Near-surface turbulence is a key determinant of gas exchange velocities (k) used to compute fluxes of climate forcing trace gases under light to moderate winds in lakes and oceans. Scaling approaches to accurately predict turbulence would enable modeling of fluxes from diverse water bodies over large spatial scales. While wind-based models have been used, heating (buoyancy flux, β +) or cooling (β-) in the upper water column are likely to moderate turbulence relative to predictions from wind. Monin-Obukhov similarity theory (MOST) estimates turbulence, as rate of dissipation of turbulent kinetic energy (ϵ), taking into account the relative contributions of wind and β. We evaluated the accuracy of MOST in tropical floodplains, lakes, and reservoirs, in temperate, boreal and Arctic lakes, and in Arctic ponds and rivers using measurements of ϵ from temperature-gradient microstructure profilers and acoustic Doppler velocimeters. Dissipation rates near the water surface were enhanced relative to law of the wall scaling under heating. The enhancement was larger than prior predictions from MOST due to low mixing efficiency near the surface when the turbulence was isotropic. Under cooling and minimal wind, ϵ was predicted from β-, however, as winds increased, ε was progressively lower than predictions from MOST. Merging variable mixing efficiency with MOST will lead to improved time series estimates of ϵ and k as needed for modeling fluxes of dissolved gases.
Speaker: Sally MacIntyre, UC Santa Barbara
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Stanford, CA 94305
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