Project aims

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:

• Develop improved bias correction methods for the MiKlip decadal prediction system
• Investigate their impact for user-relevant parameters such as droughts with the aim to improve prediction skill

Tasks and deliverables

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.
 

Progress so far

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:
 

• Differences in the performance of bias correction methods for skewed variables can not be addressed with distance measures, like the mean squared error or the mean absolute error. As long as the mean and the variance are successfully corrected, similar errors are reached even with large distributional deviations in the corrected quantities (Figure 1). Probabilistic measures, however, are able to uncover the performance differences of bias correction methods (Figure 2 and 3)
• Bias correction methods accounting for higher order moments improve probabilistic measures: the consistency of the ensemble members, the accurateness of probability forecasts for multiple categories (Figure 2) and they reduce the difference between the forecast and observed distribution function (Figure 3). In summary, bias correction methods not only reduce systematic errors, they additionally result in better calibrated ensembles