An assessment of geographical distribution of different plant functional types over North America simulated using the CLASS-CTEM modelling framework

Abstract

<p><strong>Abstract.</strong> The performance of the competition module of the CLASS-CTEM (Canadian Land Surface Scheme and Canadian Terrestrial Ecosystem Model) modelling framework is assessed at 1° spatial resolution over North America by comparing the simulated geographical distribution of plant functional types (PFTs) with two observation-based estimates. The model successfully reproduces the broad geographical distribution of trees, grasses and bare ground although limitations remain. In particular, compared to the two observation-based estimates, the simulated fractional vegetation coverage is lower in the arid south-west North American region and higher in the Arctic region. The lower than observed simulated vegetation coverage in the south-west region is attributed to lack of representation of shrubs in the model and plausible errors in the observation-based data sets. The observation-based data indicates vegetation fractional coverage of more than 60 % in this arid region, despite only 200–300 mm of precipitation that the region receives annually and observation-based leaf area index (LAI) in the region are lower than one. The higher than observed vegetation fractional coverage in the Arctic is due to the lack of representation of moss and lichen PFTs and also likely because of inadequate representation of permafrost in the model as a result of which the C<sub>3</sub> grass PFT performs overly well in the region. The model generally reproduces the broad spatial distribution and the total area covered by the two primary tree PFTs (needleleaf evergreen and broadleaf cold deciduous trees) reasonably well. The simulated fractional coverage of tree PFTs increases after 1960s in response to the CO<sub>2</sub> fertilization effect and climate warming. Differences between observed and simulated PFT coverages highlight limitations in the model and provide insight into physical and structural processes that need improvement.</p>