Genetic Programming for Estimation of Heat Flux between the Atmosphere and Sea Ice in Polar Regions

The Earth surface and atmosphere exchange heat via turbulent fluxes. An accurate description of the heat exchange is essential in modelling the weather and climate. In these models the heat fluxes are described applying the Monin-Obukhov similarity theory, where the flux depends on the air-surface temperature difference and wind speed. The theory makes idealized assumptions and the resulting estimates often have large errors. This is the case particularly in conditions when the air is warmer than the Earth surface, i.e., the atmospheric boundary layer is stably stratified, and turbulence is therefore weak. This is a common situation over snow and ice in the Arctic and Antarctic. In this paper, we present alternative models for heat flux estimation evolved by means of genetic programming (GP). To this aim, we utilize the best heat flux data collected in the Arctic and Antarctic sea ice zones. We obtain GP models that are more accurate, robust, and conceptually novel from the viewpoint of meteorology. Contrary to the Monin-Obukhov theory, the GP equations are not solely based on the air-surface temperature difference and wind speed, but include also radiative fluxes that improve the performance of the method. These results open the door to a new class of approaches to heat flux prediction with potential applications in weather and climate models.