Bias correction has become a common practice to improve numerical weather forecasting. However, bias correction (BC) methods for ensembles typically deteriorate the relation across space, time, and different variables, thereby leading to physically unrealistic atmospheric situations. Complicated additional steps based on copula methods are therefore required to correct these deficits. A simple member-by-member (MBM) post-processing method was developed that required no additional correlation-preservation steps. In this project, this MBM post-processing method will be adjusted to serve as a method for BC in the context of climate projections.
In order to encompass uncertainty, three greenhouse gas emission scenarios used in the IPCC assessment reports will be considered (i.e., RCP2.6, RCP4.5 and RCP8.5) and bias-corrected over Belgium. In order to serve as input for the HETEROFOR forest dynamics simulation experiments, a subset of ensemble members will be identified as representative scenarios.