Concluding Discussion

In this chapter we demonstrate how snow albedo parameterizations can significantly affect modeled surface-atmosphere fluxes. Grain metamor-phism provides an important control on albedo and is directly affected by changes in snowpack energy balance. Furthermore, there is a positive feedback effect between grain size and energy balance whereby increased grain size causes decreased albedo and a subsequent increase in absorption of shortwave energy. Parameterization of snow albedo as a function of snow grain size includes the effects of energy balance changes on surface layer grain size. More simple constant albedo parameterizations can result in individual flux differences of nearly This is especially critical during the spring melt season, when increased sensitivity of AQ to the albedo parameterization directly affects the melt rate.

Moreover, similar estimates of AQ can be derived from both constant and variable albedo parameterizations even at times when the individual flux estimates are different. This is because the differences in estimated latent heat flux tend to be of opposite sign and similar magnitude to the differences in estimated sensible heat flux. These differences in estimates of the individual turbulent fluxes may be unimportant from the point of view of snow-pack energy balance modelers. However, from the perspective of the atmospheric modeler, the values of the individual fluxes are important because they are treated differently in the models. Therefore, an accurate representation of snowpack albedo is key to accurate computation of surface fluxes.

The accurate detection of snow cover is crucial for quantifying the impacts of snow cover dynamics on local, regional and hemispherical land surface-atmosphere interactions. Moreover, trends in the spatial extent of snow cover may be a key indicator climate change. Snow cover and snow albedo data are needed for accurate land surface representation in climate models as well as for climate model validation. There is currently no snow observational data set that provides all the mapping capabilities required by climate models. Historical products such as the NOAA weekly snow charts (dating back to 1972) and SMMR/SSM/I (dating back to 1978) provide longer term records of snowcover useful for climatological analyses. New products, although lacking in record length, provide improved and additional observations. A global snow albedo product from the MODIS instrument is planned to begin production in year 2001. Currently, the NISE product, derived from passive microwave data, maps global distributions of both dry and wet snow on a daily basis at a spatial resolution that is useful for climate models. Another product scheduled to begin production in 2001 will come from the AMSR-E instrument (also a passive microwave instrument). This product will map global daily snow water equivalent and snow covered area at 12.5 km spatial resolution. Climate models will benefit from improved snow parameterizations and observations and the incorporation of more sophisticated snow albedo parameterizations. In addition, remote sensing algorithm developers should consider the needs of the climate modeling community when designing new snow mapping products.

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