Projected Changes in Temperature and Precipitation Extremes in China by the CMIP5 Multimodel Ensembles

Zhou, Botao; Wen, Qiuzi Han; Xu, Ying; Song, Lianchun; Zhang, Xuebin

doi:10.1175/jcli-d-13-00761.1

Cited by 312 publications

(229 citation statements)

References 33 publications

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“…In addition, the total precipitation (PRCPTOT) is also used to measure the climatology (Moberg et al 2006), which may help to explain the variation of extreme precipitation. These indices are generally considered effective in extracting climate change information, are highly sensitive to global warming, and have been widely used to identify and monitor extreme precipitation for the IPCC AR5 (Xu et al 2011; Sillmann et al 2013a,b;Zhang et al 2013;Zhou et al 2014). All extreme indices are calculated with the Statistical and Regional Dynamical Downscaling of Extremes for European Regions (STARDEX) diagnostic extremes indices software (Haylock et al 2006).…”

Section: B Extreme Precipitation Indicesmentioning

confidence: 99%

Extreme Precipitation Indices over China in CMIP5 Models. Part I: Model Evaluation

et al. 2015

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International audienceCompared to precipitation extremes calculated from a high-resolution daily observational dataset in China during 1960–2005, simulations in 31 climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) have been quantitatively assessed using skill-score metrics. Four extreme precipitation indices , including the total precipitation (PRCPTOT), maximum consecutive dry days (CDD), precipitation intensity (SDII), and fraction of total rainfall from heavy events (R95T) are analyzed. Results show that CMIP5 models still have wet biases in western and northern China. Especially in western China, the models' median relative error is about 120% for PRCPTOT; the 25th and 75th percentile errors are of 70% and 220%, respectively. However, there are dry biases in southeastern China, where the underestimation of PRCPTOT reach 200 mm. The performance of CMIP5 models is quite different between western and eastern China. The simulations are more reliable in the east than in the west in terms of spatial pattern and interannual variability. In the east, precipitation indices are more consistent with observations, and the spread among models is smaller. The multimodel ensemble constructed from a selection of the most skillful models shows improved behavior compared to the all-model ensemble. The wet bias in western and northern China and dry bias over southeastern China are all decreased. The median of errors for PRCPTOT has a decrease of 69% and 17% in the west and east, respectively. The good reproduction of the southwesterlies along the east coast of the Arabian Peninsula is revealed to be the main factor explaining the improvement of precipitation patterns and extreme events

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Section: B Extreme Precipitation Indicesmentioning

confidence: 99%

Extreme Precipitation Indices over China in CMIP5 Models. Part I: Model Evaluation

et al. 2015

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“…Using 7 CMIP3 models, projected that precipitation extremes would be more frequent and intense across China by the end of twenty-first century, with significant increases found primarily in the middle and lower reaches of the Yangtze River, the Southeast coastal region, the west part of Northwest China, and the Tibetan Plateau. In their study using 24 CMIP5 models, Zhou et al (2014) found that precipitation extremes evidenced disproportionately larger percentage increases compared to mean precipitation.…”

Section: Introductionmentioning

confidence: 96%

Projection of precipitation extremes for eight global warming targets by 17 CMIP5 models

et al. 2016

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Based on the historical and future outputs of 17 coupled model intercomparison project phase 5 (CMIP5) models, simulation of the precipitation extremes in China was evaluated under baseline climate condition compared to a gridded daily observation dataset CN05.1. The variations in precipitation extremes for eight global warming targets were also projected. The 17 individual models and the multi-model ensemble accurately reproduced the spatial distribution of precipitation extremes, although they were limited in their ability to capture the temporal characteristics. A notable dry bias existed in Southeast China, while a wet bias was present in North and Northwest China. The precipitation extremes in China were projected to be more frequent and more intense as global temperature rise reached the 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, and 5.0°C warming targets. The projected percentage changes in the annual number of days with precipitation [50 mm (R50) and total precipitation during days in which the daily precipitation exceeds the 99th percentile (R99p) are projected to increase by 25.81 and 69.14 % relative to the baseline climate for a 1.5°C warming target, and by 95.52 and 162.00 % for a 4.0°C warming target, respectively. As the global mean temperature rise increased from 1.5 to 5°C, the subregions considerably affected by the East Asian summer monsoon (e.g., Southwest China, South China, and the Yangtze-Huai River Valley) were projected to experience a more dramatic increase in extreme precipitation events, in both number of days and intensity, while North and Northwest China were projected to suffer from relatively slight increases. The model uncertainties in the projected precipitation extremes in China by 17 CMIP5 models increase as global temperature rise increases.

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“…The MME method was applied because the consensus is that it provides more robust estimates (e.g. Gleckler, Taylor, and Doutriaux 2008;Gupta et al 2013;Zhou et al 2014;Jiang, Tian, and Lang 2016). For the calculation of the MME, two samples were adopted in this study.…”

Section: Methodsmentioning

confidence: 99%

CMIP5 analysis of the interannual variability of the Pacific SST and its association with the Asian–Pacific oscillation

Zhou

2016

Atmospheric and Oceanic Science Letters

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Its association with the Asian-Pacific oscillation (APO) was also investigated. The evaluation results showed that the multi-model ensemble mean (MME) and most of the individual models perform well in reproducing the relatively stronger interannual variability of SST over the North Pacific and tropical eastern Pacific. They can also capture reasonably well the observed in-phase and out-of-phase relationships of the APO with the SST in the above two regions, respectively. Under the RCP4.5 and RCP8.5 scenarios, the interannual variability of the SST over the North Pacific and the tropical eastern Pacific is projected by the MME to be weakened during 2050-99 compared to 1950-99. The majority of the CMIP5 models show the same projection as the MME. Besides, the MME projection indicates that the present relationship between the APO and the SST over those two regions would still be dominant under both RCPs. However, considerable discrepancies exist in the changes of the relationships among the individual models.

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Projected Changes in Temperature and Precipitation Extremes in China by the CMIP5 Multimodel Ensembles

Cited by 312 publications

References 33 publications

Extreme Precipitation Indices over China in CMIP5 Models. Part I: Model Evaluation

Extreme Precipitation Indices over China in CMIP5 Models. Part I: Model Evaluation

Projection of precipitation extremes for eight global warming targets by 17 CMIP5 models

CMIP5 analysis of the interannual variability of the Pacific SST and its association with the Asian–Pacific oscillation

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