WorldClim2

WorldClim is a database of high spatial resolution global weather and climate data. These data can be used for mapping and spatial modeling. The data are provided for use in research and related activities.

Citation

Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315.

For more information about this provider:

AverageTemperature

using SpeciesDistributionToolkit
layer = SDMLayer(RasterData(WorldClim2, 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://www.worldclim.org/data/index.html

Keyword argument resolution

5 arc minutes - 5.0

30 arc seconds, approx. 1×1 km - 0.5

10 arc minutes - 10.0

2.5 arc minutes, approx 4×4 km - 2.5

Keyword argument month

This dataset can be accessed monthly, using the month keyword argument. You can list the available months using SimpleSDMDatasets.months(RasterData{WorldClim2, AverageTemperature}).

BioClim

using SpeciesDistributionToolkit
layer = SDMLayer(RasterData(WorldClim2, 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://www.worldclim.org/data/index.html

Keyword argument layer

Layer code	Description
BIO8	Mean Temperature of Wettest Quarter
BIO14	Precipitation of Driest Month
BIO16	Precipitation of Wettest Quarter
BIO18	Precipitation of Warmest Quarter
BIO19	Precipitation of Coldest Quarter
BIO10	Mean Temperature of Warmest Quarter
BIO12	Annual Precipitation
BIO13	Precipitation of Wettest Month
BIO2	Mean Diurnal Range (Mean of monthly (max temp - min temp))
BIO11	Mean Temperature of Coldest Quarter
BIO6	Min Temperature of Coldest Month
BIO4	Temperature Seasonality (standard deviation ×100)
BIO17	Precipitation of Driest Quarter
BIO7	Temperature Annual Range (BIO5-BIO6)
BIO1	Annual Mean Temperature
BIO5	Max Temperature of Warmest Month
BIO9	Mean Temperature of Driest Quarter
BIO3	Isothermality (BIO2/BIO7) (×100)
BIO15	Precipitation Seasonality (Coefficient of Variation)

Keyword argument resolution

5 arc minutes - 5.0

30 arc seconds, approx. 1×1 km - 0.5

10 arc minutes - 10.0

2.5 arc minutes, approx 4×4 km - 2.5

Projections for SSP126

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

Models: ACCESS_CM2, ACCESS_ESM1_5, BCC_CSM2_MR, CMCC_ESM2, CNRM_CM6_1, CNRM_CM6_1_HR, CNRM_ESM2_1, CanESM5, CanESM5_CanOE, EC_Earth3_Veg, EC_Earth3_Veg_LR, FIO_ESM_2_0, GFDL_ESM4, GISS_E2_1_G, GISS_E2_1_H, HadGEM3_GC31_LL, INM_CM4_8, INM_CM5_0, IPSL_CM6A_LR, MIROC6, MIROC_ES2L, MPI_ESM1_2_HR, MPI_ESM1_2_LR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2021) => Year(2040), Year(2041) => Year(2060), Year(2061) => Year(2080) and Year(2081) => Year(2100)

Projections for SSP245

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

Models: ACCESS_CM2, ACCESS_ESM1_5, BCC_CSM2_MR, CMCC_ESM2, CNRM_CM6_1, CNRM_CM6_1_HR, CNRM_ESM2_1, CanESM5, CanESM5_CanOE, EC_Earth3_Veg, EC_Earth3_Veg_LR, FIO_ESM_2_0, GISS_E2_1_G, GISS_E2_1_H, HadGEM3_GC31_LL, INM_CM4_8, INM_CM5_0, IPSL_CM6A_LR, MIROC6, MIROC_ES2L, MPI_ESM1_2_HR, MPI_ESM1_2_LR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2021) => Year(2040), Year(2041) => Year(2060), Year(2061) => Year(2080) and Year(2081) => Year(2100)

Projections for SSP370

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

Models: ACCESS_CM2, ACCESS_ESM1_5, BCC_CSM2_MR, CMCC_ESM2, CNRM_CM6_1, CNRM_CM6_1_HR, CNRM_ESM2_1, CanESM5, CanESM5_CanOE, EC_Earth3_Veg, EC_Earth3_Veg_LR, GFDL_ESM4, GISS_E2_1_G, GISS_E2_1_H, INM_CM4_8, INM_CM5_0, IPSL_CM6A_LR, MIROC6, MIROC_ES2L, MPI_ESM1_2_HR, MPI_ESM1_2_LR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2021) => Year(2040), Year(2041) => Year(2060), Year(2061) => Year(2080) and Year(2081) => Year(2100)

Projections for SSP585

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

Models: ACCESS_CM2, ACCESS_ESM1_5, BCC_CSM2_MR, CMCC_ESM2, CNRM_CM6_1, CNRM_CM6_1_HR, CNRM_ESM2_1, CanESM5, CanESM5_CanOE, EC_Earth3_Veg, EC_Earth3_Veg_LR, FIO_ESM_2_0, GISS_E2_1_G, GISS_E2_1_H, HadGEM3_GC31_LL, INM_CM4_8, INM_CM5_0, IPSL_CM6A_LR, MIROC6, MIROC_ES2L, MPI_ESM1_2_HR, MPI_ESM1_2_LR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2021) => Year(2040), Year(2041) => Year(2060), Year(2061) => Year(2080) and Year(2081) => Year(2100)

Elevation

using SpeciesDistributionToolkit
layer = SDMLayer(RasterData(WorldClim2, Elevation))  

Information about elevation, that usually comes from a DEM.

