Linking snowpack microphysics and albedo evolution

Snow aging causes reflectance to vary significantly on timescales of days. This variability influences the strength of snow albedo feedback and can affect the timing of snowmelt. However, climate models have yet to incorporate important controls on snow aging and albedo evolution. We develop a physically based model that predicts evolution of dry, pure snow specific surface area, and apply aspherical ice particle theory to link these results with albedo evolution. This is the first theoretical study to quantify the relative roles of initial size distribution, vertical temperature gradient, and snow density in snow albedo evolution. Vapor diffusion caused by curvature differences causes rapid albedo decay in the first day following snowfall. Vertical temperature gradient generally dominates grain growth processes afterward but is modulated by snow density, irregularity in particle spacing, and temperature. These processes operate as a coupled system, which we uniquely represent without abrupt transitions between regimes. Model results agree very well with measurements of isothermal snow evolution and are within reasonable range of temperature gradient observations. We show that different snow state regimes cause albedo of nonmelting snow surfaces with identical initial albedo to vary by 0.12 or more after 14 days. Lack of quality observational data illuminates the need for well-controlled snow studies that simultaneously monitor specific surface area, temperature gradient, and albedo. Accounting for snow aging processes, especially temperature gradient, will improve understanding and assessment of snow albedo feedback and climate sensitivity. The modeling framework we develop will also be useful for air-snow chemistry studies that consider specific surface area