Underestimation of Global Photosynthesis in Earth System Models Due to Representation of Vegetation Structure

Abstract

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.

Mycorrhizal distributions impact global patterns of carbon and nutrient cycling

“…The parameters associated with each PFT is determined or inferred from observable characteristics of the land surface following different land types. A spatial data product can be added as a 2D variable varying as function of latitude and longitude, but because land surface models usually work with the concept of PFTs, adding a third spatial dimension (i.e., PFT) can represent within grid cell heterogeneity and improve accuracy of land surface processes, as well as reduce model uncertainty (Braghiere et al, 2019). Here, given new spatial distributions of mycorrhizal associations based on observations at different spatial resolutions, we modified CLM5 and added mycorrhizal association types per PFT within each gridcell.…”

Impacts of Degradation on Water, Energy, and Carbon Cycling of the Amazon Tropical Forests

“…In the past, big‐leaf models have been modified to account for the long‐term impacts of selectively logged tropical forests on the carbon cycle of tropical forests (e.g., Huang & Asner, 2010; Huang et al., 2008). However, big‐leaf models generally do not represent the mechanisms that control access and availability of light and water in complex and heterogeneous forest structures (Fisher et al., 2018; D. Purves & Pacala, 2008) (but see Braghiere et al., 2019). Individual‐based models can represent the changes in the population structure and microenvironments due to degradation (R. Fischer et al., 2016; Maréchaux & Chave, 2017), but the complexity and computational burden of these simulations often limit their application to single sites.…”

Testing stomatal models at stand level in deciduous angiosperm and evergreen gymnosperm forests using CliMA Land (v0.1)

“…The second difference was that we accounted for the bidirectional reflectance distribution function effect of canopy horizontal structure by incorporating a clumping index (CI; Braghiere et al, 2021). As CI impacts the effective leaf area index (eLAI for effective value, and LAI for the true value) of an open canopy (Pinty et al, 2006;Braghiere et al, 2019Braghiere et al, , 2020: eLAI = LAI • CI, we used eLAI in our model, whereas the original mSCOPE used LAI. When CI = 1, leaves are uniformly distributed in the horizontal; when CI < 1, there are gaps between and within clusters of leaves for each tree.…”