Estimating Subsurface Thermohaline Structure of the Global Ocean Using Surface Remote Sensing Observations

Hua Su,Xin Yang,Xiao-Hai Yan,Wenfang Lu

doi:10.3390/rs11131598

Abstract

Retrieving multi-temporal and large-scale thermohaline structure information of the interior of the global ocean based on surface satellite observations is important for understanding the complex and multidimensional dynamic processes within the ocean. This study proposes a new ensemble learning algorithm, extreme gradient boosting (XGBoost), for retrieving subsurface thermohaline anomalies, including the subsurface temperature anomaly (STA) and the subsurface salinity anomaly (SSA), in the upper 2000 m of the global ocean. The model combines surface satellite observations and in situ Argo data for estimation, and uses root-mean-square error (RMSE), normalized root-mean-square error (NRMSE), and R2 as accuracy evaluations. The results show that the proposed XGBoost model can easily retrieve subsurface thermohaline anomalies and outperforms the gradient boosting decision tree (GBDT) model. The XGBoost model had good performance with average R2 values of 0.69 and 0.54, and average NRMSE values of 0.035 and 0.042, for STA and SSA estimations, respectively. The thermohaline anomaly patterns presented obvious seasonal variation signals in the upper layers (the upper 500 m); however, these signals became weaker as the depth increased. The model performance fluctuated, with the best performance in October (autumn) for both STA and SSA, and the lowest accuracy occurred in January (winter) for STA and April (spring) for SSA. The STA estimation error mainly occurred in the El Niño-Southern Oscillation (ENSO) region in the upper ocean and the boundary of the ocean basins in the deeper ocean; meanwhile, the SSA estimation error presented a relatively even distribution. The wind speed anomalies, including the u and v components, contributed more to the XGBoost model for both STA and SSA estimations than the other surface parameters; however, its importance at deeper layers decreased and the contributions of the other parameters increased. This study provides an effective remote sensing technique for subsurface thermohaline estimations and further promotes long-term remote sensing reconstructions of internal ocean parameters.

Highlights

IntroductionRapid warming has occurred in the global climate system
In recent years, rapid warming has occurred in the global climate system
Su et al [30,31] and Li et al [32] employed classic machine learning methods, such as support vector regression (SVR) and random forest (RF), to retrieve the subsurface temperature anomaly (STA) based on multi-source satellite observations, and the results showed that the models have good performance and RF outperforms SVR for global applications

Summary

Introduction

Rapid warming has occurred in the global climate system. Numerous studies have suggested that most of the heat gained by the Earth system is stored in the ocean, which leads to significant global ocean warming [3]. The heat variation and redistribution in the global subsurface and deeper ocean (300–2000 m) is of great significance to global climate change [4,5,6,7], but there are large uncertainties and discrepancies in the deeper ocean warming evaluation [8]. Evaluating the temperature and salinity distributions within the ocean can provide a valuable basis for studying ocean dynamics and other phenomena. The ocean temperature, along with the salinity, is required to compute the ocean water density [9]

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Results

Conclusion