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<p>Reliable projections of Tibetan Plateau (TP) warming are essential for regional ecosystem stability, socioeconomic development, and understanding its modulation of the Asian Monsoon and broader climate patterns. This study evaluated and selected optimal CMIP6 models by combining an observation-based physical constraint—the synergistic influence of soil moisture and sea surface temperature anomalies on summer TP surface air temperature (SAT)—with an assessment of SAT spatiotemporal variations. Compared to the conventional multi-model ensemble mean (MME) of non-optimized 18 models (MME-18), the MME of 10 optimal models (MME-10) reduces systematic cold bias and the uncertainty range in summer TP SAT projections. Among four multi-model ensemble methods, the random forest (RF) most effectively reduce historical biases. The RF-based projections using 10 optimal models show that under the SSP1-2.6 scenario, the TP warming is significant (0.38 °C/decade) in early-term future but stagnates (0.03 °C/decade) thereafter (2045–2100). Under the SSP2-4.5 scenario, the warming is significant (0.33 °C/decade) during 2015–2070, then slows to 0.14 °C/decade. Under the SSP5-8.5 scenario, rapid warming persists throughout 2015–2100 (0.60 °C/decade). The RF-based projections indicate stronger future warming (particularly in western TP) than MME-18. These results highlight the risk of underestimating TP warming without proper optimization, warranting particular attention to this vulnerable region.</p>

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Future projections of summer Tibetan Plateau temperature based on the combined influence of sea surface temperature and soil moisture

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月13日

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  • Ting Zhang,
  • Ge Liu,
  • Mingkeng Duan,
  • Sulan Nan,
  • Yuhan Feng,
  • Yuwei Zhou,
  • Hancheng Zou

摘要

Reliable projections of Tibetan Plateau (TP) warming are essential for regional ecosystem stability, socioeconomic development, and understanding its modulation of the Asian Monsoon and broader climate patterns. This study evaluated and selected optimal CMIP6 models by combining an observation-based physical constraint—the synergistic influence of soil moisture and sea surface temperature anomalies on summer TP surface air temperature (SAT)—with an assessment of SAT spatiotemporal variations. Compared to the conventional multi-model ensemble mean (MME) of non-optimized 18 models (MME-18), the MME of 10 optimal models (MME-10) reduces systematic cold bias and the uncertainty range in summer TP SAT projections. Among four multi-model ensemble methods, the random forest (RF) most effectively reduce historical biases. The RF-based projections using 10 optimal models show that under the SSP1-2.6 scenario, the TP warming is significant (0.38 °C/decade) in early-term future but stagnates (0.03 °C/decade) thereafter (2045–2100). Under the SSP2-4.5 scenario, the warming is significant (0.33 °C/decade) during 2015–2070, then slows to 0.14 °C/decade. Under the SSP5-8.5 scenario, rapid warming persists throughout 2015–2100 (0.60 °C/decade). The RF-based projections indicate stronger future warming (particularly in western TP) than MME-18. These results highlight the risk of underestimating TP warming without proper optimization, warranting particular attention to this vulnerable region.