Stimulation of terrestrial plant production by rising CO 2 concentration is projected to reduce the airborne fraction of anthropogenic CO 2 emissions. Coupled climate-carbon cycle models are sensitive to this negative feedback on atmospheric CO 2 , but model projections are uncertain because of the expectation that feedbacks through the nitrogen (N) cycle will reduce this so-called CO 2 fertilization effect. We assessed whether N limitation caused a reduced stimulation of net primary productivity (NPP) by elevated atmospheric CO 2 concentration over 11 y in a free-air CO 2 enrichment (FACE) experiment in a deciduous Liquidambar styraciflua (sweetgum) forest stand in Tennessee. During the first 6 y of the experiment, NPP was significantly enhanced in forest plots exposed to 550 ppm CO 2 compared with NPP in plots in current ambient CO 2 , and this was a consistent and sustained response. However, the enhancement of NPP under elevated CO 2 declined from 24% in 2001-2003 to 9% in 2008. Global analyses that assume a sustained CO 2 fertilization effect are no longer supported by this FACE experiment. N budget analysis supports the premise that N availability was limiting to tree growth and declining over time -an expected consequence of stand development, which was exacerbated by elevated CO 2 . Leaf-and stand-level observations provide mechanistic evidence that declining N availability constrained the tree response to elevated CO 2 ; these observations are consistent with stand-level model projections. This FACE experiment provides strong rationale and process understanding for incorporating N limitation and N feedback effects in ecosystem and global models used in climate change assessments.CO 2 fertilization | free air CO 2 enrichment | global carbon cycle | sweetgum | coupled climate-carbon cycle models P olicy decisions to mitigate climate change require dependable predictions of the forcings and feedbacks between the terrestrial biosphere and the climate (1). Currently, climate models that are coupled to terrestrial and oceanic carbon (C)-cycle models simulate a positive feedback to climate change such that the airborne fraction of anthropogenic CO 2 emissions increases with, and amplifies, climatic warming (1). However, the uncertainty in these projections is high, largely because of uncertainty in the offsetting negative feedback that may occur if stimulation of terrestrial plant production by rising CO 2 concentration increases land C storage and thereby reduces the airborne fraction of anthropogenic CO 2 emissions. Coupled climate-C-cycle models, including those used in the Intergovernmental Panel on Climate Change Fourth Assessment Report (AR4) (2), are sensitive to this negative feedback on atmospheric CO 2 (3). For example, dynamic global vegetation models (4) simulate an increased terrestrial C sink resulting from the physiological responses of plants to elevated atmospheric CO 2 concentration (eCO 2 ), and when coupled to climate models, inclusion of the CO 2 fertilization effect slows the increase in...

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Summary Tree mortality rates appear to be increasing in moist tropical forests (MTFs) with significant carbon cycle consequences. Here, we review the state of knowledge regarding MTF tree mortality, create a conceptual framework with testable hypotheses regarding the drivers, mechanisms and interactions that may underlie increasing MTF mortality rates, and identify the next steps for improved understanding and reduced prediction. Increasing mortality rates are associated with rising temperature and vapor pressure deficit, liana abundance, drought, wind events, fire and, possibly, CO2 fertilization‐induced increases in stand thinning or acceleration of trees reaching larger, more vulnerable heights. The majority of these mortality drivers may kill trees in part through carbon starvation and hydraulic failure. The relative importance of each driver is unknown. High species diversity may buffer MTFs against large‐scale mortality events, but recent and expected trends in mortality drivers give reason for concern regarding increasing mortality within MTFs. Models of tropical tree mortality are advancing the representation of hydraulics, carbon and demography, but require more empirical knowledge regarding the most common drivers and their subsequent mechanisms. We outline critical datasets and model developments required to test hypotheses regarding the underlying causes of increasing MTF mortality rates, and improve prediction of future mortality under climate change.

Predicted responses of transpiration to elevated atmospheric CO2 concentration (eCO2 ) are highly variable amongst process-based models. To better understand and constrain this variability amongst models, we conducted an intercomparison of 11 ecosystem models applied to data from two forest free-air CO2 enrichment (FACE) experiments at Duke University and Oak Ridge National Laboratory. We analysed model structures to identify the key underlying assumptions causing differences in model predictions of transpiration and canopy water use efficiency. We then compared the models against data to identify model assumptions that are incorrect or are large sources of uncertainty. We found that model-to-model and model-to-observations differences resulted from four key sets of assumptions, namely (i) the nature of the stomatal response to elevated CO2 (coupling between photosynthesis and stomata was supported by the data); (ii) the roles of the leaf and atmospheric boundary layer (models which assumed multiple conductance terms in series predicted more decoupled fluxes than observed at the broadleaf site); (iii) the treatment of canopy interception (large intermodel variability, 2-15%); and (iv) the impact of soil moisture stress (process uncertainty in how models limit carbon and water fluxes during moisture stress). Overall, model predictions of the CO2 effect on WUE were reasonable (intermodel μ = approximately 28% ± 10%) compared to the observations (μ = approximately 30% ± 13%) at the well-coupled coniferous site (Duke), but poor (intermodel μ = approximately 24% ± 6%; observations μ = approximately 38% ± 7%) at the broadleaf site (Oak Ridge). The study yields a framework for analysing and interpreting model predictions of transpiration responses to eCO2 , and highlights key improvements to these types of models.

Summary 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.