Modeling seasonal snowpack evolution in the complex terrain and forested Colorado Headwaters region: A model intercomparison study

  • Fei Chen
    National Center for Atmospheric Research Boulder Colorado USA
  • Michael Barlage
    National Center for Atmospheric Research Boulder Colorado USA
  • Mukul Tewari
    National Center for Atmospheric Research Boulder Colorado USA
  • Roy Rasmussen
    National Center for Atmospheric Research Boulder Colorado USA
  • Jiming Jin
    Department of Watershed Sciences Utah State University Logan Utah USA
  • Dennis Lettenmaier
    Civil & Environmental Engineering University of Washington Seattle Washington USA
  • Ben Livneh
    Cooperative Institute for Research in Environmental Sciences Boulder Colorado USA
  • Chiyu Lin
    Civil & Environmental Engineering University of Washington Seattle Washington USA
  • Gonzalo Miguez‐Macho
    Facultade de Física Universidade de Santiago de Compostela Santiago de Compostela Spain
  • Guo‐Yue Niu
    Department of Hydrology and Water Resources and Biosphere 2 University of Arizona Tucson Arizona USA
  • Lijuan Wen
    Cold and Arid Regions Environmental and Engineering Research Institute Lanzhou China
  • Zong‐Liang Yang
    Department of Geological Sciences University of Texas Austin Texas USA

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<jats:title>Abstract</jats:title><jats:p>Correctly modeling snow is critical for climate models and for hydrologic applications. Snowpack simulated by six land surface models (LSM: Noah, Variable Infiltration Capacity, snow‐atmosphere‐soil transfer, Land Ecosystem‐Atmosphere Feedback, Noah with Multiparameterization, and Community Land Model) were evaluated against 1 year snow water equivalent (SWE) data at 112 Snow Telemetry (SNOTEL) sites in the Colorado River Headwaters region and 4 year flux tower data at two AmeriFlux sites. All models captured the main characteristics of the seasonal SWE evolution fairly well at 112 SNOTEL sites. No single model performed the best to capture the combined features of the peak SWE, the timing of peak SWE, and the length of snow season. Evaluating only simulated SWE is deceiving and does not reveal critical deficiencies in models, because the models could produce similar SWE for starkly different reasons. Sensitivity experiments revealed that the models responded differently to variations of forest coverage. The treatment of snow albedo and its cascading effects on surface energy deficit, surface temperature, stability correction, and turbulent fluxes was a major intermodel discrepancy. Six LSMs substantially overestimated (underestimated) radiative flux (heat flux), a crucial deficiency in representing winter land‐atmosphere feedback in coupled weather and climate models. Results showed significant intermodel differences in snowmelt efficiency and sublimation efficiency, and models with high rate of snow accumulation and melt were able to reproduce the observed seasonal evolution of SWE. This study highlights that the parameterization of cascading effects of snow albedo and below‐canopy turbulence and radiation transfer is critical not only for SWE simulation but also for correctly capturing the winter land‐atmosphere interactions.</jats:p>

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