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Abstract

The gravimetry measurements from the Gravity Recovery and Climate Experiment (GRACE) satellite mission provide an essential way to monitor changes in ocean bottom pressure (OBP), which is a key variable in understanding ocean circulation. However, the coarse spatial resolution of GRACE OBP hinders resolving mass transports with refined details, particularly on the continental slope. By contrast, classical ocean forward models or reanalyses provide small-scale OBP information, but typically suffer from other problems (e.g., uncertainties in forcing fields, bathymetry, or structural errors in the dynamical formulation). In this study, we downscale the GRACE measured OBP to the eddy-permitting resolution of 0.25º using a self-supervised deep learning model by considering inputs from external high-resolution ocean models. The proposed deep learning model combines the principles of convolutional neural networks, residual learning, and an encoder-decoder structure. By a specific design of the loss function, the model learns to retain the short spatial scale signals contained in the ocean model and calibrate their magnitudes based on GRACE measurements over an area larger than the effective resolution of GRACE. We will compare the downscaled OBP signals to in-situ observations obtained from globally distributed bottom pressure recorders. The possibility of using the downscaled OBP changes for monitoring the meridional overturning circulation via boundary pressures on the continental slope in the North Atlantic will also be discussed.