Climate model output and therefore climate predictions exhibit systematic departures from the observed climate state, especially on small regional scales. This bias reduces prediction skill and restricts usability and value of the predicted quantities. These deviations however, can often be corrected with statistical post-processing methods to obtain more meaningful results. Bias and its correction for skewed variables like wind speed and precipitation is the subject of DroughtClip during the second phase of MiKlip. The main goals are:
Different known bias correction methods are compared and an implementation is planned for the central prediction and evaluation system. The utilised approaches include parametric and non-parametric techniques, in particular methods based on distribution functions and the quantile-relations between observed and predicted variables. Further, the development of an improved bias correction is planned, which additionally addresses the problem of climate model drift and reduces time dependent systematic errors. The evaluation of bias correction methods is accompanied by an analysis of prediction scores. They determine efficiently their value from an end-user perspective. The main variable of interest are drought events and how and to which extent bias correction methods help to improve their prediction.
A set of bias correction methods have been selected from published literature. They encompass a wide range of different strategies to reduce systematic departures. On basis of a simulation study the following main results are established: