23 Sep 2021

23 Sep 2021

Review status: a revised version of this preprint is currently under review for the journal TC.

SNICAR-ADv4: A physically based radiative transfer model to represent the spectral albedo of glacier ice

Chloe A. Whicker1, Mark G. Flanner1, Cheng Dang2, Charles S. Zender3, Joseph M. Cook4, and Alex S. Gardner5 Chloe A. Whicker et al.
  • 1Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, MI, USA
  • 2Joint Center for Satellite Data Assimilation, University Corporation for Atmospheric Research, Boulder, CO, USA
  • 3Department of Earth System Science, University of California, Irvine, CA, USA
  • 4Department of Environmental Science, Aarhus University, Frederiksborgvej 339C, DK-4000, Roskilde, DK
  • 5Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, United States of America

Abstract. Accurate modeling of cryospheric surface albedo is essential for our understanding of climate change as snow and ice surfaces regulate the global radiative budget and sea-level through their albedo and mass balance. Although significant progress has been made using physical principles to represent the dynamic albedo of snow, models of glacier ice albedo tend to be heavily parameterized and not explicitly connected with physical properties that govern albedo, such as the number and size of air bubbles, specific surface area (SSA), presence of abiotic and biotic light absorbing constituents (LAC), and characteristics of any overlying snow. Here, we introduce SNICAR-ADv4, an extension of the multi-layer two-stream delta-Eddington radiative transfer model with the adding-doubling solver that has been previously applied to represent snow and sea-ice spectral albedo. SNICAR-ADv4 treats spectrally resolved Fresnel reflectance and transmittance between overlying snow and higher-density glacier ice, scattering by air bubbles of varying sizes, and numerous types of LAC. SNICAR-ADv4 simulates a wide range of clean snow and ice broadband albedos (BBA), ranging from 0.88 for (30 μm) fine-grain snow to 0.03 for bare and bubble free ice under direct light. Our results indicate that representing ice with a density of 650 kg m−3 as snow with no refractive Fresnel layer, as done previously, generally overestimates the BBA by an average of 0.058. However, because most naturally occurring ice surfaces are roughened "white ice", we recommend modeling a thin snow layer over bare ice simulations. We find optimal agreement with measurements by representing cryospheric media with densities less than 650 kg m−3 as snow, and larger density media as bubbly ice with a Fresnel layer. SNICAR-ADv4 also simulates the non-linear albedo impacts from LACs with changing ice SSA, with peak impact per unit mass of LAC near SSAs of 0.1–0.01 m2 kg−1. For bare, bubble-free ice, LAC actually increase the albedo. SNICAR-ADv4 represents smooth transitions between snow, firn, and ice surfaces and accurately reproduces measured spectral albedos of a variety of glacier surfaces. This work paves the way for adapting SNICAR-ADv4 to be used in land ice model components of Earth System Models.

Chloe A. Whicker et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Review of “SNICAR-ADv4: A physically based radiative transfer model to represent the spectral albedo of glacier ice” by Whicker et al.', Anonymous Referee #1, 04 Nov 2021
    • AC1: 'Reply on RC1', Chloe A. Whicker, 25 Jan 2022
    • AC2: 'Reply on RC1', Chloe A. Whicker, 25 Jan 2022
  • RC2: 'Comment on tc-2021-272', Ruzica Dadic, 23 Dec 2021
    • AC3: 'Reply on RC2', Chloe A. Whicker, 25 Jan 2022

Chloe A. Whicker et al.

Chloe A. Whicker et al.


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Short summary
Snow and ice surfaces are important to the global climate. Current climate models use measurements to determine the reflectivity of ice. This model uses physical properties to determine the reflectivity of snow, ice, and darkly pigmented impurities that reside within the snow and ice. Therefore, the modeled reflectivity is more accurate for snow/ice columns under varying climate conditions. This model paves the way for improvements in the portrayal of snow and ice within global climate models.