Artificial Neural Network Model for Estimating Ocean Heat Content in the Sea Ice-Covered Arctic Regions Using Satellite Data

Ocean heat content (OHC) is an essential parameter to assess Earth's energy imbalance, global warming, and climate change over the historical record. An accurate estimate of the OHC in the Arctic sea ice regions is challenging due to the lack of in-situ data and satellite-based algorithms. In this study, an artificial neural network-based (ANN) novel approach is presented for estimating OHC changes at various standard depth extents (in line with the World Ocean Atlas-2018 depth levels) in the Arctic sea ice regions based on the relationships of the sea ice thermodynamic parameters from the satellite measurements and in-situ OHC estimates. Because of the potential uncertainty that arises from the inaccessible near-surface oceanic layer in the in-situ OHC estimates, a preliminary ANN model was developed with a set of approximations to account for the in-situ OHC stored in the respective depths within the inaccessible near-surface oceanic layer. The ANN model architecture was optimized for a depth extent of 700 m and adopted for the remaining depths of 20 m, 30 m, 40 m, 50 m, 100 m,150 m, 200 m, 250 m, 300 m, 350 m, 400 m, 450 m, 500 m, 550 m, 600 m, and 650 m. The new model was robust in capturing the spatial, temporal, and depth variabilities of OHC in the sea ice-covered Arctic regions with greater accuracy (mean bias error 0.022 GJ m-2, mean bias percentage error 0.015%, mean absolute error 0.182 GJ m-2, mean absolute percentage error 0.148%, root mean square error 0.24 GJ m-2, R-2 0.94, slope 0.93, and intercept 25.05 GJ m-2). This model is capable of estimating OHC and its temporal trends from satellite data which will have implications for understanding the global climate change and its impacts in the Polar Oceans.