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Download CHELSA climate data

Source: R/chelsa.R
chelsa.Rd

This function downloads, processes, and extracts variables from the CHELSA (Climatologies at High Resolution for the Earth's Land Surface Areas) dataset.

Usage

chelsa(
  x,
  vars,
  years = NULL,
  months = NULL,
  gcm = NULL,
  rcp = NULL,
  ssp = NULL,
  cruts_years = NULL,
  ...
)

Arguments

x

The output from `par_set()` defining the area or locations for extraction, the reference system, and the buffer. Leave this empty and use `par_set()` to define parameters for download.

vars

Character vector of one or more variables to download and process.

years

A character or numeric vector of years or year ranges (e.g., "1981-2010", 2015).

months

A numeric vector (1–12) specifying which months to download. If NULL and `years` are single years, all 12 months are downloaded.

gcm

General Circulation Model(s) for future projections.

rcp

Representative Concentration Pathway, given as the radiative-forcing level (e.g., 2.6, 4.5, 6.0, 8.5). For CMIP5 projections (year ranges "2041-2060", "2061-2080") it selects the RCP directly. For CMIP6/BIOCLIM+ projections it is combined with ssp to build the scenario code (e.g., ssp = 5 and rcp = 8.5 request the ssp585 scenario).

ssp

Shared Socioeconomic Pathway family for CMIP6/BIOCLIM+ data (e.g., 1, 2, 3, 5). Combined with rcp as described above. A complete code such as "585" may also be supplied directly (with rcp = NULL).

cruts_years

Numeric vector. Years to download from CHELSAcruts (must be 1901–2016).

...

Additional arguments (currently unused).

Value

If `par_set()` contained a raster/polygon/points with buffer: a `SpatRaster` stack of processed variables. If `par_set()` contained spatial points or data.frame of points without buffer: a `data.frame` of x, y, and extracted values.

Details

Available variables

Please note the distinction between "Monthly" time-series data and "Climatologies". Unlike other functions in this package, there is only one code-name for each variable and no working synonyms. The meaning of each variable code-name is provided in parentheses.

Monthly Time-Series (Available from 1979 onwards)

  • 1 - "pr" (Precipitation amount; mass per unit area)

  • 2 - "tas" (Mean daily air temperature at 2 meters)

  • 3 - "tasmax" (Mean daily maximum air temperature at 2 meters)

  • 4 - "tasmin" (Mean daily minimum air temperature at 2 meters)

  • 5 - "hurs" (Near-surface relative humidity)

  • 6 - "clt" (Total cloud cover at surface; considers entire atmospheric column)

  • 7 - "sfcWind" (Near-surface wind speed at 10m above ground)

  • 8 - "vpd" (Vapor pressure deficit)

  • 9 - "rsds" (Surface downwelling shortwave flux in air)

  • 10 - "pet_penman" (Potential evapotranspiration; Penman-Monteith equation)

  • 11 - "cmi" (Climate Moisture Index)

  • 12 - "swb" (Site water balance; cumulative available water)

Climatologies & Derived Indices (1981-2010, 2011-2040, 2041-2070, 2071-2100)

Cloud Cover

  • 13 - "clt_mean" (Mean monthly total cloud cover over 1 year)

  • 14 - "clt_max" (Maximum monthly total cloud cover)

  • 15 - "clt_min" (Minimum monthly total cloud cover)

  • 16 - "clt_range" (Annual range of monthly total cloud cover)

Climate Moisture Index

  • 17 - "cmi_mean" (Mean monthly climate moisture index)

  • 18 - "cmi_max" (Maximum monthly climate moisture index; highest surplus)

  • 19 - "cmi_min" (Minimum monthly climate moisture index; highest deficit)

  • 20 - "cmi_range" (Annual range of monthly climate moisture index)

Relative Humidity

  • 21 - "hurs_mean" (Mean monthly near-surface relative humidity)

  • 22 - "hurs_max" (Maximum monthly near-surface relative humidity)

  • 23 - "hurs_min" (Minimum monthly near-surface relative humidity)

  • 24 - "hurs_range" (Annual range of monthly near-surface relative humidity)

Potential Evapotranspiration

  • 25 - "pet_penman_mean" (Mean monthly PET)

  • 26 - "pet_penman_max" (Maximum monthly PET)

  • 27 - "pet_penman_min" (Minimum monthly PET)

  • 28 - "pet_penman_range" (Annual range of monthly PET)

Solar Radiation

  • 29 - "rsds_mean" (Mean monthly surface downwelling shortwave flux)

  • 30 - "rsds_max" (Maximum monthly surface downwelling shortwave flux)

  • 31 - "rsds_min" (Minimum monthly surface downwelling shortwave flux)

