Evaluation of Coupled Model Intercomparison Project Phase 6 model‐simulated extreme precipitation over Indonesia

Abstract

The ability of 42 global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), consisting of 20 low resolution (LR) and 22 medium resolution (MR), are evaluated for their performance in simulating mean and extreme precipitation over Indonesia. Compared to Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), the model climatologies and interannual variability are investigated individually and as multimodel ensemble means (MME-mean) at monthly and seasonal time scales for the historical simulation over the period 1988-2014. Overall, results show that both LR and MR CMIP6 model skills in simulating mean and extreme precipitation indices vary across specific Indonesian regions and seasons. The individual and MME-mean tend to overestimate the observed climatology, being largest over drier regions, yet MR models perform better compared to the LR regarding the mean bias presumably due to increased resolution. CMIP6 models tend to simulate extreme precipitation better in the dry seasons compared to the wet season. The MME-means of the LR and MR groups mostly outperform the individual models of each group in simulating wet extremes (R95p and Rx5d) but not for the dry extremes (CDD). Among the 42 CMIP6 models, three models consistently perform poorly in simulating Rx5d and R95p, namely FGOALS-g3, IPSL-CM6A-LR, and IPSL-CM6A-LR-INCA, and one model in consecutive dry day (CDD) simulation, MPI-ESM-1-2-HAM, and caution is warranted. Given the knowledge of such biases, the LR and MR CMIP6 climate models can be suitably applied to assist policy makers in their decision on climate change adaptation and mitigation action.

Future projections of extreme rainfall events in Indonesia

“…Multi-model Ensemble means (MMEM) are calculated both for the LR and MR groups of models for all the mean and extreme rainfall indices. Using the daily gridded CHIRPS data, a Taylor diagram is used to evaluate the performance of the area-mean climatology statistically from all 24 individual models and each MMEM during the four seasons (December-January-February [DJF], March-April-May [MAM], June-July-August [JJA] and September-October-November [SON]) over the historical period, substantiated by the precedent set by analogous methodologies in previous studies (Fiedler et al, 2020; Kurniadi et al, 2023;Siew et al, 2014;Tangang et al, 2020;Taylor, 2001). Subsequent analysis uses the model ensembles to investigate the future changes in extreme rainfall over Indonesia due to superior performance of MMEM described in the following section.…”

“…The future changes in extreme rainfall are examined under two scenarios, SSP245 and SSP585, representing “middle‐of‐the‐road” and “fossil‐fuelled development” scenarios (O'Neill et al, 2016). The model outputs are interpolated to a regular geographical grid of 1° × 1°, consistent with the observational rainfall dataset introduced in the following subsection, using the first‐order conservative regridding method recommended by the National Centre for Atmospheric Research Climate Data Guide (Fan et al, 2020; Kurniadi et al, 2023). All climate models used in this study are publicly available and can be accessed through the Earth System Grid Federation (ESGF) portal after registration (https://esgf-node.llnl.gov/search/cmip6/).…”

“…However, the complexity of the topography of some countries, such as those in southeast Asia, and the strong maritime influence on tropical climates means that rainfall models tend to overestimate (or underestimate) observed rainfall during the different seasons in such areas (Gouda et al, 2018; Yazdandoost et al, 2021). However, a recent study which evaluated the ability of ensemble CMIP6 models with medium spatial resolution in simulating extreme rainfall indices during the historical period in Indonesia, demonstrated improved performance compared to low resolution (LR) models (Kurniadi et al, 2023).…”

“…Here, we provide a comprehensive examination of how future extreme rainfall over Indonesia is projected to change using the most recent CMIP6 models that incorporates newly constructed climate change scenarios, and compare results between low and medium spatial resolution models. This study follows on from previous CMIP6 assessment (Kurniadi et al, 2023), but with a distinct focus on the future projections of extreme rainfall over Indonesia. The combination of the evaluation of CMIP6 models in representing extreme rainfall during the historical period and the future projections based on CMIP6 models presented here, provides the relevant information for addressing mitigation against climate extremes in Indonesia.…”

An Evaluation of the Seasonal Caribbean Hydroclimate in Low and High-Resolution CESM and other CMIP6 Models

“…Earth System (iHESP) (Small et al 2014;Chang et al 2020). These HR GCMs improve the simulation of rainfall characteristics and its associated governing mechanisms compared to their low resolution (LR) counterparts in tropical climates such as southern China (Xin et al, 2021), Malaysia (Liang et al, 2021), Indonesia (Hariadi et al, 2022;Kurniadi et al, 2022), tropical South America (Monerie et al 2020) and West Africa (Ajibola et al 2020;Ajibola et al 2022, Nkrumah et al 2022. We also use the M19 subregional classification (Fig.…”