Spatio-Temporal Variations in Carbon Isotope Discrimination Predicted by the JULES Land Surface Model.

Stable carbon isotopes in plants can help evaluate and improve the representation of carbon and water cycles in land-surface models, increasing confidence in projections of vegetation response to climate change. Here, we evaluated the predictive skills of the Joint UK Land Environmental Simulator (JULES) to capture spatio-temporal variations in carbon isotope discrimination (Δ13C) reconstructed by tree rings at 12 sites in the United Kingdom over the period 1979-2016. Modeled and measured Δ13C time series were compared at each site and their relationships with local climate investigated. Modeled Δ13C time series were significantly correlated (p < 0.05) with tree-ring Δ13C at eight sites, but JULES underestimated mean Δ13C values at all sites, by up to 2.6‰. Differences in mean Δ13C may result from post-photosynthetic isotopic fractionations that were not considered in JULES. Inter-annual variability in Δ13C was also underestimated by JULES at all sites. While modeled Δ13C typically increased over time across the UK, tree-ring Δ13C values increased only at five sites located in the northern regions but decreased at the southern-most sites. Considering all sites together, JULES captured the overall influence of environmental drivers on Δ13C but failed to capture the direction of change in Δ13C caused by air temperature, atmospheric CO2 and vapor pressure deficit at some sites. Results indicate that the representation of carbon-water coupling in JULES could be improved to reproduce both the trend and magnitude of interannual variability in isotopic records, the influence of local climate on Δ13C, and to reduce uncertainties in predicting vegetation-environment interactions.

Global decadal variability of plant carbon isotope discrimination and its link to gross primary production.

Carbon isotope discrimination (Δ13 C) in C3 woody plants is a key variable for the study of photosynthesis. Yet how Δ13 C varies at decadal scales, and across regions, and how it is related to gross primary production (GPP), are still incompletely understood. Here we address these questions by implementing a new Δ13 C modelling capability in the land-surface model JULES incorporating both photorespiratory and mesophyll-conductance fractionations. We test the ability of four leaf-internal CO2 concentration models embedded in JULES to reproduce leaf and tree-ring (TR) carbon isotopic data. We show that all the tested models tend to overestimate average Δ13 C values, and to underestimate interannual variability in Δ13 C. This is likely because they ignore the effects of soil water stress on stomatal behavior. Variations in post-photosynthetic isotopic fractionations across species, sites and years, may also partly explain the discrepancies between predicted and TR-derived Δ13 C values. Nonetheless, the "least-cost" (Prentice) model shows the lowest biases with the isotopic measurements, and lead to improved predictions of canopy-level carbon and water fluxes. Overall, modelled Δ13 C trends vary strongly between regions during the recent (1979-2016) historical period but stay nearly constant when averaged over the globe. Photorespiratory and mesophyll effects modulate the simulated global Δ13 C trend by 0.0015 ± 0.005‰ and -0.0006 ± 0.001‰ ppm-1 , respectively. These predictions contrast with previous findings based on atmospheric carbon isotope measurements. Predicted Δ13 C and GPP tend to be negatively correlated in wet-humid and cold regions, and in tropical African forests, but positively related elsewhere. The negative correlation between Δ13 C and GPP is partly due to the strong dominant influences of temperature on GPP and vapor pressure deficit on Δ13 C in those forests. Our results demonstrate that the combined analysis of Δ13 C and GPP can help understand the drivers of photosynthesis changes in different climatic regions.

Eco-evolutionary optimality as a means to improve vegetation and land-surface models.

Global 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.

Impacts of soil water stress on the acclimated stomatal limitation of photosynthesis: Insights from stable carbon isotope data.

