Enhancing Regional Climate Model Simulations through Bias Correction of Global Climate Model Boundary Conditions

Regional Climate Models (RCMs) are dependent on boundary conditions provided by Global Climate Models (GCMs). A significant challenge in regional climate modelling is the "Garbage in – garbage out" problem. Specifically, if the input boundary conditions from a GCM are unrealistic, the RCM cannot rectify this and will consequently produce inaccurate results. While we can avoid using unrealistic GCMs, this issue is critical as all GCMs, even the best performing, exhibit biases. Here we explore whether bias correction of GCM boundary conditions can mitigate this problem and enhance RCM simulations.

In this presentation, we provide evidence that bias correction of boundary conditions leads to improved RCM simulations. We investigate the impact of various bias correction techniques including multivariate bias correction, the role of the relaxation zone in propagating these corrections to the interior of the domain, the importance of maintaining physical consistency within the boundary conditions, and the impact of sub-daily corrections. Our findings demonstrate that corrected boundary conditions enhance multiple aspects of the simulated climate, including the mean climate, extremes, compound events, and synoptic systems. It is worth noting that even with these enhancements, errors in the simulated climate remain, and continued improvements in global and regional climate models are required to produce the most useful and reliable climate projections.