Evaluation of OAFlux datasets based on in situ air–sea flux tower observations over Yongxing Island in 2016

Abstract

Abstract. The Yongxing air–sea flux tower (YXASFT), which was specially designed for air–sea boundary layer observations, was constructed on Yongxing Island in the South China Sea (SCS). Surface bulk variable measurements were collected during a 1-year period from 1 February 2016 to 31 January 2017. The sensible heat flux (SHF) and latent heat flux (LHF) were further derived via the Coupled Ocean–Atmosphere Response Experiment version 3.0 (COARE3.0). This study employed the YXASFT in situ observations to evaluate the Woods Hole Oceanographic Institute (WHOI) Objectively Analyzed Air–Sea Fluxes (OAFlux) reanalysis data products. First, the reliability of COARE3.0 data in the SCS was validated using direct turbulent heat flux measurements via an eddy covariance flux (ECF) system. The LHF data derived from COARE3.0 are highly consistent with the ECF with a coefficient of determination (R2) of 0.78. Second, the overall reliabilities of the bulk OAFlux variables were diminished in the order of Ta (air temperature), U(wind speed), Qa (air humidity) and Ts (sea surface temperature) based on a combination of R2 values and biases. OAFlux overestimates (underestimates) U (Qa) throughout the year and provides better estimates for winter and spring than in the summer–autumn period, which seems to be highly correlated with the monsoon climate in the SCS. The lowest R2 is between the OAFlux-estimated and YXASFT-observed Ts, indicating that Ts is the least reliable dataset and should thus be used with considerable caution. In terms of the heat fluxes, OAFlux considerably overestimates LHF with an ocean heat loss bias of 52 w m−2 in the spring, and the seasonal OAFlux LHF performance is consistent with U and Qa. The OAFlux-estimated SHF appears to be a poor representative, with enormous overestimations in the spring and winter, while its performance is much better during the summer–autumn period. Third, analysis reveals that the biases in Qa are the most dominant factor on the LHF biases in the spring and winter, and that the biases in both Qa and U are responsible for controlling the biases in LHF during the summer–autumn period. The biases in Ts are responsible for controlling the SHF biases, and the effects of biases in Ts on the biases in SHF during the spring and winter are much greater than that in the summer–autumn period.