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 Climate change is a threat to the 500 Gt carbon stored in northern peatlands. As the region warms, the rise in mean temperature is more pronounced during the non-growing season (NGS, i.e., winter and parts of the shoulder seasons) when net ecosystem loss of carbon dioxide (CO 2 ) occurs. Many studies have investigated the impacts of climate warming on NGS CO 2 emissions, yet there is a lack of consistency amongst researchers in how the NGS period is defined. This complicates the interpretation of NGS CO 2 emissions and hinders our understanding of seasonal drivers of important terrestrial carbon exchange processes. Here, we analyze the impact of alternative definitions of the NGS for a peatland site with multiple years of CO 2 flux records. Three climatic parameters were considered to define the NGS: air temperature, soil temperature, and snow cover. Our findings reveal positive correlations between estimates of the cumulative non-growing season net ecosystem CO 2 exchange (NGS-NEE) and the length of the NGS for each alternative definition, with the greatest proportion of variability explained using snow cover ( R 2 = 0.89, p < 0.001), followed by air temperature ( R 2 = 0.79, p < 0.001) and soil temperature ( R 2 = 0.54, p = 0.006). Using these correlations, we estimate average daily NGS CO 2 emitted between 1.42 and 1.90 gCO 2 m −2 , depending on which NGS definition is used. Our results highlight the need to explicitly define the NGS based on available climatic parameters to account for regional climate and ecosystem variability.

Abstract Peatlands are important ecosystems that store approximately one third of terrestrial organic carbon. Non-growing season carbon fluxes significantly contribute to annual carbon budgets in peatlands, yet their response to climate change is poorly understood. Here, we investigate the governing environmental variables of non-growing season carbon emissions in a northern peatland. We develop a support-vector regression model using a continuous 13-year dataset of eddy covariance flux measurements from the Mer Blue Bog, Canada. We determine that only seven variables were needed to reproduce carbon fluxes, which were most sensitive to net radiation above the canopy, soil temperature, wind speed and soil moisture. We find that changes in soil temperature and photosynthesis drove changes in net carbon flux. Assessing net ecosystem carbon exchange under three representative concentration pathways, we project a 103% increase in peatland carbon loss by 2100 under a high emissions scenario. We suggest that peatland carbon losses constitute a strong positive climate feedback loop.