Year(2100)

MaximumTemperature ​

julia
using SpeciesDistributionToolkit
layer = SDMLayer(RasterData(CHELSA2, MaximumTemperature))

Maximum temperature within each grid cell, usually represented in degrees, and usually provided as part of a dataset giving daily, weekly, or monthly temporal resolution.

For more information about this dataset: https://chelsa-climate.org/

Keyword argument month

This dataset can be accessed monthly, using the month keyword argument. You can list the available months using SimpleSDMDatasets.months(RasterData{CHELSA2, MaximumTemperature}).

Projections for SSP126

Note that the future scenarios support the same keyword arguments as the contemporary data.

Models: GFDL_ESM4, IPSL_CM6A_LR, MPI_ESM1_2_HR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2011) => Year(2040), Year(2041) => Year(2070) and Year(2071) => Year(2100)

Projections for SSP370

Note that the future scenarios support the same keyword arguments as the contemporary data.

Models: GFDL_ESM4, IPSL_CM6A_LR, MPI_ESM1_2_HR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2011) => Year(2040), Year(2041) => Year(2070) and Year(2071) => Year(2100)

Projections for SSP585

Note that the future scenarios support the same keyword arguments as the contemporary data.

Models: GFDL_ESM4, IPSL_CM6A_LR, MPI_ESM1_2_HR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2011) => Year(2040), Year(2041) => Year(2070) and Year(2071) => Year(2100)

MinimumTemperature ​

julia
using SpeciesDistributionToolkit
layer = SDMLayer(RasterData(CHELSA2, MinimumTemperature))

Minimum temperature within each grid cell, usually represented in degrees, and usually provided as part of a dataset giving daily, weekly, or monthly temporal resolution.

For more information about this dataset: https://chelsa-climate.org/

Keyword argument month

This dataset can be accessed monthly, using the month keyword argument. You can list the available months using SimpleSDMDatasets.months(RasterData{CHELSA2, MinimumTemperature}).

Projections for SSP126

Note that the future scenarios support the same keyword arguments as the contemporary data.

Models: GFDL_ESM4, IPSL_CM6A_LR, MPI_ESM1_2_HR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2011) => Year(2040), Year(2041) => Year(2070) and Year(2071) => Year(2100)

Projections for SSP370

Note that the future scenarios support the same keyword arguments as the contemporary data.

Models: GFDL_ESM4, IPSL_CM6A_LR, MPI_ESM1_2_HR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2011) => Year(2040), Year(2041) => Year(2070) and Year(2071) => Year(2100)

Projections for SSP585

Note that the future scenarios support the same keyword arguments as the contemporary data.

Models: GFDL_ESM4, IPSL_CM6A_LR, MPI_ESM1_2_HR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2011) => Year(2040), Year(2041) => Year(2070) and Year(2071) => Year(2100)

Precipitation ​

julia
using SpeciesDistributionToolkit
layer = SDMLayer(RasterData(CHELSA2, Precipitation))

Precipitation (rainfall) within each grid cell, usually represented as the total amount received, and usually provided as part of a dataset giving daily, weekly, or monthly temporal resolution.

For more information about this dataset: https://chelsa-climate.org/

Keyword argument month

This dataset can be accessed monthly, using the month keyword argument. You can list the available months using SimpleSDMDatasets.months(RasterData{CHELSA2, Precipitation}).

Projections for SSP126

Note that the future scenarios support the same keyword arguments as the contemporary data.

Models: GFDL_ESM4, IPSL_CM6A_LR, MPI_ESM1_2_HR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2011) => Year(2040), Year(2041) => Year(2070) and Year(2071) => Year(2100)

Projections for SSP370

Note that the future scenarios support the same keyword arguments as the contemporary data.

Models: GFDL_ESM4, IPSL_CM6A_LR, MPI_ESM1_2_HR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2011) => Year(2040), Year(2041) => Year(2070) and Year(2071) => Year(2100)

Projections for SSP585

Note that the future scenarios support the same keyword arguments as the contemporary data.

Models: GFDL_ESM4, IPSL_CM6A_LR, MPI_ESM1_2_HR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2011) => Year(2040), Year(2041) => Year(2070) and Year(2071) => Year(2100)