Improved snow darkening coefficient for large-scale albedo modelling with Crocus

Description

Light-absorbing particles (LAPs) deposited at the snow surface significantly reduce its albedo and strongly affect the snow melt dynamics. The explicit simulation of these effects with advanced snow radiative transfer models can be associated with a large computational cost. Consequently, many albedo schemes used in snowpack models still rely on empirical parameterizations that do not account for the spatial variability of LAP deposition. In Gaillard et al. (2024), a new strategy of intermediate complexity that includes the effects of spatially variable LAP deposition on snow albedo was tested with the snowpack model Crocus. It relies on an optimization of the snow darkening coefficient that controls the evolution of snow albedo in the visible range. A global dataset of LAP-informed and spatially variable values of the snow darkening coefficient was constructed. The revised snow darkening coefficient improved snow albedo simulations at the ten sites considered in the study by 10%, with the largest improvements found in the Arctic (more than 25%). The uncertainties in the values of the snow darkening coefficient resulting from the inter-annual variability of LAP deposition on snow were also computed.

The data are distributed in a NetCDF file (gamma_tot_publish_v2.nc). More details about the dataset and the file format are given in the file readme_gamma_v2.pdf.