Evaluation of bulk surface flux algorithms for light wind conditions usingdata from the Coupled Ocean-Atmosphere Response Experiment (COARE)

Five bulk surface flux formulae algorithms, currently used in large- and sm all-scale atmospheric and coupled ocean-atmosphere models, are tested with the same observations. Over the past decade many improvements have been mad e to the well-known bulk formulae used for estimating surface fluxes. This paper concentrates upon those improvements that have centred upon light win d conditions. This condition is especially important because light winds ar e often found in the equatorial zone of tropical oceans, particularly in th e western Pacific Ocean area known as the Pacific warm pool. The Pacific wa rm pool is important to the maintenance of the general circulation of the a tmosphere and to the initial phases of the El Nino Southern Oscillation (EN SO) phenomenon. The input data to the bulk algorithms are mean quantities o f 15 m air temperature, moisture, wind velocity, and 2 cm depth sea surface temperature obtained by the RV Moana Wave during the Coupled Ocean-Atmosph ere Response Experiment (COARE; 1 November 1992-28 February 1993). The outp ut from the bulk formulae are tested against 15 m eddy correlation flux obs ervations which were also part of the RV Moana Wave near-surface observatio nal package. The RV Moana Wave was stationed in the Pacific warm pool for t hree periods during the COARE. Four formulae used exactly the same conditio ning of the sea-surface-temperature input variable that was based upon uppe r ocean observations during the COARE. A surface renewal formulation had it s own sea-surface-temperature adjustment scheme. The tests show that four o f the five estimates of latent-heat flux magnitude are within 8% of observe d values. For sensible heat, about 10% of the latent-heat flux magnitude, t he range was 8-23%, and for momentum the range was 8-31% of observed values . Momentum flux was, as in the past, the most difficult to estimate. All ap proaches also used extensions to higher wind speeds. The test of those cond itions was limited by a small dataset. It is shown that, for most of the al gorithms tested, the bulk flux either overestimates or underestimates the c ovariance flux, depending upon the magnitude of the flux. In the COARE regi on this overestimation or underestimation is effectively a wind-speed depen dent bias in the model due to the surface flux parametrization. An error an alysis indicates that there may be substantial errors in the bulk flux due to instrument uncertainties in the inputs to the formula. These two conclus ions have an important implication concerning deterministic atmospheric mod elling. Chaos theory and other approaches to the accuracy-of-prediction pro blem show that small differences in the initial value (or nudging) of the d eterministic predictive integration can lead to widely varying (but coheren t) results. Since surface fluxes are a major input to atmospheric, ocean, a nd coupled models, the consequence of this effective bias error in surface flux, a bias-magnitude uncertainty due to natural variability, and errors a ssociated with the inputs to surface flux parametrizations must be consider ed in determining the accuracy of deterministic models of the atmosphere an d ocean. Otherwise the results of the integration, especially if long-term, when compared with the real atmosphere may have wide differences. From the se considerations, it is suggested that current general-circulation models and climate models undergo surface flux sensitivity studies to test the eff ect of bias errors as discussed, and errors associated with inputs to the s urface flux algorithms as well as natural variability.