Explore the vast range of titles available.

For over 40 years, Plant Functional Types (PFTs) have been used to discretize the ∼400,000 species of terrestrial plants into “similar” classes. Within Earth System Models (ESMs), PFTs simplify terrestrial biosphere modeling in combination with soil information and other site characteristics. However, in flux analysis studies, PFT schemes are often implemented as the sole analytical lens to clarify complex behavior. This usage assumes that PFTs adequately enable a mapping between climate inputs and flux outputs. Here, we show that random forest models, trained using aggregated climate and flux measurements from 245 eddy‐covariance sites, cannot accurately predict PFT groupings, regardless of the nature of the PFT scheme. Similarly, PFTs provide negligible benefit when using site climate to predict site flux regimes and vice versa. While use of PFT classifications is convenient, our results suggest they do not aid analytical skill, which has important implications for future terrestrial flux studies. Plain Language Summary To understand how the land surface behaves, we often divide plants into a small number (20 or less) of ”similar” groups, such as evergreen forests, or grasslands, known as Plant Functional Types (PFTs). The idea is that landscapes with similar large‐scale characteristics will behave in the same way. In land surface models, these PFT groups determine how the simulated plants react to the climate in combination with soil information and other characteristics, yet analysis of observations often use PFT groups alone to try to explain variations in results between different experimental sites. We use machine learning to show that while PFTs might be visually compelling, they do not necessarily represent behavior groupings and might actually hide real world behavior if used for analysis. As such, we suggest that future studies instead try to look at more specific site characteristics when trying to explain analysis results. Key Points Plant Functional Types (PFTs), as often used in land flux studies, are not easily empirically associated with site climate and/or flux regimes A broad selection of alternative vegetation/land cover classifications do not offer greater predictability The disconnect between PFTs and climate/flux regimes has implications for modeling and analysis of terrestrial systems

Increasing atmospheric CO 2 stimulates photosynthesis which can increase net primary production (NPP), but at longer timescales may not necessarily increase plant biomass. Here we analyse the four decade-long CO 2 -enrichment experiments in woody ecosystems that measured total NPP and biomass. CO 2 enrichment increased biomass increment by 1.05 ± 0.26 kg C m −2 over a full decade, a 29.1 ± 11.7% stimulation of biomass gain in these early-secondary-succession temperate ecosystems. This response is predictable by combining the CO 2 response of NPP (0.16 ± 0.03 kg C m −2  y −1 ) and the CO 2 -independent, linear slope between biomass increment and cumulative NPP (0.55 ± 0.17). An ensemble of terrestrial ecosystem models fail to predict both terms correctly. Allocation to wood was a driver of across-site, and across-model, response variability and together with CO 2 -independence of biomass retention highlights the value of understanding drivers of wood allocation under ambient conditions to correctly interpret and predict CO 2 responses. It is unclear whether CO 2 -stimulation of photosynthesis can propagate through slower ecosystem processes and lead to long-term increases in terrestrial carbon. Here the authors show that CO 2 -stimulation of photosynthesis leads to a 30% increase in forest regrowth over a decade of CO 2 enrichment.

Plant responses to elevated atmospheric carbon dioxide (eCO₂) have been hypothesized as a key mechanism that may ameliorate the impact of future drought. Yet, despite decades of experiments, the question of whether eCO₂ reduces plant water use, yielding ‘water savings’ that can be used to maintain plant function during periods of water stress, remains unresolved. In this Viewpoint, we identify the experimental challenges and limitations to our understanding of plant responses to drought under eCO₂. In particular, we argue that future studies need to move beyond exploring whether eCO₂ played ‘a role’ or ‘no role’ in responses to drought, but instead more carefully consider the timescales and conditions that would induce an influence. We also argue that considering emergent differences in soil water content may be an insufficient means of assessing the impact of eCO₂. We identify eCO₂ impact during severe drought (e.g. to the point of mortality), interactions with future changes in vapour pressure deficit and uncertainty about changes in leaf area as key gaps in our current understanding. New insights into CO₂ × drought interactions are essential to better constrain model theory that governs future climate model projections of land–atmosphere interactions during periods of water stress.

