• Energy Research

AbstractRealistic simulation of leaf photosynthetic and respiratory processes is needed for accurate prediction of the global carbon cycle. These two processes systematically acclimate to long‐term environmental changes by adjusting photosynthetic and respiratory traits (e.g., the maximum photosynthetic capacity at 25°C (Vcmax,25) and the leaf respiration rate at 25°C (R25)) following increasingly well‐understood principles. While some land surface models (LSMs) now account for thermal acclimation, they do so by assigning empirical parameterizations for individual plant functional types (PFTs). Here, we have implemented an Eco‐Evolutionary Optimality (EEO)‐based scheme to represent the universal acclimation of photosynthesis and leaf respiration to multiple environmental effects, and that therefore requires no PFT‐specific parameterizations, in a standard version of the widely used LSM, Noah MP. We evaluated model performance with plant trait data from a 5‐year experiment and extensive global field measurements, and carbon flux measurements from FLUXNET2015. We show that observed R25 and Vcmax,25 vary substantially both temporally and spatially within the same PFT (C.V. >20%). Our EEO‐based scheme captures 62% of the temporal and 70% of the spatial variations in Vcmax,25 (73% and 54% of the variations in R25). The standard scheme underestimates gross primary production by 10% versus 2% for the EEO‐based scheme and generates a larger spread in r (correlation coefficient) across flux sites (0.79 ± 0.16 vs. 0.84 ± 0.1, mean ± S.D.). The standard scheme greatly overestimates canopy respiration (bias: ∼200% vs. 8% for the EEO scheme), resulting in less CO2 uptake by terrestrial ecosystems. Our approach thus simulates climate‐carbon coupling more realistically, with fewer parameters.

ABSTRACTThe coupling between carbon uptake and water loss through stomata implies that gross primary production (GPP) can be limited by soil water availability through reduced leaf area and/or stomatal conductance. Ecosystem and land‐surface models commonly assume that GPP is highest under well‐watered conditions and apply a stress function to reduce GPP as soil moisture declines. Optimality considerations, however, suggest that the stress function should depend on climatic aridity: ecosystems adapted to more arid climates should use water more conservatively when soil moisture is high, but maintain unchanged GPP down to a lower critical soil‐moisture threshold. We use eddy‐covariance flux data to test this hypothesis. We investigate how the light‐use efficiency (LUE) of GPP depends on soil moisture across ecosystems representing a wide range of climatic aridity. ‘Well‐watered’ GPP is estimated using the sub‐daily P model, a first‐principles LUE model driven by atmospheric data and remotely sensed vegetation cover. Breakpoint regression is used to relate daily β(θ) (the ratio of flux data–derived GPP to modelled well‐watered GPP) to soil moisture estimated via a generic water balance model. The resulting piecewise function describing β(θ) varies with aridity, as hypothesised. Unstressed LUE, even when soil moisture is high, declines with increasing aridity index (AI). So does the critical soil‐moisture threshold. Moreover, for any AI value, there exists a soil moisture level at which β(θ) is maximised. This level declines as AI increases. This behaviour is captured by universal non‐linear functions relating both unstressed LUE and the critical soil‐moisture threshold to AI. Applying these aridity‐based functions to predict the site‐level response of LUE to soil moisture substantially improves GPP simulation under both water‐stressed and unstressed conditions, suggesting a route towards a robust, universal model representation of the effects of low soil moisture on leaf‐level photosynthesis.

SummaryGlobal vegetation and land‐surface models embody interdisciplinary scientific understanding of the behaviour of plants and ecosystems, and are indispensable to project the impacts of environmental change on vegetation and the interactions between vegetation and climate. However, systematic errors and persistently large differences among carbon and water cycle projections by different models highlight the limitations of current process formulations. In this review, focusing on core plant functions in the terrestrial carbon and water cycles, we show how unifying hypotheses derived from eco‐evolutionary optimality (EEO) principles can provide novel, parameter‐sparse representations of plant and vegetation processes. We present case studies that demonstrate how EEO generates parsimonious representations of core, leaf‐level processes that are individually testable and supported by evidence. EEO approaches to photosynthesis and primary production, dark respiration and stomatal behaviour are ripe for implementation in global models. EEO approaches to other important traits, including the leaf economics spectrum and applications of EEO at the community level are active research areas. Independently tested modules emerging from EEO studies could profitably be integrated into modelling frameworks that account for the multiple time scales on which plants and plant communities adjust to environmental change.

Summary Leaf dark respiration (Rd) acclimates to environmental changes. However, the magnitude, controls and time scales of acclimation remain unclear and are inconsistently treated in ecosystem models. We hypothesized that Rd and Rubisco carboxylation capacity (Vcmax) at 25°C (Rd,25, Vcmax,25) are coordinated so that Rd,25 variations support Vcmax,25 at a level allowing full light use, with Vcmax,25 reflecting daytime conditions (for photosynthesis), and Rd,25/Vcmax,25 reflecting night‐time conditions (for starch degradation and sucrose export). We tested this hypothesis temporally using a 5‐yr warming experiment, and spatially using an extensive field‐measurement data set. We compared the results to three published alternatives: Rd,25 declines linearly with daily average prior temperature; Rd at average prior night temperatures tends towards a constant value; and Rd,25/Vcmax,25 is constant. Our hypothesis accounted for more variation in observed Rd,25 over time (R2 = 0.74) and space (R2 = 0.68) than the alternatives. Night‐time temperature dominated the seasonal time‐course of Rd, with an apparent response time scale of c. 2 wk. Vcmax dominated the spatial patterns. Our acclimation hypothesis results in a smaller increase in global Rd in response to rising CO2 and warming than is projected by the two of three alternative hypotheses, and by current models.