A Surface Quasi‐Geostrophic‐Based Dynamical‐Statistical Framework to Retrieve Interior Temperature/Salinity From Ocean Surface

Abstract

AbstractConventional methods to reconstruct ocean interior temperature/salinity (T/S) from surface data are mostly “pure data‐driven.” On the other hand, the reconstruction methods based on surface quasi‐geostrophic (SQG) dynamics present promising results in retrieving mesoscale density structures. It is rarely considered to incorporate SQG‐based reconstructions to facilitate the estimation of subsurface T/S. Since the SQG equation does not contain explicit terms of T/S, a density incorporation tool of least square‐multivariate empirical orthogonal functions (LS‐mEOFs) algorithm is proposed to retrieve T/S fields based on concurrent surface T/S and vertical density reconstructions. The LS‐mEOFs plus SQG‐based density reconstruction is developed into a novel dynamical‐statistical framework of subsurface T/S reconstruction. In this study, the framework is evaluated based on the eddy‐resolving ocean general circulation model for the Earth simulator simulation. The density reconstructions of SQG, interior + SQG (isQG), and SQG‐mEOF‐R are incorporated respectively. The results are compared with two linear algorithms of multivariate linear regression and mEOF‐R and two machine learning algorithms of fruit fly optimized generalized regression neural network and random forest. The LS‐mEOFs plus isQG and LS‐mEOFs plus SQG‐mEOF‐R present robust T/S reconstruction in the selected regions of Northwest Pacific and Southeast Pacific. Especially, the Southeast Pacific is abundant of subsurface‐intensified eddies, where the T/S fields are poorly retrieved by machine learning algorithms. It is encouraging that the SQG‐based dynamical‐statistical framework can outperform the machine learning algorithms in retrieving those complicated T/S structures. The proposed framework is applicable to 3D mesoscale T/S reconstruction with the advent of surface water and ocean topography mission.