Improving the Reliability and Added Value of Dynamical Downscaling via Correction of Large-Scale Errors: A Norwegian Perspective

Abstract

Heavy precipitation and associated flooding are major concerns for western Norway under both present climate conditions and projected future scenarios. In winter, these events are mostly caused by North Atlantic low-pressure systems. However, the storm track in this region is systematically biased in most global climate models. Further, these models are unable to capture the interactions between synoptic systems and the complex topography due to their coarse resolution, which contributes to poor representation of precipitation. In this study we employed a correction technique to remove the mean biases from multiple variables in a global Earth system model (Norwegian Earth System Model), and we subsequently dynamically downscaled a 38-year period. The effect of the bias correction was a more realistic storm track and an improved precipitation distribution in southern Norway. Our results indicate that it is possible to enhance the reliability of regional climate simulations with positive implications for regional climate projections and climate change impact studies.