Bias Correction of Precipitation Extremes Conditioned on Synoptic Weather Patterns.

When using the precipitation extremes simulated by a regional climate model (RCM) in climate impact studies, the bias has to be first corrected. Commonly used bias correction methods are designed to match statistics of the simulated precipitation with observations. This study proposes a new approach to account for the potential change of different precipitation types; the approach accounts for different precipitation mechanisms having different bias characteristics. Different precipitation types are identified by self-organizing map classification of the weather synoptic patterns. The rationale for using synoptic patterns to classify the extreme precipitation is that these large-scale circulation patterns may be better simulated by the RCM than precipitation data itself. Considering the simulations of a very high resolution Weather Research and Forecasting (WRF) RCM for a domain over south-eastern Australia, a slight change in the proportions of the synoptic weather patterns was found, indicating a possible change in the composition of the total extreme precipitation in the future. A new Synoptic Pattern Bias Correction (SPBC) approach was developed that could account for these changes. The SPBC method lead to marginally different results compared to a conventional bias correction method, namely quantile mapping. To understand under what conditions significant differences will result between the SPBC method and quantile mapping a comprehensive synthetic study has been defined. The properties of the bias, the changes in the synoptic patterns and the differences in the rainfall amounts from the synoptic patterns were among some of the factors that were explored in a synthetic study. From over 600,000 synthetic cases, 46% were found to have significant differences in the future simulations from the two bias correction methods. It was also found that the differences between the methods depends on several factors including the change in proportion of each precipitation type, the difference in the correction factor for different precipitation types and the magnitude of the overall change in precipitation extremes. Among these factors, the between-cluster difference in the correction factor seems to dominate the significance of the results.