The impact of vegetation structure on the absorption of shortwave radiation in Earth System Models (ESMs) is potentially important for accurate modeling of the carbon cycle and hence climate projections. A proportion of incident shortwave radiation is used by plants to photosynthesize and canopy structure has a direct impact on the fraction of this radiation which is absorbed. This paper evaluates how modeled carbon assimilation of the terrestrial biosphere is impacted when clumping derived from satellite data is incorporated. We evaluated impacts of clumping on photosynthesis using the Joint UK Land Environment Simulator, the land surface scheme of the UK Earth System Model. At the global level, Gross Primary Productivity (GPP) increased by 5.53 ± 1.02 PgC/year with the strongest absolute increase in the tropics. This is contrary to previous studies that have shown a decrease in photosynthesis when similar clumping data sets have been used to modify light interception in models. In our study additional transmission of light through upper canopy layers leads to enhanced absorption in lower layers in which photosynthesis tends to be light limited. We show that this result is related to the complexity of canopy scheme being used. Plain Language Summary Plants need sunlight to photosynthesize; however, the way in which light absorption is typically described by climate models is not very realistic because it does not take into account structural differences in forest canopies. Identifying more realistic ways to represent these processes in forests would allow us to better predict photosynthesis and to have a greater understanding of the impact of future climate change. In our paper we discuss a method to include information about vegetation structure derived from satellites in climate models. Our results indicate that such models underestimate the amount of light reaching plants in the lower layers of dense forests. Consequently, global photosynthesis is likely underestimated in climate models due to a lack of consideration of plant structural variability.