Abstract. In this study we analyzed turbulent heat fluxes over a seasonal ice cover on boreal lake located in southern Finland. Eddy covariance (EC) measurements from four ice-on seasons between 2014 and 2019 are compared to three different bulk transfer models: one with a constant transfer coefficient, and two with stability adjusted transfer coefficients: the Lake Heat Flux Analyzer and SEA-ICE. All three models correlate to the EC results well in general, although typically underestimating the magnitude and the variance of the flux in comparison to the EC observations. Differences between the models are small, with the constant transfer coefficient model performing slightly better than the stability adjusted models. Small difference in temperature and humidity between surface and air results in low correlation between models and EC. During melting periods (surface temperature T0 > 0 °C), the model performance for LE decreases when comparing to the freezing periods (T0 < 0 °C), while the opposite is true for H. At low wind speed EC shows relatively high fluxes (±20 W m−2) for H and LE due to non-local effects that the bulk models are not able to reproduce. Finally, the uncertainty in the estimation of the surface temperature and humidity affects the bulk heat fluxes, especially when the difference between surface and air values are small.

Abstract. Lake ice melting and breakup form a fast, nonlinear process with important mechanical, chemical, and biological consequences. The process is difficult to study in the field due to safety issues, and therefore relatively little is known about its details. In the present work, ice monitoring was based on foot, hydrocopter, and boat to get a full time-series of the evolution of ice structure and geochemical properties through the melting period. The field observations were made in Lake Pääjärvi during the ice decay periods in 2018 and 2022. In 2022, the maximum thickness of ice was 55 cm with 60 % snow-ice, and based on the data and heat budget analysis, the ice melted by 33 cm from the surface and 22 cm from the bottom while porosity increased to 40–50 % at breakup. In 2018, the snow-ice layer was small and bottom and internal melting dominated during the decay. Due to global warming, the ice breakup date became earlier. The mean melting rates were 1.31 cm d–1 in 2022 and 1.55 cm d–1 in 2018. In 2022 the electrical conductivity (EC) in ice was 11.4±5.79 S cm–1, one order of magnitude lower than in the lake water, and ice pH was 6.44±0.28, lower by 0.4 than in water. pH and EC of ice and lake water decreased along the ice decay except slight increases in ice due to flushing by lake water. Chlorophyll a was less than 0.5 g L–1 in porous ice, approximately one-third of that in the lake water. These results are important for further development of numerical models and understanding the process of ice decay with consequences to lake ecology and to safety of ice cover for human activities.

Abstract. In this study we analyzed turbulent heat fluxes over a seasonal ice cover on a boreal lake located in southern Finland. Eddy covariance (EC) flux measurements of sensible (H) and latent heat (LE) from four ice-on seasons between 2014 and 2019 are compared to three different bulk transfer models: one with a constant transfer coefficient and two with stability-adjusted transfer coefficients: the Lake Heat Flux Analyzer and SEA-ICE. All three models correlate well with the EC results in general while typically underestimating the magnitude and the standard deviation of the flux in comparison to the EC observations. Differences between the models are small, with the constant transfer coefficient model performing slightly better than the stability-adjusted models. Small difference in temperature and humidity between surface and air results in low correlation between models and EC. During melting periods (surface temperature T0>0 ∘C), the model performance for LE decreases when compared to the freezing periods (T0<0 ∘C), while the opposite is true for H. At low wind speed, EC shows relatively high fluxes (±20 W m−2) for H and LE due to non-local effects that the bulk models are not able to reproduce. The complex topography of the lake surroundings creates local violations of the Monin–Obukhov similarity theory, which helps explain this counterintuitive result. Finally, the uncertainty in the estimation of the surface temperature and humidity affects the bulk heat fluxes, especially when the differences between surface and air values are small.

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