The temperature response of photosynthesis is one of the key factors determining predicted responses to warming in global vegetation models (GVMs). The response may vary geographically, owing to genetic adaptation to climate, and temporally, as a result of acclimation to changes in ambient temperature. Our goal was to develop a robust quantitative global model representing acclimation and adaptation of photosynthetic temperature responses. We quantified and modelled key mechanisms responsible for photosynthetic temperature acclimation and adaptation using a global dataset of photosynthetic CO2 response curves, including data from 141 C3 species from tropical rainforest to Arctic tundra. We separated temperature acclimation and adaptation processes by considering seasonal and common‐garden datasets, respectively. The observed global variation in the temperature optimum of photosynthesis was primarily explained by biochemical limitations to photosynthesis, rather than stomatal conductance or respiration. We found acclimation to growth temperature to be a stronger driver of this variation than adaptation to temperature at climate of origin. We developed a summary model to represent photosynthetic temperature responses and showed that it predicted the observed global variation in optimal temperatures with high accuracy. This novel algorithm should enable improved prediction of the function of global ecosystems in a warming climate.

The maximum photosynthetic carboxylation rate (V cmax) is an influential plant trait that has multiple scaling hypotheses, which is a source of uncertainty in predictive understanding of global gross primary production (GPP). Four trait-scaling hypotheses (plant functional type, nutrient limitation, environmental filtering, and plant plasticity) with nine specific implementations were used to predict global V cmax distributions and their impact on global GPP in the Sheffield Dynamic Global Vegetation Model (SDGVM). Global GPP varied from 108.1 to 128.2 PgC yr−1, 65% of the range of a recent model inter-comparison of global GPP. The variation in GPP propagated through to a 27% coefficient of variation in net biome productivity (NBP). All hypotheses produced global GPP that was highly correlated (r = 0.85–0.91) with three proxies of global GPP. Plant functional type-based nutrient limitation, underpinned by a core SDGVM hypothesis that plant nitrogen (N) status is inversely related to increasing costs of N acquisition with increasing soil carbon, adequately reproduced global GPP distributions. Further improvement could be achieved with accurate representation of water sensitivity and agriculture in SDGVM. Mismatch between environmental filtering (the most data-driven hypothesis) and GPP suggested that greater effort is needed understand V cmax variation in the field, particularly in northern latitudes.

• Knowledge of how water stress impacts the carbon and water cycles is a key uncertainty in terrestrial biosphere models. • We tested a new profit maximization model, where photosynthetic uptake of CO₂ is optimally traded against plant hydraulic function, as an alternative to the empirical functions commonly used in models to regulate gas exchange during periods of water stress. We conducted a multi-site evaluation of this model at the ecosystem scale, before and during major droughts in Europe. Additionally, we asked whether the maximum hydraulic conductance in the soil–plant continuum k max (a key model parameter which is not commonly measured) could be predicted from long-term site climate. • Compared with a control model with an empirical soil moisture function, the profit maximization model improved the simulation of evapotranspiration during the growing season, reducing the normalized mean square error by c. 63%, across mesic and xeric sites. We also showed that k max could be estimated from long-term climate, with improvements in the simulation of evapotranspiration at eight out of the 10 forest sites during drought. • Although the generalization of this approach is contingent upon determining k max, it presents a mechanistic trait-based alternative to regulate canopy gas exchange in global models.

Recent experimental evidence suggests that during heat extremes, wooded ecosystems may decouple photosynthesis and transpiration, reducing photosynthesis to near zero but increasing transpiration into the boundary layer. This feedback may act to dampen, rather than amplify, heat extremes in wooded ecosystems. We examined eddy covariance databases (OzFlux and FLUXNET2015) to identify whether there was field-based evidence to support these experimental findings. We focused on two types of heat extremes: (i) the 3 days leading up to a temperature extreme, defined as including a daily maximum temperature >37 ∘C (similar to the widely used TXx metric), and (ii) heatwaves, defined as 3 or more consecutive days above 35 ∘C. When focusing on (i), we found some evidence of reduced photosynthesis and sustained or increased latent heat fluxes at seven Australian evergreen wooded flux sites. However, when considering the role of vapour pressure deficit and focusing on (ii), we were unable to conclusively disentangle the decoupling between photosynthesis and latent heat flux from the effect of increasing the vapour pressure deficit. Outside of Australia, the Tier-1 FLUXNET2015 database provided limited scope to tackle this issue as it does not sample sufficient high temperature events with which to probe the physiological response of trees to extreme heat. Thus, further work is required to determine whether this photosynthetic decoupling occurs widely, ideally by matching experimental species with those found at eddy covariance tower sites. Such measurements would allow this decoupling mechanism to be probed experimentally and at the ecosystem scale. Transpiration during heatwaves remains a key issue to resolve, as no land surface model includes a decoupling mechanism, and any potential dampening of the land–atmosphere amplification is thus not included in climate model projections.

