Seasonal-spatial variations in satellite-derived global subsurface temperature anomalies

Abstract

Improving ocean interior observation resolution via satellite remote sensing is essential because of the limitation and sparsity of ocean interior observation. Retrieving the multi-temporal and large-scale thermal structure information of the subsurface ocean on the basis of satellite remote sensing is of great importance in understanding the complex and multi-dimensional dynamic processes within the ocean. This task requires a robust model with strong spatiotemporal applicability to provide technical support based on satellite observations. This study adopts a random forest regression model, namely, an advanced machine learning algorithm, to predict global Subsurface Temperature Anomaly (STA) at different depth levels (upper 1,000 m) in different seasons in 2010 from multisource sea surface parameters (sea surface height anomaly, SSHA; sea surface temperature anomaly, SSTA; sea surface salinity anomaly, SSSA; sea surface wind anomaly, SSWA) based on satellite observations. We use the in-situ Argo data for performance measurement and accuracy validation by combined use of the root mean square error (RMSE), normalized root-mean-square error (NRMSE) and coefficient of determination (R2) at global and ocean basin scales. For model accuracy, the results show that the average R2 and NRMSE of 16 depth levels are 0.53/0.60/0.54/0.66 and 0.051/0.031/0.043/0.044 for global ocean in spring/summer/autumn/winter. With the evolution of seasons, the model performance promotes first, declines, and then promotes, a trend that may be caused by the El Niño and La Niña phenomena and the transformation between them. The best performance of the model occurs in the Indian Ocean with the average R2 and RMSE of 0.71 and 0.18 °C, respectively, whereas accuracy in the Atlantic is the lowest, with average R2 and RMSE of 0.46 and 0.25 °C at different depth levels in different seasons. This study suggests that the random forest model is suitable for retrieving ocean subsurface temperature anomalies in different seasons and can achieve good performance in different ocean basins. STA has distinctive variation signal in the upper ocean (above 300 m) and spatial heterogeneity is considerable in different seasons. However, in the subsurface and deeper layers (below 300 m), STA variation signal is weak over different seasons. This study can provide a basis for remote sensing estimation of STA and further promote the reconstruction of long-term and large-scale ocean internal parameter information (such as thermohaline structure). It can also help develop the subsurface and deeper ocean remote sensing technique.