For more information about this dataset: https://www.worldclim.org/data/index.html

Keyword argument resolution

5 arc minutes - 5.0

30 arc seconds, approx. 1×1 km - 0.5

10 arc minutes - 10.0

2.5 arc minutes, approx 4×4 km - 2.5

MaximumTemperature

using SpeciesDistributionToolkit
layer = SDMLayer(RasterData(WorldClim2, 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://www.worldclim.org/data/index.html

Keyword argument resolution

5 arc minutes - 5.0

30 arc seconds, approx. 1×1 km - 0.5

10 arc minutes - 10.0

2.5 arc minutes, approx 4×4 km - 2.5

Keyword argument month

This dataset can be accessed monthly, using the month keyword argument. You can list the available months using SimpleSDMDatasets.months(RasterData{WorldClim2, MaximumTemperature}).

Projections for SSP126

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

Models: ACCESS_CM2, ACCESS_ESM1_5, BCC_CSM2_MR, CMCC_ESM2, CNRM_CM6_1, CNRM_CM6_1_HR, CNRM_ESM2_1, CanESM5, CanESM5_CanOE, EC_Earth3_Veg, EC_Earth3_Veg_LR, FIO_ESM_2_0, GFDL_ESM4, GISS_E2_1_G, GISS_E2_1_H, HadGEM3_GC31_LL, INM_CM4_8, INM_CM5_0, IPSL_CM6A_LR, MIROC6, MIROC_ES2L, MPI_ESM1_2_HR, MPI_ESM1_2_LR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2021) => Year(2040), Year(2041) => Year(2060), Year(2061) => Year(2080) and Year(2081) => Year(2100)

Projections for SSP245

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

Models: ACCESS_CM2, ACCESS_ESM1_5, BCC_CSM2_MR, CMCC_ESM2, CNRM_CM6_1, CNRM_CM6_1_HR, CNRM_ESM2_1, CanESM5, CanESM5_CanOE, EC_Earth3_Veg, EC_Earth3_Veg_LR, FIO_ESM_2_0, GISS_E2_1_G, GISS_E2_1_H, HadGEM3_GC31_LL, INM_CM4_8, INM_CM5_0, IPSL_CM6A_LR, MIROC6, MIROC_ES2L, MPI_ESM1_2_HR, MPI_ESM1_2_LR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2021) => Year(2040), Year(2041) => Year(2060), Year(2061) => Year(2080) and Year(2081) => Year(2100)

Projections for SSP370

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

Models: ACCESS_CM2, ACCESS_ESM1_5, BCC_CSM2_MR, CMCC_ESM2, CNRM_CM6_1, CNRM_CM6_1_HR, CNRM_ESM2_1, CanESM5, CanESM5_CanOE, EC_Earth3_Veg, EC_Earth3_Veg_LR, GFDL_ESM4, GISS_E2_1_G, GISS_E2_1_H, INM_CM4_8, INM_CM5_0, IPSL_CM6A_LR, MIROC6, MIROC_ES2L, MPI_ESM1_2_HR, MPI_ESM1_2_LR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2021) => Year(2040), Year(2041) => Year(2060), Year(2061) => Year(2080) and Year(2081) => Year(2100)

Projections for SSP585

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

Models: ACCESS_CM2, ACCESS_ESM1_5, BCC_CSM2_MR, CMCC_ESM2, CNRM_CM6_1, CNRM_CM6_1_HR, CNRM_ESM2_1, CanESM5, CanESM5_CanOE, EC_Earth3_Veg, EC_Earth3_Veg_LR, FIO_ESM_2_0, GISS_E2_1_G, GISS_E2_1_H, HadGEM3_GC31_LL, INM_CM4_8, INM_CM5_0, IPSL_CM6A_LR, MIROC6, MIROC_ES2L, MPI_ESM1_2_HR, MPI_ESM1_2_LR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2021) => Year(2040), Year(2041) => Year(2060), Year(2061) => Year(2080) and Year(2081) => Year(2100)

MinimumTemperature

using SpeciesDistributionToolkit
layer = SDMLayer(RasterData(WorldClim2, 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://www.worldclim.org/data/index.html

Keyword argument resolution

5 arc minutes - 5.0

30 arc seconds, approx. 1×1 km - 0.5

10 arc minutes - 10.0

2.5 arc minutes, approx 4×4 km - 2.5

Keyword argument month

This dataset can be accessed monthly, using the month keyword argument. You can list the available months using SimpleSDMDatasets.months(RasterData{WorldClim2, MinimumTemperature}).

