Future Projections and Uncertainty Assessment of Precipitation Extremes in the Korean Peninsula from the CMIP6 Ensemble with a Statistical Framework

Abstract

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.

Future Projections and Uncertainty Assessment of Precipitation Extremes in Iran from the CMIP6 Ensemble

“…In applying the PI-weighting method, we follow ways of selecting two shape parameters [43], based on the p-value of the chi-square statistic and entropy. The methods are simple and intuitively appealing, although they may need more justification to use.…”

“…This leave-one-out procedure needs a huge computing time. To overcome this computational burden, we follow a relatively simple method proposed by [43] to determine the shape parameters. More details in computing the distance S ij and in selecting the shape parameters are given in the Supplementary Material.…”

“…In calculating the PI-weights, considering only one or two climate variables over a relatively small area can lead to an overfitting problem [40,42]. To avoid this problem, we thus consider five climate variables as in Table 1 over Middle East, while our focus is the annual maximum daily precipitation (AMP1) over Iran as in [43] did. The AMP1 (or annual Rx1day [44]) is defined as the annual maximum precipitation (in mm) among the daily rainfall amount in the year.…”

Macroscale impact assessment of flood damage reduction from climate change adaptation policies

“…Recent data have demonstrated that the intensity and frequency of extreme rainfall events have increased due to climate change. Furthermore, it is anticipated that the intensity and frequency of precipitation will continue to increase in the future (Li et al, 2021; Shin et al, 2021).…”

Evaluating the effects of CMIP6 model uncertainty on extreme flows of the Caspian Hyrcanian forest watersheds by BMA method

“…Their results showed a 22.5% and 19.2% increase in runoff under the SSP2-4.5 and SSP5-8.5 scenarios, respectively. Shin et al (2021) predicted extreme PCP events on the Korean Peninsula. They Utilized 21 CMIP6 GCMs under three scenarios: SSP2-4.5, SSP3-7.0, and SSP5-8.5.…”