Impacts of SST Warming in tropical Indian Ocean on CMIP5 model-projected summer rainfall changes over Central Asia

Zhao, Yong; Zhang, Huqiang

doi:10.1007/s00382-015-2765-0

Cited by 60 publications

(55 citation statements)

References 43 publications

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“…The scatter diagrams show that the SWJ position change and summer rainfall have a strong correlation in the future when they also correlate well in the historical experiments, which indicates that the relationships between the SWJ changes and summer rainfall continue to exist in the future. Based on the ensemble results, we compare the current projected summer rainfall over CA with three other methods used by previous studies (Wu et al ., ; Huang et al ., ; Zhao and Zhang, ). The results display changes in summer rainfall that are consistent with those observed in Kazakhstan and Xinjiang Province of northwestern China, with more rainfall, and the large differences observed in the rest of the countries of CA, which indicate that in Kazakhstan and Xinjiang, the projected rainfall changes are relatively more credible.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Because of the lack of high‐quality meteorological station observational rainfall data over CA, we use Climate Research Unit (CRU) version 3.10 monthly precipitation data across the global land area, with a horizontal resolution of 0.5 × 0.5° (Mitchell and Jones, ). The CRU data are widely used in evaluating the skills of modelled rainfall in CMIP5 models over CA (Wu et al ., ; Huang et al ., ; Zhao and Zhang, ). The National Centers for Environment Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis data set (Kalnay et al ., ) are used to reveal the variations in the SWJ and the related circulations.…”

Section: Study Domain Data and Methodsmentioning

confidence: 97%

“…In the Access1‐0, bcc‐csm1‐1, CMCC‐CM, MPI‐ESM‐MR models, the SSTs become warmer over the Arabian Sea than over the Bay of Bengal. Although the SWJ position changes are also related to the eastern tropical Pacific, our previous study indicates that the SST warming over the Indian Ocean plays a more important role in influencing summer rainfall over CA (Zhao and Zhang, ).…”

Section: Responses Of the Swj To Sst Warming In The Futurementioning

confidence: 97%

“…(a) Changes in the summer rainfall (mm day −1 , 2051–2010 minus 1961–2004) in the selected models from Huang et al (), (b) as (a) but using models from Wu et al (), (c) as (a) but using models from Zhao and Zhang (), (d) as (a), but using the models from current study.…”

Section: Change In the Swj And Its Effects On The Projected Summer Ramentioning

confidence: 99%

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Evaluation of the subtropical westerly jet and its effects on the projected summer rainfall over central Asia using multi‐CMIP5 models

Zhao

Yao

et al. 2018

Intl Journal of Climatology

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Study Domain Data and Methodsmentioning

confidence: 97%

Section: Responses Of the Swj To Sst Warming In The Futurementioning

confidence: 97%

Section: Change In the Swj And Its Effects On The Projected Summer Ramentioning

confidence: 99%

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Evaluation of the subtropical westerly jet and its effects on the projected summer rainfall over central Asia using multi‐CMIP5 models

Zhao

Yao

et al. 2018

Intl Journal of Climatology

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“…Table lists the models used in this study, as given in Zhao and Zhang (). The outputs of 25 CMIP5 models, including monthly precipitation, wind vectors, specific humidity and evaporation, are used, which are available at https://esgf-node.llnl.gov/search/cmip5.…”

Section: Methodsmentioning

confidence: 98%

Projected changes in the annual cycle of precipitation over central Asia by CMIP5 models

Zhao

et al. 2018

Intl Journal of Climatology

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Future changes in the annual cycle of the precipitation in central Asia (CA) were estimated based on the historical and representative concentration pathway 8.5 (RCP8.5) experiments from 25 models of the Coupled Model Intercomparison Project phase 5. Compared with the Global Precipitation Climatology Project observations, the historical (1979–1999) experiments showed that most models can capture the migration of rainfall centres, but remarkable discrepancies exist in the location and intensity of rainfall centres between simulations and observations. Considering the skill scores of precipitation and pattern correlations of circulations, which are closely related to the precipitation for each month, for the 25 models, the four best models (e.g., CanESM2, CMCC‐CMS, MIROC5 and MPI‐ESM‐LR) with relatively good performance were selected. The four models' ensemble mean indicated that the migration and location of the precipitation centres were better reproduced, except the intensity of the centres was overestimated, compared with the result that only took the precipitation into consideration. Based on the four best models' ensemble mean under RCP8.5 scenarios, precipitation was projected to increase dramatically over most of the CA region in the boreal cold seasons (November, December, January, February, March and April) and May, with the maximum in December at the end of 21st century (2079–2099), and several positive centres were located in the Pamirs Plateau, the Tianshan Mountains and the northern Himalayas. The precipitation changes were weak in the boreal warm seasons (June, July, August, September and October), with a dry centre located in the Pamirs Plateau and a wet centre in the northern Himalayas. These projected changes can be directly attributed to the overlay of anomalous moisture convergence and evaporation, and other possible mechanisms of dynamic and thermodynamic factors need to be further elucidated.

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Dryland climate change: Recent progress and challenges

Huang

et al. 2017

Reviews of Geophysics

638

331

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Drylands are home to more than 38% of the world's population and are one of the most sensitive areas to climate change and human activities. This review describes recent progress in dryland climate change research. Recent findings indicate that the long‐term trend of the aridity index (AI) is mainly attributable to increased greenhouse gas emissions, while anthropogenic aerosols exert small effects but alter its attributions. Atmosphere‐land interactions determine the intensity of regional response. The largest warming during the last 100 years was observed over drylands and accounted for more than half of the continental warming. The global pattern and interdecadal variability of aridity changes are modulated by oceanic oscillations. The different phases of those oceanic oscillations induce significant changes in land‐sea and north‐south thermal contrasts, which affect the intensity of the westerlies and planetary waves and the blocking frequency, thereby altering global changes in temperature and precipitation. During 1948–2008, the drylands in the Americas became wetter due to enhanced westerlies, whereas the drylands in the Eastern Hemisphere became drier because of the weakened East Asian summer monsoon. Drylands as defined by the AI have expanded over the last 60 years and are projected to expand in the 21st century. The largest expansion of drylands has occurred in semiarid regions since the early 1960s. Dryland expansion will lead to reduced carbon sequestration and enhanced regional warming. The increasing aridity, enhanced warming, and rapidly growing population will exacerbate the risk of land degradation and desertification in the near future in developing countries.

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Impacts of SST Warming in tropical Indian Ocean on CMIP5 model-projected summer rainfall changes over Central Asia

Cited by 60 publications

References 43 publications

Evaluation of the subtropical westerly jet and its effects on the projected summer rainfall over central Asia using multi‐CMIP5 models

Evaluation of the subtropical westerly jet and its effects on the projected summer rainfall over central Asia using multi‐CMIP5 models

Projected changes in the annual cycle of precipitation over central Asia by CMIP5 models

Dryland climate change: Recent progress and challenges

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