Unraveling Impact of North Indian Ocean on Tibetan Plateau Summer Precipitation From Its Pattern Diversity

Abstract The sea surface temperature anomaly (SSTA) in the North Indian Ocean (NIO) region associated with Indian Ocean Basin Mode (IOBM) is well‐known as a key factor in impacting the Tibetan Plateau summer precipitation (TPSP). However, either the commonly used IOBM index or the basin‐mean index of SSTA in the NIO region captures only a small portion of TPSP variability, making it underrated in indicating the TPSP. In this study, we demonstrate a significant and predictable relationship between the NIO SSTA and TPSP, when further considering the NIO pattern diversity. This relationship can be depicted by two physics‐based indices accounting for the diverse NIO patterns, which can effectively capture their distinct spatial features and associated impacts on the TPSP. Such an extended diversity view of NIO provides potential predictability of the TPSP.

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Publication Details

Journal
Geophysical Research Letters
Published
2025-10-16
DOI
https://doi.org/10.1029/2025gl118116
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
0.00

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article

Unraveling Impact of North Indian Ocean on Tibetan Plateau Summer Precipitation From Its Pattern Diversity

Hong‐Li Ren, Jieru Ma, Lin Wang, Minghong Liu et al.
Geophysical Research Letters
Climate variability and models
article

Unraveling Impact of North Indian Ocean on Tibetan Plateau Summer Precipitation From Its Pattern Diversity

Hong‐Li Ren, Jieru Ma, Lin Wang, Minghong Liu, Jun Du, Run Wang, Duo Bian
article en

Abstract

Abstract The sea surface temperature anomaly (SSTA) in the North Indian Ocean (NIO) region associated with Indian Ocean Basin Mode (IOBM) is well‐known as a key factor in impacting the Tibetan Plateau summer precipitation (TPSP). However, either the commonly used IOBM index or the basin‐mean index of SSTA in the NIO region captures only a small portion of TPSP variability, making it underrated in indicating the TPSP. In this study, we demonstrate a significant and predictable relationship between the NIO SSTA and TPSP, when further considering the NIO pattern diversity. This relationship can be depicted by two physics‐based indices accounting for the diverse NIO patterns, which can effectively capture their distinct spatial features and associated impacts on the TPSP. Such an extended diversity view of NIO provides potential predictability of the TPSP.

Geophysical Research LettersVol. 52(20)
China Meteorological Administration (CN), Climate Centre (NL), Chinese Academy of Meteorological Sciences (CN), Institute of Atmospheric Physics (CN), Astronomical Observatory (RS), Shiga Medical Center (JP)
National Natural Science Foundation of China, National Key Research and Development Program of China, State Key Laboratory of Severe Weather
Openalex Percentile: Top 73%
Climate variability and models
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