An empirical model calibration approach is presented that aims to approximate missing biosphere processes in a global land surface model without the need for substantial model structural changes. The strategy is implemented here using the NASA Catchment‐CN land surface model and Moderate Resolution Imaging Spectroradiometer (MODIS) observations of the fraction of absorbed photosynthetically active radiation (FPAR). Existing plant functional types (PFTs) of the Catchment‐CN model are divided into three subtypes, based on the bias between the model‐simulated and MODIS‐observed FPAR. Separate sets of vegetation parameters for each subtype are then calibrated at a small number of grid cells with homogeneous, single‐PFT land cover, using MODIS FPAR reference observations from 2003 to 2009. The effectiveness of the empirical approach at improving the realism of modeled vegetation dynamics is investigated with two global model simulations for the period 2010–2016, one using the newly calibrated parameter values and the other using the original values. Globally, the calibrated parameters reduce the root mean square error (RMSE) of the modeled FPAR with respect to MODIS by 0.029 (～10%) on average. In some regions, substantially larger RMSE reductions are achieved. RMSE reductions are primarily driven by model bias reductions, with neutral effects on the temporal correlation skill. While the empirical approach is suitable for achieving consistent model improvements, it is shown to be sensitive to the characteristics of the model error, specifically a dominance of the bias component in the case of Catchment‐CN. Ultimately, more fundamental model structural changes may be required to achieve better improvements. Plain Language Summary Plants impact the exchange of water, energy, and carbon between the land surface and the atmosphere and are thus one of the key factors controlling land‐atmosphere interactions. Because vegetation also evolves more slowly than the atmosphere, being able to correctly model vegetation activity is important to make accurate predictions of atmospheric behavior, for example, for weather or seasonal forecasts. This study presents an approach to introduce more vegetation types in a land surface model and to use satellite observations of vegetation activity to calibrate the parameters that describe the behavior of each vegetation type. We show that using this approach results in better model simulations of vegetation activity globally compared with observations. We also show that changing the vegetation has wide‐reaching consequences on other model components, including the water and carbon cycle at the land‐atmosphere boundary.
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