Modulation of Preceding Tibetan Plateau Tropospheric Thermal Anomaly on Summer Temperature Anomalies in the Yangtze and Huaihe River Valleys

ABSTRACT Anomalous warm surface air temperatures (SATs) appeared in eastern China, especially the middle and lower Yangtze and Huaihe River valleys (MLYHRV), in the summer of 2022. Using the 2022 SAT anomaly as a case, this study analyses the reasons for the variation of summer SATs in the MLYHRV and explores the precursory signals for the summer MLYHRV SAT and related mechanisms. The findings indicate that the preceding May mid‐upper (500–300 hPa) tropospheric temperature (TT) over the Arabian Sea and India and the western Tibetan Plateau (TP) can reflect the persistence of the TT anomaly over the western TP and its eastward extension to the Japan Sea from May to summer, which causes downstream East Asian circulation (i.e., the South Asian high and western Pacific subtropical high) and related SAT anomalies over the MLYHRV during summer (especially July–August). In addition, the May La Niña SST can reflect the summer La Niña SST pattern and therefore affect the summer MLYHRV SAT by modulating the East Asian circulations. Using TT and SST precursory signals, a statistical prediction model was developed to predict the summer MLYHRV SAT. This model based on the TT and SST signals shows better prediction ability than the model based only on the traditional La Niña SST signal. The May ASI‐WTP TT may be regarded as a crucial supplementary precursor for forecasting the summer MLYHRV SAT.

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Publication Details

Journal
International Journal of Climatology
Published
2024-12-22
DOI
https://doi.org/10.1002/joc.8733
Citations
1
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
0.28

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article

Modulation of Preceding Tibetan Plateau Tropospheric Thermal Anomaly on Summer Temperature Anomalies in the Yangtze and Huaihe River Valleys

Yuwei Zhou, Jingpeng Liu, Di Li, Changyan Zhou et al.
1 citations
International Journal of Climatology
Climate variability and models
0.28
article

Modulation of Preceding Tibetan Plateau Tropospheric Thermal Anomaly on Summer Temperature Anomalies in the Yangtze and Huaihe River Valleys

Yuwei Zhou, Jingpeng Liu, Di Li, Changyan Zhou, Ge Liu, Ting Zhang, Dongmei Qi, Yuhan Feng
article en
1 citations

Abstract

ABSTRACT Anomalous warm surface air temperatures (SATs) appeared in eastern China, especially the middle and lower Yangtze and Huaihe River valleys (MLYHRV), in the summer of 2022. Using the 2022 SAT anomaly as a case, this study analyses the reasons for the variation of summer SATs in the MLYHRV and explores the precursory signals for the summer MLYHRV SAT and related mechanisms. The findings indicate that the preceding May mid‐upper (500–300 hPa) tropospheric temperature (TT) over the Arabian Sea and India and the western Tibetan Plateau (TP) can reflect the persistence of the TT anomaly over the western TP and its eastward extension to the Japan Sea from May to summer, which causes downstream East Asian circulation (i.e., the South Asian high and western Pacific subtropical high) and related SAT anomalies over the MLYHRV during summer (especially July–August). In addition, the May La Niña SST can reflect the summer La Niña SST pattern and therefore affect the summer MLYHRV SAT by modulating the East Asian circulations. Using TT and SST precursory signals, a statistical prediction model was developed to predict the summer MLYHRV SAT. This model based on the TT and SST signals shows better prediction ability than the model based only on the traditional La Niña SST signal. The May ASI‐WTP TT may be regarded as a crucial supplementary precursor for forecasting the summer MLYHRV SAT.

International Journal of ClimatologyVol. 45(4)
China Meteorological Administration (CN), Nanjing University of Information Science and Technology (CN), Chinese Academy of Meteorological Sciences (CN)
Natural Science Foundation of Sichuan Province, National Key Research and Development Program of China
Life below water
Openalex Percentile: Top 39%
Climate variability and models
0.28
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