Correcting lateral boundary biases in regional climate modelling: the effect of the relaxation zone

Regional climate models (RCM) are an important tool for simulating atmospheric information at finer resolutions often of greater relevance to local scale climate change impact assessment studies. The lateral and lower boundary conditions, which form the inputs to the RCM downscaling application, are outputs from the global climate model (GCM). These boundary variables are known to be biased in GCMs, providing the potential to use a statistical approach that corrects these biases before use in downscaling. An array of bias correction techniques have been developed to remove these biases before being used to drive the RCM, but questions remain on their efficacy in terms of the final downscaled output. This study assesses the impact of these bias correction strategies by focussing on how these corrections are translated as one proceeds from the lateral boundaries into the model interior. Of specific interest is the change in the correction from generation of the lateral boundary conditions as well as how correction information moves through the relaxation zone and into the interior of the model. Here we show that bias correction information passing into the regional climate model is limited by interpolations required to generate lateral boundary conditions and dominant outflow wind conditions in the boundaries. This work suggests that these limitations should be addressed in order for bias correction of lateral boundary conditions to robustly influence RCM simulations of climate in the interior of the model domain.