Correcting lateral boundary biases in regional climate modelling: the effect of the relaxation zone
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Correcting lateral boundary biases in regional climate modelling: the effect of the relaxation zone Eytan Rocheta1 · Jason P. Evans2 · Ashish Sharma1 Received: 19 July 2019 / Accepted: 24 July 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract 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. Keywords Bias correction · Regional climate model · Lateral boundary conditions · Relaxation zone
1 Introduction RCM simulation performance over long timescales is known to be linked to the quality of the lateral boundary conditions (LBCs) supplied to the model (Errico et al. 1993; Wu et al. 2005). Models run for extended periods are more sensitive to the LBCs than initial conditions (IC) specification as, over time, the influence of IC decreases yet the impact of LBCs remains the same (Vukicevic and Errico 1990; Wu et al. 2005). A known drawback in this approach is the extent of bias that is associated with global climate model simulations, including the atmospheric and surface variables that serve as the lateral and lower boundaries in a downscaling * Ashish Sharma [email protected] 1
School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Climate Change Research Centre and ARC Centre of Excellence for Climate System Science, University of New South Wales, Sydney, NSW, Australia
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study. As such, bias correction of RCM inputs has become more common in order to improve the conditions which drive the RCM and provide more useful dynamical downscaled output. This process involves
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