A comparison is conducted between nine monthly turbulent air-sea flux products. The analysis includes in situ-based (Florida State University fluxes, FSU3 and National Oceanography Centre, NOC), satellite-based (Hamburg Ocean-Atmosphere Parameters from Satellite data, HOAPS2 and the French Research Institute for Exploitation of the Sea, IFREMER), hybrid (Goddard Satellite-based Surface Turbulent Fluxes, GSSTF2 and objectively analysed fluxes, OAFLUX), and reanalysis (National Centers for Environmental Prediction, NCEPR2, Japanese 25-year reanalysis, JRA, and European Centre for Medium Range Weather Forecasts reanalysis, ERA-40) products. Objectives include documenting the varying analysis methodologies and quantifying the differences and similarities between the nine products. Recommendations are made for developers of future flux products and to guide users to select products most suitable for their application.The comparison examines turbulent fluxes of heat and momentum along with the forcing variables (air temperature, wind speed, humidity, and ocean skin temperature) that are necessary to estimate turbulent fluxes. The wide range of turbulent flux parameterisations, sampling patterns, and averaging techniques within the products are described, including some of the difficulties product differences pose when trying to compare or apply the individual products. Global comparisons of monthly means tend to reveal similar spatial patterns in latent heat flux (LHF) and sensible heat flux (SHF) for the nine products; however, the magnitudes and patterns of variability (expressed as maps of standard deviations) are widely different. Basin scale and regional analysis further reveals large differences in the products (in some cases the interquartile ranges (IQRs) do not overlap for different products), but also reveals potential sources of the differences. For example, some of the variations in LHF can be explained by large differences in the distribution of specific humidity between the products. As a final analysis, we examine how each product represents the variations in turbulent fluxes in the equatorial Pacific (EP) Ocean. This analysis provides an example of how the choice of a flux product, and understanding the strengths and weaknesses of that product, can alter research findings.

Seasonal-to-multidecadal applications that require ocean surface energy fluxes often require accuracies of surface turbulent fluxes to be 5 W m 22 or better. While there is little doubt that uncertainties in the flux algorithms and input data can cause considerable errors, the impact of temporal averaging has been more controversial. The biases resulting from using monthly averaged winds, temperatures, and humidities in the bulk aerodynamic formula (i.e., the so-called classical method) to estimate the monthly mean latent heat fluxes are shown to be substantial and spatially varying in a manner that is consistent with most prior work. These averaging-related biases are linked to nonnegligible submonthly covariances between the wind, temperature, and humidity. To provide additional insight into the averaging-related bias, the methodology behind the third-generation Florida State University monthly mean surface flux product (FSU3) is detailed to highlight additional sources of errors in gridded datasets. The FSU3 latent heat fluxes suffer from this averaging-related bias, which can be as large as 90 W m 22 in western boundary current regions during winter and can exceed 40 W m 22 in synoptically active portions of the tropics. The regional impacts of these biases on the mixed layer temperature tendency are shown to demonstrate that the error resulting from applying the classical method is physically substantial.

The authors' modeling shows that changes in sea surface temperature (SST) gradients and surface roughness between oil-free water and oil slicks influence the motion of the slick. Physically significant changes occur in surface wind speed, surface wind divergence, wind stress curl, and Ekman transport mostly because of SST gradients and changes in surface roughness between the water and the slick. These remarkable changes might affect the speed and direction of surface oil. For example, the strongest surface wind divergence (convergence) occurring in the transition zones owing to the presence of an oil slick will induce an atmospheric secondary circulation over the oil region, which in turn might affect the surface oil movement. SST-related changes to wind stress curl and Ekman transport in the transition zones appear to increase approximately linearly with the magnitude of SST gradients. Both surface roughness difference and SST gradients give rise to a net convergence of Ekman transport for oil cover. The SST gradient could play a more important role than surface roughness in changes of Ekman transport when SST gradients are large enough (e.g., several degrees per 10 km). The resulting changes in Ekman transport also induce the changes of surface oil movement. Sensitivity experiments show that appropriate selections of modeled parameters and geostrophic winds do not change the conclusions. The results from this idealized study indicate that the feedbacks from the surface oil presence to the oil motion itself are not trivial and should be further investigated for consideration in future oil-tracking modeling systems.

SUMMARYThe drift of plastic envelopes floating close to the sea surface is related to the gradient wind. The results indicate that in westerly situations a shallow layer near the sea surface moves in approximately the same direction as the gradient wind with about 2 per cent of its velocity.

The exchange of heat and momentum through the air-sea surface are critical aspects of ocean forcing and ocean modeling. Over most of the global oceans, there are few in situ observations that can be used to estimate these fluxes. This chapter provides background on the calculation and application of air-sea fluxes, as well as the use of remote sensing to calculate these fluxes. Wind variability makes a large contribution to variability in surface fluxes, and the remote sensing of winds is relatively mature compared to the air sea differences in temperature and humidity, which are the other key variables. Therefore, the remote sensing of wind is presented in greater detail. These details enable the reader to understand how the improper use of satellite winds can result in regional and seasonal biases in fluxes, and how to calculate fluxes in a manner that removes these biases. Examples are given of high-resolution applications of fluxes, which are used to indicate the strengths and weakness of satellite-based calculations of ocean surface fluxes.