Usando a decomposição em modos empíricos para determinação de fluxos turbulentos entre oceano/atmosfera

Abstract

Turbulent fluxes may be directly determined as the statistical covariance between quantities locally observed. Besides environmental and instrumental difficulties associated with taking high frequency measurements over the ocean, there is a source of uncertainty inherent to the estimation of turbulent fluxes in the atmosphere, and it is their contamination by nonturbulent motion. This problem is directly related to the time window over which the covariances are determined and to the cospectral gap that, in theory, separates turbulent and nonturbulent events. In this work, we use a methodology based in the Empirical Mode Decomposition, which allows the precise identification of the cospectral gap for each temporal interval over which the fluxes are determined. Furthermore, this novel methodology allows filtering out oscillation modes associated with nonturbulent events, therefore allowing the use of a time window over which the large turbulent eddies are completely sampled. To test the method, data from two oceanic cruises have been used. One is from project HalocAST-2010 (over Eastern Pacific), and the other is from project Acex 2012 (over Southwestern Atlantic). The use of the new method in 4-h time series resulted in an increase of the absolute values of the fluxes of sensible heat, latent heat and momentum, with respect to those determined with the traditionally used 10-minute time series. For CO2 fluxes, it has been observed a large reduction of the average absolute fluxes, suggesting that such measurement may be largely contaminated by nonturbulent fluxes. When compared to bulk estimates, fluxes obtained by the new methodology show reduced scatter with respect to those determined from fixed 10-minute windows. The scatter reduction of the CO2 flux estimates allowed the determination of a functional relationship between piston velocity and wind speed, which is not possible to be obtained from the 10-minute estimates.