Ensheng Weng

Consequences of More Extreme Precipitation Regimes for Terrestrial Ecosystems

ABSTRACT 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.

Evaluation of 11 terrestrial carbon-nitrogen cycle models against observations from two temperate Free-Air CO2 Enrichment studies

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.

Dynamic disequilibrium of the terrestrial carbon cycle under global change.

In this review, we propose a new framework, dynamic disequilibrium of the carbon cycles, to assess future land carbon-sink dynamics. The framework recognizes internal ecosystem processes that drive the carbon cycle toward equilibrium, such as donor pool-dominated transfer; and external forces that create disequilibrium, such as disturbances and global change. Dynamic disequilibrium within one disturbance-recovery episode causes temporal changes in the carbon source and sink at yearly and decadal scales, but has no impacts on longer-term carbon sequestration unless disturbance regimes shift. Such shifts can result in long-term regional carbon loss or gain and be quantified by stochastic statistics for use in prognostic modeling. If the regime shifts result in ecosystem state changes in regions with large carbon reserves at risk, the global carbon cycle might be destabilized.

Where does the carbon go? A model-data intercomparison of vegetation carbon allocation and turnover processes at two temperate forest free-air CO2 enrichment sites.

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.

Parameter identifiability, constraint, and equifinality in data assimilation with ecosystem models

One of the most desirable goals of scientific endeavor is to discover laws or principles behind ‘‘mystified’’ phenomena. A cherished example is the discovery of the law of universal gravitation by Isaac Newton, which can precisely describe falling of an apple from a tree and predict the existence of Neptune. Scientists pursue mechanistic understanding of natural phenomena in an attempt to develop relatively simple equations with a small number of parameters to describe patterns in nature and to predict changes in the future. In this context, uncertainty had been considered to be incompatible with science (Klir 2006). Not until the early 20th century was the notion gradually changed when physicists studied the behavior of matter and energy on the scale of atoms and subatomic particles in quantum mechanics. In 1927, Heisenberg observed that the electron could not be considered as in an exact location, but rather in points of probable location in its orbital, which can be described by a probability distribution (Heisenberg 1958). Quantum mechanics lets scientists realize that inherent uncertainty exists in nature and is an unavoidable and essential property of most systems. Since then, scientists have developed methods to analyze and describe uncertainty. Ecosystem ecologists have recently directed attention to studying uncertainty in ecosystem processes. The Bayesian paradigm allows ecologists to generate a posteriori probabilistic density functions (PDF) for parameters of ecosystem models by assimilating a priori PDFs and measurements (Dowd and Meyer 2003). Xu et al. (2006), for example, evaluated uncertainty in parameter estimation and projected carbon sinks by a Bayesian framework using six data sets and a terrestrial ecosystem (TECO) model. The Bayesian framework has been applied to assimilation of eddy-flux data into simplified photosynthesis and evapotranspiration model (SIPNET) to evaluate information content of the net ecosystem exchange (NEE) observations for constraints of process parameters (e.g., Braswell et al. 2005) and to partition NEE into its component fluxes (Sacks et al. 2006). Verstraeten et al. (2008) evaluate error propagation and uncertainty of evaporation, soil moisture content, and net ecosystem productivity with remotely sensed data assimilation. Nevertheless, uncertainty in data assimilation with ecosystem models has not been systematically explored. Cressie et al. (2009) proposed a general framework to account for multiple sources of uncertainty in measurements, in sampling, in specification of the process, in parameters, and in initial and boundary conditions. They proposed to separate the multiple sources of uncertainty using a conditional-probabilistic approach. With this approach, ecologists need to build a hierarchical statistical model based on the Bayesian theorem, and to use Markov chain Monte Carlos (MCMC) techniques for sampling before probability distributions of interested parameters or projected state variables can be obtained for quantification of uncertainty. It is an elegant framework for quantifying uncertainties in the parameters and processes of ecological models. At the core of uncertainty analysis is parameter identifiability. When parameters can be constrained by a set of data with a given model structure, we can identify maximum likelihood values of the parameters and then those parameters are identifiable. Conversely, there is an issue of equifinality in data assimilation (Beven 2006) that different models, or different parameter values of the same model, may fit data equally well without the ability to distinguish which models or parameter values are better than others. Thus, the issue of identifiability is reflected by parameter constraint and equifinality. This essay first reviews the current status of our knowledge on parameter identifiability and then discusses major factors that influence it. To enrich discussion, we use examples in ecosystem ecology that are different from the one on population dynamics of harbor seals in Cressie et al. (2009).

