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  • 1
    Publication Date: 2014-04-23
    Description: Subgrid snow cover is one of the key parameters in global land models since snow cover has large impacts on the surface energy and moisture budgets, and hence the surface temperature. In this study, the Subgrid Snow Distribution (SSNOWD) snow cover parameterization was incorporated into the Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO) land surface model. SSNOWD assumes that the subgrid snow water equivalent (SWE) distribution follows a lognormal distribution function, and its parameters are physically derived from geoclimatic information. Two 29-yr global offline simulations, with and without SSNOWD, were performed while forced with the Japanese 25-yr Reanalysis (JRA-25) dataset combined with an observed precipitation dataset. The simulated spatial patterns of mean monthly snow cover fraction were compared with satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) observations. The snow cover fraction was improved by the inclusion of SSNOWD, particularly for the accumulation season and/or regions with relatively small amounts of snowfall; snow cover fraction was typically underestimated in the simulation without SSNOWD. In the Northern Hemisphere, the daily snow-covered area was validated using Interactive Multisensor Snow and Ice Mapping System (IMS) snow analysis datasets. In the simulation with SSNOWD, snow-covered area largely agreed with the IMS snow analysis and the seasonal cycle in the Northern Hemisphere was improved. This was because SSNOWD formulates the snow cover fraction differently for the accumulation season and ablation season, and represents the hysteresis of the snow cover fraction between different seasons. The effects of including SSNOWD on hydrological properties and snow mass were also examined.
    Print ISSN: 0894-8755
    Electronic ISSN: 1520-0442
    Topics: Geography , Geosciences , Physics
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  • 2
    Publication Date: 2009-04-01
    Description: The effect of vertical and time interpolations of external forcings on the accuracy of regional simulations is examined. Two different treatments of the forcings, one with conventional lateral boundary nudging and the other with spectral nudging, are studied. The main result is that the accuracy of the regional simulation increases very slowly as the number of forcing field levels increase when no spectral nudging is used. Thus, for better simulation, it is desirable to have as many forcing levels as possible. By contrast, spectral nudging improves the regional model simulation when reasonably large numbers of forcing field levels, at least up to nine levels, are given. The accuracy worsens drastically when the number of forcing levels is reduced to less than nine. To improve the simulation, in particular when the forcing field is given at a coarse vertical resolution and at lower time frequency, an incremental interpolation method is introduced. The incremental interpolation in the vertical direction significantly improves the regional simulation at all numbers of forcing field levels. The improvement is largest at very low vertical resolution. Incremental interpolation in time also works excellently, allowing the use of daily output for reasonably accurate downscaling. By using a combination of spectral nudging and incremental interpolation, it is possible to make a reasonably accurate downscaling from the forcing given daily at three–five levels in the vertical direction with low overhead. This considerably reduces the amount of data currently believed to be required to downscale global model integrations.
    Print ISSN: 0027-0644
    Electronic ISSN: 1520-0493
    Topics: Geography , Geosciences , Physics
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