Evaluation of CMIP6 GCMs performance to simulate precipitation over Southeast Asia

Pimonsree, Sittichai; Kamworapan, Suchada; Gheewala, Shabbir H.; Thongbhakdi, Amornpong; Prueksakorn, Kritana

doi:10.1016/j.atmosres.2022.106522

Cited by 28 publications

(13 citation statements)

References 59 publications

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“…To understand the ability of each climate model to simulate precipitation in the LMRB, the simulation bias of each model for annual mean precipitation over the historical period was first analyzed, as shown in Figure 2. The bias of each model is very pronounced, up to 1500 mm for both overestimation and underestimation, which is in general agreement with the model assessment of Pimonsree et al using the GPCC reference data [102]. Spatially, overestimation occurs mainly in the upper LMRB; i.e., in the areas with less annual precipitation, such as ACC, ACE, Can, CMC, GFD, INM4, INM5, MIR, NoL, and Tai, the models overestimate the middle and lower part of the R1 region by more than 1000 mm.…”

Section: Evaluation Of Simulation Deviationsupporting

confidence: 89%

Spatiotemporal Projections of Precipitation in the Lancang–Mekong River Basin Based on CMIP6 Models

Sun,

Liu,

Zhang

et al. 2023

Remote Sensing

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The Lancang–Mekong River Basin (LMRB) is the largest international river in Southeast Asia, supporting a population of about 70 million people. Precipitation is the main source of water resources in the basin, with significant impacts on ecology, production, and livelihoods in the basin. In this study, future precipitation was projected using the Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models. The initial bias of each model was corrected using the daily bias-correction (DBC) method, and then the models were ensembled using the Bayesian model-averaging (BMA) method. The evaluation, based on metrics such as climatology bias, root-mean-square error (RMSE), mean absolute error (MAE), and correlation coefficient (COR), showed that the ensemble precipitation performs better than the individual models. Precipitation under four future Shared Socioeconomic Pathway scenarios (SSP126, SSP245, SSP370, SSP585) displayed an increasing trend throughout the LMRB. The anomalies in annual precipitation in 2061–2090 under each scenario are 136 mm, 142 mm, 114 mm, and 227 mm, in that order. Precipitation in spring and winter shows a trend of increasing in the northern LMRB and decreasing in the southern LMRB, and precipitation in summer and autumn shows a significant trend of increasing in almost the whole basin (significance level 0.05). Spring precipitation in the Mekong Delta decreases in all scenarios. The ratio of wet-season precipitation to dry-season precipitation shows an increasing trend for all scenarios, indicating that the difference between wet-season precipitation and dry-season precipitation will increase in the future. For daily precipitation, the Lancang River Basin (LRB) is dominated by a 3–5% increase in the number of days with 5–10 mm/d of precipitation and the Mekong River Basin (MRB) by a 3–5% increase in the number of days with 10–20 mm/d of precipitation under four SSP scenarios in 2061–2090. There are important changes in the spatial distribution of future precipitation, with the 2500 mm isohyet expanding outwards in a circular pattern and the center of the 1500 mm isohyet moving westwards; i.e., areas with annual precipitation exceeding 2500 mm and 1500 mm will expand. For dry-season precipitation, the 500 mm isohyet shrinks, mainly in a circular pattern towards the center, while the 300 mm isohyet moves mainly towards the east, indicating that areas of dry-season precipitation below 500 mm and 300 mm will expand. In the future, the LMRB will generally become wetter in the wet season and drier in the dry season.

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Section: Evaluation Of Simulation Deviationsupporting

confidence: 89%

Spatiotemporal Projections of Precipitation in the Lancang–Mekong River Basin Based on CMIP6 Models

Sun,

Liu,

Zhang

et al. 2023

Remote Sensing

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“…For Borneo region, ACCESS‐CM2 (Figure 3c) and FGOALS‐f3‐L (Figure 3j) fail to simulate the wet humid weather in the southwest part. Study of Pimonsree et al (2023) discussed that the performances of CMIP6 are different within countries in SEA, which results in different performance of every TCMs. However, both the ensemble TCMs and TICMs still able to simulate the general described SEA rainfall pattern during winter, with the precipitation simulated by TICMs are slightly higher compared to TCMs.…”

Section: Resultsmentioning

confidence: 99%

“…Li et al (2021b) suggests that the spring rainfall in Indochina Peninsula has a negative association with El Niño. Moreover, warmer climate is proven to increase the occurrence of extreme climate events due to the changes of ENSO characteristics (Ge et al, 2021;Kang et al, 2019;Ngai et al, 2020b;Power et al, 2013). The likelihood of the rainfall frequency and intensity in various regions of Southeast Asia (SEA) will undergo significant changes in the future.…”

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confidence: 99%

Expansion of winter ENSO‐associated rainfall affected area in Southeast Asia under warmer climate

