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New features and enhancements in Community Land Model (CLM5) snow albedo modeling: description, sensitivity, and evaluation
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  • Cenlin He,
  • Mark Flanner,
  • David M Lawrence,
  • Yu Gu
Cenlin He
National Center for Atmospheric Research

Corresponding Author:[email protected]

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Mark Flanner
University of Michigan-Ann Arbor
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David M Lawrence
National Center for Atmospheric Research (UCAR)
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Yu Gu
Joint Institute for Regional Earth System Science and Engineering
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We enhance the Community Land Model (CLM) snow albedo modeling by implementing several new features with more realistic and physical representations of snow-aerosol-radiation interactions. Specifically, we incorporate the following model enhancements: (1) updating ice and aerosol optical properties with more realistic and accurate datasets, (2) adding multiple dust types, (3) adding multiple surface downward solar spectra to account for different atmospheric conditions, (4) incorporating a more accurate adding-doubling radiative transfer solver, (5) adding nonspherical snow grain representation, (6) adding black carbon-snow and dust-snow internal mixing representations, and (7) adding a hyperspectral (480-band versus the default 5-band) modeling capability. These model features/enhancements are included as new CLM physics/namelist options, which allows for quantification of model sensitivity to snow albedo processes and for multi-physics model ensemble analyses for uncertainty assessment. The model updates will be included in the next CLM version release. Sensitivity analyses reveal stronger impacts of using the new adding-doubling solver, nonspherical snow grains, and aerosol-snow internal mixing than the other new features/enhancements. These enhanced snow albedo representations improve the CLM simulated global snowpack evolution and land surface conditions, with reduced biases in simulated snow surface albedo, snow cover, snow water equivalent, snow depth, and surface temperature, particularly over northern mid-latitude mountainous regions and polar regions.
08 Jun 2023Submitted to ESS Open Archive
08 Jun 2023Published in ESS Open Archive