Spatial-temporal evolution and projection of climate extremes in South Korea based on multi-GCM ensemble data

Ahmad, Mirza Junaid; Choi, Kyung Sook

doi:10.1016/j.atmosres.2023.106772

Cited by 3 publications

(2 citation statements)

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“…QDM is considered an improved method to perform bias correction by addressing the issue of non-stationarity in the GCM projection data while conserving the long-term relative change in simulated quantiles of GCM output data [7,26]. Moreover, this method is less prone to inflating relative trends in extreme climate variables than other quantile mapping bias correction algorithms [27].…”

Section: Bias Correctionmentioning

confidence: 99%

“…South Korea is one of the vulnerable countries to climate change that unprecedented natural disasters have recently and continuously impacted due to its geographical location in the tropical monsoon climate region and varying climatic characteristics [4,6,7]. The pattern of extreme climate events in South Korea has become frequent in recent times, such that it causes loss of lives and massive damage to properties [4].…”

Section: Introductionmentioning

confidence: 99%

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Performance-Based Evaluation of CMIP5 and CMIP6 Global Climate Models and Their Multi-Model Ensembles to Simulate and Project Seasonal and Annual Climate Variables in the Chungcheong Region of South Korea

Adelodun,

Ahmad,

Odey

et al. 2023

Atmosphere

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Extreme climate change events are major causes of devastating impacts on socioeconomic well-being and ecosystem damage. Therefore, understanding the performance of appropriate climate models representing local climate characteristics is critical for future projections. Thus, this study analyses the performance of 24 GCMs from the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and 6) and their multi-model ensembles in simulating climate variables including average rainfall, maximum (Tmax), and minimum (Tmin) temperatures at annual and seasonal scales over the Chungcheong region of South Korea from 1975 to 2015. A trend analysis was conducted to estimate the future trends in climate variables in the 2060s (2021–2060) and 2080s (2061–2100). Inverse distance weighting and quantile delta mapping were applied to bias-correct the GCM data. Further, six major evaluating indices comprising temporal and spatial performance assessments were used, after which a comprehensive GCM ranking was applied. The results showed that CMIP6 models performed better in simulating rainfall, Tmax, and Tmin at both temporal and spatial scales. For CMIP5, the top three performing models were GISS, ACCESS1-3, and MRI-CGCM3 for rain; CanESM2, GISS, and MPI-ESM-L-R for Tmax; and GFDL, MRI-CGCM3, and CanESM2 for Tmin. However, the top three performing models in the CMIP6 were MRI-ESM2-0, BCC_CSM, and GFDL for rain; MIROC6, BCC_CSM, and MRI-ESM2-0 for Tmax, and GFDL, MPI_ESM_HR, and MRI-ESM2-0 for Tmin. The multi-model ensembles (an average of the top three GCMs) performed better in simulating rain and Tmin for both CMIP5 and CMIP6 compared with multi-model ensembles (an average of all the GCMs), which only performed slightly better in simulating Tmax. The trend analysis of future projection indicates an increase in rain, Tmax, and Tmin; however, with distinct changes under similar radiative forcing levels in both CMIP5 and CMIP6 models. The projections under RCP4.5 and RCP8.5 increase more than the SSP2-4.5 and SSP5-8.5 scenarios for most climate conditions but are more pronounced, especially for rain, under RCP8.5 than SSP5-8.5 in the far future (2080s). This study provides insightful findings on selecting appropriate GCMs to generate reliable climate projections for local climate conditions in the Chungcheong region of South Korea.

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Section: Bias Correctionmentioning

confidence: 99%