Ice cover in seasonally frozen lakes plays an important role in ecosystem processes, winter activities, and climate monitoring. A new study presents an extension of the air2water model that enables the simulation of lake ice dynamics alongside lake surface water temperature.
The updated model represents both black ice, formed by the direct freezing of lake water, and white ice, which develops when snow accumulates on the ice surface and later freezes. The approach maintains the main advantage of the original air2water model: it requires only limited input data—air temperature and precipitation—and relies on a relatively small number of parameters.
The model was evaluated using long-term observations (1960–2023) of lake surface temperature and ice thickness from three Finnish lakes. Results show good agreement with measurements, with temperature errors close to 1 °C and ice thickness errors of about 10 cm. Reliable ice thickness estimates were also obtained when the model was calibrated using only temperature data.
These findings highlight the potential of the extended air2water model as an efficient and data-light alternative to more complex lake ice models.
The full paper is available here: