How much of the surface energy partitioning can be explained by controls imposed by thermodynamics?

Natural processes within the Earth system have been shown to organise themselves to achieve a state of thermodynamic optimality. Here we test these physical principles for convective flux exchange within the surface – atmosphere system.  We propose an idealised modelling framework where the convective exchange is conceptualised as the outcome of a heat engine operated between the hotter Earth’s surface and the cooler atmosphere. We use the first and second law of thermodynamics in conjunction with the surface energy balance which give rise to thermodynamic constraints on turbulent flux exchange. This new constraint is associated with the maximum power that can be generated within the heat engine to sustain convective motion. We use daily radiative forcing from NASA-CERES dataset as the input to our approach and estimated the surface energy partitioning on land into turbulent fluxes and emitted longwave radiation. The former is closely related to convective exchange within the atmosphere driving the hydrologic cycling while the latter directly relates to the surface temperature of the Earth.  We compare our estimates of surface temperatures, latent and sensible heat fluxes with observation based datasets and found a very good agreement over land at a global scale. Our findings show that physical principles of thermodynamics alone can explain the surface energy partitioning to a large extent. We further show an application of this approach in removing the cloud radiative effects (CRE) from surface temperatures. We used clear-sky fluxes from the NASA-CERES dataset as a forcing to our thermodynamically constrained energy balance model and estimated "clear-sky" temperatures. These temperatures removes the effect of radiative cooling by clouds on surface temperatures and can be used as useful variable to infer the hydrological sensitivity from observations. Our work implies that thermodynamically constrained idealised models can be used to identify the dominant physical controls on climate system to better understand land-atmosphere interactions and climate sensitivities.