Amplification of the hydrological cycle as a consequence of global warming is forecast to lead to more extreme intra-annual precipitation regimes characterized by larger rainfall events and longer intervals between events. We present a conceptual framework, based on past investigations and ecological theory, for predicting the consequences of this underappreciated aspect of climate change. We consider a broad range of terrestrial ecosystems that vary in their overall water balance. More extreme rainfall regimes are expected to increase the duration and severity of soil water stress in mesic ecosystems as intervals between rainfall events increase. In contrast, xeric ecosystems may exhibit the opposite response to extreme events. Larger but less frequent rainfall events may result in proportional reductions in evaporative losses in xeric systems, and thus may lead to greater soil water availability. Hydric (wetland) ecosystems are predicted to experience reduced periods of anoxia in response to prolonged intervals between rainfall events. Understanding these contingent effects of ecosystem water balance is necessary for predicting how more extreme precipitation regimes will modify ecosystem processes and alter interactions with related global change drivers.

We analysed the responses of 11 ecosystem models to elevated atmospheric [CO2] (eCO2) at two temperate forest ecosystems (Duke and Oak Ridge National Laboratory (ORNL) Free-Air CO2 Enrichment (FACE) experiments) to test alternative representations of carbon (C)–nitrogen (N) cycle processes. We decomposed the model responses into component processes affecting the response to eCO2 and confronted these with observations from the FACE experiments. Most of the models reproduced the observed initial enhancement of net primary production (NPP) at both sites, but none was able to simulate both the sustained 10-yr enhancement at Duke and the declining response at ORNL: models generally showed signs of progressive N limitation as a result of lower than observed plant N uptake. Nonetheless, many models showed qualitative agreement with observed component processes. The results suggest that improved representation of above-ground–below-ground interactions and better constraints on plant stoichiometry are important for a predictive understanding of eCO2 effects. Improved accuracy of soil organic matter inventories is pivotal to reduce uncertainty in the observed C–N budgets. The two FACE experiments are insufficient to fully constrain terrestrial responses to eCO2, given the complexity of factors leading to the observed diverging trends, and the consequential inability of the models to explain these trends. Nevertheless, the ecosystem models were able to capture important features of the experiments, lending some support to their projections.

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.

[1] Accurately simulating gross primary productivity (GPP) in terrestrial ecosystem models is critical because errors in simulated GPP propagate through the model to introduce additional errors in simulated biomass and other fluxes. We evaluated simulated, daily average GPP from 26 models against estimated GPP at 39 eddy covariance flux tower sites across the United States and Canada. None of the models in this study match estimated GPP within observed uncertainty. On average, models overestimate GPP in winter, spring, and fall, and underestimate GPP in summer. Models overpredicted GPP under dry conditions and for temperatures below 0 C. Improvements in simulated soil moisture and ecosystem response to drought or humidity stress will improve simulated GPP under dry conditions. Adding a low-temperature response to shut down GPP for temperatures below 0 C will reduce the positive bias in winter, spring, and fall and improve simulated phenology. The negative bias in summer and poor overall performance resulted from mismatches between simulated and observed light use efficiency (LUE). Improving simulated GPP requires better leaf-to-canopy scaling and better values of model parameters that control the maximum potential GPP, such as ɛ max (LUE), V cmax (unstressed Rubisco catalytic capacity) or J max (the maximum electron transport rate).

Elevated atmospheric CO2 concentration (eCO2) has the potential to increase vegetation carbon storage if increased net primary production causes increased long-lived biomass. Model predictions of eCO2 effects on vegetation carbon storage depend on how allocation and turnover processes are represented.We used data from two temperate forest free-air CO2 enrichment (FACE) experiments to evaluate representations of allocation and turnover in 11 ecosystem models.Observed eCO2 effects on allocation were dynamic. Allocation schemes based on functional relationships among biomass fractions that vary with resource availability were best able to capture the general features of the observations. Allocation schemes based on constant fractions or resource limitations performed less well, with some models having unintended outcomes. Few models represent turnover processes mechanistically and there was wide variation in predictions of tissue lifespan. Consequently, models did not perform well at predicting eCO2 effects on vegetation carbon storage.Our recommendations to reduce uncertainty include: use of allocation schemes constrained by biomass fractions; careful testing of allocation schemes; and synthesis of allocation and turnover data in terms of model parameters. Data from intensively studied ecosystem manipulation experiments are invaluable for constraining models and we recommend that such experiments should attempt to fully quantify carbon, water and nutrient budgets.