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 We analyzed 14 days of observations from sonic anemometry and high-resolution fiber-optic distributed sensing collected in the stable 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, controls 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 nonequilibrium. The nondimensional 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 suggests that the vertical heat transport always remains coupled to the surface, but its efficiency and the resulting flux vary. Forty-three percent 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. Significance Statement Climate warming is amplified in polar regions, but quantifying heat transfer is complicated since mixing is weak and sporadic. We aim to understand when and why measurements taken in the lowest meters above snow can be used to quantify the surface heat transfer and if common modeling concepts are valid. We found that the measured heat transfer for weak winds and clear skies is connected to the snow surface only to a small degree in 66% of all cases at a much (−65%) reduced magnitude compared to strong wind or cloudy cases. However, 43% of the total heat is exchanged during these conditions and thus must be included in surface heat budgets. Classic modeling concepts systematically underpredicted the exchanged heat budget by 12% across all conditions which limits their utility in polar regions.