An Intelligent Reconstruction Method for Ocean Subsurface Temperature Empowered by isQG

Abstract

High-precision reconstruction of subsurface ocean temperatures is of great significance for the identification and monitoring of mesoscale phenomena and the prediction of climate change trends. Although advanced artificial intelligent (AI) algorithms have been widely applied to reconstruct subsurface thermohaline fields from surface inputs, most of them cannot be trained on in situ data because of the sparsity and discontinuity of Argo profiles. Considering Argo profiles as labels, only one-dimensional AI algorithms are applicable thus far, and they cannot fully capture the spatiotemporal evolutionary characteristics of the ocean. As a basic property of mesoscale to larger-scale oceans, quasi-geostrophic (QG) dynamics can help estimate the interior parameters of the ocean. Combining QG with AI algorithms is promising for improving the performance of subsurface reconstruction, which has not been fully exploited by current studies. In this study, a hybrid dynamic-statistical reconstruction framework named interior+surface quasi-geostrophic (isQG)-empowered ResNet is proposed, which is based on the one-dimensional residual network (1D-ResNet) and incorporates the density anomalies reconstructed by the isQG method. The results demonstrate the following. First, the model performance is better than that of the conventional 1D-ResNet model, with the layer-averaged root mean square error being reduced by approximately 0.18 $^{\circ }$C. Shapley additive explanations analysis verifies the important contribution of isQG factors. Second, the predicted temperature profiles are in good agreement with the Argo in situ profiles, reasonably reflecting the vertical temperature variations in actual marine conditions. Third, the reconstructed 3-D temperature field can reasonably represent mesoscale phenomena (e.g., mesoscale eddies) and is in line with the GLORYS12V1 reanalysis data. It is suggested that the proposed method is a feasible and stable framework for subsurface reconstruction and is, thus, helpful for analyzing the three-dimensional structures of mesoscale phenomena.