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Process-based carbon dynamic models are rarely validated against traditional forest growth and yield data and are difficult to use as a practical tool for forest management. To bridge the gap between empirical and process-based models, a simulation using a hybrid model of TRIPLEX1.0 was performed for the forest growth and yield of the boreal forest ecosystem in the Lake Abitibi Model Forest in northeastern Ontario. The model was tested using field measurements, forest inventory data, and the normal yield table. The model simulations of tree height and diameter at breast height (DBH) showed a good agreement with measurements for black spruce (Picea mariana (Mill.) BSP), jack pine (Pinus banksiana Lamb.), and trembling aspen (Populus tremuloides Michx.). The coefficients of determination (R 2 ) between simulated values and permanent sample plot measurements were 0.92 for height and 0.95 for DBH. At the landscape scale, model predictions were compared with forest inventory data and the normal yield table. The R 2 ranged from 0.73 to 0.89 for tree height and from 0.72 to 0.85 for DBH. The simulated basal area is consistent with the normal yield table. The R 2 for basal area ranged from 0.82 to 0.96 for black spruce, jack pine, and trembling aspen for each site class. This study demonstrated the feasibility of testing the performance of the process-based carbon dynamic model using traditional forest growth and yield data and the ability of the TRIPLEX1.0 model for predicting growth and yield variables. The current work also introduces a means to test model accuracy and its prediction of forest stand variables to provide a complement to empirical growth and yield models for forest management practices, as well as for investigating climate change impacts on forest growth and yield in regions without sufficient established permanent sample plots and remote areas without suitable field measurements.
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Abstract The role and impact that boundary layer and shallow cumulus clouds have on the medium-range forecast of a large-scale weather system is discussed in this study. A mesoscale version of the Global Environmental Multiscale (GEM) atmospheric model is used to produce a 5-day numerical forecast of a midlatitude large-scale weather system that occurred over the Pacific Ocean during February 2003. In this version of GEM, four different schemes are used to represent (i) boundary layer clouds (including stratus, stratocumulus, and small cumulus clouds), (ii) shallow cumulus clouds (overshooting cumulus), (iii) deep convection, and (iv) nonconvective clouds. Two of these schemes, that is, the so-called MoisTKE and Kuo Transient schemes for boundary layer and overshooting cumulus clouds, respectively, have been recently introduced in GEM and are described in more detail. The results show that GEM, with this new cloud package, is able to represent the wide variety of clouds observed in association with the large-scale weather system. In particular, it is found that the Kuo Transient scheme is mostly responsible for the shallow/intermediate cumulus clouds in the rear portion of the large-scale system, whereas MoisTKE produces the low-level stratocumulus clouds ahead of the system. Several diagnostics for the rear portion of the system reveal that the role of MoisTKE is mainly to increase the vertical transport (diffusion) associated with the boundary layer clouds, while Kuo Transient is acting in a manner more consistent with convective stabilization. As a consequence, MoisTKE is not able to remove the low-level shallow cloud layer that is incorrectly produced by the GEM nonconvective condensation scheme. Kuo Transient, in contrast, led to a significant reduction of these nonconvective clouds, in better agreement with satellite observations. This improved representation of stratocumulus and cumulus clouds does not have a large impact on the overall sea level pressure patterns of the large-scale weather system. Precipitation in the rear portion of the system, however, is found to be smoother when MoisTKE is used, and significantly less when the Kuo Transient scheme is switched on.