Monday, 10 January 2000: 11:15 AM
Although detailed process-oriented snow models exist, general circulation
models (GCMs) use relatively simple schemes for computational considerations.
Therefore it is critical to assess their simulations for two related reasons.
First, GCM results have been used to study climate change and water resource
issues. Second, GCMs are known to be highly sensitive to snow processes.
Two widely-used land-surface models are selected in this study, namely,
the Biosphere-Atmosphere Transfer Scheme (BATS) and the NCAR Land
Surface Model (LSM), each including a snow submodel. Both BATS and LSM have
been coupled to the NCAR CCM3, and long-term integrations (more than 10 years)
are available for analyses. For both North America and Eurasia, LSM-CCM3
produces less snow mass than BATS-CCM3 during the snow season, which generally
coincides with more absorbed solar radiation, higher 2-m air temperature, less snowfall, and smaller latent
heat flux in LSM-CCM3. In terms of continental averages, the snow mass is
more accurately simulated in BATS than in LSM during the accumulation period.
Because LSM underestimates peak snow mass, its ablation rate appears to agree
with observations, while the BATS melting rate is slower than observed. Both
models predict snow depth greater than the estimates from the Nimbus-7 Scanning
Multichannel Microwave Radiometer (SMMR). Some of the differences in the snow
simulations between BATS and LSM can be attributed to differences in
precipitation, surface air temperature, and the surface energy balance terms,
resulting from the fully-coupled land surface-atmosphere interactions. To
filter out this feedback effect, an intercomparison of both land models is
made in off-line mode (i.e. uncoupled to the CCM3) using the long-term snow
cover and meteorological data from a grassland catchment at the Valdai
water-balance research site in Russia. I will first discuss results from the
models in their original structure, and then show the impacts from using
different methods of layering, albedo and areal snow cover extent. The
off-line runs confirm that LSM predicts less snow mass than BATS, mainly due
to two reasons. First, the iterative surface energy balance in LSM tends to
predict higher surface temperature and greater snow melt. Second, the albedo
parameterization in LSM generally leads to lower albedo values than in BATS.
Guided by our recent survey of the existing snow models
(http://www.atmo.arizona.edu/~zly/snow.html), a versatile multi-layer snow
model is being developed to address the level of complexity required in GCM
snow modeling.
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