Abstract

Abstract. Most models simulating snow albedo assume a flat and smooth surface, neglecting surface roughness. However, the presence of macroscopic roughness leads to a systematic decrease in albedo due to two effects: (1) photons are trapped in concavities (multiple reflection effect) and (2) when the sun is low, the roughness sides facing the sun experience an overall decrease in the local incidence angle relative to a smooth surface, promoting higher absorption, whilst the other sides have weak contributions because of the increased incidence angle or because they are shadowed (called the effective-angle effect here). This paper aims to quantify the impact of surface roughness on albedo and to assess the respective role of these two effects, with (1) observations over varying amounts of surface roughness and (2) simulations using the new rough surface ray-tracing (RSRT) model, based on a Monte Carlo method for photon transport calculation. The observations include spectral albedo (400–1050 nm) over manually created roughness surfaces with multiple geometrical characteristics. Measurements highlight that even a low fraction of surface roughness features (7 % of the surface) causes an albedo decrease of 0.02 at 1000 nm when the solar zenith angle (θs) is larger than 50∘. For higher fractions (13 %, 27 % and 63 %), and when the roughness orientation is perpendicular to the sun, the decrease is of 0.03–0.04 at 700 nm and of 0.06–0.10 at 1000 nm. The impact is 20 % lower when roughness orientation is parallel to the sun. The observations are subsequently compared to RSRT simulations. Accounting for surface roughness improves the model observation agreement by a factor of 2 at 700 and 1000 nm (errors of 0.03 and 0.04, respectively) compared to simulations considering a flat smooth surface. The model is used to explore the albedo sensitivity to surface roughness with varying snow properties and illumination conditions. Both multiple reflections and the effective-angle effect have a greater impact with low specific surface area (SSA; <10 m2 kg−1). The effective-angle effect also increases rapidly with θs at large θs. This latter effect is larger when the overall slope of the surface is facing away from the sun and has a roughness orientation perpendicular to the sun. For a snowpack where artificial surface roughness features were created, we showed that a broadband albedo decrease of 0.05 may cause an increase in the net shortwave radiation of 80 % (from 15 to 27 W m−2). This paper highlights the necessity of considering surface roughness in the estimation of the surface energy budget and opens the way for considering natural rough surfaces in snow modelling.

Highlights

  • Spectral albedo quantifies the proportion of solar energy reflected by a surface for each wavelength and governs the quantity of solar radiation absorbed in the snowpack

  • (2) What is the impact of specific surface area (SSA) and slope uncertainties on the quantification of roughness effects (Sect. 4.3)? (3) What are the respective roles of the effective-angle effect and multiple reflections according to snow properties and illumination conditions (Sect. 4.4)? The impact of roughness on the absorbed energy is investigated (Sect. 4.5)

  • Our observations show that the presence of macroscopic surface roughness significantly decreases snow albedo

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Summary

Introduction

Spectral albedo quantifies the proportion of solar energy reflected by a surface for each wavelength and governs the quantity of solar radiation absorbed in the snowpack. Snow spectral albedo generally depends, in a complex way, on several factors, including (1) the snow physical and chemical properties, mainly the specific surface area (SSA) of snow grains (Gallet et al, 2009), the snow grain shapes (Tanikawa et al, 2006; Jin et al, 2008; Libois et al, 2013, 2014) and the concentration of snow light-absorbing particles (referred to as LAPs; Skiles et al, 2018); (2) the spectral and angular characteristics of the incident radiation (Warren, 1982); and (3) the presence of macroscopic surface roughness (Kuhn, 1985; Warren et al, 1998; Mondet and Fily, 1999). The effects of roughness are often neglected due to the difficulty of characterising the actual surface roughness within the footprint of the sensor

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