Trevor F. Keenan

Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise

Terrestrial plants remove CO2 from the atmosphere through photosynthesis, a process that is accompanied by the loss of water vapour from leaves. The ratio of water loss to carbon gain, or water-use efficiency, is a key characteristic of ecosystem function that is central to the global cycles of water, energy and carbon. Here we analyse direct, long-term measurements of whole-ecosystem carbon and water exchange. We find a substantial increase in water-use efficiency in temperate and boreal forests of the Northern Hemisphere over the past two decades. We systematically assess various competing hypotheses to explain this trend, and find that the observed increase is most consistent with a strong CO2 fertilization effect. The results suggest a partial closure of stomata—small pores on the leaf surface that regulate gas exchange—to maintain a near-constant concentration of CO2 inside the leaf even under continually increasing atmospheric CO2 levels. The observed increase in forest water-use efficiency is larger than that predicted by existing theory and 13 terrestrial biosphere models. The increase is associated with trends of increasing ecosystem-level photosynthesis and net carbon uptake, and decreasing evapotranspiration. Our findings suggest a shift in the carbon- and water-based economics of terrestrial vegetation, which may require a reassessment of the role of stomatal control in regulating interactions between forests and climate change, and a re-evaluation of coupled vegetation–climate models.

Seasonal dynamics and age of stemwood nonstructural carbohydrates in temperate forest trees.

Nonstructural carbohydrate reserves support tree metabolism and growth when current photosynthates are insufficient, offering resilience in times of stress. We monitored stemwood nonstructural carbohydrate (starch and sugars) concentrations of the dominant tree species at three sites in the northeastern United States. We estimated the mean age of the starch and sugars in a subset of trees using the radiocarbon ((14) C) bomb spike. With these data, we then tested different carbon (C) allocation schemes in a process-based model of forest C cycling. We found that the nonstructural carbohydrates are both highly dynamic and about a decade old. Seasonal dynamics in starch (two to four times higher in the growing season, lower in the dormant season) mirrored those of sugars. Radiocarbon-based estimates indicated that the mean age of the starch and sugars in red maple (Acer rubrum) was 7-14 yr. A two-pool (fast and slow cycling reserves) model structure gave reasonable estimates of the size and mean residence time of the total NSC pool, and greatly improved model predictions of interannual variability in woody biomass increment, compared with zero- or one-pool structures used in the majority of existing models. This highlights the importance of nonstructural carbohydrates in the context of forest ecosystem carbon cycling.

A worldwide analysis of within‐canopy variations in leaf structural, chemical and physiological traits across plant functional types

Extensive within-canopy light gradients importantly affect the photosynthetic productivity of leaves in different canopy positions and lead to light-dependent increases in foliage photosynthetic capacity per area (AA). However, the controls on AA variations by changes in underlying traits are poorly known. We constructed an unprecedented worldwide database including 831 within-canopy gradients with standardized light estimates for 304 species belonging to major vascular plant functional types, and analyzed within-canopy variations in 12 key foliage structural, chemical and physiological traits by quantitative separation of the contributions of different traits to photosynthetic acclimation. Although the light-dependent increase in AA is surprisingly similar in different plant functional types, they differ fundamentally in the share of the controls on AA by constituent traits. Species with high rates of canopy development and leaf turnover, exhibiting highly dynamic light environments, actively change AA by nitrogen reallocation among and partitioning within leaves. By contrast, species with slow leaf turnover exhibit a passive AA acclimation response, primarily determined by the acclimation of leaf structure to growth light. This review emphasizes that different combinations of traits are responsible for within-canopy photosynthetic acclimation in different plant functional types, and solves an old enigma of the role of mass- vs area-based traits in vegetation acclimation.

Tracking forest phenology and seasonal physiology using digital repeat photography: a critical assessment