Eddy covariance flux towers measure the exchange of water, energy, and carbon fluxes between the land and atmosphere. They have become invaluable for theory development and evaluating land models. However, flux tower data as measured (even after site post-processing) are not directly suitable for land surface modelling due to data gaps in model forcing variables, inappropriate gap-filling, formatting, and varying data quality. Here we present a quality-control and data-formatting pipeline for tower data from FLUXNET2015, La Thuile, and OzFlux syntheses and the resultant 170-site globally distributed flux tower dataset specifically designed for use in land modelling. The dataset underpins the second phase of the Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER), an international model intercomparison project encompassing >20 land surface and biosphere models. The dataset is provided in the Assistance for Land-surface Modelling Activities (ALMA) NetCDF format and is CF-NetCDF compliant. For forcing land surface models, the dataset provides fully gap-filled meteorological data that have had periods of low data quality removed. Additional constraints required for land models, such as reference measurement heights, vegetation types, and satellite-based monthly leaf area index estimates, are also included. For model evaluation, the dataset provides estimates of key water, carbon, and energy variables, with the latent and sensible heat fluxes additionally corrected for energy balance closure. The dataset provides a total of 1040 site years covering the period 1992–2018, with individual sites spanning from 1 to 21 years. The dataset is available at http://doi.org/10.25914/5fdb0902607e1 (Ukkola et al., 2021).

Ecosystems are projected to face extreme high temperatures more frequently in the near future. Various biotic coping strategies exist to prevent heat stress. Controlled experiments have recently provided evidence for continued transpiration in woody plants during high air temperatures, even when photosynthesis is inhibited. Such a decoupling of photosynthesis and transpiration would represent an effective strategy (‘known as leaf or canopy cooling’) to prevent lethal leaf temperatures. At the ecosystem scale, continued transpiration might dampen the development and propagation of heat extremes despite further desiccating soils. However, at the ecosystem scale, evidence for the occurrence of this decoupling is still limited. Here, we aim to investigate this mechanism using eddy-covariance data of thirteen woody ecosystems located in Australia and a causal graph discovery algorithm. Working at half-hourly time resolution, we find evidence for a decoupling of photosynthesis and transpiration in four ecosystems which can be classified as Mediterranean woodlands. The decoupling occurred at air temperatures above 35 ∘ C. At the nine other investigated woody sites, we found that vegetation CO 2 exchange remained coupled to transpiration at the observed high air temperatures. Ecosystem characteristics suggest that the canopy energy balance plays a crucial role in determining the occurrence of a decoupling. Our results highlight the value of causal-inference approaches for the analysis of complex physiological processes. With regard to projected increasing temperatures and especially extreme events in future climates, further vegetation types might be pushed to threatening canopy temperatures. Our findings suggest that the coupling of leaf-level photosynthesis and stomatal conductance, common in land surface schemes, may need be re-examined when applied to high-temperature events.

Simulations of photosynthesis by terrestrial biosphere models typically need a specification of the maximum carboxylation rate (V cmax). Estimating this parameter using A–C i curves (net photosynthesis, A, vs intercellular CO2 concentration, C i) is laborious, which limits availability of V cmax data. However, many multispecies field datasets include net photosynthetic rate at saturating irradiance and at ambient atmospheric CO2 concentration (A sat) measurements, from which V cmax can be extracted using a ‘one-point method’. We used a global dataset of A–C i curves (564 species from 46 field sites, covering a range of plant functional types) to test the validity of an alternative approach to estimate V cmax from A sat via this ‘one-point method’. If leaf respiration during the day (R day) is known exactly, V cmax can be estimated with an r 2 value of 0.98 and a root-mean-squared error (RMSE) of 8.19 μmolm−2 s−1. However, R day typically must be estimated. Estimating R day as 1.5% of V cmax, we found that V cmax could be estimated with an r 2 of 0.95 and an RMSE of 17.1 μmolm−2 s−1. The one-point method provides a robust means to expand current databases of fieldmeasured V cmax, giving new potential to improve vegetation models and quantify the environmental drivers of V cmax variation.