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<p>Marine heatwaves (MHWs) are prolonged periods of anomalously warm ocean temperatures that threaten marine ecosystems and regional economies. Reliable and efficient forecasting with local details across multiple temporal scales is crucial to mitigate their negative impacts. However, current dynamical methods are computationally intensive and struggle to capture local stochastic climate anomalies, while statistical methods fail to explicitly model atmospheric interactions governing MHW evolution. We present MARINA, a multi-temporal resolution forecasting model that integrates physical insights into a statistical framework, enabling skillful and efficient MHW forecasting. To effectively train MARINA, we built MT-MHW, a multi-temporal resolution MHW dataset comprising 3.22 million data points and incorporating key meteorological variables from multiple weather stations. Together, MT-MHW enables MARINA to model the complex interactions among meteorological factors that govern MHW evolution across multiple timescales in regional areas. MARINA facilitates the low-cost integration of dynamical knowledge into statistical models, enabling detailed MHW forecasting across previously unavailable temporal scales and enhancing region-specific disaster prevention.</p>

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A lightweight physics-aware framework for multi-scale marine heatwaves forecasting

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月13日

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  • Xiu Su,
  • Yuemin Wu,
  • Zhongze Wu,
  • Yitian Long,
  • Yichao Cao,
  • Yue Liao,
  • Xi Lin,
  • Jun Long,
  • Shuo Jiang,
  • Shan You,
  • Chang Xu

摘要

Marine heatwaves (MHWs) are prolonged periods of anomalously warm ocean temperatures that threaten marine ecosystems and regional economies. Reliable and efficient forecasting with local details across multiple temporal scales is crucial to mitigate their negative impacts. However, current dynamical methods are computationally intensive and struggle to capture local stochastic climate anomalies, while statistical methods fail to explicitly model atmospheric interactions governing MHW evolution. We present MARINA, a multi-temporal resolution forecasting model that integrates physical insights into a statistical framework, enabling skillful and efficient MHW forecasting. To effectively train MARINA, we built MT-MHW, a multi-temporal resolution MHW dataset comprising 3.22 million data points and incorporating key meteorological variables from multiple weather stations. Together, MT-MHW enables MARINA to model the complex interactions among meteorological factors that govern MHW evolution across multiple timescales in regional areas. MARINA facilitates the low-cost integration of dynamical knowledge into statistical models, enabling detailed MHW forecasting across previously unavailable temporal scales and enhancing region-specific disaster prevention.