Future changes in precipitation extremes over Southeast Asia: insights from CMIP6 multi-model ensemble

Ge, Fei; Zhu, Shoupeng; Luo, Haolin; Zhi, Xiefei; Wang, Hao

doi:10.1088/1748-9326/abd7ad

Cited by 108 publications

(74 citation statements)

References 73 publications

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“…These were total wet-day precipitation (PRCPTOT), precipitation of very wet days (R95p), simple daily intensity index (SDII), and consecutive dry days (CDD). These four extreme precipitation indices can reflect the basic characteristics of precipitation and its extremes and have been widely used in previous studies (Alexander and Arblaster 2009;Sillmann et al 2013;Jiang et al 2015;Sun et al 2015;Ge et al 2017;Kim et al 2020). More details of these extreme precipitation indices can be obtained at the ETCCDI website (http://etccdi.pacificclimate.org/ indices.shtml).…”

Section: Extreme Precipitation Indicesmentioning

confidence: 99%

“…The climate dynamics in the Indochina and South China (INCSC) region are extremely complex, as the region is influenced by three regional monsoon systems (the Indian summer monsoon, East Asian summer monsoon, and the western North Pacific summer monsoon) (Chen and Yoon 2000;Yang et al 2020). In addition, the distinctive topography and complex air-sea interactions make the spatiotemporal variation of precipitation and extreme precipitation in the INCSC region more complicated than in other regions (Buckley et al 2014;Räsänen et al 2016;Ge et al 2017). Since extreme precipitation events severely impact economic activity and human lives in the INCSC region, comprehending and quantifying extreme precipitation in the region is of vital importance for implementing proactive measures to reduce the risks from extreme precipitation (Asian Development Bank 2009; Krishnan et al 2015;Pai and Sridhar 2015;Kim et al 2018).…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Extreme Precipitation Indices over Indochina and South China in CMIP6 Models

Tang

Duan

2021

Journal of Climate

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Precipitation extremes over the Indochina and South China (INCSC) region simulated by 40 global climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) were quantitatively assessed based on the skill score metrics of four extreme precipitation indices when compared with observational results from a high-resolution daily precipitation dataset for 1958–2014. The results show that it is difficult for most of the CMIP6 models to reproduce the observed spatial pattern of extreme precipitation indices in the INCSC region. The interannual variability of the extreme precipitation indices is relatively better simulated for South China than for Indochina. In general, most of the CMIP6 models perform better in South China compared with Indochina when taking both the simulations of spatial pattern and interannual variability into consideration. Only three models (EC-Earth3, EC-Earth3-Veg, and NorESM2-MM) can successfully reproduce both the spatial pattern and the interannual variability for the INCSC region. Through model ranking, the multi-model ensemble generated by a selection of the most skillful models leads to a more realistic simulation of the extreme precipitation indices both in South China and Indochina. Better simulation of the meridional wind component over South China and the water vapor convergence over Indochina can partly reduce the wet biases, resulting in a more realistic simulation of extreme precipitation indices over the INCSC region.

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Section: Extreme Precipitation Indicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment of Extreme Precipitation Indices over Indochina and South China in CMIP6 Models

Tang

Duan

2021

Journal of Climate

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“…Global Climate Models (GCMs) from the Coupled Model Intercomparison Project (CMIP) have been used as a primary tool for examining the past and future changes in the mean and extreme of climate at global and regional scales. A plethora of studies (e.g., Ge et al 2021;Klutse et al 2021;Li et al 2021a;Saeed et al 2021a, b;Akinsanola et al 2020;Almazroui 2020;Kim et al 2020;Rastogi et al 2020;Srivastava et al 2020;Wehner et al 2020;Betts et al 2018;Diffenbaugh et al 2017) concluded that under the continuously rising global temperature, catastrophic extreme climatic events are happening with ever greater intensity and frequency across the globe. However, representing the characteristics of climate extremes in the past as well as in projections using multiple GCMs has remained an interesting topic in climate change research (e.g., Chen et al 2021;Sillmann et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Projected Changes in Climate Extremes Using CMIP6 Simulations Over SREX Regions

