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Inverse relationship between present-day tropical precipitation and its sensitivity to greenhouse warming

Future changes in rainfall have serious impacts on human adaptation to climate change, but quantification of these changes is subject to large uncertainties in climate model projections. To narrow these uncertainties, significant efforts have been made to understand the intermodel differences in fut...

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Bibliographic Details
Published in:Nature climate change 2018, Vol.8 (1), p.64-69
Main Authors: Ham, Yoo-Geun, Kug, Jong-Seong, Choi, Jun-Young, Jin, Fei-Fei, Watanabe, Masahiro
Format: Article
Language:English
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Summary:Future changes in rainfall have serious impacts on human adaptation to climate change, but quantification of these changes is subject to large uncertainties in climate model projections. To narrow these uncertainties, significant efforts have been made to understand the intermodel differences in future rainfall changes. Here, we show a strong inverse relationship between present-day precipitation and its future change to possibly calibrate future precipitation change by removing the present-day bias in climate models. The results of the models with less tropical (40° S–40° N) present-day precipitation are closely linked to the dryness over the equatorial central-eastern Pacific, and project weaker regional precipitation increase due to the anthropogenic greenhouse forcing 1 – 6 with stronger zonal Walker circulation. This induces Indo-western Pacific warming through Bjerknes feedback, which reduces relative humidity by the enhanced atmospheric boundary-layer mixing in the future projection. This increases the air–sea humidity difference to enhance tropical evaporation and the resultant precipitation. Our estimation of the sensitivity of the tropical precipitation per 1 K warming, after removing a common bias in the present-day simulation, is about 50% greater than the original future multi-model projection. A present-day bias identified in climate projections means that future tropical rainfall may be underestimated. This bias can be addressed by constraining projections with observations.
ISSN:1758-678X
1758-6798
DOI:10.1038/s41558-017-0033-5