This paper proposes a convolutional neural network (CNN) method to estimate subsurface temperature (ST) in the Pacific Ocean from a suite of satellite remote sensing measurements. These include sea surface temperature(SST), sea surface height (SSH), and sea surface salinity (SSS). We propose using the multisource sea surface parameters to establish a monthly CNN model to reconstruct the ocean subsurface temperature (ST) and use Argo data for accurate validation. The results show that the CNN can accurately estimate the ST of the Pacific Ocean by using the model. We trained the model for 12 months. The most prominent months are January, April, July, and October with average mean square error (MSE) values of 0.2659, 0.3129, 0.5318, and 0.5160, and the average coefficients of determination (R 2 ) were 0.968, 0.971, 0.949, and 0.967, respectively. This study improves the accuracy of ST estimation and the good results based on reanalysis indicate that the model is promising to be applied to satellite observations. INDEX TERMS Convolutional neural network, ocean data, satellite measurements, subsurface temperature.

A coupled ocean-wave-sea spray model system is used to investigate the impacts of sea spray and sea surface roughness on the response of the upper ocean to the passage of the super typhoon Haitang. Sea spray mediated heat and momentum fluxes are derived from an improved version of Fairall’s heat fluxes formulation (Zhang et al., 2017) and Andreas’s sea spray-mediated momentum flux models. For winds ranging from low to extremely high speeds, a new parameterization scheme for the sea surface roughness is developed, in which the effects of wave state and sea spray are introduced. In this formulation, the drag coefficient has minimal values over the right quadrant of the typhoon track, along which the typhoon-generated waves are longer, smoother, and older, compared to other quadrants. Using traditional interfacial air-sea turbulent (sensible, latent, and momentum) fluxes, the sea surface cooling response to typhoon Haitang is overestimated by 1 °C, which can be compensated by the effects of sea spray and ocean waves on the right side of the storm. Inclusion of sea spray-mediated turbulent fluxes and sea surface roughness, modulated by ocean waves, gives enhanced cooling along the left edges of the cooling area by 0.2 °C, consistent with the upper ocean temperature observations.

A one-dimensional turbulent model is used to investigate the effect of sea spray mediated turbulent fluxes on upper ocean temperature during the passage of typhoon Yagi over the Kuroshio Extension area in 2006. Both a macroscopical sea spray momentum flux algorithm and a microphysical heat and moisture flux algorithm are included in this turbulent model. Numerical results show that the model can well reproduce the upper ocean temperature, which is consistent with the data from the Kuroshio Extension Observatory. Besides, the sea surface temperature is decreased by about 0.5°C during the typhoon passage, which also agrees with the sea surface temperature dataset derived from Advanced Microwave Scanning Radiometer for the Earth Observing and Reynolds. Diagnostic analysis indicates that sea spray acts as an additional source of the air-sea turbulent fluxes and plays a key role in increasing the turbulent kinetic energy in the upper ocean, which enhances the temperature diffusion there. Therefore, sea spray is also an important factor in determining the upper mixed layer depth during the typhoon passage.

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