The seasonal dynamics of freshwater lake ice and its interactions with air and snow are studied in two small subarctic lakes with comparable surface areas but contrasting depths (4.3 versus 91 m). Two, 2.9 m long thermistor chain sensors (Snow and Ice Mass Balance Apparatuses), were used to remotely measure air, snow, ice, and water temperatures every 15-min between December 2021 and March 2022. Results showed that freeze-up occurred later in the deeper lake (Ryan Lake) and earlier in the shallow lake (Landing Lake). Ice growth was significantly faster in Ryan Lake than in Landing Lake, due to cold water temperatures (mean (Tw¯) =0.65 to 0.96°C) persisting beneath the ice. In Landing Lake, basal ice growth was hindered because of warm water temperatures (Tw¯=1.5 to 2.1°C) caused by heat released from lake sediments. Variability in air temperatures at both lakes had significant influences on the thermal regimes of ice and snow, particularly in Ryan Lake, where ice temperatures were more sensitive to rapid changes in air temperatures. This finding suggests that conductive heat transfer through the air-water continuum may be more sensitive to variability in air temperatures in deeper lakes with colder water temperatures than in shallow lakes with warmer water temperatures, if snow depths and densities are comparable. This study highlights the significance of lake morphology and rapid air temperature variability on influencing ice growth processes. Conclusions drawn aim to improve the representation of ice growth processes in regional and global climate models, and to improve ice safety for northern communities.

The seasonal dynamics of freshwater lake ice and its interactions with air and snow are studied in two small subarctic lakes with comparable surface areas but contrasting depths (4.3 versus 91 m). Two, 2.9 m long thermistor chain sensors (Snow and Ice Mass Balance Apparatuses), were used to remotely measure air, snow, ice, and water temperatures every 15-min between December 2021 and March 2022. Results showed that freeze-up occurred later in the deeper lake (Ryan Lake) and earlier in the shallow lake (Landing Lake). Ice growth was significantly faster in Ryan Lake than in Landing Lake, due to cold water temperatures (mean (Tw¯) =0.65 to 0.96°C) persisting beneath the ice. In Landing Lake, basal ice growth was hindered because of warm water temperatures (Tw¯=1.5 to 2.1°C) caused by heat released from lake sediments. Variability in air temperatures at both lakes had significant influences on the thermal regimes of ice and snow, particularly in Ryan Lake, where ice temperatures were more sensitive to rapid changes in air temperatures. This finding suggests that conductive heat transfer through the air-water continuum may be more sensitive to variability in air temperatures in deeper lakes with colder water temperatures than in shallow lakes with warmer water temperatures, if snow depths and densities are comparable. This study highlights the significance of lake morphology and rapid air temperature variability on influencing ice growth processes. Conclusions drawn aim to improve the representation of ice growth processes in regional and global climate models, and to improve ice safety for northern communities.

Abstract. Ice thickness across lake ice is mainly influenced by the presence of snow and its distribution, which affects the rate of lake ice growth. The distribution of snow depth over lake ice varies due to wind redistribution and snowpack metamorphism, affecting the variability of lake ice thickness. Accurate and consistent snow depth data on lake ice are sparse and challenging to obtain. However, high spatial resolution lake snow depth observations are necessary for the next generation of thermodynamic lake ice models to improve the understanding of how the varying distribution of snow depth influences lake ice formation and growth. This study was conducted using ground-penetrating radar (GPR) acquisitions with ∼9 cm sampling resolution along transects totalling ∼44 km to map snow depth over four Canadian sub-arctic freshwater lakes. The lake snow depth derived from GPR two-way travel time (TWT) resulted in an average relative error of under 10 % when compared to 2430 in situ snow depth observations for the early and late winter season. The snow depth derived from GPR TWTs for the early winter season was estimated with a root mean square error (RMSE) of 1.6 cm and a mean bias error of 0.01 cm, while the accuracy for the late winter season on a deeper snowpack was estimated with a RMSE of 2.9 cm and a mean bias error of 0.4 cm. The GPR-derived snow depths were interpolated to create 1 m spatial resolution snow depth maps. The findings showed improved lake snow depth retrieval accuracy and introduced a fast and efficient method to obtain high spatial resolution snow depth information. The results suggest that GPR acquisitions can be used to derive lake snow depth, providing a viable alternative to manual snow depth monitoring methods. The findings can lead to an improved understanding of snow and lake ice interactions, which is essential for northern communities' safety and wellbeing and the scientific modelling community.

Abstract. Ice thickness across lake ice is mainly influenced by the presence of snow and its distribution, which affects the rate of lake ice growth. The distribution of snow depth over lake ice varies due to wind redistribution and snowpack metamorphism, affecting the variability of lake ice thickness. Accurate and consistent snow depth data on lake ice are sparse and challenging to obtain. However, high spatial resolution lake snow depth observations are necessary for the next generation of thermodynamic lake ice models to improve the understanding of how the varying distribution of snow depth influences lake ice formation and growth. This study was conducted using ground-penetrating radar (GPR) acquisitions with ∼9 cm sampling resolution along transects totalling ∼44 km to map snow depth over four Canadian sub-arctic freshwater lakes. The lake snow depth derived from GPR two-way travel time (TWT) resulted in an average relative error of under 10 % when compared to 2430 in situ snow depth observations for the early and late winter season. The snow depth derived from GPR TWTs for the early winter season was estimated with a root mean square error (RMSE) of 1.6 cm and a mean bias error of 0.01 cm, while the accuracy for the late winter season on a deeper snowpack was estimated with a RMSE of 2.9 cm and a mean bias error of 0.4 cm. The GPR-derived snow depths were interpolated to create 1 m spatial resolution snow depth maps. The findings showed improved lake snow depth retrieval accuracy and introduced a fast and efficient method to obtain high spatial resolution snow depth information. The results suggest that GPR acquisitions can be used to derive lake snow depth, providing a viable alternative to manual snow depth monitoring methods. The findings can lead to an improved understanding of snow and lake ice interactions, which is essential for northern communities' safety and wellbeing and the scientific modelling community.