Greenland supraglacial lakes albedo-depth parameterization from multi-source remote sensing: an application of lake-albedo feedback modeling

Surface melt is a key control on ice sheet mass balance through meltwater runoff, and the surface-to-bed meltwater connection disturbs ice dynamics. The presence of supraglacial lakes (SGLs), a crucial component of the hydrological system, reduces the surface albedo, resulting in heightened solar radiation absorption and consequently enhancing mass loss. However, it is difficult to quantify lake-albedo feedback because little is known about the bottom ablation process, which is difficult to observe and is currently not incorporated in the regional climate models. This research mainly focuses on the simulation of SGL based on the improved GlacierLake_v2 model. Firstly, the specific albedo-depth parameterization for SGL is developed in the western Greenland ice sheet based on satellite observations, and a meteorologically driven runoff module to calculate meltwater input is also incorporated in GlacierLake_v2. Secondly, the SGL-albedo feedback (the melt rate difference between SGL and bare ice) is quantified in Lake BlueSnow, and its influencing factors are explored.

The lake albedo is calculated using narrow-to-broadband conversion, and the lake depth is extracted from ICESat-2. The albedo-depth parameterization is described by an exponential function. Compared to the measured bottom ablation, the GlacierLake_v2 achieves superior performance over the original GlacierLake model, with RMSE reduced by more than 50%. The SGL-albedo feedback exhibits an exponential decline as lake depth increases. Summer snowfall rapidly suppresses the ice sheet surface melt rate while exerting little influence on the lake bottom melting, thereby triggering strong SGL albedo feedback. We are currently developing a distributed SGL model aimed at simulating lake evolution in both horizontal and vertical dimensions and at the volume estimation of buried lakes. There is also the prospect of integrating GlacierLake_v2 into the more comprehensive hydrological model to decrease the uncertainty in surface mass loss predictions.