Different vegetation models impact the atmospheric response of a regional
climate model in different ways, and hence have an impact upon the ability
of that model to match an observed climatology. Using a multivariate
principal-component analysis, we investigate the relationships between
several land-surface models (BATS, LSM) coupled to a regional climate model,
and observed climate parameters over the North Slope of Alaska. In this
application, annual cycle simulations at 20 km spatial resolution are
compared with European Centre for Medium-Range Weather Forecasts (ECMWF)
climatology. Initial results demonstrate broad agreement between all models;
however, small-scale regional variations between land-surface models
indicate the strengths and weaknesses of the land-surface treatments in a
climate system model. Specifically, we found that the greater
surface-moisture availability and temperature-dependent albedo formulation
of the LSM model allow for a higher proportion of low-level cloud, and a
later, more rapid transition from the winter to the summer regime. Crucial
to this transition is the seasonal cycle of incoming solar radiation. These
preliminary results indicate the importance of the land-surface hydrologic
cycle in modelling the seasonal transitions.