Separating the albedo-reducing effect of different light-absorbing particles on snow using deep learning

Abstract

Several different types of light-absorbing particles (LAPs) darken snow surfaces, enhancing snowmelt on glaciers and snowfields. LAPs are often present as a mixture of biotic and abiotic components at the snow surface, yet methods to separate their respective abundance and albedo-reducing effects are lacking. Here, we present a new optimisation method enabling the retrievals of dust, black carbon, and red algal abundances and their respective darkening effects from spectral albedo. This method includes a deep-learning emulator of a radiative transfer model (RTM) and an inversion algorithm. The emulator alone can be used as a fast and lightweight alternative to the full RTM with the possibility to add new features, such as new light-absorbing particles. The inversion method was applied to 180 ground field spectra collected on snowfields in southern Norway, with a mean absolute error on spectral albedo of 0.0056, and surface parameters that closely matched expectations from qualitative assessments of the surface. The emulator predictions of surface parameters were used to quantify the albedo-reducing effect of algal blooms, mineral dust, and dark particles represented by black carbon. Among these 180 surfaces, the albedo reduction due to light-absorbing particles was highly variable and reached up to 0.13, 0.21, and 0.25 for red algal blooms, mineral dust, and dark particles respectively. In addition, the effect of a single LAP was attenuated by the presence of other LAPs by up to 2–3 times. These results demonstrate the importance of considering the individual types of light-absorbing particles and their concomitant interactions for forecasting snow albedo.