Bias correction method for climate change impact assessments in the Philippines

Abstract

Basin-scale climate change impact studies mainly rely on general circulation models (GCMs) with various emission scenarios. Bias in GCMs should be removed for regional or local scale circulation study. This study describes selection of appropriate GCMs over a focused region to decrease analysis uncertainty. An efficient and comprehensive statistical bias correction method is developed that covers extreme rainfall, normal rainfall and frequency of dry days. Heavy rainfall is corrected by fitting a generalized Pareto distribution (GPD) to peak over threshold series. We used a gamma distribution for normal rainfall correction on a monthly scale and frequency by rank order statistics. Validation was done via long-term seasonal climatological rainfall, ranking extreme rainfall, and return-period estimates by the GPD. In this way, we analyzed climate change impacts at the basin scale in the Philippines.