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Scientists who want to know future climate can use multimodel ensemble (MME) methods that combine projections from individual simulation models. To predict the future changes of extreme rainfall in Iran, we examined the observations and 24 models of the Coupled Model Inter-Comparison Project Phase 6 (CMIP6) over the Middle East. We applied generalized extreme value (GEV) distribution to series of annual maximum daily precipitation (AMP1) data obtained from both of models and the observations. We also employed multivariate bias-correction under three shared socioeconomic pathway (SSP) scenarios (namely, SSP2-4.5, SSP3-7.0, and SSP5-8.5). We used a model averaging method that takes both performance and independence of model into account, which is called PI-weighting. Return levels for 20 and 50 years, as well as the return periods of the AMP1 relative to the reference years (1971–2014), were estimated for three future periods. These are period 1 (2021–2050), period 2 (2046–2075), and period 3 (2071–2100). From this study, we predict that over Iran the relative increases of 20-year return level of the AMP1 in the spatial median from the past observations to the year 2100 will be approximately 15.6% in the SSP2-4.5, 23.2% in the SSP3-7.0, and 28.7% in the SSP5-8.5 scenarios, respectively. We also realized that a 1-in-20 year (or 1-in-50 year) AMP1 observed in the reference years in Iran will likely become a 1-in-12 (1-in-26) year, a 1-in-10 (1-in-22) year, and a 1-in-9 (1-in-20) year event by 2100 under the SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, respectively. We project that heavy rainfall will be more prominent in the western and southwestern parts of Iran.
Juyoung Hong; Khadijeh Javan; Yonggwan Shin; Jeong-Soo Park. Future Projections and Uncertainty Assessment of Precipitation Extremes in Iran from the CMIP6 Ensemble. Atmosphere 2021, 12, 1052 .
AMA StyleJuyoung Hong, Khadijeh Javan, Yonggwan Shin, Jeong-Soo Park. Future Projections and Uncertainty Assessment of Precipitation Extremes in Iran from the CMIP6 Ensemble. Atmosphere. 2021; 12 (8):1052.
Chicago/Turabian StyleJuyoung Hong; Khadijeh Javan; Yonggwan Shin; Jeong-Soo Park. 2021. "Future Projections and Uncertainty Assessment of Precipitation Extremes in Iran from the CMIP6 Ensemble." Atmosphere 12, no. 8: 1052.
Scientists occasionally predict projected changes in extreme climate using multi-model ensemble methods that combine predictions from individual simulation models. To predict future changes in precipitation extremes in the Korean peninsula, we examined the observed data and 21 models of the Coupled Model Inter-Comparison Project Phase 6 (CMIP6) over East Asia. We applied generalized extreme value distribution (GEVD) to a series of annual maximum daily precipitation (AMP1) data. Multivariate bias-corrected simulation data under three shared socioeconomic pathway (SSP) scenarios—namely, SSP2-4.5, SSP3-7.0, and SSP5-8.5—were used. We employed a model weighting method that accounts for both performance and independence (PI-weighting). In calculating the PI-weights, two shape parameters should be determined, but usually, a perfect model test method requires a considerable amount of computing time. To address this problem, we suggest simple ways for selecting two shape parameters based on the chi-square statistic and entropy. Variance decomposition was applied to quantify the uncertainty of projecting the future AMP1. Return levels spanning over 20 and 50 years, as well as the return periods relative to the reference years (1973–2010), were estimated for three overlapping periods in the future, namely, period 1 (2021–2050), period 2 (2046–2075), and period 3 (2071–2100). From these analyses, we estimated that the relative increases in the observations for the spatial median 20-year return level will be approximately 18.4% in the SSP2-4.5, 25.9% in the SSP3-7.0, and 41.7% in the SSP5-8.5 scenarios, respectively, by the end of the 21st century. We predict that severe rainfall will be more prominent in the southern and central parts of the Korean peninsula.
Yonggwan Shin; Yire Shin; Juyoung Hong; Maeng-Ki Kim; Young-Hwa Byun; Kyung-On Boo; Il-Ung Chung; Doo-Sun Park; Jeong-Soo Park. Future Projections and Uncertainty Assessment of Precipitation Extremes in the Korean Peninsula from the CMIP6 Ensemble with a Statistical Framework. Atmosphere 2021, 12, 97 .
AMA StyleYonggwan Shin, Yire Shin, Juyoung Hong, Maeng-Ki Kim, Young-Hwa Byun, Kyung-On Boo, Il-Ung Chung, Doo-Sun Park, Jeong-Soo Park. Future Projections and Uncertainty Assessment of Precipitation Extremes in the Korean Peninsula from the CMIP6 Ensemble with a Statistical Framework. Atmosphere. 2021; 12 (1):97.
Chicago/Turabian StyleYonggwan Shin; Yire Shin; Juyoung Hong; Maeng-Ki Kim; Young-Hwa Byun; Kyung-On Boo; Il-Ung Chung; Doo-Sun Park; Jeong-Soo Park. 2021. "Future Projections and Uncertainty Assessment of Precipitation Extremes in the Korean Peninsula from the CMIP6 Ensemble with a Statistical Framework." Atmosphere 12, no. 1: 97.
A model weighting scheme is important in multi-model climate ensembles for projecting future changes. The climate model output typically needs to be bias corrected before it can be used. When a bias-correction (BC) is applied, equal model weights are usually derived because some BC methods cause the observations and historical simulation to match perfectly. This equal weighting is sometimes criticized because it does not take into account the model performance. Unequal weights reflecting model performance may be obtained from raw data before BC is applied. However, we have observed that certain models produce excessively high weights, while the weights generated in all other models are extremely low. This phenomenon may be partly due to the fact that some models are more fit or calibrated to the observations for a given applications. To address these problems, we consider, in this study, a hybrid weighting scheme including both equal and unequal weights. The proposed approach applies an “imperfect” correction to the historical data in computing their weights, while it applies ordinary BC to the future data in computing the ensemble prediction. We employ a quantile mapping method for the BC and a Bayesian model averaging for performance-based weighting. Furthermore, techniques for selecting the optimal correction rate based on the chi-square test statistic and the continuous ranked probability score are examined. Comparisons with ordinary ensembles are provided using a perfect model test. The usefulness of the proposed method is illustrated using the annual maximum daily precipitation as observed in the Korean peninsula and simulated by 21 models from the Coupled Model Intercomparison Project Phase 6.
Yonggwan Shin; Youngsaeng Lee; Jeong-Soo Park. A Weighting Scheme in A Multi-Model Ensemble for Bias-Corrected Climate Simulation. Atmosphere 2020, 11, 775 .
AMA StyleYonggwan Shin, Youngsaeng Lee, Jeong-Soo Park. A Weighting Scheme in A Multi-Model Ensemble for Bias-Corrected Climate Simulation. Atmosphere. 2020; 11 (8):775.
Chicago/Turabian StyleYonggwan Shin; Youngsaeng Lee; Jeong-Soo Park. 2020. "A Weighting Scheme in A Multi-Model Ensemble for Bias-Corrected Climate Simulation." Atmosphere 11, no. 8: 775.