Improved Understanding of Extreme Precipitation over Indonesia and Future Projections Using GCMs

Abstract

Global warming is expected to cause a non-uniform increase in the distribution of extreme rainfall events, posing significant hydro-meteorological hazards. Understanding past and future extreme rainfall events is vital for Indonesia due to the socio-economic implications of such hazards. This study investigates historical variations in extreme rainfall patterns in Indonesia, particularly their association with prominent climatic phenomena such as El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). Subsequently, we aim to simulate the projected alterations in extreme rainfall patterns for Indonesia, utilising Global Climate Models (GCMs), after evaluating their historical performance in depicting the extreme events. In the past, extreme rainfall in Indonesia has been significantly influenced by natural variability, resulting in drier (during El Niño and positive IOD events) and wetter conditions (during La Niña and negative IOD). These findings elucidate that extreme rainfall is influenced by independent ENSO and IOD more on the northeast and southwest of the country, respectively. Notably, historical extreme rainfall events exhibit their most pronounced effects during the dry seasons (JJA-SON) with comparatively weaker impacts during the wet seasons (DJF-MAM) when subjected to the influences of ENSO and IOD. In the future, extreme rainfall events in Indonesian are simulated using the most recent GCMs. With the better performance of Multi-Model Ensemble Mean (MMEM) of GCMs compared to individual models, future extreme rainfall events are projected using MMEM to vary seasonally across time periods, spatial resolutions, and climate scenarios. Future extreme wet events are simulated to increase during the wet season across most of Indonesia. Conversely, extreme dry events are expected to increase countrywide during the dry season but decrease (increase) in the half-north (half-south) of the country during the wet season. The medium-resolution (MR) models project smaller changes in extreme wet indices than the low-resolution (LR) models but simulate a more prolonged extreme dry index. Future extreme wet and dry events are anticipated to become more frequent and intense, exceeding historical records, particularly under a "business-as-usual" scenario. These projections necessitate policymakers' careful planning and judicious policy formulation for effective climate change adaptation and mitigation, which is crucial for the country's future development.