6.4 Observational Analysis of Air-sea Fluxes Under High Winds Offshore South China Sea and Implications for Flux Parameterizations.

Wednesday, 13 June 2018: 12:00 AM
Meeting Room 19-20 (Renaissance Oklahoma City Convention Center Hotel)
Xueyan Bi, Institute of Tropical and Marine Meteorology/Guangdong Provincial Key Laboratory, Guangzhou, China

This paper investigates turbulent fluxes, mainly heat fluxes [sensible heat flux (H) and latent heat flux (LE)], derived from eddy covariance method based on data collected at an offshore observation tower during five tropical cyclones (TCs) episodes in the South China Sea from 2010 to 2014. This study represents the first direct in-situ measurements of continuous heat fluxes during TC passing. The highest 10 m winds measured is about 17 m s-1. Our observations show that LE was the main consumer of available energy during the TCs. In general, H(LE) is larger than zero (heat transfers from sea to air) and increases with increasing u_10. The maximum H(LE) measured is about 100 (400) W m-2. The values of H during TC Nalgae(2011) are larger than those during other TCs. We think that TC Nalgae(2011) formed in higher latitude and came the observation tower with colder air mass, which caused the larger air-sea temperature difference and H. Unlike previous observations reported in literature that the exchange coefficients of sensible heat flux, latent heat flux and enthalpy flux are equal (C_H=C_E=C_K) and C_(H() (C_E,C_K) is independent of u_10, our results show that C_K>C_H>C_E slightly when u_10>7 m s-1 and C_H slightly decreases with increasing u_10 when 5 m s-1 <u_10< 17 m s-1. Our observations show that the median of C_H/C_D is about 0.5 (1.1) when u_10>9 m s-1 (<9 m s-1). The value of C_H/C_D=0.5 significantly below the 0.75 threshold for hurricane development suggested by Emanuel [1995]. Zhang et al. [2008] reported the similar lower value of C_K/C_D=0.63. These observations suggest that TC deveplopment may have heat flux sources other than turbulent fluxes or Emanuel’s model assumptions should be revisited. More observations and analyses are needed.
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