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