Digital repeat photography is becoming widely used for near-surface remote sensing of vegetation. Canopy greenness, which has been used extensively for phenological applications, can be readily quantified from camera images. Important questions remain, however, as to whether the observed changes in canopy greenness are directly related to changes in leaf-level traits, changes in canopy structure, or some combination thereof. We investigated relationships between canopy greenness and various metrics of canopy structure and function, using five years (2008–2012) of automated digital imagery, ground observations of phenological transitions, leaf area index (LAI) measurements, and eddy covariance estimates of gross ecosystem photosynthesis from the Harvard Forest, a temperate deciduous forest in the northeastern United States. Additionally, we sampled canopy sunlit leaves on a weekly basis throughout the growing season of 2011. We measured physiological and morphological traits including leaf size, mass (wet/dry), nitrogen content, chlorophyll fluorescence, and spectral reflectance and characterized individual leaf color with flatbed scanner imagery. Our results show that observed spring and autumn phenological transition dates are well captured by information extracted from digital repeat photography. However, spring development of both LAI and the measured physiological and morphological traits are shown to lag behind spring increases in canopy greenness, which rises very quickly to its maximum value before leaves are even half their final size. Based on the hypothesis that changes in canopy greenness represent the aggregate effect of changes in both leaf-level properties (specifically, leaf color) and changes in canopy structure (specifically, LAI), we developed a two end-member mixing model. With just a single free parameter, the model was able to reproduce the observed seasonal trajectory of canopy greenness. This analysis shows that canopy greenness is relatively insensitive to changes in LAI at high LAI levels, which we further demonstrate by assessing the impact of an ice storm on both LAI and canopy greenness. Our study provides new insights into the mechanisms driving seasonal changes in canopy greenness retrieved from digital camera imagery. The nonlinear relationship between canopy greenness and canopy LAI has important implications both for phenological research applications and for assessing responses of vegetation to disturbances.

Warm spring reduced carbon cycle impact of the 2012 US summer drought

Significance Carbon uptake by terrestrial ecosystems mitigates the impact of anthropogenic fossil fuel emissions on atmospheric CO2 concentrations, but the strength of this carbon sink is highly sensitive to large-scale extreme climate events. In 2012, the United States experienced the most severe drought since the Dust Bowl period, along with the warmest spring on record. Here, we quantify the impact of this climate anomaly on the carbon cycle. Our results show that warming-induced earlier vegetation activity increased spring carbon uptake, and thus compensated for reduced carbon uptake during the summer drought in 2012. This compensation, however, came at the cost of soil moisture depletion from increased spring evapotranspiration that likely enhanced summer heating through land-atmosphere coupling. The global terrestrial carbon sink offsets one-third of the world’s fossil fuel emissions, but the strength of this sink is highly sensitive to large-scale extreme events. In 2012, the contiguous United States experienced exceptionally warm temperatures and the most severe drought since the Dust Bowl era of the 1930s, resulting in substantial economic damage. It is crucial to understand the dynamics of such events because warmer temperatures and a higher prevalence of drought are projected in a changing climate. Here, we combine an extensive network of direct ecosystem flux measurements with satellite remote sensing and atmospheric inverse modeling to quantify the impact of the warmer spring and summer drought on biosphere-atmosphere carbon and water exchange in 2012. We consistently find that earlier vegetation activity increased spring carbon uptake and compensated for the reduced uptake during the summer drought, which mitigated the impact on net annual carbon uptake. The early phenological development in the Eastern Temperate Forests played a major role for the continental-scale carbon balance in 2012. The warm spring also depleted soil water resources earlier, and thus exacerbated water limitations during summer. Our results show that the detrimental effects of severe summer drought on ecosystem carbon storage can be mitigated by warming-induced increases in spring carbon uptake. However, the results also suggest that the positive carbon cycle effect of warm spring enhances water limitations and can increase summer heating through biosphere–atmosphere feedbacks.

Predictability of the terrestrial carbon cycle

Terrestrial ecosystems sequester roughly 30% of anthropogenic carbon emission. However this estimate has not been directly deduced from studies of terrestrial ecosystems themselves, but inferred from atmospheric and oceanic data. This raises a question: to what extent is the terrestrial carbon cycle intrinsically predictable? In this paper, we investigated fundamental properties of the terrestrial carbon cycle, examined its intrinsic predictability, and proposed a suite of future research directions to improve empirical understanding and model predictive ability. Specifically, we isolated endogenous internal processes of the terrestrial carbon cycle from exogenous forcing variables. The internal processes share five fundamental properties (i.e., compartmentalization, carbon input through photosynthesis, partitioning among pools, donor pool-dominant transfers, and the first-order decay) among all types of ecosystems on the Earth. The five properties together result in an emergent constraint on predictability of various carbon cycle components in response to five classes of exogenous forcing. Future observational and experimental research should be focused on those less predictive components while modeling research needs to improve model predictive ability for those highly predictive components. We argue that an understanding of predictability should provide guidance on future observational, experimental and modeling research.

