The impact of turbulent transport efficiency on surface vertical heat fluxes in the Arctic stable boundary layer predicted from similarity theory and machine-learning

Abstract

Abstract We analyzed 14 days of observations from sonic anemometry and high-resolution fiber optic distributed sensing collected in the stable polar boundary layer (SBL). The study sought to evaluate if and under which conditions the sensible heat flux is related to the temperature gradient. Machine learning methods were employed to identify drivers of and model heat fluxes. We found the recently proposed coupling metric Ω defined as the ratio of the buoyancy length scale and measurement height to delineate physically meaningful transport regimes. The regime transition marks the point where static stability in addition to the vertical turbulence strength control the heat transport, which is rather gradual than abrupt. The maximum downward heat flux is reached when one third of turbulent eddies exceed the opposing buoyancy forces in the SBL. We found evidence that even for large Ω a substantial fraction of the turbulent transport is non-equilibrium. The non-dimensional temperature gradient is better explained by variations in Ω than ζ = zL−1 from Monin-Obukhov Similarity theory. Its continuous organization with Ω across stabilities suggest that the vertical heat transport always remains coupled to the surface, but its efficiency and the resulting flux vary. 43% of the total enthalpy is exchanged during conditions of limited transport efficiency in the very SBL despite the small flux magnitude of ≤ 7 W m−2, which underlines the importance of quantifying the weak surface exchange for polar regions. When predicting sensible heat fluxes using mean quantities from weather stations, the net longwave radiative forcing and the horizontal wind speed are the most important predictors representing stratification and bulk shear.