Comparing the performance of the Canadian land surface scheme @class) for two subarctic terrain types

• Published in: Atmosphere-ocean Vol. 38; no. 1; pp. 181 - 204
• Main Authors: Bellisario, Lianne M., Boudreau, L. Dale, Verseghy, Diana L., Rouse, Wayne R., Blanken, Peter D.
• Format: Journal Article
• Language: English
• Published: Taylor & Francis Group 01.03.2000
• ISSN: 0705-5900; 1480-9214
• DOI: 10.1080/07055900.2000.9649645

The Canadian Land Surface Scheme (CLASS) is tested for two major terrain types found in the northern Hudson Bay Lowland. Soil temperature and energy balance measurements for sedge fen wetland and dwarf willow-birch forest near Churchill, Manitoba, are compared with simulations both with and without the new organic soil parametrization that has been developed for CLASS (Letts et al., 2000), for eight datasets spanning six years (1990 through 1995). With the exception of the sensible heat flux at the sedge site, the new version of CLASS with the organic soil parametrization improves the energy budget simulation at each of the research sites. Both the latent (Q E ) and ground (Q G ) heat flux were modelled well; however, some modifications were required to simulate the continued evaporation from these sites once the water table receded below the first soil layer. The sensible heat flux (Q H ) was the least well simulated component of the energy balance in both versions. Temperatures for the top two soil layers were consistently overestimated by the mineral soil parametrization for both terrain types, whereas the organic soil parametrization showed significant improvement. The new water table diagnostic algorithm in the organic soil version satisfactorily estimates the position of the water table, even under a large range of climatic conditions. The inclusion of organic soil parameters in CLASS, and the subsequent improved handling of soil moisture, is a significant contribution to model development, and provides a physically-based capability for simulating northern peatlands within land surface models.