Abstract
Lakes in the Arctic are important reservoirs of heat with much lower albedo in summer and larger absorption of solar radiation than surrounding tundra vegetation. In the winter, lakes that do not freeze to their bed have a mean annual bed temperature > 0 °C in an otherwise frozen landscape. Under climate warming scenarios, we expect Arctic lakes to accelerate thawing underlying permafrost due to warming waters in the summer and in the winter. Previous studies of Arctic lakes have focused on ice cover and thickness, the ice decay process, catchment hydrology, lake water balance, and eddy covariance measurements, but little work has been done in the Arctic to model lake heat balance. We applied the LAKE 2.0 model to simulate water temperatures in three Arctic lakes in Northern Alaska over several years and tested the sensitivity of the model to several perturbations of input meteorological variables (precipitation, shortwave radiation, and air temperature). The LAKE model is a one-dimensional model that explicitly solves vertical profiles of water state variables on a grid. We used a combination of meteorological data from local and remote weather stations, as well as data derived from remote sensing, to drive the model. We validated modelled water temperatures with data of observed lake temperatures at several depths. Our validation of the LAKE model completes a necessary step toward modelling changes in Arctic lake ice regimes, lake heat balance, and thermal interactions with permafrost. The sensitivity analysis shows us that the LAKE model is not highly sensitive to the weather data perturbations used in this study. Our results show that snow depth and lake ice strongly affect water temperatures during the frozen season which dominates the annual thermal regime. These findings suggest that reductions in lake ice thickness and duration could lead to more heat storage by lakes and enhanced permafrost degradation.
Highlights
Previous studies of Arctic lakes have focused on ice cover and thickness, the ice decay process, catchment hydrology, lake water balance, and eddy covariance measurements, but little work has been done in the Arctic to model lake heat balance
The sensitivity analysis shows us that the LAKE model is not highly sensitive to the weather data perturbations used in this study
There is mismatch towards the 170 end of each frozen season which is likely explained by ice rafting moving the temperature sensor into shallower water, which has been observed at many Arctic lakes (Jones, personal communication)
Summary
Lakes in the Arctic are important reservoirs of heat (Williamson et al, 2009) that affect permafrost thaw and carbon and methane emissions (Rowland et al, 2011; Abnizova et al, 2012). Lake water temperatures regulate heat fluxes, biogeochemical activity, and are influenced by meteorological conditions and the surface radiative balance (Abnizova et al, 2012; Jeffries et al, 1999; Arp et al, 2011; Wik et al, 2016; Rouse et al., 1997). Increasing lake temperatures can thaw underlying permafrost, creating taliks and enhancing surface-groundwater interactions (Rowland et al, 2011; Jorgenson et al, 2006; Jorgenson and Shur, 2007). Understanding and modeling water temperatures in permafrost landscapes is critical to be able to predict future talik development, permafrost thaw, and greenhouse gas releases (Grosse et al, 2013). Most high-fidelity physics models require an increase in computing time. Grant et al, (2021) stressed the important role of one-dimensional models serving as an optimal solution/tool when it comes to perform multiple runs for large numbers of lakes under different scenarios of climate change
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