Assessing multivariate bias corrections of climate simulations on various impact models under climate change

Abstract. Atmospheric variables simulated from climate models often present biases relative to the same variables calculated by reanalysis in the past. In order to use these models to assess the impact of climate change on processes of interest, it is necessary to correct these biases. Currently, the bias correction methods used operationally correct one-dimensional time series and are therefore applied separately, physical variable by physical variable and site by site. Multivariate bias correction methods have been developed to better take into account dependencies between variables and in space. Although the performance of multivariate bias correction methods for adjusting the statistical properties of simulated climate variables has already been evaluated, their effectiveness for different impact models has been little investigated. In this work, we propose a comparison between two multivariate bias correction methods (R2D2 and dOTC) in three different configurations (intervariable, spatial and spatial-intervariable) and a univariate correction (CDF-t) through several highly multivariate impact models (phenological stage, reference evapotranspiration, soil water content, fire weather index) integrating the weather conditions over a whole season. Our results show that CDF-t does a fair job in most situations and that there is no single best MBC method. The performances of multivariate bias correction methods depend both on some characteristics of the studied process and on the configuration of the chosen bias correction method. When all characteristics are important (multivariate, time cumulative and spatial) it is found that dOTC in its spatial-intervariable configuration brings improvements in most cases and no significant improvement in some rare cases. We did not find any multivariate cases where the spatial-intervariable configuration for dOTC performs less well than CDF-t.