Projected extreme climate indices in the java island using cmip5 models

Abstract

Climate change has brought great environmental impacts that cause economic disruption as it causes extreme climate phenomena such as floods and droughts. The projection of precipitation and temperature is crucial to develop the adaptation and mitigation options, as well as to improve the operational strategies in various sectors. This study used Coupled Model Intercomparison Project Phase 5 (CMIP5) that consists of 29 GCMs to make the projection of precipitation and temperature (2011–2100), along with daily observational data from 16 stations over the Java island for 20 years (1986–2005) to evaluate the models. Spatial and temporal correlation method was used to evaluate the climate models and 5 GCMs with the best performance were selected to project the precipitation and temperature. A bias correction method called Simple Quantile Mapping (SQM) was used to adjust the climate models to better represent the observational data. Representative Concentration Pathway (RCP)4.5 dan RCP8.5 scenarios were chosen and the extreme weather events were depicted using the Expert Team for Climate Change Detection and Indices (ETCCDI), which includes annual total precipitation (Prcptot), consecutive dry days (CDD), consecutive wet days (CWD), monthly maximum temperature (TXx) and monthly minimum temperature (TNn). Using the multi model ensemble (MME) from the 5 best GCMs, the projection of 5 extreme climate indices over Java island shows a relative increase to the historical period.