Echam4

The SCF parameterization is analyzed in the framework of the ECHAM4 GCM of the Max Planck Institute for Meteorology, Hamburg. The structure of this GCM is described in detail by Roeckner et al. (1992) and Roeckner et al. (1996). It has evolved from the spectral numerical weather forecasting model of the European Centre for Medium-Range Weather Forecasts, and has been extensively modified for climate applications. For the present study, the processes affecting the surface albedo are particularly interesting and are summarized below.

In ECHAM4, the background surface albedo asb for every grid element is constant in time. asb is calculated on the basis of three blended data sets as described in Claussen et al. (1994). In snow covered regions, the surface albedo is modified according to where as is the snow albedo and asb the background albedo. The snow cover fraction fsis calculated according to Equation 2:

where is the water equivalent of snow in metres and is the critical snow depth (=0.01 m). The albedo of snow and ice covered surfaces as is a function of the surface type (ts), surface temperature (Tg), and the fractional forest area over land. A maximum value is assumed for temperatures below 263.15 K and a minimum value asmjn for temperatures above the freezing point. Snow albedo for a surface temperature between To = and is obtained by linear interpolation:

where asmjn and asmax are as given in Table 1 and further depend on the forest fraction as follows:

taking into account the fraction of the grid-cell covered with forest with and as given in Table 1.

Table 1. Minimal (amjn) and maximal (amax) surface albedos used in Eq. 4. af is the fractional forest area

surface type

asmin

asmax

sea ice

0.5

0.75

land ice

0.6

0.8

snow on land af=0

0.4

0.8

snow on land af=l

0.3

0.4

2.2 Experimental design

All model simulations are performed with the 3-dimensional ECHAM4 GCM at T42 resolution and were carried out at the Swiss Scientific Computing Center (CSCS) in Manno, Switzerland, on a NEC SX4 vector computer. Initialization is made by using atmospheric data from the ECMWF analysis. Sea surface temperature and sea-ice coverage are prescribed from the atmospheric intercomparison project (AMIP) dataset (Gates, 1992).Each simulation covers a period of eleven years and three months. Since these simulations require a spin-up time of about one year in order to reach an equilibrium climate, the first 15 months were discarded. Therefore, all climatological means refer to a ten-year period.

3. DATA

3.1 Snow water depth

In this study, the model outputs are validated against the global snow depth (SDH) climatology of the U.S. Air Force Environmental Technical Application Center (USAF/ ETAC) as documented in Foster and Davy (1988). This data set provides a mid-monthly mean SDH climatology with the highest spatial resolution (1° x 1° equal-angle grid) currently available, which was compiled from a comprehensive set of station data for the months of September through to June. The USAF data is generally considered to be one of the most reliable and accurate snow depth climatologies available (Douville et al., 1995b) and is used in several studies dealing with the validation of snow models (Douville et al., 1995b; Marshall et al., 1994; Foster et al., 1996). Throughout the United States, Canada and Eurasia, there is high confidence in the observations, as they generally contain more than 5 years of data and are of good coverage. In areas of sparse data coverage, SDHs are estimated using precipitation and satellite analyses of snow extent.

These areas are generally assumed to have a lower confidence level (e.g., Arctic and Antarctica).

3.2 Snow cover fraction

Northern hemispheric monthly SCF data is averaged from weekly values of the "weekly digital Northern Hemisphere snow and ice product" compiled by the National Oceanic and Atmospheric Administration (NOAA) and National Environmental Satellite, Data and Information Service (NESDIS) from 1973 - 1996. NOAA charts are based on a visual interpretation of photographic copies of visible satellite imagery by trained meteorologists. The data are given on a regular 1° x l°-grid. In general, the NOAA charts are considered to be the most accurate means of obtaining snow extent information on large regional to hemisphere scales. Furthermore, they comprise the longest satellite-based record available and has been intensively used in former studies (Gutzler and Rosen, 1992; Iwasaki, 1991; Kukla and Robinson, 1981; Masuda etal., 1993; Robinson etal., 1993).

The principal shortcomings in using visible satellite imagery to chart snow cover are (i) the inability to detect snow cover when solar radiation is small, (ii) difficulties in discriminating snow from clouds, and (iii) the underestimation of snow cover where dense forests mask the underlying snow. Moreover, problems arise when the snow cover is unstable or rapid changes occur. These deficiencies should be taken into account when interpreting the results in later sections.

3.3 Albedo data

The Surface Radiation Budget (SRB, Whitlock et al., 1995) dataset provides incoming and reflected shortwave radiation and thus albedo at the surface. In order to derive surface radiation fluxes and surface albedo from top-of-atmosphere (TOA) radiation fluxes, the algorithm developed by Staylor (Darnell et al., 1992) at the NASA Langley Research Center is used. The SRB dataset is computed from data which covers the period 1984 -1990. They are given on the International Satellite Cloud Climatology Project (ISCCP) equal-area grid which comprises 6596 gridboxes. Close to the equator,its resolution is2.5°x2.5°.

The comparison between simulated and observed data requires the interpolation from a 1° x l°-grid (snow water equivalent and snow cover fraction) and from the ISCCP-grid (surface albedo) on the T42 grid or T106 grid. The necessary interpolations are performed using an area-weighted interpolation. Reliable results are expected for small-scale grids while the ISCCP-grid raises some problems due to coarse resolution, primarily in the higher latitudes. This should be considered when comparing modelled and observed surface albedos in northern Eurasia or Canada/Alaska.

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