Projections for SSP126

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

Models: ACCESS_CM2, ACCESS_ESM1_5, BCC_CSM2_MR, CMCC_ESM2, CNRM_CM6_1, CNRM_CM6_1_HR, CNRM_ESM2_1, CanESM5, CanESM5_CanOE, EC_Earth3_Veg, EC_Earth3_Veg_LR, FIO_ESM_2_0, GFDL_ESM4, GISS_E2_1_G, GISS_E2_1_H, HadGEM3_GC31_LL, INM_CM4_8, INM_CM5_0, IPSL_CM6A_LR, MIROC6, MIROC_ES2L, MPI_ESM1_2_HR, MPI_ESM1_2_LR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2021) => Year(2040), Year(2041) => Year(2060), Year(2061) => Year(2080) and Year(2081) => Year(2100)

Projections for SSP245

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

Models: ACCESS_CM2, ACCESS_ESM1_5, BCC_CSM2_MR, CMCC_ESM2, CNRM_CM6_1, CNRM_CM6_1_HR, CNRM_ESM2_1, CanESM5, CanESM5_CanOE, EC_Earth3_Veg, EC_Earth3_Veg_LR, FIO_ESM_2_0, GISS_E2_1_G, GISS_E2_1_H, HadGEM3_GC31_LL, INM_CM4_8, INM_CM5_0, IPSL_CM6A_LR, MIROC6, MIROC_ES2L, MPI_ESM1_2_HR, MPI_ESM1_2_LR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2021) => Year(2040), Year(2041) => Year(2060), Year(2061) => Year(2080) and Year(2081) => Year(2100)

Projections for SSP370

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

Models: ACCESS_CM2, ACCESS_ESM1_5, BCC_CSM2_MR, CMCC_ESM2, CNRM_CM6_1, CNRM_CM6_1_HR, CNRM_ESM2_1, CanESM5, CanESM5_CanOE, EC_Earth3_Veg, EC_Earth3_Veg_LR, GFDL_ESM4, GISS_E2_1_G, GISS_E2_1_H, INM_CM4_8, INM_CM5_0, IPSL_CM6A_LR, MIROC6, MIROC_ES2L, MPI_ESM1_2_HR, MPI_ESM1_2_LR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2021) => Year(2040), Year(2041) => Year(2060), Year(2061) => Year(2080) and Year(2081) => Year(2100)

Projections for SSP585

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

Models: ACCESS_CM2, ACCESS_ESM1_5, BCC_CSM2_MR, CMCC_ESM2, CNRM_CM6_1, CNRM_CM6_1_HR, CNRM_ESM2_1, CanESM5, CanESM5_CanOE, EC_Earth3_Veg, EC_Earth3_Veg_LR, FIO_ESM_2_0, GISS_E2_1_G, GISS_E2_1_H, HadGEM3_GC31_LL, INM_CM4_8, INM_CM5_0, IPSL_CM6A_LR, MIROC6, MIROC_ES2L, MPI_ESM1_2_HR, MPI_ESM1_2_LR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2021) => Year(2040), Year(2041) => Year(2060), Year(2061) => Year(2080) and Year(2081) => Year(2100)

Precipitation

using SpeciesDistributionToolkit
layer = SDMLayer(RasterData(WorldClim2, 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://www.worldclim.org/data/index.html

Keyword argument resolution

5 arc minutes - 5.0

30 arc seconds, approx. 1×1 km - 0.5

10 arc minutes - 10.0

2.5 arc minutes, approx 4×4 km - 2.5

Keyword argument month

This dataset can be accessed monthly, using the month keyword argument. You can list the available months using SimpleSDMDatasets.months(RasterData{WorldClim2, Precipitation}).

