A Software for Correcting Systematic Biases in Rcm Input Boundary Conditions

Abstract

Bias-correction approaches have been widely applied to Global Climate Model (GCM) or Regional Climate Model (RCM) outputs in order to overcome the limitations of climate models in resolving small-scale climate features. These methods were specifically designed to correct surface fields such as precipitation and temperature without regard to the physical mechanisms between variables and have not been applied to the full atmospheric fields required for input boundary conditions for RCM simulations. To address these limitations, this study developed open-source Python software that corrects RCM input boundary variables using reanalysis and raw GCM datasets as inputs. The bias correction technique used is based on a novel approach, Sub-Daily Multivariate Bias Correction (SDMBC), which corrects the inter-variable relationships and distribution of atmospheric variables at a sub-daily time scale. This paper describes the software package, which simplifies the implementation of the bias correction process, and provides a simple example of its application.

Keywords: Bias correction alternative, sub-daily, regional climate model, lateral boundary conditions, Python package