The model–data fusion pitfall: assuming certainty in an uncertain world

Model–data fusion is a powerful framework by which to combine models with various data streams (including observations at different spatial or temporal scales), and account for associated uncertainties. The approach can be used to constrain estimates of model states, rate constants, and driver sensitivities. The number of applications of model–data fusion in environmental biology and ecology has been rising steadily, offering insights into both model and data strengths and limitations. For reliable model–data fusion-based results, however, the approach taken must fully account for both model and data uncertainties in a statistically rigorous and transparent manner. Here we review and outline the cornerstones of a rigorous model–data fusion approach, highlighting the importance of properly accounting for uncertainty. We conclude by suggesting a code of best practices, which should serve to guide future efforts.

The timing of autumn senescence is affected by the timing of spring phenology: implications for predictive models.

Autumn senescence regulates multiple aspects of ecosystem function, along with associated feedbacks to the climate system. Despite its importance, current understanding of the drivers of senescence is limited, leading to a large spread in predictions of how the timing of senescence, and thus the length of the growing season, will change under future climate conditions. The most commonly held paradigm is that temperature and photoperiod are the primary controls, which suggests a future extension of the autumnal growing season as global temperatures rise. Here, using two decades of ground- and satellite-based observations of temperate deciduous forest phenology, we show that the timing of autumn senescence is correlated with the timing of spring budburst across the entire eastern United States. On a year-to-year basis, an earlier/later spring was associated with an earlier/later autumn senescence, both for individual species and at a regional scale. We use the observed relationship to develop a novel model of autumn phenology. In contrast to current phenology models, this model predicts that the potential response of autumn phenology to future climate change is strongly limited by the impact of climate change on spring phenology. Current models of autumn phenology therefore may overpredict future increases in the length of the growing season, with subsequent impacts for modeling future CO2 uptake and evapotranspiration.

Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake

Terrestrial ecosystems play a significant role in the global carbon cycle and offset a large fraction of anthropogenic CO2 emissions. The terrestrial carbon sink is increasing, yet the mechanisms responsible for its enhancement, and implications for the growth rate of atmospheric CO2, remain unclear. Here using global carbon budget estimates, ground, atmospheric and satellite observations, and multiple global vegetation models, we report a recent pause in the growth rate of atmospheric CO2, and a decline in the fraction of anthropogenic emissions that remain in the atmosphere, despite increasing anthropogenic emissions. We attribute the observed decline to increases in the terrestrial sink during the past decade, associated with the effects of rising atmospheric CO2 on vegetation and the slowdown in the rate of warming on global respiration. The pause in the atmospheric CO2 growth rate provides further evidence of the roles of CO2 fertilization and warming-induced respiration, and highlights the need to protect both existing carbon stocks and regions, where the sink is growing rapidly.

Evaluation of continental carbon cycle simulations with North American flux tower observations

Terrestrial biosphere models can help identify physical processes that control carbon dynamics, including land-atmosphere CO2 fluxes, and have great potential to predict the terrestrial ecosystem response to changing climate. The skill of models that provide continental-scale carbon flux estimates, however, remains largely untested. This paper evaluates the performance of continental-scale flux estimates from 17 models against observations from 36 North American flux towers. Fluxes extracted from regional model simulations were compared with co-located flux tower observations at monthly and annual time increments. Site-level model simulations were used to help interpret sources of the mismatch between the regional simulations and site-based observations. On average, the regional model runs overestimated the annual gross primary productivity (5%) and total respiration (15%), and they significantly underestimated the annual net carbon uptake (64%) during the time period 2000- 2005. Comparison with site-level simulations implicated choices specific to regional model simulations as contributors to the gross flux biases, but not the net carbon uptake bias. The models performed the best at simulating carbon exchange at deciduous broadleaf sites, likely because a number of models used prescribed phenology to simulate seasonal fluxes. The models did not perform as well for crop, grass, and evergreen sites. The regional models matched the observations most closely in terms of seasonal correlation and seasonal magnitude of variation, but they have very little skill at interannual correlation and minimal skill at interannual magnitude of variability. The comparison of site vs. regional-level model runs demonstrated that (1) the interannual correlation is higher for site-level model runs, but the skill remains low; and (2) the underestimation of year-to-year variability for all fluxes is an inherent weakness of the models. The best-performing regional models that did not use flux tower calibration were CLM-CN, CASA-GFEDv2, and SIB3.1. Two flux tower calibrated, empirical models, EC-MOD and MOD17þ, performed as well as the best process-based models. This suggests that (1) empirical, calibrated models can perform as well as complex, process-based models and (2) combining process- based model structure with relevant constraining data could significantly improve model performance.