Mei‐yu Front Assessment in <scp>CMIP6</scp> Earth System Models During the East Asian Summer Monsoon

ABSTRACT The East Asian Summer Monsoon (EASM) plays a pivotal role in redistributing water across East Asia, including contributing a considerable flood risk due to the potential for localized extreme precipitation. To gain insights into future EASM changes, it is crucial to explore the dynamics of a core driver of extreme precipitation during the EASM, the Mei‐yu front (MYF). While prior studies have examined various aspects of EASM in climate models, the comprehensive assessment of the dynamically important, that is, MYF remains largely unexplored. In this study, we evaluate the Mei‐yu front representation in 38 CMIP6 models from May to August using the ECMWF Reanalysis version 5 (ERA5) as reference. Our findings reveal that several CMIP6 models struggle to accurately reproduce the MYF climatology, with performance varying by month. By categorizing models based on the east–west bias of MYF position in May, we identify distinct monthly evolutions in these biases during the EASM season. Our study shows a significant association between the misrepresentation of the MYF climatology in CMIP6 models and the misrepresentation of the Western North Pacific High, particularly its western edge. Other potential sources of biases are based on the misrepresentation of other large‐scale circulation patterns, such as the South Asian High, and are also investigated. Furthermore, the performance evaluation of different aspects of the EASM is compared to previous studies, and the transferability of those principle evaluation findings is discussed.

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Publication Details

Journal
International Journal of Climatology
Published
2025-03-09
DOI
https://doi.org/10.1002/joc.8810
Citations
1
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
1.11

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article

Mei‐yu Front Assessment in CMIP6 Earth System Models During the East Asian Summer Monsoon

Kelvin S. Ng, Yaocun Zhang, Kevin I. Hodges, Gregor C. Leckebusch
1 citations
International Journal of Climatology
Climate variability and models
1.11
article

Mei‐yu Front Assessment in CMIP6 Earth System Models During the East Asian Summer Monsoon

Kelvin S. Ng, Yaocun Zhang, Kevin I. Hodges, Gregor C. Leckebusch
article en
1 citations

Abstract

ABSTRACT The East Asian Summer Monsoon (EASM) plays a pivotal role in redistributing water across East Asia, including contributing a considerable flood risk due to the potential for localized extreme precipitation. To gain insights into future EASM changes, it is crucial to explore the dynamics of a core driver of extreme precipitation during the EASM, the Mei‐yu front (MYF). While prior studies have examined various aspects of EASM in climate models, the comprehensive assessment of the dynamically important, that is, MYF remains largely unexplored. In this study, we evaluate the Mei‐yu front representation in 38 CMIP6 models from May to August using the ECMWF Reanalysis version 5 (ERA5) as reference. Our findings reveal that several CMIP6 models struggle to accurately reproduce the MYF climatology, with performance varying by month. By categorizing models based on the east–west bias of MYF position in May, we identify distinct monthly evolutions in these biases during the EASM season. Our study shows a significant association between the misrepresentation of the MYF climatology in CMIP6 models and the misrepresentation of the Western North Pacific High, particularly its western edge. Other potential sources of biases are based on the misrepresentation of other large‐scale circulation patterns, such as the South Asian High, and are also investigated. Furthermore, the performance evaluation of different aspects of the EASM is compared to previous studies, and the transferability of those principle evaluation findings is discussed.

International Journal of ClimatologyVol. 45(7)
National Centre for Atmospheric Science (GB), University of Reading (GB), University of Birmingham (GB), Nanjing University (CN)
Newton Fund
Openalex Percentile: Top 27%
Climate variability and models
1.11
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