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:

Lee, Youngsaeng ^[1]; Shin, Younggwan ^[2]; Boo, Kyung‐On ^[3]; Park, Jeong‐Soo  ^[2]

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+ Show Author Affiliations

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:

Sun Jan 19 00:00:00 EST 2020

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