Estimating ocean heat content from the ocean thermal expansion parameters using satellite data
Abstract. Ocean heat content (OHC) is a depth-integrated physical oceanographic variable used to precisely measure ocean warming. Because of the limitations associated with in-situ CTD data and Ocean Reanalysis system products, satellite-based approaches have gained importance in estimating the daily to decadal variability of OHC over the vast oceanic region. Efforts to minimize the biases in satellite-based OHC estimates are needed to realize the actual response of the ocean to the brunt of climate change. In the current study, an attempt has been made to better implement the satellite-based ocean thermal expansion method to estimate OHC at 17 depth extents ranging from the surface to 700 m. To achieve this objective, an artificial neural network (ANN) model was developed to derive thermosteric sea level (TSL) from a given dataset of sea surface temperature, sea surface salinity, geographical coordinates, and climatological TSL. The model-derived TSL data were used to estimate OHC changes based on the thermal expansion efficiency of heat. Statistical analysis showed high correlation coefficients and low errors in satellite-derived TSL / OHC at 700 m water depth (N 388469, R 0.9926 / 0.9922, RMSE 1.16 m / 1.56 GJ m-2, MBE -0.1917 m / -0.2400 GJ m-2, MBPE -0.4560 % / -0.0290 %, MAE 0.763 m / 1.029 GJ m-2, and MAPE 2.34 % / 0.13 %) and nearly similar results at the remaining depth extents. These results suggest that the proposed ANN models are capable of accurately estimating OHC changes on real-time data and three-dimensional distribution patterns of depth-integrated OHC trends in the global ocean. In addition, the first-ever attempt to estimate the ocean thermal expansion component (i.e., TSL) from satellite data was successful and the model-derived TSL can be used to obtain high-end sea-level rise products in the global ocean.
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