Compute Bioclimatic Variables from ERA5-Land Data

End-to-end pipeline that reads ERA5-Land hourly GRIB/NetCDF files, aggregates them to CHELSA-compatible monthly climate variables, and computes the 19 standard bioclimatic variables (BIO01–BIO19).

Usage

era5_bioclim(
  t2m_files,
  tp_files,
  year,
  output = tempdir(),
  to_celsius = TRUE,
  variables = 1:19,
  ncores = 1L,
  save_monthly = FALSE
)

Arguments

t2m_files

Character vector of 12 file paths to monthly ERA5-Land hourly 2-m temperature files (one per calendar month, January–December). Each file may be GRIB or NetCDF.

tp_files

Character vector of 12 file paths to monthly ERA5-Land hourly total precipitation files (same order as t2m_files).

year

Integer: the calendar year (used to determine days per month).

output

Character path to an output directory for GeoTIFF files. Defaults to a temporary directory.

to_celsius

Logical: convert temperatures to Celsius? Default TRUE for WorldClim-convention bioclimatic variables.

variables

Integer vector of bioclimatic variables to compute (1–19). Default 1:19 (all).

ncores

Integer: OpenMP threads for aggregation. Default 1L.

save_monthly

Logical: write intermediate monthly GeoTIFFs? Default FALSE.

Value

A terra::SpatRaster with one layer per bioclimatic variable.

Details

The pipeline proceeds in three stages:

1. Monthly aggregation: For each of the 12 calendar months, hourly t2m is aggregated to tas, tasmax, and tasmin; hourly tp is summed to pr.

2. Stack: The 12 monthly layers are assembled into terra::SpatRaster objects with 12 bands each.

3. Bioclim: bioclim_raster computes BIO01–BIO19.

See also

era5_t2m_to_monthly, era5_tp_to_monthly, bioclim_raster

Examples

if (FALSE) { # \dontrun{
# Paths to ERA5-Land GRIB files on the HPC cluster
t2m_files <- sprintf("era5land_t2m_hourly_2020_%02d.grib", 1:12)
tp_files  <- sprintf("era5land_tp_hourly_2020_%02d.grib", 1:12)

bio <- era5_bioclim(t2m_files, tp_files, year = 2020L, ncores = 4L)
terra::plot(bio[[1]])  # BIO01
} # }