Atmospheric aridity and drought both influence physiological function in plant leaves, but their relative contributions to changes in the ratio of leaf internal to ambient partial pressure of CO2 (χ) - an index of adjustments in both stomatal conductance and photosynthetic rate to environmental conditions - are difficult to disentangle. Many stomatal models predicting χ include the influence of only one of these drivers. In particular, the least-cost optimality hypothesis considers the effect of atmospheric demand for water on χ but does not predict how soils with reduced water further influence χ, potentially leading to an overestimation of χ under dry conditions. Here, we use a large network of stable carbon isotope measurements in C3 woody plants to examine the acclimated response of χ to soil water stress. We estimate the ratio of cost factors for carboxylation and transpiration (ß) expected from the theory to explain the variance in the data, and investigate the responses of ß (and thus χ) to soil water content and suction across seed plant groups, leaf phenological types and regions. Overall, ß decreases linearly with soil drying, implying that the cost of water transport along the soil-plant-atmosphere continuum increases as water available in the soil decreases. However, despite contrasting hydraulic strategies, the stomatal responses of angiosperms and gymnosperms to soil water tend to converge, consistent with the optimality theory. The prediction of ß as a simple, empirical function of soil water significantly improves χ predictions by up to 6.3 ± 2.3% (mean ± SD of adjusted-R2 ) over 1980-2018 and results in a reduction of around 2% of mean χ values across the globe. Our results highlight the importance of soil water status on stomatal functions and plant water-use efficiency, and suggest the implementation of trait-based hydraulic functions into the model to account for soil water stress.

Historical changes in the stomatal limitation of photosynthesis: empirical support for an optimality principle.

The ratio of leaf internal (ci ) to ambient (ca ) partial pressure of CO2 , defined here as χ, is an index of adjustments in both leaf stomatal conductance and photosynthetic rate to environmental conditions. Measurements and proxies of this ratio can be used to constrain vegetation model uncertainties for predicting terrestrial carbon uptake and water use. We test a theory based on the least-cost optimality hypothesis for modelling historical changes in χ over the 1951-2014 period, across different tree species and environmental conditions, as reconstructed from stable carbon isotopic measurements across a global network of 103 absolutely dated tree-ring chronologies. The theory predicts optimal χ as a function of air temperature, vapour pressure deficit, ca and atmospheric pressure. The theoretical model predicts 39% of the variance in χ values across sites and years, but underestimates the intersite variability in the reconstructed χ trends, resulting in only 8% of the variance in χ trends across years explained by the model. Overall, our results support theoretical predictions that variations in χ are tightly regulated by the four environmental drivers. They also suggest that explicitly accounting for the effects of plant-available soil water and other site-specific characteristics might improve the predictions.

Observed and modelled historical trends in the water-use efficiency of plants and ecosystems.

Plant water-use efficiency (WUE, the carbon gained through photosynthesis per unit of water lost through transpiration) is a tracer of the plant physiological controls on the exchange of water and carbon dioxide between terrestrial ecosystems and the atmosphere. At the leaf level, rising CO2 concentrations tend to increase carbon uptake (in the absence of other limitations) and to reduce stomatal conductance, both effects leading to an increase in leaf WUE. At the ecosystem level, indirect effects (e.g. increased leaf area index, soil water savings) may amplify or dampen the direct effect of CO2 . Thus, the extent to which changes in leaf WUE translate to changes at the ecosystem scale remains unclear. The differences in the magnitude of increase in leaf versus ecosystem WUE as reported by several studies are much larger than would be expected with current understanding of tree physiology and scaling, indicating unresolved issues. Moreover, current vegetation models produce inconsistent and often unrealistic magnitudes and patterns of variability in leaf and ecosystem WUE, calling for a better assessment of the underlying approaches. Here, we review the causes of variations in observed and modelled historical trends in WUE over the continuum of scales from leaf to ecosystem, including methodological issues, with the aim of elucidating the reasons for discrepancies observed within and across spatial scales. We emphasize that even though physiological responses to changing environmental drivers should be interpreted differently depending on the observational scale, there are large uncertainties in each data set which are often underestimated. Assumptions made by the vegetation models about the main processes influencing WUE strongly impact the modelled historical trends. We provide recommendations for improving long-term observation-based estimates of WUE that will better inform the representation of WUE in vegetation models.