CMIP5 downscaling and its uncertainty in China

Yue, Tianxiang; Zhao, Na; Fan, Zemeng; Li, Jing; Chen, ChuanFa; Lu, Yimin; Wang, Chenliang; Xu, Bing; Wilson, John P.

doi:10.1016/j.gloplacha.2016.09.003

Cited by 38 publications

(23 citation statements)

References 36 publications

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“…However, the CMIP5 GCMs overestimate the magnitude of precipitation along the eastern edge of the TP (Chen and Frauenfeld, ) and in southernmost parts of the TP (Jiang et al ., ). The largest uncertainty of China domain in the CMIP5 data occurred on the TP with a mean error of annual precipitation as high as 770.51 mm (Yue et al ., ). The selected 24 GCMs overestimate the mean annual precipitation by 62.0–183.0% over the TP for the period 1961–2005 (Su et al ., ).…”

Section: Recent Progress Of Precipitation On the Tp From Observationsmentioning

confidence: 97%

Precipitation over the Tibetan Plateau during recent decades: a review based on observations and simulations

Wang

Pang

Yang

2017

Intl Journal of Climatology

193

111

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Schematic diagram of precipitation on the Tibetan Plateau (TP). Precipitation on the TP has a significant influence on climate, water resources, and terrestrial ecosystem. Here, we review recent progresses of precipitation on the TP based on observations (meteorological stations and satellite remote sensing) and simulations (global climate models and downscaling models), and discuss some mechanisms accounting for precipitation changes. Finally, we conclude a strategy for future research to further study the TP precipitation. Such review provides crucial implications for hydrological and precipitation studies over the TP.

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Section: Recent Progress Of Precipitation On the Tp From Observationsmentioning

confidence: 97%

Precipitation over the Tibetan Plateau during recent decades: a review based on observations and simulations

Wang

Pang

Yang

2017

Intl Journal of Climatology

193

111

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“…On the other hand, climate change is a global problem coupling with the human system and natural system (Bossel, ), with numerous and complex influencing factors. It is difficult to predict future climate accurately, for example, models in Coupled Model Intercomparison Project Phase 5 cannot simulate temperature and precipitation well in China; mean annual temperature was underestimated approximately 1.8°C; and mean annual precipitation was overestimated approximately 263 mm by Coupled Model Intercomparison Project Phase 5 (T. X. Yue et al, ). But there is no doubt that too little or too much precipitation is the primary cause of urban drought and flood disasters.…”

Section: Discussionmentioning

confidence: 99%

Assessing urban flood and drought risks under climate change, China

Chen

Yuan

et al. 2019

Hydrological Processes

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Risk analysis of urban flood and drought can provide useful guidance for urban rainwater management. Based on an analysis of urban climate characteristics in 2,264 Chinese cities from 1958 to 2017, this study evaluated urban flood and drought risks. The results demonstrated that the annual average values of precipitation, aridity index, frequency and intensity of extreme precipitation and extreme drought events differed significantly in these cities. The values of the above six climatic indicators in the cities ranged from 9.29–2639.30 mm, 0.47–54.73, 1.08–8.79 time, 7.82–107.25 mm, 0.76–2.99 time, and 10.30–131.19 days, respectively. The geographical patterns of urban precipitation, aridity index, intensity and frequency of extreme precipitation and drought events in China fit well to the Hu‐Huanyong Line that was created in 1940s to identify the pattern of population distribution. Extreme precipitation in most cities has upward trends, except for those around the Hu‐Huanyong Line. The extreme drought events had upward trends in the cities east of the Hu‐Huanyong Line, but there were downward trends in the cities west of the line. The risk assessment indicated that 3.80% cities were facing serious flood and 6.01% cities were facing serious drought risks, which are located in the coast of southern China and northwestern China, respectively, and other 90.19% cities were facing different types of drought and flood risks in terms of their intensity and frequency.

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“…The HASM method [11,41,42] was utilized to obtain high-resolution and high-accuracy climate data by interpolating the station observational climate data and downscaling the climate scenario data. The HLZ ecosystem model [9], as a bioclimatic classification scheme [48], has been widely used to simulate the distributions of terrestrial ecosystems [35,36,49], predict land cover change [47], and support land use planning [20][21][22][23][24][25].…”

Section: Discussionmentioning

confidence: 99%

“…The TIN model uses a linear function to calculate the value of each point but ignores nonlinear information. HASM is a new method based on the differential geometry theory that could overcome the drawbacks of the IDW, Kriging, Spline and TIN methods [40], and the accuracy of the interpolated and downscaled climate data could be generally increased by 5% compared with that of the other three methods [41,42]. Thus, the HASM method was selected to obtain the spatial grid data of MAB, TAP and PER with a spatial resolution of 0.125 • × 0.125 • by combining the longitude, latitude and elevation data during the periods from 1981 to 2010 (T0), 2011 to 2040 (T1), 2041 to 2070 (T2) and 2071 to 2100 (T3); the specific methods are described in the references [40][41][42].…”

Section: Data Sourcesmentioning

confidence: 99%

Shifts of the Mean Centers of Potential Vegetation Ecosystems under Future Climate Change in Eurasia

Fei

2019

Forests

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Climate change dominantly controls the spatial distributions of potential vegetation ecosystems; the shift trends in the mean centers of potential vegetation ecosystems could be used to explain their responses to climate change. In terms of the climate observation data of Eurasia for the period from 1981 to 2010 and the climate scenario data for the period from 2011 to 2100 under the three Representative Concentration Pathways (RCPs) scenarios of RCP2.6, RCP4.5 and, RCP8.5, which were released by the Coupled Model Intercomparison Project Phase 5 (CMIP5), the Holdridge Life Zone (HLZ) ecosystem model was improved to quantitatively classify the potential vegetation types, and the shift model of mean center was adopted to compute the trends in the spatiotemporal shifts of potential vegetation types in Eurasia. The results showed that the mean centers of the major potential vegetation ecosystems would be distributed in the central and southern parts of Eurasia. Under the RCP2.6, RCP4.5, and RCP8.5 scenarios, the potential shift distances of the mean centers of the vegetation types under the RCP8.5 scenario would be the largest, and those of the polar/nival area, subpolar/alpine moist tundra, warm temperate dry forest, subtropical moist forest, cool temperate moist forest, cool temperate wet forest, subtropical wet forest, subtropical thorn woodland, warm temperate moist forest and subtropical dry forest would be larger than those in the other potential vegetation types in Eurasia. Moreover, the shift directions of the mean centers of the major potential vegetation types would generally shift northward, and subtropical dry forest, warm temperate moist forest and subpolar/alpine moist tundra would be the most sensitive to change among all vegetation types under the three scenarios for the period from 2011 to 2100.

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CMIP5 downscaling and its uncertainty in China

Cited by 38 publications

References 36 publications

Precipitation over the Tibetan Plateau during recent decades: a review based on observations and simulations

Precipitation over the Tibetan Plateau during recent decades: a review based on observations and simulations

Assessing urban flood and drought risks under climate change, China

Shifts of the Mean Centers of Potential Vegetation Ecosystems under Future Climate Change in Eurasia

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