  • 32 - "rsds_range" (Annual range of monthly surface downwelling shortwave flux)

Wind Speed

  • 33 - "sfcWind_mean" (Mean monthly near-surface wind speed)

  • 34 - "sfcWind_max" (Maximum monthly near-surface wind speed)

  • 35 - "sfcWind_min" (Minimum monthly near-surface wind speed)

  • 36 - "sfcWind_range" (Annual range of monthly near-surface wind speed)

Vapor Pressure Deficit

  • 37 - "vpd_mean" (Mean monthly vapor pressure deficit)

  • 38 - "vpd_max" (Maximum monthly vapor pressure deficit)

  • 39 - "vpd_min" (Minimum monthly vapor pressure deficit)

  • 40 - "vpd_range" (Annual range of monthly vapor pressure deficit)

Growing Season Characteristics (TREELIM model)

  • 41 - "gsl" (Growing season length; days)

  • 42 - "gsp" (Accumulated precipitation during growing season)

  • 43 - "gst" (Mean temperature of the growing season)

  • 44 - "fgd" (First day of the growing season; Julian day)

  • 45 - "lgd" (Last day of the growing season; Julian day)

Growing Degree Days (GDD)

  • 46 - "gdd0" (Heat sum of all days > 0°C accumulated over 1 year)

  • 47 - "gdd5" (Heat sum of all days > 5°C accumulated over 1 year)

  • 48 - "gdd10" (Heat sum of all days > 10°C accumulated over 1 year)

  • 49 - "ngd0" (Number of days with tas > 0°C)

  • 50 - "ngd5" (Number of days with tas > 5°C)

  • 51 - "ngd10" (Number of days with tas > 10°C)

  • 52 - "gdgfgd0" (First growing degree day > 0°C; Julian day)

  • 53 - "gdgfgd5" (First growing degree day > 5°C; Julian day)

  • 54 - "gdgfgd10" (First growing degree day > 10°C; Julian day)

  • 55 - "gddlgd0" (Last growing degree day > 0°C; Julian day)

  • 56 - "gddlgd5" (Last growing degree day > 5°C; Julian day)

  • 57 - "gddlgd10" (Last growing degree day > 10°C; Julian day)

Snow and Frost

  • 58 - "scd" (Snow cover days; count)

  • 59 - "swe" (Snow water equivalent; accumulated amount of liquid water if snow melted)

  • 60 - "fcf" (Frost change frequency; events where tmin/tmax cross 0°C)

Biological Productivity

  • 61 - "npp" (Net primary productivity; g C m^-2 yr^-1)

Climate Classifications

  • 62 - "kg0" (Köppen-Geiger climate category)

  • 63 - "kg1" (Köppen-Geiger without As/Aw differentiation)

  • 64 - "kg2" (Köppen-Geiger after Peel et al. 2007)

  • 65 - "kg3" (Wissmann 1939 classification)

  • 66 - "kg4" (Thornthwaite 1931 classification)

  • 67 - "kg5" (Troll-Pfaffen classification)

Citation:
Standard bioclimatic variables: Karger D, Conrad O, Böhner J et al (2017). "Climatologies at high resolution for the earth’s land surface areas." Scientific Data 4, 170122. https://doi.org/10.1038/sdata.2017.122

BIOCLIM+ dataset: Brun P, Zimmermann NE, Hari C, Pellissier L, Karger DN (2022). "Global climate-related predictors at kilometer resolution for the past and future." Earth System Science Data 14, 5573-5603. https://doi.org/10.5194/essd-14-5573-2022

Note: Users should verify the terms of use for CHELSA data provided at https://chelsa-climate.org/

Examples

# \donttest{

# climatic values for one specific year/month
processed <- par_set(zooregion = "Madagascan") %>%
chelsa(vars=c("tas"), years = 2018, months = 1)

# climatic values for a long period (real "climate"), if months are not specified
# all the months are downloaded (12 layers per variable)
processed <- par_set(country = "Iceland") %>%
chelsa(vars=c("pr", "tas"), years = "1981-2010", months = 1)

# bioclimatic variables are available only over these extended periods, and not
# for the single years
processed <- par_set(country = "Iceland") %>%
chelsa(vars=c("bio1"), years = "1981-2010", months = 1)

# to download a specified set of variables, leave only "bio" and then the
# package will ask which variables to download (all 19 or a selection) in the console
processed <- par_set(country = "Iceland") %>%
chelsa(vars=c("bio"), years = "1981-2010", months = 1)

# climatic values for the future (SSP, RCP and GCM must be specified)
processed <- par_set(country = "Italy", crs = 3035) %>%
chelsa(vars=c("pr", "tas"), years = "2041-2070", months = 1,
 ssp = 5, rcp = 8.5, gcm = "GFDL-ESM4")
   # }