National Oceanic and
Atmospheric Administration
United States Department of Commerce


FY 1999

The surface-layer heat balance in the equatorial Pacific Ocean, Part I: Mean seasonal cycle

Wang, W., and M.J. McPhaden

J. Phys. Oceanogr., 29(8), 1812–1831, doi: 10.1175/1520-0485(1999)029<1812:TSLHBI>2.0.CO;2 (1999)

The surface-layer heat balance in the equatorial Pacific is examined in order to determine the processes responsible for the mean seasonal cycle of sea surface temperature (SST). Principal datasets include multiyear time series of surface winds, upper-ocean temperature, and velocity obtained from Tropical Atmosphere Ocean (TAO) buoy array at four locations along the equator in the western (165°E), central (170°W), and eastern (140° and 110°W) Pacific. A blended satellite-in situ SST product and climatological surface heat fluxes based on the Comprehensive Ocean-Atmosphere Data Set are also used. Changes in heat storage, horizontal heat advection, and heat fluxes at the surface are estimated directly from data; vertical fluxes of heat out of the base of the mixed layer are calculated as a residual. Results indicate that, of the terms that can be directly estimated, the net surface heat flux is generally the largest term in heat balance. Zonal heat advection is important at all locations and is generally a cooling term except in the eastern Pacific where the springtime reversal of the South Equatorial Current leads to warming. Meridional heat advection is largest in the eastern Pacific where it is dominated by seasonally varying tropical instability waves, which tend to warm the equator. The inferred vertical heat fluxes out the base of the mixed layer are comparable in magnitude to the surface fluxes, except in the western Pacific where they are close to zero. From these inferred vertical fluxes, the authors estimate the mean seasonal cycles in vertical eddy diffusivities and entrainment velocities, which, in the eastern Pacific, mimic the mean seasonal cycle of the surface winds. Implications for modeling and predicting SST are addressed.

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