SELF v1.0: a minimal physical model for predicting time of freeze-up in lakes

Abstract. Predicting the freezing time in lakes is achieved by means of complex mechanistic models or simplified statistical regressions considering integral quantities. Here, we propose a minimal model (SELF) built on sound physical grounds that focuses pre-freezing period goes from mixed conditions (lake temperature at 4 ?C) to formation ice (0 ?C surface) dimictic lakes. The based energy balance involving two main processes governing inverse stratification dynamics: cooling water due heat loss and wind-driven mixing surface layer. They play opposite roles determining required for contribute large interannual variability observed phenology. More intense does indeed accelerate rate decrease lake (LSWT), while stronger wind deepens layer, increasing capacity thus reducing LSWT. A characterization process obtained with Monte Carlo simulation random sequences fluxes. results, interpreted through an approximate analytical solution model, elucidate general tendency system, suggesting power law dependence duration This simple yet physically characterized single calibration parameter, efficiency transfer change potential lake. Thus, SELF can be used as prognostic tool phenology freezing.