Preprints
https://doi.org/10.5194/gmd-2021-182
https://doi.org/10.5194/gmd-2021-182

Submitted as: model description paper 16 Aug 2021

Submitted as: model description paper | 16 Aug 2021

Review status: this preprint is currently under review for the journal GMD.

SNICAR-AD v3: A Community Tool for Modeling Spectral Snow Albedo

Mark G. Flanner1, Julian Arnheim2, Joseph M. Cook3, Cheng Dang4, Cenlin He5, Xianglei Huang1, Deepak Singh6, S. McKenzie Skiles7, Chloe A. Whicker1, and Charles S. Zender8 Mark G. Flanner et al.
  • 1Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, MI, USA
  • 2Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY, USA
  • 3Department of Environmental Science, Aarhus University, Roskilde, Denmark
  • 4Joint Center For Satellite Data Assimilation, UCAR, Boulder, CO, USA
  • 5Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
  • 6Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra, India
  • 7Department of Geography, University of Utah, Salt Lake City, UT, USA
  • 8Department of Earth System Science, University of California, Irvine, CA, USA

Abstract. The Snow, Ice, and Aerosol Radiative (SNICAR) model has been used in various capacities over the last 15 years to model the spectral albedo of snow with light-absorbing constituents (LAC). Recent studies have extended the model to include an adding-doubling two-stream solver and representations of non-spherical ice particles, carbon dioxide snow, snow algae, and new types of mineral dust, volcanic ash, and brown carbon. New options also exist for ice refractive indices and solar zenith angle-dependent surface spectral irradiances used to derive broadband albedo. The model spectral range was also extended deeper into the ultraviolet for studies of extraterrestrial and high-altitude cryospheric surfaces. Until now, however, these improvements and capabilities have not been merged into a unified code base. Here, we document the formulation and evaluation of the publicly-available SNICAR-ADv3 source code, web-based model, and accompanying library of particle optical properties. The use of non-spherical ice grains, which scatter less strongly into the forward direction, reduce the simulated albedo perturbations from LAC by ~9–31 %, depending on which of the three available non-spherical shapes are applied. The model compares very well against measurements of snow albedo from seven studies, though key properties affecting snow albedo are not fully constrained with measurements, including ice effective grain size of the top sub-millimeter of the snowpack, mixing state of LAC with respect to ice grains, and site-specific LAC optical properties. The new default ice refractive indices produce extremely high pure snow albedo (> 0.99) in the blue and ultraviolet part of the spectrum, with such values measured so far only in Antarctica. More work is needed particularly in the representation of snow algae, including experimental verification of how different pigment expressions and algal cell concentrations affect snow albedo. Representations and measurements of the influence of liquid water on spectral snow albedo are also needed.

Mark G. Flanner et al.

Status: open (until 11 Oct 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-182', Marie Dumont, 15 Sep 2021 reply
  • RC2: 'Comment on gmd-2021-182', Ghislain Picard, 28 Sep 2021 reply

Mark G. Flanner et al.

Mark G. Flanner et al.

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Short summary
We present the technical formulation and evaluation of a publicly-available code and web-based model to simulate the spectral albedo of snow. Our model accounts for numerous features of the snow state and ambient conditions, including the the presence of light-absorbing matter like black and brown carbon, mineral dust, volcanic ash, and snow algae. Carbon dioxide snow, found on Mars, is also represented. The model accurately reproduces spectral measurements of clean and contaminated snow.