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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.

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.

Carbon isotope discrimination (Δ13C) in C3 woody plants is a key variable for the study of photosynthesis. Yet how Δ13C 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 Δ13C 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 Δ13C values, and to underestimate interannual variability in Δ13C. 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 Δ13C 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 Δ13C 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 Δ13C 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 Δ13C 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 Δ13C and GPP is partly due to the strong dominant influences of temperature on GPP and vapor pressure deficit on Δ13C in those forests. Our results demonstrate that the combined analysis of Δ13C and GPP can help understand the drivers of photosynthesis changes in different climatic regions. [ABSTRACT FROM AUTHOR]

The representation of snow processes in forest growth models is necessary to accurately predict the hydrological cycle in boreal ecosystems and the isotopic signature of soil water extracted by trees, photosynthates and tree-ring cellulose. Yet, most process-based models do not include a snow module, consequently their simulations may be biased in cold environments. Here, we modified the MAIDENiso model to incorporate a new snow module that simulates snow accumulation, melting and sublimation, as well as thermal exchanges driving freezing and thawing of the snow and the soil. We tested these implementations in two sites in East and West Canada for black spruce (Picea mariana) and white spruce (Picea glauca) forests, respectively. The new snow module improves the skills of the model to predict components of the hydrological cycle. The model is now able to reproduce the spring discharge peak and to simulate stable oxygen isotopes in tree-ring cellulose more realistically than in the original, snow-free version of the model. The new implementation also results in simulations with a higher contribution from the source water on the oxygen isotopic composition of the simulated cellulose, leading to more accurate estimates. Future work may include the development of inverse modelling with the new version of MAIDENiso to produce robust reconstructions of the hydrological cycle and isotope processes in cold environments. [ABSTRACT FROM AUTHOR]

Abstract: Estimating summer temperature fluctuations over long timescales in southern South America is essential for better understanding the past climate variations in the Southern Hemisphere. Here we developed robust 212 year long basal area increment (BAI) and δ13C chronologies from living temperature‐sensitive Fitzroya cupressoides on the eastern slope of the northern Patagonian Andes (41°S). After removing the increasing trend from the growth records likely due to the CO2 fertilization effect, we tested the potential to reconstruct past summer temperature variations using BAI and δ13C as predictors. The reconstruction based on δ13C records has the strongest predictive skills and explains as much as 62% of the total variance in instrumental summer temperature (n = 81, p < 0.001). The temperature signal recorded in tree‐ring growth is not substantially different to that present in δ13C and consequently does not provide additional information to improve the regression models. Our δ13C‐based reconstruction shows cold summer temperatures in the second part of the 19th century and in the mid‐20th century followed by a warmer period. Notably, the 20th and the early 21st centuries were warmer (+0.6°C) than the 19th century. Reconstructed summer temperature variations are modulated by low‐latitude (El Niño–Southern Oscillation) and high‐latitude (Southern Annular Mode) climate forcings. Our reconstruction based on δ13C agrees well with previous ring width based temperature reconstructions in the region and comparatively enhances the low‐frequency variations in the records. The present study provides the first reconstruction of summer temperature in South America south of 40°S for the period 1800–2011 entirely based on isotopic records. [ABSTRACT FROM AUTHOR]

Oxygen isotopes in tree rings (δ18OTR) are widely used to reconstruct past climates. However, the complexity of climatic and biological processes controlling isotopic fractionation is not yet fully understood. Here, we use the MAIDENiso model to decipher the variability in δ18OTR of two temperature-sensitive species of relevant palaeoclimatological interest (Picea mariana and Nothofagus pumilio) and growing at cold high latitudes in North and South America. In this first modelling study on δ18OTR values in both northeastern Canada (53.86° N) and western Argentina (41.10° S), we specifically aim at (1) evaluating the predictive skill of MAIDENiso to simulate δ18OTR values, (2) identifying the physical processes controlling δ18OTR by mechanistic modelling and (3) defining the origin of the temperature signal recorded in the two species. Although the linear regression models used here to predict daily δ18O of precipitation (δ18OP) may need to be improved in the future, the resulting daily δ18OP values adequately reproduce observed (from weather stations) and simulated (by global circulation model) δ18OP series. The δ18OTR values of the two species are correctly simulated using the δ18OP estimation as MAIDENiso input, although some offset in mean δ18OTR levels is observed for the South American site. For both species, the variability in δ18OTR series is primarily linked to the effect of temperature on isotopic enrichment of the leaf water. We show that MAIDENiso is a powerful tool for investigating isotopic fractionation processes but that the lack of a denser isotope-enabled monitoring network recording oxygen fractionation in the soil-vegetation-atmosphere compartments limits our capacity to decipher the processes at play. This study proves that the eco-physiological modelling of δ18OTR values is necessary to interpret the recorded climate signal more reliably. [ABSTRACT FROM AUTHOR]

Oxygen isotopes in tree-rings (δ18OTR) are widely used to reconstruct past climates. However, the complexity of climatic and biological processes controlling isotopic fractionation is not yet fully understood. Here, we use the MAIDENiso model to decipher the variability of δ18OTR of two temperature-sensitive species of relevant paleoclimatological interest (Picea mariana and Nothofagus pumilio) and growing at cold high-latitudes in North and South America. In this first modelling study on δ18OTR values in both northeastern Canada (53.86° N) and western Argentina (41.10° S), we specifically aim at: (1) evaluating the predictive skill of MAIDENiso to simulate δ18OTR values, (2) identifying the physical processes controlling δ18OTR by mechanistic modelling and, (3) defining the origin of the temperature signal recorded in the two species. Although the linear regression models used here to predict daily δ18O of precipitation (δ18OP) may need to be improved in the future, the resulting daily δ18OP values adequately reproduce observed (from weather stations) and simulated (by global circulation model) δ18OP series. The δ18OTR values of the two species are correctly simulated using the δ18OP estimation as MAIDENiso input, although some offset in mean δ18OTR levels is observed for the South American site. For both species, the variability of δ18OTR series is more likely linked to the effect of temperature on isotopic enrichment of the leaf water rather than on the isotopic composition of the source water. We show that MAIDENiso is a powerful tool for investigating isotopic fractionation processes but that the lack of a denser isotope-enabled monitoring network recording oxygen fractionation in the soil-vegetation-atmosphere compartments limits our capacity to decipher the processes at play. This study proves that the eco-physiological modelling of δ18OTR values is necessary to interpret the recorded climate signal more reliably. [ABSTRACT FROM AUTHOR]