Ecosystem Photosynthesis in Land‐Surface Models: A First‐Principles Approach Incorporating Acclimation. (13th January 2022)

• Record Type: Journal Article
• Authors: Mengoli, Giulia; Agustí‐Panareda, Anna; Boussetta, Souhail; Harrison, Sandy P.; Trotta, Carlo; Prentice, I. Colin
• Is Part Of: Journal of advances in modeling earth systems. Volume 14:Number 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-01-13
• Subjects: optimality‐based model -- acclimation -- sub‐daily simulations -- PFT‐independent -- gross primary production -- land surface model; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2021MS002767
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: Vegetation regulates land‐atmosphere, water, and energy exchanges and is an essential component of land‐surface models (LSMs). However, LSMs have been handicapped by assumptions that equate acclimated photosynthetic responses to the environment with the fast responses observable in the laboratory. The effects of acclimation can be taken into account by including PFT‐specific values of photosynthetic parameters, but at the cost of increasing parameter requirements. Here, we develop an alternative approach for including acclimation in LSMs by adopting the P model, an existing light‐use efficiency model for gross primary production (GPP) that implicitly predicts the acclimation of photosynthetic parameters on a weekly to monthly timescale via optimality principles. We demonstrate that it is possible to explicitly separate the fast and slow photosynthetic responses to environmental conditions, allowing the simulation of GPP at the sub‐daily timesteps required for coupling in an LSM. The resulting model reproduces the diurnal cycles of GPP recorded by eddy‐covariance flux towers in a temperate grassland and boreal, temperate and tropical forests. The best performance is achieved when biochemical capacities are adjusted to match recent midday conditions. Comparison between this model and the operational LSM in the European Centre for Medium‐range Weather Forecasts climate model shows that the new model has better predictive power in most of the sites and years analyzed, particularly in summer and autumn. Our analyses suggest a simple and parameter‐sparse method to include both instantaneous and acclimated responses within an LSM framework, with potential applications in weather, climate, and carbon‐cycle modeling. Plain Language Summary: Vegetation regulates the exchanges of energy, water, and carbon dioxide between the land and the atmosphere. Numerical climate models represent these processes, focusing mainly on their rapid variations in response to changes in the environment (including temperature and light) on timescales of seconds to hours. However, plants also adjust their physiology to environmental changes over longer periods within the season. Here, we have adapted a simple model that formulates plant behavior in terms of optimal trade‐offs between different processes, so it simulates processes on both timescales. This model correctly reproduces the daily cycle of carbon dioxide uptake by plants, as recorded in different kinds of vegetation. We show that plants optimize their behavior for midday conditions, when the light is greatest, and adjust to longer‐term environmental variations on a timescale of a week to a month. The model conveniently avoids the need to give specific, fixed values to physiological variables (such as photosynthetic capacity) for different types of plants. The optimality assumptions mean that the model gives equally good results in tropical, temperate, and boreal forests, and in grasslands, using the same equations, and a very small number of input variables. Key Points: Optimality theory is used to develop a model incorporating fast and acclimated responses of photosynthesis and stomatal conductance Biogeochemical photosynthetic capacities adjust to midday light conditions The new parameter‐sparse model simulates gross primary production on sub‐daily timesteps across a range of vegetation types and climates