Subsurface Temperature Reconstruction for the Global Ocean from 1993 to 2020 Using Satellite Observations and Deep Learning

Abstract

The reconstruction of the ocean’s 3D thermal structure is essential to the study of ocean interior processes and global climate change. Satellite remote sensing technology can collect large-scale, high-resolution ocean observation data, but only at the surface layer. Based on empirical statistical and artificial intelligence models, deep ocean remote sensing techniques allow us to retrieve and reconstruct the 3D ocean temperature structure by combining surface remote sensing observations with in situ float observations. This study proposed a new deep learning method, Convolutional Long Short-Term Memory (ConvLSTM) neural networks, which combines multisource remote sensing observations and Argo gridded data to reconstruct and produce a new long-time-series global ocean subsurface temperature (ST) dataset for the upper 2000 m from 1993 to 2020, which is named the Deep Ocean Remote Sensing (DORS) product. The data-driven ConvLSTM model can learn the spatiotemporal features of ocean observation data, significantly improves the model’s robustness and generalization ability, and outperforms the LighGBM model for the data reconstruction. The validation results show our DORS dataset has high accuracy with an average R2 and RMSE of 0.99/0.34 °C compared to the Argo gridded dataset, and the average R2 and NRMSE validated by the EN4-Profile dataset over the time series are 0.94/0.05 °C. Furthermore, the ST structure between DORS and Argo has good consistency in the 3D spatial morphology and distribution pattern, indicating that the DORS dataset has high quality and strong reliability, and well fills the pre-Argo data gaps. We effectively track the global ocean warming in the upper 2000 m from 1993 to 2020 based on the DORS dataset, and we further examine and understand the spatial patterns, evolution trends, and vertical characteristics of global ST changes. From 1993 to 2020, the average global ocean temperature warming trend is 0.063 °C/decade for the upper 2000 m. The 3D temperature trends revealed significant spatial heterogeneity across different ocean basins. Since 2005, the warming signal has become more significant in the subsurface and deeper ocean. From a remote sensing standpoint, the DORS product can provide new and robust data support for ocean interior process and climate change studies.

Reconstruction of subsurface ocean state variables using Convolutional Neural Networks with combined satellite and in situ data

“…The CNN model and its architecture should be adapted accordingly to this input, i.e., changing from one-dimensional to two-or threedimensional convolutional layers. Prior studies (Su et al, 2021b;Su et al, 2022;Sun et al, 2022) have used spatial and temporal dimensions in a more explicit manner than in this study. However, much research is still required to fully evaluate and understand the dominant patterns and modes within the surface data.…”

Micro-Climate Computed Machine and Deep Learning Models for Prediction of Surface Water Temperature Using Satellite Data in Mundan Water Reservoir

“…Traditional RNN, GRU, LSTM, or CNN cannot fully exploit the temporal and spatial properties of open surface data using satellite images because data from various open surfaces water bodies is often spatially nonlinear and temporally dependent. Following that, the ConvLSTM algorithm was proposed to extend the capabilities of LSTM [43][44][45]. By using the inputs and past states of its local neighbors, the ConvLSTM predicts the future state of each cell in the grid.…”

NeXtNow: A Convolutional Deep Learning Model for the Prediction of Weather Radar Data for Nowcasting Purposes

“…On the right, there is a visualization of the missing data projected onto the map, in which the red color indicates that the values are missing all day for all acquisitions at all elevation levels. The histogram in Figure 5 reveals the imbalance between the larger values and smaller values and also highlights the fact that during the cleaning process, the data points were grouped into categories ([0-5), [5][6][7][8][9][10], [10][11][12][13][14][15]. .…”

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