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A Causality-guided Statistical Approach for Modeling Extreme Mei-yu Rainfall Based on Known Large-scale Modes-A Pilot
Kelvin S Ng1, Gregor C Leckebusch1, Kevin I Hodges2
1School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, B15 2TT UK.
Advances in Atmospheric Sciences
|May 23, 2022
Summary
Extreme Mei-yu rainfall (MYR) prediction can be improved using causality-guided statistical models based on large-scale climate modes (LSCMs). This approach enhances climate model simulations of regional extreme rainfall events.
Area of Science:
- Climate Science
- Meteorology
- Atmospheric Science
Background:
- Extreme Mei-yu rainfall (MYR) events pose significant risks to China's economy and society.
- Current climate models often fail to accurately simulate regional extreme rainfall like MYR.
- Large-scale climate modes (LSCMs) are generally well-represented in climate models.
Purpose of the Study:
- To develop and validate causality-guided statistical models for predicting MYR using LSCMs.
- To improve the simulation of MYR in climate models through statistical model application.
- To explore the potential of causality-guided approaches for understanding complex climate phenomena.
Main Methods:
- Constructed statistical models guided by causal relationships between MYR and LSCMs.
- Utilized known LSCMs as predictors in the statistical models.
- Assessed the relevancy of predictors and the importance of temporal resolution.
Main Results:
- Skillful causality-guided statistical models for MYR prediction were successfully developed.
- The selected predictors were consistent with existing scientific literature.
- The significance of temporal resolution in MYR modeling was confirmed.
Conclusions:
- The causality-guided approach is reliable for studying complex systems like the East Asian summer monsoon (EASM).
- This method offers a new avenue for investigating intricate interactions within the EASM.
- Future research can build upon this approach to enhance climate modeling and prediction capabilities.
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