Science & Technology

Newest CMIP6 Climate Models Overestimate Future Afro-Asian Monsoon Rainfall and Runoff

Spatial Distribution of Changes in Rainfall and Runoff

The shadings and percentages within the subplots are the fractions of land space that can expertise a major enhance in rainfall (left) and runoff (proper) within the unconstrained (blue) and constrained (crimson) projections. Credit: IAP

Climate projections are crucial for adaptation and mitigation planning. The output of the newest spherical of the Coupled Model Intercomparison Project, part 6 (CMIP6) has been extensively utilized in local weather projections.

However, a subset of CMIP6 fashions is “too hot” and the projected warming because of greenhouse gases is extreme. It was beforehand unclear the way to tackle the “hot model” drawback on the regional scale.

The newest CMIP6 local weather fashions are likely to overestimate future Afro-Asian summer time monsoon (AfroASM) rainfall and runoff as a result of present-day biases in warming patterns, based on a analysis staff from the Chinese Academy of Sciences’ Institute of Atmospheric Physics (IAP). By constraining biases, nevertheless, the rainfall enhance is 70% of the uncooked projection.

The examine might be printed at this time (May 10, 2022) within the journal Nature Communications.

The AfroASM contains the West African monsoon, South Asian monsoon, and East Asian monsoon.

The analysis staff recognized the main mode of variability amongst CMIP6 fashions in projecting future adjustments in AfroASM rainfall. They discovered that projection uncertainty was associated to the bias in present-day interhemispheric thermal distinction (ITC). Since large-scale monsoon circulation is pushed by ITC as a result of moist static power gradients, fashions with a bigger ITC pattern over the previous thirty years are likely to mission extra precipitation will increase.

Since most CMIP6 fashions are likely to overestimate present-day ITC traits, the staff corrected the uncooked projection by designing an emergent constraint method. The enhance in precipitation within the constrained projection is ~70% of the ensemble imply of the CMIP6 fashions. The space of land with a major enhance in precipitation is ~57% of the uncooked projection.

The analysis staff additional prolonged its evaluation to runoff, which is a mirror of potential water availability. In the constrained projection, ~27% of land space within the AfroASM area will witness a major enhance in potential water availability, which is ~66% of the uncooked projection. Regionally, the influence of the observational constraint is most pronounced within the West African monsoon area the place the fraction of land space with elevated water availability is ~55% of the uncooked projection.

This examine offers an answer for tackling the “hot model” drawback at regional scales. The emergent constraint method reported within the examine relies on the bodily hyperlink between a modeled however observable variable within the current day and a projected variable sooner or later local weather system.

“This technique is useful for correcting the bias of CMIP6 models and finally increase the reliability of rainfall projection in the Afro-Asian summer monsoon region. The underlying physical mechanism is the impact of equilibrium climate sensitivity on the interhemispheric thermal contrast in both the historical and future periods,” mentioned Dr. ZHOU Tianjun from IAP, corresponding creator of the examine.

“Smaller increases in precipitation and runoff will likely reduce flooding risk, while also posing a challenge to future water resource management,” mentioned CHEN Ziming, a Ph.D. scholar on the University of the Chinese Academy of Sciences, first creator of the examine.

Reference: “Observationally constrained projection of Afro-Asian monsoon precipitation” by Ziming Chen, Tianjun Zhou, Xiaolong Chen, Wenxia Zhang, Lixia Zhang, Mingna Wu and Liwei Zou, 10 May 2022, Nature Communications.
DOI: 10.1038/s41467-022-30106-z

The examine was supported by the National Key Research and Development Program of China and the National Natural Science Foundation of China.

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