Abstract
Extreme Mei-yu rainfall (MYR) can cause catastrophic impacts to the economic development and societal welfare in China. While significant improvements have been made in climate models, they often struggle to simulate local-to-regional extreme rainfall (e.g., MYR). Yet, large-scale climate modes (LSCMs) are relatively well represented in climate models. Since there exists a close relationship between MYR and various LSCMs, it might be possible to develop causality-guided statistical models for MYR prediction based on LSCMs. These statistical models could then be applied to climate model simulations to improve the representation of MYR in climate models.
In this pilot study, it is demonstrated that skillful causality-guided statistical models for MYR can be constructed based on known LSCMs. The relevancy of the selected predictors for statistical models are found to be consistent with the literature. The importance of temporal resolution in constructing statistical models for MYR is also shown and is in good agreement with the literature. The results demonstrate the reliability of the causality-guided approach in studying complex circulation systems such as the East Asian summer monsoon (EASM). Some limitations and possible improvements of the current approach are discussed. The application of the causality-guided approach opens up a new possibility to uncover the complex interactions in the EASM in future studies.
In this pilot study, it is demonstrated that skillful causality-guided statistical models for MYR can be constructed based on known LSCMs. The relevancy of the selected predictors for statistical models are found to be consistent with the literature. The importance of temporal resolution in constructing statistical models for MYR is also shown and is in good agreement with the literature. The results demonstrate the reliability of the causality-guided approach in studying complex circulation systems such as the East Asian summer monsoon (EASM). Some limitations and possible improvements of the current approach are discussed. The application of the causality-guided approach opens up a new possibility to uncover the complex interactions in the EASM in future studies.
| Original language | English |
|---|---|
| Pages (from-to) | 1925-1940 |
| Journal | Advances in Atmospheric Science |
| Volume | 39 |
| Issue number | 11 |
| Early online date | 14 May 2022 |
| DOIs | |
| Publication status | E-pub ahead of print - 14 May 2022 |
Bibliographical note
Funding Information:The authors thank two anonymous reviewers and an associate editor-in-chief for their valuable comments. This work was supported by the UK-China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. The authors thank Dr. Jia WU at National Climate Center, China Meteorological Administration for providing CN05.1. The calculations described in this paper were performed using the Blue-BEAR HPC service at the University of Birmingham.
Publisher Copyright:
© 2022, The Author(s).
Keywords
- extreme rainfall
- Mei-yu front
- causality-guided approach
- large-scale climate modes
ASJC Scopus subject areas
- Atmospheric Science
