Multivariate bias correction of regional climate model boundary conditions

Improving modeling capacities requires a better understanding of both the physical relationship between the variables and climate models with a higher degree of skill than is currently achieved by Global Climate Models (GCMs). Although Regional Climate Models (RCMs) are commonly used to resolve finer scales, their application is restricted by the inherent systematic biases within the GCM datasets that can be propagated into the RCM simulation through the model input boundaries. Hence, it is advisable to remove the systematic biases in the GCM simulations prior to downscaling, forming improved input boundary conditions for the RCMs. Various mathematical approaches have been formulated to correct such biases. Most of the techniques, however, correct each variable independently leading to physical inconsistencies across the variables in dynamically linked fields. Here, we investigate bias corrections ranging from simple to more complex techniques to correct biases of RCM input boundary conditions. The results show that substantial improvements in model performance are achieved after applying bias correction to the boundaries of RCM. This work identifies that the effectiveness of increasingly sophisticated techniques is able to improve the simulated rainfall characteristics. An RCM with multivariate bias correction, which corrects temporal persistence and inter-variable relationships, better represents extreme events relative to univariate bias correction techniques, which do not account for the physical relationship between the variables.

Automated summary

To improve modeling capacities, a better understanding of physical relationships and higher skill climate models are needed. Regional Climate Models (RCMs) are commonly used to resolve finer scales, but their application is restricted by systematic biases within Global Climate Models (GCMs) datasets. Hence, it is advisable to remove these biases in GCM simulations prior to downscaling. Various techniques have been formulated to correct the biases, but most correct each variable independently, leading to physical inconsistencies. This study investigates bias corrections ranging from simple to complex techniques and shows that applying bias correction to RCM boundaries significantly improves model performance, with multivariate bias correction better representing extreme events.