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Extract specific time periods from climate model data

Source: R/htr_slice_period.R
htr_slice_period.Rd

This function extracts specific time periods from merged climate model files using CDO (Climate Data Operators). It is essential for focusing analysis on particular time ranges of interest, such as future projection periods or specific historical periods.

Usage

htr_slice_period(
  hpc = NA,
  file = NA,
  indir,
  outdir,
  freq,
  scenario,
  year_start,
  year_end,
  overwrite = TRUE
)

Arguments

hpc

Character string or NA. Indicates High Performance Computing mode:

  • NA: Standard processing mode

  • "array": HPC array job mode (requires file parameter)

  • "parallel": HPC parallel mode

file

Character string or NA. Specific file to process when hpc = "array". Not used in other modes.

indir

Character string. Directory containing merged NetCDF files to be time-sliced. Files should be continuous time series created by htr_merge_files().

outdir

Character string. Directory where time-sliced files will be saved.

freq

Character string. CMIP6 frequency identifier to filter files (e.g., "Omon" for ocean monthly, "day" for daily, "Amon" for atmosphere monthly).

scenario

Character string. CMIP6 scenario identifier to filter files (e.g., "historical", "ssp126", "ssp245", "ssp585"). Use partial strings to match multiple scenarios (e.g., "ssp" for all SSP scenarios).

year_start

Numeric. Starting year for the time slice (inclusive).

year_end

Numeric. Ending year for the time slice (inclusive).

overwrite

Logical. If TRUE (default), removes original files after successful time slicing. If FALSE, keeps original files.

Value

No return value. The function creates time-sliced files in the specified output directory with updated filenames reflecting the new time range (e.g., "merged" becomes "_YYYYMMDD-YYYYMMDD.nc").

Details

Climate model data often spans long time periods, but analysis typically focuses on specific time ranges. This function uses the CDO selyear operator to extract the specified year range from merged time series files.

The function:

  1. Filters files by frequency and scenario

  2. For each matching file, checks if it contains data outside the target period

  3. If trimming is needed, uses cdo selyear,year_start/year_end to extract the period

  4. Updates filenames to reflect the new time range

  5. Optionally removes original files if overwrite = TRUE

The CDO command used is: cdo selyear,year_start/year_end input_file output_file

Files are only processed if they contain data outside the specified time range, making the function efficient for large datasets.

Note

  • Requires CDO (Climate Data Operators) to be installed and accessible from the system PATH

  • Input files must follow CMIP6 naming conventions for proper metadata extraction

  • Files are only processed if they contain data outside the specified time range

  • Uses parallel processing when hpc is not set to "array"

  • WARNING: Setting overwrite = TRUE will delete original files

  • Progress messages show which model and scenario combinations are being processed

References

CDO User Guide: https://code.mpimet.mpg.de/projects/cdo/embedded/cdo.pdf CDO selyear operator: https://code.mpimet.mpg.de/projects/cdo/embedded/cdo.pdf#page=124

Author

Dave Schoeman and Tin Buenafe

Examples

if (FALSE) { # \dontrun{
# Extract 21st century projection period
htr_slice_period(
  hpc = NA,
  indir = file.path(base_dir, "data", "proc", "merged", "tos"),
  outdir = file.path(base_dir, "data", "proc", "sliced", "tos"),
  freq = "Omon", # ocean monthly
  scenario = "ssp",
  year_start = 2020,
  year_end = 2100,
  overwrite = FALSE
)

# Extract historical baseline period
htr_slice_period(
  hpc = NA,
  indir = file.path(base_dir, "data", "proc", "merged", "tos"),
  outdir = file.path(base_dir, "data", "proc", "sliced", "tos"),
  freq = "Omon",
  scenario = "historical",
  year_start = 1995,
  year_end = 2014,
  overwrite = TRUE
)
} # }