AFAllan Frei

Allan Frei

*This is an algorithmically generated author record based on publicly available information.

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The organizations/institutes from the scholar's latest article

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Allan Frei has a total of 2 co-authors. Here are the top co-authors by number of shared publications:

  • David A. Robinson

    Department of Geography State University of New Jersey–Rutgers Piscataway New Jersey USA

    1 shared publication
  • James A. Miller

    National Snow and Ice Data Center, Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder Colorado USA

    1 shared publication

Latest Publications

  • Open Access

    Improved simulations of snow extent in the second phase of the Atmospheric Model Intercomparison Project (AMIP‐2)

    by Allan Frei, James A. Miller, David A. Robinson

    Simulations of snow‐covered area (SCA) over Northern Hemisphere lands by a suite of general circulation models (GCMs) are evaluated. Results from GCM experiments submitted by an international array of research groups participating in the second phase of the Atmospheric Model Intercomparison Project (AMIP‐2) are compared to a data set derived primarily from visible band satellite imagery provided by the United States National Oceanic and Atmospheric Administration. At continental to hemispheric scales we find improvements over AMIP‐1 models, including the elimination of temporal and spatial biases in simulations of the seasonal cycle of SCA, as well as improved simulations of the magnitude of interannual variability. At regional spatial scales, while no consistent model biases are identified over North America, regions over Eurasia are identified where models consistently either underestimate or overestimate SCA at the southern boundary of the seasonal snowpack. The region of greatest model bias is eastern Asia. While SCA biases are associated with temperature and precipitation biases, over only one region do we find a relationship between the magnitudes of SCA biases and the magnitudes of temperature and/or precipitation biases.