et al. 2021

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This paper presents projected changes in extreme temperature and precipitation events by using Coupled Model Intercomparison Project phase 6 (CMIP6) data for mid-century (2036–2065) and end-century (2070–2099) periods with respect to the reference period (1985–2014). Four indices namely, Annual maximum of maximum temperature (TXx), Extreme heat wave days frequency (HWFI), Annual maximum consecutive 5-day precipitation (RX5day), and Consecutive Dry Days (CDD) were investigated under four socioeconomic scenarios (SSP1-2.6; SSP2-4.5; SSP3-7.0; SSP5-8.5) over the entire globe and its 26 Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) regions. The projections show an increase in intensity and frequency of hot temperature and precipitation extremes over land. The intensity of the hottest days (as measured by TXx) is projected to increase more in extratropical regions than in the tropics, while the frequency of extremely hot days (as measured by HWFI) is projected to increase more in the tropics. Drought frequency (as measured by CDD) is projected to increase more over Brazil, the Mediterranean, South Africa, and Australia. Meanwhile, the Asian monsoon regions (i.e., South Asia, East Asia, and Southeast Asia) become more prone to extreme flash flooding events later in the twenty-first century as shown by the higher RX5day index projections. The projected changes in extremes reveal large spatial variability within each SREX region. The spatial variability of the studied extreme events increases with increasing greenhouse gas concentration (GHG) and is higher at the end of the twenty-first century. The projected change in the extremes and the pattern of their spatial variability is minimum under the low-emission scenario SSP1-2.6. Our results indicate that an increased concentration of GHG leads to substantial increases in the extremes and their intensities. Hence, limiting CO2 emissions could substantially limit the risks associated with increases in extreme events in the twenty-first century.

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“…Recent studies [8][9][10][11][12][13][14][15][16][17] have showed strong evidence of climate change impacts on the rainfall regime. Alterations in rainfall regime propagate through the hydrologic cycle, and thus induce changing streamflow regime.…”

Section: Introductionmentioning

confidence: 99%

Nonstationary Analyses of the Maximum and Minimum Streamflow in Tamsui River Basin, Taiwan

Shiau

Liu

2021

Water

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This study aims to detect non-stationarity of the maximum and minimum streamflow regime in Tamsui River basin, northern Taiwan. Seven streamflow gauge stations, with at least 27-year daily records, are used to characterize annual maximum 1- and 2-day flows and annual minimum 1-, 7-, and 30-day flows. The generalized additive models for location, scale, and shape (GAMLSS) are used to dynamically detect evolution of probability distributions of the maximum and minimum flow indices with time. Results of time-covariate models indicate that stationarity is only noted in the 4 maximum flow indices out of 35 indices. This phenomenon indicates that the minimum flow indices are vulnerable to changing environments. A 16-category distributional-change scheme is employed to classify distributional changes of flow indices. A probabilistic distribution with complex variations of mean and variance is prevalent in the Tamsui River basin since approximate one third of flow indices (34.3%) belong to this category. To evaluate impacts of dams on streamflow regime, a dimensionless index called the reservoir index (RI) serves as an alternative covariate to model nonstationary probability distribution. Results of RI-covariate models indicate that 7 out of 15 flow indices are independent of RI and 80% of the best-fitted RI-covariate models are generally worse than the time-covariate models. This fact reveals that the dam is not the only factor in altering the streamflow regime in the Tamsui River, which is a significant alteration, especially the minimum flow indices. The obtained distributional changes of flow indices clearly indicate changes in probability distributions with time. Non-stationarity in the Tamsui River is induced by climate change and complex anthropogenic interferences.

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Future changes in precipitation extremes over Southeast Asia: insights from CMIP6 multi-model ensemble

Cited by 108 publications

References 73 publications

Assessment of Extreme Precipitation Indices over Indochina and South China in CMIP6 Models

Assessment of Extreme Precipitation Indices over Indochina and South China in CMIP6 Models

Projected Changes in Climate Extremes Using CMIP6 Simulations Over SREX Regions

Nonstationary Analyses of the Maximum and Minimum Streamflow in Tamsui River Basin, Taiwan

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