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Bias Correction

ClimateTools provides several bias-correction methods aimed at different goals. Some methods mainly correct distributions, while others target time-scale variability or upper-tail behavior.

Before choosing a method, make sure that:

  • the observational reference and the model simulation cover a comparable calibration period
  • the datasets have compatible units
  • the grids are compatible, or have already been regridded to a common target

Which Method Should You Use?

Use this quick guide:

  • qqmap: default day-of-year quantile mapping for many temperature and precipitation workflows
  • qqmap_bulk: bulk quantile mapping without day-of-year grouping
  • biascorrect_extremes: upper-tail-aware correction when extremes matter
  • tvc: correction of temporal covariance across time scales while preserving event order

Quantile Mapping with qqmap

qqmap performs day-of-year-based quantile mapping.

qq = qqmap(obs, ref, fut;
    method="additive",
    detrend=true,
    window=15,
    rankn=50,
    qmin=0.01,
    qmax=0.99)

Typical choices:

  • method="additive" for temperature-like variables
  • method="multiplicative" for precipitation-like variables

Important parameters:

  • window: day-of-year neighborhood used for seasonal grouping
  • rankn: number of quantiles used in the mapping
  • detrend: whether to remove and reapply a polynomial trend during correction

Use qqmap when seasonal behavior matters and you want the correction to vary through the annual cycle.

Bulk Quantile Mapping with qqmap_bulk

qq_bulk = qqmap_bulk(obs, ref, fut; method="multiplicative")

This method uses the full sample rather than a moving day-of-year window. It is simpler, but it does not adapt the correction seasonally.

Extreme-Value Tail Correction with biascorrect_extremes

Use biascorrect_extremes when the upper tail needs special treatment beyond standard quantile mapping.

dext = biascorrect_extremes(obs, ref, fut;
    detrend=false,
    P=0.95,
    runlength=2,
    frac=0.25,
    power=1.0)

The function combines multiplicative quantile mapping with an explicit generalized Pareto distribution correction of extreme values.

The function also accepts optional external extreme-value parameters with columns lat, lon, mu, sigma, and xi:

using DataFrames

gevparams = DataFrame(
    lat=[45.0, 46.0],
    lon=[-73.0, -72.0],
    mu=[20.0, 21.5],
    sigma=[4.0, 4.5],
    xi=[0.10, 0.08],
)

dext = biascorrect_extremes(obs, ref, fut; gevparams=gevparams)

This method is most appropriate when impact-relevant extremes are central to the study.

Time Variability Correction with tvc

tvc applies the Time Variability Correction method of Shao et al. (2024), DOI 10.1029/2023MS003640.

The method is designed to correct covariance across and between multiple time scales while preserving the event sequence of the validation series.

corrected = tvc(obs, raw_train, raw_val;
    scales=[365, 183, 92, 46, 23, 12, 6, 3, 2],
    eig_floor=1e-8,
    keep_original=false)

For repeated applications to multiple validation series, fit once and reuse the fitted model:

model = fit_tvc(obs_series, raw_train_series; scales=[365, 183, 92, 46, 23, 12, 6, 3, 2])
corrected_series = apply_tvc(model, raw_val_series)

tvc returns a series on the validation time axis. The leading warm-up segment, of length sum(scales) - length(scales), is filled with NaN because the multiscale rolling decomposition needs prior samples before the corrected series is well-defined.

Practical Workflow

For scenario construction, the usual order is:

  1. subset the calibration period
  2. regrid observations and simulations to a common grid
  3. choose a correction method
  4. validate the corrected output before computing derived indicators

See Building Climate Scenarios for the full workflow.

Validation Checklist

After correction, compare the corrected data against the observational reference over the calibration period.

Useful checks include:

  • changes in mean and standard deviation
  • annual maxima and minima
  • wet-day fractions or dry-spell lengths
  • map patterns at representative dates
  • time-series slices at representative grid points

See Validation and Diagnostics for more detailed guidance.