Multimodel ensemble projection of precipitation in eastern China under A1B emission scenario

Niu, Xiaorui; Tang, Jianping; Lee, Dong‐Kyou; Gao, Xuejie; Wu, Jiangning; Hong, Song‐You; Gutowski, William J.; McGregor, John L.

doi:10.1002/2015jd023853

Cited by 35 publications

(32 citation statements)

References 64 publications

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“…Uncertainty in projected climate change mainly arises from the internal variability of the climate system, the model uncertainty, and the scenario uncertainty (Niu et al, 2015;Woldemeskel et al, 2016). In this study, all RCMs are driven by the same GCM under the same scenario, and thus, the uncertainty of the climate projections is mainly caused by inter-RCM and internal variability.…”

Section: Analysis Methodsmentioning

confidence: 99%

“…go.kr/cordex/download.do, last access: 18 May 2018). A series of studies based on RCMs within CORDEX-EA has been conducted to project extreme and mean precipitation and temperature in East Asia (Jin et al, 2016;Lee et al, 2014;Niu et al, 2015;Park et al, 2016;Tang et al, 2016;Um et al, 2017), but little attention has been paid to quantify the contributions of the uncertainty in future climate projection over China.…”

Section: H Gu Et Al: Ensemble Future Climate Projections and Uncertmentioning

confidence: 99%

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High-resolution ensemble projections and uncertainty assessment of regional climate change over China in CORDEX East Asia

Yang

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. An ensemble simulation of five regional climate models (RCMs) from the coordinated regional downscaling experiment in East Asia is evaluated and used to project future regional climate change in China. The influences of model uncertainty and internal variability on projections are also identified. The RCMs simulate the historical climate and future climate projections under the Representative Concentration Pathway (RCP) RCP4.5 scenario. The simulations for five subregions in China, including northeastern China, northern China, southern China, northwestern China, and the Tibetan Plateau, are highlighted in this study. Results show that (1) RCMs can capture the climatology, annual cycle, and interannual variability of temperature and precipitation and that a multimodel ensemble (MME) outperforms that of an individual RCM. The added values for RCMs are confirmed by comparing the performance of RCMs and global climate models (GCMs) in reproducing annual and seasonal mean precipitation and temperature during the historical period. (2) For future (2030)(2031)(2032)(2033)(2034)(2035)(2036)(2037)(2038)(2039)(2040)(2041)(2042)(2043)(2044)(2045)(2046)(2047)(2048)(2049) climate, the MME indicates consistent warming trends at around 1 • C in the entire domain and projects pronounced warming in northern and western China. The annual precipitation is likely to increase in most of the simulation region, except for the Tibetan Plateau. (3) Generally, the future projected change in annual and seasonal mean temperature by RCMs is nearly consistent with the results from the driving GCM. However, changes in annual and seasonal mean precipitation exhibit significant inter-RCM differences and possess a larger magnitude and variability than the driving GCM. Even opposite signals for projected changes in average precipitation between the MME and the driving GCM are shown over southern China, northeastern China, and the Tibetan Plateau. (4) The uncertainty in projected mean temperature mainly arises from the internal variability over northern and southern China and the model uncertainty over the other three subregions. For the projected mean precipitation, the dominant uncertainty source is the internal variability over most regions, except for the Tibetan Plateau, where the model uncertainty reaches up to 60 %. Moreover, the model uncertainty increases with prediction lead time across all subregions.

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Section: Analysis Methodsmentioning

confidence: 99%

Section: H Gu Et Al: Ensemble Future Climate Projections and Uncertmentioning

confidence: 99%

High-resolution ensemble projections and uncertainty assessment of regional climate change over China in CORDEX East Asia

Yang

et al. 2018

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Niu et al . () constructed future precipitation in China using five RCMs driven by the one GCM, and found that similar changes in summer precipitation were projected by all RCMs over western China, but model behaviours were quite different over eastern China of the Eastern Asian Summer Monsoon region.…”