Leong,

Juneng,

Tangang

et al. 2024

Intl Journal of Climatology

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This paper examines the changes of El Niño–Southern Oscillation (ENSO) associated winter rainfall relationship in Southeast Asia (SEA) in the historical period (1981–2010) and future period (2071–2100) based on the Coupled Model Intercomparison Project Phase 6 (CMIP6) models simulations. Twenty general circulation models (GCMs) are chosen from the Scenario Model Intercomparison Project (ScenarioMIP) highest emission scenario—Shared Socioeconomic Pathway (SSP) 5‐8.5. This study focuses only on the GCMs that have been able to replicate the consistent associated patterns observed, referred to as “teleconnection‐consistent models” (TCMs). During historical period, ENSO has significant negative association with SEA winter rainfall, particularly over Indochina mainland, Maritime Continent (MC) and northern Borneo. The significant negative‐associated teleconnection affected area has increased under future climate, particularly over southern part of South China Sea (SCS). The weakening of south westerlies at Western North Pacific (WNP) centred near 20°N reduces the moisture transport from Indochina into south of China, increase the negative correlated affected area in the SEA regions above equator. The northeasterlies/easterlies that prevailed from the WNP anticyclone (AC) brings significant moisture flux into southern Borneo, causing increment of significant positive‐associated moisture flux convergence to that region. The weakening of upward motion in TCM results in less warm and moist air rising over SEA MC, reducing the rainfall availability. The 1° eastward expansion of WC downward branch eastern edge leads to suppression of convective activities, increasing the dry affected area. The results suggest that the ENSO has a larger influence on the negative‐associated SEA rainfall and atmospheric circulation in the December–January–February (DJF) late‐21st century when compared to the 1980–2010.

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“…Model evaluation is an essential part of CMIP6 model selection since simulating past performance well is a necessary (but insufficient) condition to have more confidence in future performance. Different metrics are employed to quantify model skill in simulating various climate variables at either global (Kim et al, 2020;Ridder et al, 2021;Wang et al, 2021b;Donat et al, 2023) or regional scales [e.g., Australia (Deng et al, 2021;Di Virgilio et al, 2022) Europe (Ossó et al, 2023;Palmer et al, 2023); South America (Díaz et al, 2021); Asia (Dong and Dong, 2021); Southeast Asia (Desmet and Ngo-Duc, 2022;Pimonsree et al, 2023)]. However, the lack of consistency in the list of metrics used makes it difficult to perform one-to-one comparisons between studies or to track model performance across various regions.…”

Section: Introductionmentioning

confidence: 99%

“…Note that over SEA, observations are sparse with large uncertainties, particularly for rainfall (Nguyen et al, 2020), making GCM evaluations more complicated (Nguyen et al, 2022;. To date, the performance of various CMIP6 GCMs has been evaluated and ranked over the whole region of SEA (Desmet and Ngo-Duc, 2022;Pimonsree et al, 2023) and its sub-regions [e.g., Philippines (Ignacio-Reardon and Luo, 2023); Thailand (Kamworapan et al, 2021); Vietnam (Nguyen-Duy et al, 2023)]. Although there are groups of GCMs that consistently perform well (e.g., EC-Earth3, EC-Earth3-Veg, GFDL-ESM4, MPI-ESM1-2-HR, E3SM1-0, CESM2) and poorly (e.g., FGOALS-g3, CanESM, NESM3, IPSL-CM6A-LR) across available literature, their ranking varies differently given inconsistencies in evaluation metrics and observational reference datasets.…”

Section: Introductionmentioning

confidence: 99%

Selecting CMIP6 GCMs for CORDEX Dynamical Downscaling over Southeast Asia Using a Standardised Benchmarking Framework

Nguyen,

Alexander,

Thatcher

et al. 2024

Preprint

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Abstract. Downscaling global climate models (GCMs) provides crucial, high-resolution data needed for informed decision-making at regional scales. However, there is no uniform approach to select the most suitable GCMs. Over Southeast Asia (SEA), observations are sparse and have large uncertainties, complicating GCM selection especially for rainfall. To guide this selection, we apply a standardised benchmarking framework to select CMIP6 GCMs for dynamical downscaling over SEA, addressing current observational limitations. This framework identifies fit-for-purpose models through a two-step process: (a) selecting models that meet minimum performance requirements in simulating the fundamental characteristics of rainfall (e.g., bias, spatial pattern, annual cycle, and trend) and (b) selecting models from (a) to further assess whether key precipitation drivers (monsoon) and teleconnections from modes of variability are captured [El Niño-Southern-Oscillation (ENSO) and Indian Ocean Dipole (IOD)]. GCMs generally exhibit wet biases, particularly over the complex terrain of the Maritime Continent. Evaluations from the first step identify 19 out of 32 GCMs that meet our minimum performance expectations in simulating rainfall. These models also consistently capture atmospheric circulations and teleconnections with modes of variability over the region but overestimate their strength. Ultimately, we identify eight GCMs meeting our performance expectations. There are obvious, high-performing GCMs from allied modelling groups, highlighting the dependency of the subset of models identified from the framework. Therefore, further tests on model independence, data availability, and future climate change spread are conducted, resulting in a final sub-set of two independent models that align with our a priori expectations for downscaling over CORDEX-SEA.

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Evaluation of CMIP6 GCMs performance to simulate precipitation over Southeast Asia

Cited by 28 publications

References 59 publications

Spatiotemporal Projections of Precipitation in the Lancang–Mekong River Basin Based on CMIP6 Models

Spatiotemporal Projections of Precipitation in the Lancang–Mekong River Basin Based on CMIP6 Models

Expansion of winter ENSO‐associated rainfall affected area in Southeast Asia under warmer climate

Selecting CMIP6 GCMs for CORDEX Dynamical Downscaling over Southeast Asia Using a Standardised Benchmarking Framework

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