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<p>Minimum annual sea surface temperature (T<sub>min</sub>) plays a critical role in shaping marine ecological processes, yet its spatial and temporal variability remains insufficiently understood in many regional seas. This study investigates the spatiotemporal trends of T<sub>min</sub> and its timing in the Eastern China seas from 1985 to 2022. Results reveal distinct spatial patterns, with warming trends (~ 0.3&#xa0;°C/decade) associated with northward-flowing warm currents, and significant cooling trends (up to − 0.4&#xa0;°C/decade) along coastal regions influenced by cold coastal currents. T<sub>min</sub> and its timing were strongly correlated with preceding atmospheric conditions from December onward, including air temperature, wind components, and surface heat fluxes. These relationships enabled the development of an artificial neural network model capable of predicting T<sub>min</sub> and its timing with lead times of one to two months. Ecologically, lower T<sub>min</sub> values were associated with increased chlorophyll concentrations and a higher frequency of marine cold spells. These findings offer new insights into the mechanisms driving T<sub>min</sub> variability and highlight its potential predictability, providing a basis for improving early warning systems and supporting ecosystem-based management in a changing climate.</p>

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Spatiotemporal trends of annual minimum sea surface temperature in the Eastern China seas

  • 期刊论文

  • 发表日期：

    2025年9月3日

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  • Wenxiang Ding,
  • Caiyun Zhang,
  • Yongxin Chen,
  • Jingrui Mo,
  • Rui Zeng,
  • Qiong Wu

摘要

Minimum annual sea surface temperature (Tmin) plays a critical role in shaping marine ecological processes, yet its spatial and temporal variability remains insufficiently understood in many regional seas. This study investigates the spatiotemporal trends of Tmin and its timing in the Eastern China seas from 1985 to 2022. Results reveal distinct spatial patterns, with warming trends (~ 0.3 °C/decade) associated with northward-flowing warm currents, and significant cooling trends (up to − 0.4 °C/decade) along coastal regions influenced by cold coastal currents. Tmin and its timing were strongly correlated with preceding atmospheric conditions from December onward, including air temperature, wind components, and surface heat fluxes. These relationships enabled the development of an artificial neural network model capable of predicting Tmin and its timing with lead times of one to two months. Ecologically, lower Tmin values were associated with increased chlorophyll concentrations and a higher frequency of marine cold spells. These findings offer new insights into the mechanisms driving Tmin variability and highlight its potential predictability, providing a basis for improving early warning systems and supporting ecosystem-based management in a changing climate.