Abstract
Ocean latent heat flux (LHF) is an essential variable for air–sea interactions, which establishes the link between energy balance, water and carbon cycle. The low-latitude ocean is the main heat source of the global ocean and has a great influence on global climate change and energy transmission. Thus, an accuracy estimation of high-resolution ocean LHF over low-latitude area is vital to the understanding of energy and water cycle, and it remains a challenge. To reduce the uncertainties of individual LHF products over low-latitude areas, four machine learning (ML) methods (Artificial Neutral Network (ANN), Random forest (RF), Bayesian Ridge regression and Random Sample Consensus (RANSAC) regression) were applied to estimate low-latitude monthly ocean LHF by using two satellite products (JOFURO-3 and GSSTF-3) and two reanalysis products (MERRA-2 and ERA-I). We validated the estimated ocean LHF using 115 widely distributed buoy sites from three buoy site arrays (TAO, PIRATA and RAMA). The validation results demonstrate that the performance of LHF estimations derived from the ML methods (including ANN, RF, BR and RANSAC) were significantly better than individual LHF products, indicated by R2 increasing by 3.7–46.4%. Among them, the LHF estimation using the ANN method increased the R2 of the four-individual ocean LHF products (ranging from 0.56 to 0.79) to 0.88 and decreased the RMSE (ranging from 19.1 to 37.5) to 11 W m−2. Compared to three other ML methods (RF, BR and RANSAC), ANN method exhibited the best performance according to the validation results. The results of relative uncertainty analysis using the triangle cornered hat (TCH) method show that the ensemble LHF product using ML methods has lower relative uncertainty than individual LHF product in most area. The ANN was employed to implement the mapping of annual average ocean LHF over low-latitude at a spatial resolution of 0.25° during 2003–2007. The ocean LHF fusion products estimated from ANN methods were 10–30 W m−2 lower than those of the four original ocean products (MERRA-2, JOFURO-3, ERA-I and GSSTF-3) and were more similar to observations.
Highlights
Ocean latent heat flux (LHF) plays a key role in the transformation of energy and vapor at the interface of the atmosphere and ocean [1,2,3]
The results of relative uncertainty analysis using the triangle cornered hat (TCH) method show that the ensemble LHF product using machine learning (ML) methods has lower relative uncertainty than individual LHF product in most area
LHF estimation by ensemble of satellite and reanalysis products (MERRA-2, JOFURO-3, ERA-I and GSSTF-3) and evaluate the performance of fusion products based on reference product (OAFlux) and buoy observations
Summary
Ocean latent heat flux (LHF) plays a key role in the transformation of energy and vapor at the interface of the atmosphere and ocean [1,2,3]. The study of sea–air heat flux can deepen the understanding of the ocean circulation driving model, elucidate the role of the ocean in balancing global energy and develop numerical prediction work on climate change. Both the atmospheric model and the ocean model require accurate LHF estimates for numerical simulation and forecasting [4,5,6,7]. Accurate LHF estimation of low-latitude regions is essential for climate and hydrology applications. Ocean LHF in low-latitude regions has an important impact on global climate change
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