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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. [ABSTRACT FROM AUTHOR]

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.

ABSTRACT Present and future climate change information is required to develop adaptation and mitigation strategies at national and international levels. This study assessed the simulated surface air temperature ( SAT) and precipitation ( PR) over China from 24 models involved in the Coupled Model Intercomparison Project Phase 5 ( CMIP5). The reliability ensemble average ( REA) is applied to project the SAT and PR change under representative concentration pathway ( RCP) scenarios over China in the 21st century. The results show that most CMIP5 models tend to underestimate SAT and overestimate PR in China. Models generally agree better with the observed SAT than PR. For SAT, the ensemble prediction shows that warming is expected all over China for all RCPs. The warming trend from 2006 to 2099 in China is 0.87 ± 0.14 °C 100 year−1, 2.47 ± 0.48 °C 100 year−1, 5.85 ± 0.73 °C 100 year−1 for RCP 2.6, RCP 4.5 and RCP 8.5, respectively. Northern regions experience more warming than southern regions. The Songhua River basin warms the most, considering the ten studied basins for RCP 4.5 and RCP 8.5. Under RCP 2.6, the largest warming trend occurs in the Huaihe River basin. For PR, the spatial pattern of PR change has zonal characteristics. The girds with the maximum linear trend, i.e. >7.5 mm decade−1, are concentrated in the upper Yangtze River basin. For temporal scale, PR in China is also projected to increase during the 21st century by 4.89 ± 2.30% 100 year−1, 8.67 ± 6.27% 100 year−1 and 13.39 ± 12.58% 100 year−1 for RCP 2.6, RCP 4.5 and RCP 8.5, respectively. PR tends to decrease in the Yangtze River basin, Southeast River Drainage and Pearl River basin during the early period (2011-2030) for all RCPs, largely increase thereafter. However, uncertainties are unavoidable for SAT and PR projections. The PR uncertainty exceeds the temperature uncertainty. More studies regarding the analysis of narrowing uncertainties are essential for a better understanding of climate change. [ABSTRACT FROM AUTHOR]

Present and future climate change information is required to develop adaptation and mitigation strategies at national and international levels. This study assessed the simulated surface air temperature (SAT) and precipitation (PR) over China from 24 models involved in the Coupled Model Intercomparison Project Phase 5 (CMIP5). The reliability ensemble average (REA) is applied to project the SAT and PR change under representative concentration pathway (RCP) scenarios over China in the 21st century. The results show that most CMIP5 models tend to underestimate SAT and overestimate PR in China. Models generally agree better with the observed SAT than PR. For SAT, the ensemble prediction shows that warming is expected all over China for all RCPs. The warming trend from 2006 to 2099 in China is 0.87 ±0.14 °C 100 year-1, 2.47 ±0.48 °C100 year-1, 5.85 ±0.73 °C100 year-1 for RCP 2.6, RCP 4.5 and RCP 8.5, respectively. Northern regions experience more warming than southern regions. The Songhua River basin warms the most, considering the ten studied basins for RCP 4.5 and RCP 8.5. Under RCP 2.6, the largest warming trend occurs in the Huaihe River basin. For PR, the spatial pattern of PR change has zonal characteristics. The girds with the maximum linear trend, i.e. >7.5 mm decade-1, are concentrated in the upper Yangtze River basin. For temporal scale, PR in China is also projected to increase during the 21st century by 4.89±2.30% 100 year-1, 8.67±6.27% 100 year-1 and 13.39±12.58% 100 year-1 for RCP 2.6, RCP 4.5 and RCP 8.5, respectively. PR tends to decrease in the Yangtze River basin, Southeast River Drainage and Pearl River basin during the early period (2011-2030) for all RCPs, largely increase thereafter. However, uncertainties are unavoidable for SAT and PR projections. The PR uncertainty exceeds the temperature uncertainty. More studies regarding the analysis of narrowing uncertainties are essential for a better understanding of climate change. [ABSTRACT FROM AUTHOR]

Assessing the performance of climate models in surface air temperature (SAT) simulation and projection have received increasing attention during the recent decades. This paper assesses the performance of the Coupled Model Intercomparison Project phase 5 (CMIP5) in simulating intra-annual, annual and decadal temperature over Northern Eurasia from 1901 to 2005. We evaluate the skill of different multi-model ensemble techniques and use the best technique to project the future SAT changes under different emission scenarios. The results show that most of the general circulation models (GCMs) overestimate the annual mean SAT in Northern Eurasia and the difference between the observation and the simulations primarily comes from the winter season. Most of the GCMs can approximately capture the decadal SAT trend; however, the accuracy of annual SAT simulation is relatively low. The correlation coefficient R between each GCM simulation and the annual observation is in the range of 0.20 to 0.56. The Taylor diagram shows that the ensemble results generated by the simple model averaging (SMA), reliability ensemble averaging (REA) and Bayesian model averaging (BMA) methods are superior to any single GCM output; and the decadal SAT change generated by SMA, REA and BMA are almost identical during 1901–2005. Heuristically, the uncertainty of BMA simulation is the smallest among the three multi-model ensemble simulations. The future SAT projection generated by the BMA shows that the SAT in Northern Eurasia will increase in the 21st century by around 1.03 °C/100 yr, 3.11 °C/100 yr and 7.14 °C/100 yr under the RCP 2.6, RCP 4.5 and RCP 8.5 scenarios, respectively; and the warming accelerates with the increasing latitude. In addition, the spring season contributes most to the decadal warming occurring under the RCP 2.6 and RCP 4.5 scenarios, while the winter season contributes most to the decadal warming occurring under the RCP 8.5 scenario. Generally, the uncertainty of the SAT projections increases with time in the 21st century.