A multivariate comparison of two land-surface models integrated into an Arctic Regional Climate System model

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.