Should multivariate bias corrections of climate simulations account for
changes of rank correlation over time?
Inter-variable dependencies are key properties to characterise many
climate phenomena - such as compound events - and their future changes.
Yet, climate simulations often have statistical biases. Hence,
univariate (1dBC) and multivariate bias correction (MBC) methods are
regularly applied. Inter-variable properties (e.g., correlations) can be
altered by BC corrections. Then, it is necessary to assess how
hypotheses of BC methods on climate change affect the adjustments. This
can lead to better choices of BC methods.
Here, we investigate whether an MBC method should try reproducing,
preserving or modifying the changes in rank correlations between daily
temperature and precipitation over Europe. An original “perfect model
experiment” is set up and applied to two different climate simulation
ensembles over 2001-2100: 40 runs from the CESM global climate model and
11 runs from the CMIP6 exercise. The results highlight that, within the
multi-run single GCM ensemble (CESM), accounting for correlation changes
bring valuable information for long-term projections but that a
stationary hypothesis provides less biased correlations, up to
medium-term projections (2060). For the multi-model ensemble (CMIP6),
the non-stationary hypothesis provides larger biases than the stationary
approach, up to the end of the century. Not correcting the model rank
correlations (1dBC) provides the worst results.
Whenever an ensemble is available, the best results come from accounting
for the “robust’ part of the change signal (i.e., average change from
different runs). This pleads for using ensembles and their robust
information, in order to perform robust bias corrections.