Section: Introductionmentioning

confidence: 99%

“…Systematic studies have been performed to quantify and reduce the uncertainties in the regional climate projections (e.g. Déqué et al, 2007;Marengo et al, 2010;Gao et al, 2012a;Wang et al, 2014;Niu et al, 2015;Wu and Huang, 2016). In China, significant contributions of driving GCMs to the uncertainties in precipitation change were detected by Gao et al (2012a), using one RCM to downscale multi-GCMs for the precipitation change projections.…”

Section: Introductionmentioning

confidence: 99%

Climate change projections over China using regional climate models forced by two CMIP5 global models. Part II: projections of future climate

Hui

Tang

Wang

et al. 2018

Intl Journal of Climatology

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The changes in mean and extreme climate in China during 2020–2060 are detected with both Weather Research and Forecasting and RegCM4, by downscaling the simulations from EC‐EATTH and IPSL‐CM5A under both the RCP4.5 and RCP8.5 scenarios. The climate changes under the two scenarios exhibit similar patterns, with stronger intensity under the RCP8.5 scenario. For the mean precipitation, increases are projected in most regions, with the largest relative increase in the Tarim Basin. Slight drought mainly occurs in the south‐eastern part of China. The frequency of drizzle rain is expected to decrease in all the sub‐regions, but the moderate to heavy rainfall as well as the storm would occur more frequently, especially on the Tibetan Plateau. The whole country would experience much warmer climate in the future, with the strongest warming over the Tibetan Plateau. By detecting the changes in climate extremes, it is indicated that less dry extremes would occur in the wet areas of China, while more dry events in the arid and semiarid regions. The wet extreme indices would increase in most regions, especially in the wet areas. The surface air temperature tends to become extremely warmer in the future over the whole country, with the strongest change over the Tibetan Plateau. The changes in mean and extreme climate depend strongly on the driving global climate models, with wetter and warmer climate in the downscalings over IPSL‐CM5A, and the model physics of the regional climate models also exert great impact on the projections. Finally, the possible mechanisms for the changes of extreme precipitation are discussed. The enhanced summer monsoon in the future transports more moisture to China, which could lead to more summer precipitation. As a result, the wet extremes tend to increase.

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“…In this study, all RCMs are driven by the same GCM under the same scenario, so the uncertainty of the climate projections is mainly caused by the inter-RCM variability and internal variability (Niu et al, 2015). The method developed by Hawkins 25 and Sutton (2009; was used for separating these two sources of uncertainty.…”

Section: Analysis Methods 20mentioning

confidence: 99%

High-resolution ensemble projections and uncertainty assessment of regional climate change over China in CORDEX East Asia

Gu¹,

Yu²,

Yang³

et al. 2017

Preprint

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Abstract. An ensemble simulation of 5 regional climate models (RCMs) from the Coordinated Regional Downscaling Experiment in East Asia (CORDEX-East Asia) was evaluated and used for future regional climate change projection in China. Meanwhile, the contributions of model uncertainty and internal variability are identified. The RCMs simulated both the historical climate (1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008) likely to increase in most of the simulation region, except the Tibetan Plateau which decreases -7.8%. Compare with the similar seasonal temperature changes with the driving global climate model (GCM), the seasonal precipitation change shows significant inter-RCM difference and has larger magnitude and variability than driving GCM. The model uncertainty for future temperature projection is clearly dominant over the northeast, northwest China and Tibetan Plateau, reaching up to 70%, and it contribute about 40% of the total uncertainty over north and south China. For precipitation, the internal 20 variability is dominant over most regions except for the Tibetan Plateau which the model uncertainties reach up to 60%. In addition, the model uncertainty increases with prediction lead time over all subregions.

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Multimodel ensemble projection of precipitation in eastern China under A1B emission scenario

Cited by 35 publications

References 64 publications

High-resolution ensemble projections and uncertainty assessment of regional climate change over China in CORDEX East Asia

High-resolution ensemble projections and uncertainty assessment of regional climate change over China in CORDEX East Asia

Climate change projections over China using regional climate models forced by two CMIP5 global models. Part II: projections of future climate

High-resolution ensemble projections and uncertainty assessment of regional climate change over China in CORDEX East Asia

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