Abstract
Abstract. Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth system model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a GPP (photosynthesis per unit ground area) model, the P-model, that combines the Farquhar–von Caemmerer–Berry model for C3 photosynthesis with an optimality principle for the carbon assimilation–transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C3 vegetation type. The model builds on the theory developed in Prentice et al. (2014) and Wang et al. (2017a) and is extended to include low temperature effects on the intrinsic quantum yield and an empirical soil moisture stress factor. The model is forced with site-level data of the fraction of absorbed photosynthetically active radiation (fAPAR) and meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs, the R2 for predicted versus observed GPP based on the full model setup is 0.75 (8 d mean, 126 sites) – similar to comparable satellite-data-driven GPP models but without predefined vegetation-type-specific parameters. The R2 is reduced to 0.70 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R2 for the P-model-predicted LUE is 0.32 (means by site) and 0.48 (means by vegetation type). Applying this model for global-scale simulations yields a total global GPP of 106–122 Pg C yr−1 (mean of 2001–2011), depending on the fAPAR forcing data. The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosynthesis across a wide range of conditions. The model is available as an R package (rpmodel).
Highlights
Realistic, reliable, and robust estimates of terrestrial photosynthesis are required to understand variations in the carbon cycle, monitor forest and cropland productivity, and predict impacts of global environmental change on ecosystem function (Prentice et al, 2015)
Process-based dynamic vegetation models (DVMs) and Earth system models (ESMs) in use today almost always use some form of the Farquhar–von Caemmerer–Berry (FvCB) model for C3 photosynthesis (Farquhar et al, 1980; von Caemmerer and Farquhar, 1981), in combination with stomatal conductance models (Ball et al, 1987; Leuning, 1995; Medlyn et al, 2011) that couple water and carbon fluxes at the leaf surface
This is due to assumptions that have to be made about photosynthetic parameters that are not predicted by the FvCB model: stomatal conductance and the maximum rates of RuBisCO carboxylation (Vcmax) and electron transport (Jmax) for ribulose-1,5-bisphosphate (RuBP) regeneration, which together determine the relationship between ci and A
Summary
Reliable, and robust estimates of terrestrial photosynthesis are required to understand variations in the carbon cycle, monitor forest and cropland productivity, and predict impacts of global environmental change on ecosystem function (Prentice et al, 2015). The FvCB model is standard for leaf-scale photosynthesis and its environmental response on timescales of minutes to hours, DVMs and ESMs using FvCB produce divergent results for ecosystem-level fluxes and their response to the environment at longer timescales (Rogers et al, 2017). This is due to assumptions that have to be made about photosynthetic parameters that are not predicted by the FvCB model: stomatal conductance (gs) and the maximum rates of RuBisCO carboxylation (Vcmax) and electron transport (Jmax) for ribulose-1,5-bisphosphate (RuBP) regeneration, which together determine the relationship between ci and A. Common approaches for determining the values of Vcmax and Jmax in DVMs and ESMs are to prescribe fixed values per plant functional type (PFT) and attempt to simulate the distribution of PFTs in space, or to use empirical relationships between leaf N and Vcmax and simulate leaf N internally or prescribe it per PFT (Smith and Dukes, 2013; Rogers, 2014)
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