Uncertainty of Rate of Change in Korean Future Rainfall Extremes Using Non-Stationary GEV Model
| Autor: | Jeonghyeon Choi, Sangdan Kim, Okjeong Lee, Jeongeun Won, Jiyu Seo, Jungmin Lee, Suhyung Jang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Atmospheric Science
non-stationarity Posterior probability Bayesian inference rate of change Climate change lcsh:QC851-999 Environmental Science (miscellaneous) GEV distribution Confidence interval Dew point climate change Climatology Generalized extreme value distribution rainfall extremes Environmental science lcsh:Meteorology. Climatology Climate model uncertainty Scale parameter |
| Zdroj: | Atmosphere Volume 12 Issue 2 Atmosphere, Vol 12, Iss 227, p 227 (2021) |
| ISSN: | 2073-4433 |
| DOI: | 10.3390/atmos12020227 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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