Uncertainty of Rate of Change in Korean Future Rainfall Extremes Using Non-Stationary GEV Model

Jiyu Seo,Sangdan Kim,Okjeong Lee,Jeongeun Won,Jungmin Lee,Jeonghyeon Choi,Suhyung Jang

doi:10.3390/atmos12020227

Abstract

Interest in future rainfall extremes is increasing, but the lack of consistency in the future rainfall extremes outputs simulated in climate models increases the difficulty of establishing climate change adaptation measures for floods. In this study, a methodology is proposed to investigate future rainfall extremes using future surface air temperature (SAT) or dew point temperature (DPT). The non-stationarity of rainfall extremes is reflected through non-stationary frequency analysis using SAT or DPT as a co-variate. Among the parameters of generalized extreme value (GEV) distribution, the scale parameter is applied as a function of co-variate. Future daily rainfall extremes are projected from 16 future SAT and DPT ensembles obtained from two global climate models, four regional climate models, and two representative concentration pathway climate change scenarios. Compared with using only future rainfall data, it turns out that the proposed method using future temperature data can reduce the uncertainty of future rainfall extremes outputs if the value of the reference co-variate is properly set. In addition, the confidence interval of the rate of change of future rainfall extremes is quantified using the posterior distribution of the parameters of the GEV distribution sampled using Bayesian inference.

Highlights

In most studies investigating future rainfall extremes, it is assumed that future rainfall data simulated from global climate models (GCMs) or regional climate models (RCMs) are “observed” data in the future [1,2]
As applied in Lee et al [10] and Ouarda et al [36], if an optimal model is selected using the Akaike Information Criterion (AIC), which mainly considers the aspect of fitness of the observed AMR, the stationary model s is selected as the optimal model for the AMR at Chuncheon and Cheonan sites
The very large deviations between the statistics of rainfall extremes obusing using futurefuture rainfall time series produced fromfrom various climate models create a lot of tained rainfall time series produced various climate models create a lot tained using future rainfall time change series produced from various The climate models create a lot confusion in establishing climate adaptation measures

Summary

Introduction

In most studies investigating future rainfall extremes, it is assumed that future rainfall data simulated from global climate models (GCMs) or regional climate models (RCMs) are “observed” data in the future [1,2]. A series of attempts have been made to investigate future rainfall extremes based on the relationship between temperature and rainfall extremes [12,13,14]. The basic concept commonly included in these attempts is based on the fact that investigating the behavior of rainfall extremes under global warming conditions from the relationship between observed temperature and observed rainfall extremes is an approach to obtain more consistent future outputs [15,16]. Hosseinzadehtalaei et al [17] estimated rainfall quantiles from a stationary frequency analysis using the rainfall projection output for the 30 years from 2071 to 2100. Stationary assumptions for segmented future periods can be found in many studies on rainfall extremes related to climate change [18,19,20]. The non-stationarity of time series of rainfall extremes is sometimes expressed explicitly as a function of time, but it can be related to climate variables observed at the same time or the preceding time when rainfall extremes occurred [21]

Results

Discussion

Conclusion