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Title: Future projections and uncertainty assessment of precipitation extremes in the Korean peninsula from the CMIP5 ensemble

Abstract

Abstract Projections of changes in extreme climate are sometimes predicted by multimodel ensemble methods that combine forecasts from individual simulation models using weighted averaging. One method to assign weight to each model is the Bayesian model averaging (BMA) in which posterior probability is used. For the cases of extreme climate, the generalized extreme value distribution (GEVD) is typically used. We applied the approach of GEV‐embedded BMA to a series of 35 years of the annual maximum daily precipitation data (both historical data and data gathered from simulation experiments for future periods) over the Korean peninsula as simulated by the models in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Simulation data under two Representative Concentration Pathway (RCP) scenarios, namely RCP4.5 and RCP8.5, were used. Observed data and 17 CMIP5 models for 12 gird cells in Korea have been examined to predict future changes in precipitation extremes. A simple regional frequency analysis of pooling observations from three stations in each cell was employed to reduce the estimation variance and local fluctuations. A bias correction technique using the regression‐type transfer function was applied to these simulation data. Return levels spanning over 20 and 50 years, as well as the return periods relative to themore » reference years (1971–2005), were estimated for two future periods, namely Period 1 (2021–2050) and Period 2 (2066–2095). From these analyses, relative increase observed in the spatially averaged 20‐year (50‐year) return level was approximately 23% (16%) and 45% (36%) in the RCP4.5 and RCP8.5 experiments, respectively, by the end of the 21st century. We concluded that extreme rainfalls will likely occur two times and four times more frequently in the RCP4.5 and RCP8.5 scenarios, respectively, as compared to in the reference years by the end of the 21st century.« less

Authors:
 [1];  [2];  [3]; ORCiD logo [2]
  1. Digital Transformation Department Korea Electric Power Corporation South Korea, Department of Statistics Chonnam National University Gwangju South Korea
  2. Department of Statistics Chonnam National University Gwangju South Korea
  3. Korea Meteorological Administration South Korea
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1592488
Alternate Identifier(s):
OSTI ID: 1592489
Resource Type:
Published Article
Journal Name:
Atmospheric Science Letters
Additional Journal Information:
Journal Name: Atmospheric Science Letters Journal Volume: 21 Journal Issue: 2; Journal ID: ISSN 1530-261X
Publisher:
Wiley Blackwell (John Wiley & Sons)
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Lee, Youngsaeng, Shin, Younggwan, Boo, Kyung‐On, and Park, Jeong‐Soo. Future projections and uncertainty assessment of precipitation extremes in the Korean peninsula from the CMIP5 ensemble. United Kingdom: N. p., 2020. Web. doi:10.1002/asl.954.
Lee, Youngsaeng, Shin, Younggwan, Boo, Kyung‐On, & Park, Jeong‐Soo. Future projections and uncertainty assessment of precipitation extremes in the Korean peninsula from the CMIP5 ensemble. United Kingdom. https://doi.org/10.1002/asl.954
Lee, Youngsaeng, Shin, Younggwan, Boo, Kyung‐On, and Park, Jeong‐Soo. Sun . "Future projections and uncertainty assessment of precipitation extremes in the Korean peninsula from the CMIP5 ensemble". United Kingdom. https://doi.org/10.1002/asl.954.
@article{osti_1592488,
title = {Future projections and uncertainty assessment of precipitation extremes in the Korean peninsula from the CMIP5 ensemble},
author = {Lee, Youngsaeng and Shin, Younggwan and Boo, Kyung‐On and Park, Jeong‐Soo},
abstractNote = {Abstract Projections of changes in extreme climate are sometimes predicted by multimodel ensemble methods that combine forecasts from individual simulation models using weighted averaging. One method to assign weight to each model is the Bayesian model averaging (BMA) in which posterior probability is used. For the cases of extreme climate, the generalized extreme value distribution (GEVD) is typically used. We applied the approach of GEV‐embedded BMA to a series of 35 years of the annual maximum daily precipitation data (both historical data and data gathered from simulation experiments for future periods) over the Korean peninsula as simulated by the models in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Simulation data under two Representative Concentration Pathway (RCP) scenarios, namely RCP4.5 and RCP8.5, were used. Observed data and 17 CMIP5 models for 12 gird cells in Korea have been examined to predict future changes in precipitation extremes. A simple regional frequency analysis of pooling observations from three stations in each cell was employed to reduce the estimation variance and local fluctuations. A bias correction technique using the regression‐type transfer function was applied to these simulation data. Return levels spanning over 20 and 50 years, as well as the return periods relative to the reference years (1971–2005), were estimated for two future periods, namely Period 1 (2021–2050) and Period 2 (2066–2095). From these analyses, relative increase observed in the spatially averaged 20‐year (50‐year) return level was approximately 23% (16%) and 45% (36%) in the RCP4.5 and RCP8.5 experiments, respectively, by the end of the 21st century. We concluded that extreme rainfalls will likely occur two times and four times more frequently in the RCP4.5 and RCP8.5 scenarios, respectively, as compared to in the reference years by the end of the 21st century.},
doi = {10.1002/asl.954},
journal = {Atmospheric Science Letters},
number = 2,
volume = 21,
place = {United Kingdom},
year = {2020},
month = {1}
}

Journal Article:
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https://doi.org/10.1002/asl.954

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