Projections for SSP126

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

Models: ACCESS_CM2, ACCESS_ESM1_5, BCC_CSM2_MR, CMCC_ESM2, CNRM_CM6_1, CNRM_CM6_1_HR, CNRM_ESM2_1, CanESM5, CanESM5_CanOE, EC_Earth3_Veg, EC_Earth3_Veg_LR, FIO_ESM_2_0, GFDL_ESM4, GISS_E2_1_G, GISS_E2_1_H, HadGEM3_GC31_LL, INM_CM4_8, INM_CM5_0, IPSL_CM6A_LR, MIROC6, MIROC_ES2L, MPI_ESM1_2_HR, MPI_ESM1_2_LR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2021) => Year(2040), Year(2041) => Year(2060), Year(2061) => Year(2080) and Year(2081) => Year(2100)

Projections for SSP245

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

Models: ACCESS_CM2, ACCESS_ESM1_5, BCC_CSM2_MR, CMCC_ESM2, CNRM_CM6_1, CNRM_CM6_1_HR, CNRM_ESM2_1, CanESM5, CanESM5_CanOE, EC_Earth3_Veg, EC_Earth3_Veg_LR, FIO_ESM_2_0, GISS_E2_1_G, GISS_E2_1_H, HadGEM3_GC31_LL, INM_CM4_8, INM_CM5_0, IPSL_CM6A_LR, MIROC6, MIROC_ES2L, MPI_ESM1_2_HR, MPI_ESM1_2_LR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2021) => Year(2040), Year(2041) => Year(2060), Year(2061) => Year(2080) and Year(2081) => Year(2100)

Projections for SSP370

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

Models: ACCESS_CM2, ACCESS_ESM1_5, BCC_CSM2_MR, CMCC_ESM2, CNRM_CM6_1, CNRM_CM6_1_HR, CNRM_ESM2_1, CanESM5, CanESM5_CanOE, EC_Earth3_Veg, EC_Earth3_Veg_LR, GFDL_ESM4, GISS_E2_1_G, GISS_E2_1_H, INM_CM4_8, INM_CM5_0, IPSL_CM6A_LR, MIROC6, MIROC_ES2L, MPI_ESM1_2_HR, MPI_ESM1_2_LR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2021) => Year(2040), Year(2041) => Year(2060), Year(2061) => Year(2080) and Year(2081) => Year(2100)

Projections for SSP585

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

Models: ACCESS_CM2, ACCESS_ESM1_5, BCC_CSM2_MR, CMCC_ESM2, CNRM_CM6_1, CNRM_CM6_1_HR, CNRM_ESM2_1, CanESM5, CanESM5_CanOE, EC_Earth3_Veg, EC_Earth3_Veg_LR, FIO_ESM_2_0, GISS_E2_1_G, GISS_E2_1_H, HadGEM3_GC31_LL, INM_CM4_8, INM_CM5_0, IPSL_CM6A_LR, MIROC6, MIROC_ES2L, MPI_ESM1_2_HR, MPI_ESM1_2_LR, MRI_ESM2_0 and UKESM1_0_LL

Timespans: Year(2021) => Year(2040), Year(2041) => Year(2060), Year(2061) => Year(2080) and Year(2081) => Year(2100)

SolarRadiation

using SpeciesDistributionToolkit
layer = SDMLayer(RasterData(WorldClim2, SolarRadiation))  

WorldClim is a database of high spatial resolution global weather and climate data. These data can be used for mapping and spatial modeling. The data are provided for use in research and related activities.

Citation

Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315.

For more information about this dataset: https://www.worldclim.org/data/index.html

Keyword argument resolution

5 arc minutes - 5.0

30 arc seconds, approx. 1×1 km - 0.5

10 arc minutes - 10.0

2.5 arc minutes, approx 4×4 km - 2.5

Keyword argument month

This dataset can be accessed monthly, using the month keyword argument. You can list the available months using SimpleSDMDatasets.months(RasterData{WorldClim2, SolarRadiation}).

WaterVaporPressure

using SpeciesDistributionToolkit
layer = SDMLayer(RasterData(WorldClim2, WaterVaporPressure))  

WorldClim is a database of high spatial resolution global weather and climate data. These data can be used for mapping and spatial modeling. The data are provided for use in research and related activities.

Citation

Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315.

For more information about this dataset: https://www.worldclim.org/data/index.html

Keyword argument resolution

5 arc minutes - 5.0

30 arc seconds, approx. 1×1 km - 0.5

10 arc minutes - 10.0

2.5 arc minutes, approx 4×4 km - 2.5

Keyword argument month

This dataset can be accessed monthly, using the month keyword argument. You can list the available months using SimpleSDMDatasets.months(RasterData{WorldClim2, WaterVaporPressure}).

WindSpeed

using SpeciesDistributionToolkit
layer = SDMLayer(RasterData(WorldClim2, WindSpeed))  

WorldClim is a database of high spatial resolution global weather and climate data. These data can be used for mapping and spatial modeling. The data are provided for use in research and related activities.

Citation

Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315.

For more information about this dataset: https://www.worldclim.org/data/index.html

Keyword argument resolution

5 arc minutes - 5.0

30 arc seconds, approx. 1×1 km - 0.5

10 arc minutes - 10.0

2.5 arc minutes, approx 4×4 km - 2.5

Keyword argument month

This dataset can be accessed monthly, using the month keyword argument. You can list the available months using SimpleSDMDatasets.months(RasterData{WorldClim2, WindSpeed}).