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Title: Improving snow albedo modeling in the E3SM land model (version 2.0) and assessing its impacts on snow and surface fluxes over the Tibetan Plateau

Journal Article · · Geoscientific Model Development (Online)
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo [4]; ORCiD logo [1];  [5]; ORCiD logo [3]; ORCiD logo [6]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. Univ. of Colorado, Boulder, CO (United States)
  3. Univ. of California, Santa Barbara, CA (United States)
  4. National Center for Atmospheric Research (NCAR), Boulder, CO (United States)
  5. University Corporation for Atmospheric Research (UCAR), Boulder, CO (United States)
  6. Univ. of California, Los Angeles, CA (United States)

With the highest albedo of the land surface, snow plays a vital role in Earth's surface energy budget and water cycle. Snow albedo is primarily controlled by snow grain properties (e.g., size and shape) and light-absorbing particles (LAPs) such as black carbon (BC) and dust. The mixing state of LAPs in snow also has impacts on LAP-induced snow albedo reduction and surface radiative forcing (RF). However, most land surface models assume that snow grain shape is spherical and LAPs are externally mixed with the snow grains. This study improves the snow radiative transfer model in the Energy Exascale Earth System Model version 2.0 (E3SM v2.0) Land Model (ELM v2.0) by considering non-spherical snow grain shapes (i.e., spheroid, hexagonal plate, and Koch snowflake) and internal mixing of dust–snow, and it systematically evaluates the impacts on the surface energy budget and water cycle over the Tibetan Plateau (TP). A series of ELM simulations with different treatments of snow grain shape, mixing state of BC–snow and dust–snow, and sub-grid topographic effects (TOP) on solar radiation are performed. Compared with two remote sensing snow products derived from the Moderate Resolution Imaging Spectroradiometer, the control ELM simulation (ELM_Control) with the default configurations of spherical snow grain shape, internal mixing of BC–snow, external mixing of dust–snow, and without TOP as well as the ELM simulation with new model features (ELM_New) can both capture the overall snow distribution reasonably. Additionally, ELM_New overall shows smaller biases in snow cover fraction than ELM_Control in spring when snowmelt is important for water management. The estimated LAP-induced RF in ELM_New ranges from 0 to 19.3 W m-2 with the area-weighted average value of 1.5 W m-2 that is comparable to the reported values in existing studies. The Koch snowflake shape, among other non-spherical shapes, shows the largest difference from the spherical shape in spring when snow processes related to the surface energy budget and water cycle have high importance. The impacts of the mixing state of LAP in snow are smaller than the shape effects and depend on snow grain shape. Compared to external mixing, internal mixing of LAP–snow can lead to larger snow albedo reduction and snowmelt, which further affect the surface energy budget and water cycle. The individual contributions of non-spherical snow shape, mixing state of LAP–snow, and local topography impacts on the snow and surface fluxes have different signs and magnitudes, and their combined effects may be negative or positive due to complex and nonlinear interactions among the factors. Overall, the changes in net solar radiation in spring due to individual and combined effects range from -28.6 to 16.9 W m-2 and -29.7 to 12.2 W m-2, respectively. This study advances understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offers guidance for improving snow simulations and RF estimates in Earth system models under climate change.

Research Organization:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES); US National Oceanic and Atmospheric Administration; National Aeronautics and Space Administration (NASA)
Grant/Contract Number:
AC05-76RL01830; NOAA-OAR-CPO-2019-2005530; NA19OAR4310243; 80NSSC21K0997; 80NSSC20K1349; 80NSSC20K1722; 80NSSC18K1489
OSTI ID:
1922360
Report Number(s):
PNNL-SA-170799
Journal Information:
Geoscientific Model Development (Online), Vol. 16, Issue 1; ISSN 1991-9603
Publisher:
Copernicus Publications, EGUCopyright Statement
Country of Publication:
United States
Language:
English

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