How well do GCMs represent low-frequency rainfall variability?

General circulation models (GCMs) provide reliable simulations of global and continental scale atmospheric variables, yet have limited skill in simulating variables important for water resource management at regional to catchment scales. GCM simulations suffer from a range of uncertainties leading to transient (changing over time) and systemic (consistent over time) biases in the output when compared to observed records. An important GCM bias in managing water resources infrastructure is the under-representation of interannual variability, or persistence, a characteristic central to the simulation of floods and droughts. This study presents a performance metric, the aggregated persistence score (APS), which is used to assess the reliability of GCMs in simulating precipitation persistence. The APS identifies regions where GCMs poorly represent the amount of interannual variability seen in the observed precipitation. This study calculated the APS at monthly aggregations for GCM precipitation as well as GCM precipitation that was bias corrected to better represent low-frequency variability. It was found that there were (1) large spatial variations in the skill of GCMs to capture observed rainfall persistence, (2) widespread under-simulation of rainfall persistence characteristics in GCMs, and (3) substantial improvement in rainfall persistence after applying bias correction.

Figure 9. Equally weighted multimodel mean APS for (a) GCM and (b) GCM_NBC. Stippling indicates regions of statistically significant observed persistence at the 90% level. ‘‘Abs’’ represents the absolute value of the APS, and ‘‘Pos’’ and ‘‘Neg’’ represent the positive and negative components of the APS.