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    Location: Data prodocts >> heat flux >> project description: heat flux

    Project Description: OAFlux surface heat fluxes

    Ocean latent and sensible heat fluxes

    OAFlux latent and sensible heat fluxes products are constructed from the use of the best possible surface meteorological variables and best possible bulk flux algorithm (Yu et a. 2008, make a link to the online technical report).
    Notable features of the OAFlux analysis:
    • The best possible estimates for flux-related surface meteorological variables are obtained by OAFlux through applying an advanced objective analysis that seeks optimal synthesis of satellite and NWP data sources (Note that the present satellite observing capacity cannot provide direct measurements of near-surface air and humidity, and so auxiliary datasets have to be supplied to complete the flux estimates)
    • The objective analysis denotes the process of synthesizing measurements/estimates from various sources. The methodology governing the objective analysis is the Gauss – Markov theorem, a standard statistical estimation theory that states, when combining data in a linear fashion, the linear least squares estimator is the most efficient estimator. Such process reduces error in each input data source and produces an estimate that has the minimum variance. That means that the OAFlux analysis takes data errors into account when constructing improved estimates of flux-related surface meteorological variables.
    • The latent and sensible heat flux estimates are computed from the objectively analyzed surface meteorological variables by using the COARE bulk flux algorithm 3.0 (Fairall et al. 2003).
    OAFlux latent heat flux
    OAFlux Sensible heat flux
    Net heat flux, Qnet, is computed as:

           Qnet = SW- LW - LH - SH.

    where SW denotes net downward shortwave radiation, LW net upward longwave radiation, LH latent heat flux, and SH sensible heat flux. The unit is W/m2.

    • Currently, Qnet is a combination of OAFlux LH and SH with ISCCP SW and LW. The resulting Qnet datasets (1-degree grid, daily and monthly) are good to depict flux variability on synoptic –seasonal-interannual timescales (see Yu et al. 2006; 2007). However, Qnet is not balanced; there is a net residual of O(30W/m2) when averaged over the global oceans.
    • Work is underway on developing OAFlux radiation estimates to achieve a globally balanced net heat flux product.
    Net Heat Flux

    Yu, L., X. Jin, and R. A. Weller, 2008: Multidecade Global Flux Datasets from the Objectively Analyzed Air-sea Fluxes (OAFlux) Project: Latent and sensible heat fluxes, ocean evaporation, and related surface meteorological variables. Woods Hole Oceanographic Institution, OAFlux Project Technical Report. OA-2008-01, 64pp. Woods Hole. Massachusetts.[PDF]

    Yu, L., X. Jin, and R.A. Weller, 2007: Annual, Seasonal, and Interannual Variability of Air–Sea Heat Fluxes in the Indian Ocean. J. Climate, Special issue on the Climate Variability and Predictability of the Indian Ocean, 20, 3190–3209. [Abstract]  [PDF] [Reprint]

    Yu, L., X. Jin, and R. A. Weller, 2006: Role of net surface heat flux in the seasonal evolution of sea surface temperature in the Atlantic Ocean. J. Climate. 19, 6153–6169. [Abstract] [PDF] [Reprint]