Assessment of Climate Change Impacts on Extreme Precipitation Events: Applications of CMIP5 Climate Projections Statistically Downscaled over South Korea

Abstract

Climate change may accelerate the water cycle at a global scale, resulting in more frequent extreme climate events. This study analyzed changes in extreme precipitation events employing climate projections statistically downscaled at a station-space scale in South Korea. Among the CMIP5 climate projections, based on spatial resolution, this study selected 26 climate projections that provide daily precipitation under the representative concentration pathway (RCP) 4.5. The results show that a 20-year return period of precipitation event during a reference period (1980∼2005) corresponds to a 16.6 yr for 2011 to 2040, 14.1 yr for 2041 to 2070, and 12.8 yr for 2071 to 2100, indicating more frequent extreme maximum daily precipitation may occur in the future. In addition, we found that the probability density functions of the future periods are located out of the 10% confidence interval of the PDF for the reference period. The result indicates that the design standard under the reference climate is not managed to cope with climate change, and accordingly the revision of the design standard is required to improve sustainability in infrastructures.

A Projection of Extreme Precipitation Based on a Selection of CMIP5 GCMs over North Korea

“…The upper tail of GEV-PDF in the future would become thicker due to the decrease of the shape parameters, and therefore, the occurrence probability of extreme precipitation would increase. Generally, the PDF changes with time due to climate change, which means that the location and scale parameters of GEV distribution also increase over time, and the shape parameter becomes smaller [17]. Similar to previous studies, changes to the GEV parameters caused the location parameters from the present to F3 to increase from 93.5 to 127.0 and the scale parameters to increase from 35.0 to 50.2.…”

“…The APCC (APEC Climate Center) Integrated Modeling (AIMS) produced downscaled climate projection data of South Korea using two BCSD (Bias-Correction Spatial Disaggregation) methods [17]; the SQM (Simple Quantile Method) and Spatial Disaggregation with Quantile Delta Mapping (SDQDM) [33] which can retain long-term temporal trends in climate. This study used the SDQDM method to downscale future projections of daily precipitation and temperature of 25 GCMs from CMIP5 under RCP 8.5 to locations of the 27 weather stations.…”

“…For impact assessment concerning extremities of climate, it is necessary to quantify uncertainties caused by different dynamic systems, grid sizes simulated and parametrization physics. To quantify the uncertainty in climate change scenarios, many studies recommend the use of multi-models [13][14][15][16][17] or the selection of representative scenarios [18,19]. Several studies have suggested that scenarios should be weighted appropriately for the ensemble results [20,21].…”

Change in Extreme Precipitation over North Korea Using Multiple Climate Change Scenarios

“…Therefore, in order to perform impact assessments for extreme climate events, it is necessary to quantify the uncertainty due to differences in dynamic system, grid size, and parameterization and physicalization processes and to produce climate scenarios at local scales with appropriate downscaling. To quantify the uncertainty among climate change scenarios, many studies recommend the use of multiple models [9][10][11][12][13].…”

Assessment of Future Climate Change Projections Using Multiple Global Climate Models

“…For CMIP5, the climate projection scenarios are named as RCP scenarios which are used in the most recent IPCC Fifth Assessment Report (AR5) [24]. Four different scenarios for climate change projection are represented as RCP2.6, RCP4.5, RCP6.0, and RCP 8.5 and were proposed considering the effect of variability in the emission of greenhouse gases, aerosol concentrations in the atmosphere, Land-use change, population changes, changes in GDP, technological advancement as a whole [25]. The Representative Concentration Pathways emphasize on their primary purpose to provide time-dependent atmospheric greenhouse gas concentrations projections [26].…”