The effect of averaging on bulk estimates of heat and momentum fluxes for the tropical western Pacific Ocean

Abstract

The magnitudes of bulk air-sea surface flux estimates calculat•;d using three temporal averaging methods were compared. The reference method is a simple average of P axes computed from hourly values of bulk meteorological parameters termed the sampling method (SM). In • ontrast, the scaler averaging method (SAM) computes the average flux from the average of the bulk data; th•:s it ignores correlations between variables. The vector averaging method (VAM) is similar to the SAM but us,:s the magnitude of the average wind vector rather than the average magnitude of the hourly wind vectors. 'Fhe data used in this study were collected during the Tropical Ocean and Global Atmospheric pilot cruise of the R/V Wecoma in the equatorial Pacific Ocean near 0øN, 145øE from February 17 to March 10, 1990. The ratios of the SAM and VAM values relative to the SM values were studied as.a function of averaging periods from 2-72 hours. The ratios vary little for times exceeding 36 hours. For averaging periods of 72 hours, the SAM estimates of Q•r, QJ•, %, and xy were 102%, 61%, 21%, and 69% of the SM estimates, respectively; the VAM ratios were even lower. These results suggest that air-sea surface scaler fluxes and stress components ,:omputed from monthly, weekly, and even daily averaged bulk meteorological parameters can be seriously in ,mor in equatorial, temporally variable wind regimes. 20,211 Weather Rev., 106, 223-232, 1978. Analysis of the convectively modified GATE boundary layer using in situ and acoustic sounder data, Mon. Weather Rev., 107, 985-993, 1979. LEDVINA ET AL.: AVERAOINO OF BULK FLUX ESTIMATES FOR THE TROPICAL PACIFIC 20,217 Hanawa, K., Accuracy of air-sea heat and momentum fluxes in the sea south of Japan, J. Oceanog•. Soc. Jpn, 65, 767-774, 1987. Hanawa, K., and Y. Toba, Critical examination of estimation methods of long-term mean air-sea heat and momentum transfers, Ocean Air Interactions, 1, 79-93, 1997. Hellerman, S., An updated estimate of the wind stress on the world ocean, Mon. Weather Rev., 95, 607-626, 1967. Hellerman, S., and M. Rosenstein, Normal monthly wind stress over the world ocean with error estimates, J. Phys. Oceanogr., 13, 1093-1104, 1983. R.H., and G.Y. Young, Heat and moisture budgets of tropical mesoscale anvil clouds, J. Atmos. Sci., 40, 2138-2147, 1983. Kondo, J., Air-sea bulk transfer coefficients in diabatic conditions, Boundary Layer Meteorol., 9, 91-112, 1975. Liu, W.T., K.B. Katsaros, and J.A. Businger, Bulk parameterization of the air-sea exchanges of heat and water vapor including the molecular constraints at the interface, J. Atmos. Sci., 36, 1722-1735, 1979. Marsden, R.F., and S. Pond, Synoptic estimates of air-sea fluxes, J. Mar. Res., 41,349-373, 1983. Smith, S.D., Coefficients for sea surface wind stress, and wind profiles as a function of wind speed and temperature, J. Geophys. Res., 93, 15,467-15,472, 1988. Weare, B.C., Uncertainties in estimates of surface heat fluxes derived from marine reports over the tropical and subtropical oceans, Tellus, Set. A, 41A, 357-37...

Air–Sea fluxes from ICOADS: the construction of a new gridded dataset with uncertainty estimates

“…Most early flux datasets used the classical approach to calculate monthly mean fluxes from monthly mean variables (Hsiung, 1986;Oberhuber, 1988) as it is computationally efficient, requiring the iterative flux calculation to be performed only once for each month and grid cell. However, biases are introduced in the flux fields due to the loss of the synoptic correlation between the basic variables in the nonlinear flux formulae (Ledvina et al, 1993;Josey et al, 1995;Gulev, 1997). This led to the adoption of the sampling approach (da Silva et al, 1994;Josey et al, 1999).…”

Near‐surface retrieval of air temperature and specific humidity using multisensor microwave satellite observations

“…With multiple NOAA and DMSP satellites in orbit since 1987, the work also provides the potential to improve the spatial and temporal sampling of Q a and T a during this period. Achieving 6 hour resolution for these quantities could reduce the heat flux error that occurs when atmospheric variables are averaged into daily, weekly or monthly quantities [ Ledvina et al , 1993] and enable new studies that require diurnal cycle information.…”

Impact of Synoptic Atmospheric Forcing on the Mean Ocean Circulation

“…It has long been recognized that neglecting high-frequency winds can lead to 64 large errors in the estimate of surface wind stress (e.g., Esbenson and Reynolds 1981; 65 Thompson et al 1983; Hanawa and Toba 1987; Ledvina et al 1993;Gulev 1994). For 66 example, Gulev (1994) found that the time-mean wind stress at Ocean Weather the monthly and 6-hourly winds respectively, and found that the net wind power input 76 increases by over 70% when the 6-hourly winds are used in the stress calculation.…”