Evaluation of CMIP6 GCMs performance to simulate precipitation over Southeast Asia
2
Issued Date
2023-02-01
Resource Type
ISSN
01698095
Scopus ID
2-s2.0-85142704582
Journal Title
Atmospheric Research
Volume
282
Rights Holder(s)
SCOPUS
Bibliographic Citation
Atmospheric Research Vol.282 (2023)
Suggested Citation
Pimonsree S., Kamworapan S., Gheewala S.H., Thongbhakdi A., Prueksakorn K. Evaluation of CMIP6 GCMs performance to simulate precipitation over Southeast Asia. Atmospheric Research Vol.282 (2023). doi:10.1016/j.atmosres.2022.106522 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/81833
Title
Evaluation of CMIP6 GCMs performance to simulate precipitation over Southeast Asia
Other Contributor(s)
Abstract
The performance for simulating precipitation of 27 global climate models (GCMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) is evaluated over the Southeast Asia (SEA) region and nine SEA countries for the period 1975–2014 by comparing observation data from the Global Precipitation Climatology Centre (GPCC). The performance ranking of the GCMs was conducted with the ranking scores method using eight performance metrics that cover both spatial and temporal patterns. Compared to GPCC, the results show that some GCMs in CMIP6 reasonably capture the spatial precipitation patterns, with more in maritime and less in mainland. However, the output of most models presents considerable overestimation (wet bias) in Thailand, Cambodia, central Myanmar and maritime countries, and underestimation (dry bias) in Indonesia, Lao, northern Vietnam and western Myanmar. In addition, the findings illustrate that many models can reproduce the annual cycle shape and inter-annual variability which are consistent with GPCC; however, only 2 out of 27 models can detect increasing trends such as GPCC in every study domain. By model ranking, the best models vary from area domain to area domain. TaiESM1 performs best among the 27 GCMs over SEA region as well as Thailand. The model that has the best performance in most counties, i.e., Cambodia, Indonesia, Lao, and Vietnam is EC-Earth3-Veg-LR. EC-Earth3 is the best model in Brunei, Malaysia while CESM2-FV2 is the best model in Myanmar and the Philippines. It is also discovered that the mean ensemble of all GCMs has limited skills for all study domains. The results of this study can be used to support selection of the suitable models for simulating precipitation in specific study domains.