Growth and yield models are an essential tool for predicting the long-term response of forests to management and disturbance. Model evaluation and calibration are challenging, however, given data limitations for observing stand structural changes with age across heterogeneous forest landscapes. Here we present an approach for calibrating the lodgepole pine (LP) forest model in the Central Rockies variant of the Forest Vegetation Simulator (FVS-CR) using Forest Inventory and Analysis (FIA) plot data from the US Forest Service. Previous evaluation showed the FVS-CR LP model is generally successful in reproducing known patterns of stand dynamics. However, the default model settings tend to result in unrealistic stand successional behavior and over-estimate the density, basal area, and especially carbon density of mature lodgepole pine forest stands as compared to expectations. Here we develop a generalized model calibration procedure based on simulating bare-ground re-growth of a single mixed lodgepole stand and comparing to a forest growth chronosequence constructed from FIA plot measurements in Colorado and Wyoming. We set parameters such as basal area increment and maximum tree size to match corresponding characteristics observed directly in the FIA plot data. The remaining ‘free’ parameters (notably small tree height increment and tree mortality parameters) were then tuned for best fit against the FIA chronosequence in terms of five different stand characteristics: live and dead basal area, trees per hectare, quadratic mean diameter, and average height. Improving model fit against all five stand characteristics simultaneously required substantial increases to mortality-related parameters, particularly for the shade-tolerant species present in mixed stands. These parameters had to be adjusted empirically rather than based on literature values, suggesting some underlying model structural challenges. However, the resulting recalibrated FVS-CR LP model achieves much better representation of expected lodgepole stand structure and successional behavior, and can more credibly be used to evaluate carbon storage outcomes for different forest management choices in the region.