The oceanic data assimilation (DA) system has been developed to optimally combine numerical-model predictions with actual measurements from the ocean to create the best estimates of current ocean conditions and their uncertainties, improving our ability to forecast and understand the global climate variations. We developed DeepDA, a global oceanic DA system using deep learning, by integrating a partial convolutional neural network and a generative adversarial network. Partial convolution serves as an observation operator, mapping irregular observational data onto gridded fields, while generative adversarial network incorporates observational information from previous time frames. Our observing system simulation experiments, using simulated observations for the DA, revealed that DeepDA markedly reduces analysis error of the oceanic temperature, outperforming both background and observed values. DeepDA’s real-case global temperature reanalysis spanning from 1981 to 2020 accurately reconstructs observed global climatological temperature fields, along with their seasonal cycles, major oceanic temperature variabilities and global warming trend. Developed solely with a long-term control simulation, DeepDA lowers technical hurdles in creating global ocean reanalysis datasets using multiple numerical models’ physical constraints, thereby diminishing systematic uncertainties in estimating global oceanic states over decades with these models.

海洋数据同化（DA）系统的开发旨在将数值模型预测与海洋实际测量最佳地结合起来，以对当前海洋状况及其不确定性做出最佳估计，从而提高我们预测和了解全球气候变化的能力。我们通过集成部分卷积神经网络和生成对抗网络，开发了 DeepDA，这是一种使用深度学习的全球海洋 DA 系统。部分卷积充当观察算子，将不规则的观察数据映射到网格字段上，而生成对抗网络则合并来自先前时间帧的观察信息。我们的观测系统模拟实验使用 DA 的模拟观测结果表明，DeepDA 显着降低了海洋温度的分析误差，优于背景值和观测值。 DeepDA对1981年至2020年的真实全球温度再分析准确地重建了观测到的全球气候温度场及其季节周期、主要海洋温度变化和全球变暖趋势。 DeepDA 仅通过长期控制模拟开发，降低了使用多个数值模型的物理约束创建全球海洋再分析数据集的技术障碍，从而减少了使用这些模型估计数十年全球海洋状态的系统不确定性。