Estimating high-resolution ocean subsurface temperature has great importance for the refined study of ocean climate variability and change. However, the insufficient resolution and accuracy of subsurface temperature data greatly limits our comprehensive understanding of mesoscale and other fine-scale ocean processes. In this study, we integrated multiple remote sensing data and in situ observations to compare four models within two frameworks (gradient boosting and deep learning). The optimal model, Deep Forest, was selected to generate a high-resolution subsurface temperature dataset (DORS0.25°) for the upper 2000 m from 1993 to 2023. DORS0.25° exhibits excellent reconstruction accuracy, with an average R2 of 0.980 and RMSE of 0.579 °C, and the monthly average accuracy is higher than IAP and ORAS5 datasets. Particularly, DORS0.25° can effectively capture detailed ocean warming characteristics in complex dynamic regions such as the Gulf Stream and the Kuroshio Extension, facilitating the study of mesoscale processes and warming within the global-scale ocean. Moreover, the research highlights that the rate of warming over the past decade has been significant, and ocean warming has consistently reached new highs since 2019. This study has demonstrated that DORS0.25° is a crucial dataset for understanding and monitoring the spatiotemporal characteristics and processes of global ocean warming, providing valuable data support for the sustainable development of the marine environment and climate change actions.

中文翻译：

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利用森林深处结合遥感和现场观测重建全球海洋高分辨率地下温度


估算高分辨率海洋地下温度对于海洋气候变率和变化的精细研究具有重要意义。然而，地下温度数据的分辨率和精度不足极大地限制了我们对中尺度和其他细尺度海洋过程的全面理解。在这项研究中，我们整合了多个遥感数据和现场观测，以比较两个框架（梯度提升和深度学习）内的四种模型。选择最佳模型“Deep Forest”生成1993年至2023年高层2000米的高分辨率地下温度数据集（DORS0.25°）。DORS0.25°表现出出色的重建精度，平均R2为0.980， RMSE为0.579℃，月平均精度高于IAP和ORAS5数据集。特别是，DORS0.25°可以有效捕捉墨西哥湾流、黑潮延伸带等复杂动态区域的详细海洋变暖特征，有助于研究全球尺度海洋的中尺度过程和变暖。此外，研究还强调，过去十年的变暖速度非常快，自 2019 年以来，海洋变暖持续创下新高。这项研究表明，DORS0.25° 是了解和监测时空特征的重要数据集。全球海洋变暖过程，为海洋环境可持续发展和应对气候变化行动提供宝贵的数据支持。