Soil hydrological properties regulate grassland ecosystem responses to multifactor global change: A modeling analysis

[1] We conducted a modeling study to evaluate how soil hydrological properties regulate water and carbon dynamics of grassland ecosystems in response to multifactor global change. We first calibrated a process-based terrestrial ecosystem (TECO) model against data from two experiments with warming and clipping or doubled precipitation in Great Plains. The calibrated model was used to simulate responses of soil moisture, evaporation, transpiration, runoff, net primary production (NPP), ecosystem respiration (R h ), and net ecosystem production (NEP) to changes in precipitation amounts and intensity, increased temperature, and elevated atmospheric [CO 2 ] along a soil texture gradient (sand, sandy loam, loam, silt loam, and clay loam). Soil available water capacity (AWC), which is the difference between field capacity and wilting point, was used as the index to represent soil hydrological properties of the five soil texture types. Simulation results showed that soil AWC altered partitioning of precipitation among runoff, evaporation, and transpiration, and consequently regulated ecosystem responses to global environmental changes. The fractions of precipitation that were used for evaporation and transpiration increased with soil AWC but decreased for runoff. High AWC could greatly buffer water stress during long drought periods, particularly after a large rainfall event. NPP, R h , and NEP usually increased with AWC under ambient and 50% increased precipitation scenarios. With the halved precipitation amount, NPP, R h , and NEP only increased from 7% to 7.5% of AWC followed by declines. Warming and CO 2 effects on soil moisture, evapotranspiration, and runoff were magnified by soil AWC. Regulatory patterns of AWC on responses of NPP, R h , and NEP to warming were complex. In general, CO 2 effects on NPP, R h , and NEP increased with soil AWC. Our results indicate that variations in soil texture may be one of the major causes underlying variable responses of ecosystems to global changes observed from different experiments.

Joint control of terrestrial gross primary productivity by plant phenology and physiology

Significance Terrestrial gross primary productivity (GPP), the total photosynthetic CO2 fixation at ecosystem level, fuels all life on land. However, its spatiotemporal variability is poorly understood, because GPP is determined by many processes related to plant phenology and physiological activities. In this study, we find that plant phenological and physiological properties can be integrated in a robust index—the product of the length of CO2 uptake period and the seasonal maximal photosynthesis—to explain the GPP variability over space and time in response to climate extremes and during recovery after disturbance. Terrestrial gross primary productivity (GPP) varies greatly over time and space. A better understanding of this variability is necessary for more accurate predictions of the future climate–carbon cycle feedback. Recent studies have suggested that variability in GPP is driven by a broad range of biotic and abiotic factors operating mainly through changes in vegetation phenology and physiological processes. However, it is still unclear how plant phenology and physiology can be integrated to explain the spatiotemporal variability of terrestrial GPP. Based on analyses of eddy–covariance and satellite-derived data, we decomposed annual terrestrial GPP into the length of the CO2 uptake period (CUP) and the seasonal maximal capacity of CO2 uptake (GPPmax). The product of CUP and GPPmax explained >90% of the temporal GPP variability in most areas of North America during 2000–2010 and the spatial GPP variation among globally distributed eddy flux tower sites. It also explained GPP response to the European heatwave in 2003 (r2 = 0.90) and GPP recovery after a fire disturbance in South Dakota (r2 = 0.88). Additional analysis of the eddy–covariance flux data shows that the interbiome variation in annual GPP is better explained by that in GPPmax than CUP. These findings indicate that terrestrial GPP is jointly controlled by ecosystem-level plant phenology and photosynthetic capacity, and greater understanding of GPPmax and CUP responses to environmental and biological variations will, thus, improve predictions of GPP over time and space.

Relative information contributions of model vs. data to short‐ and long‐term forecasts of forest carbon dynamics

Biogeochemical models have been used to evaluate long-term ecosystem responses to global change on decadal and century time scales. Recently, data assimilation has been applied to improve these models for ecological forecasting. It is not clear what the relative information contributions of model (structure and parameters) vs. data are to constraints of short- and long-term forecasting. In this study, we assimilated eight sets of 10-year data (foliage, woody, and fine root biomass, litter fall, forest floor carbon [C], microbial C, soil C, and soil respiration) collected from Duke Forest into a Terrestrial Ecosystem model (TECO). The relative information contribution was measured by Shannon information index calculated from probability density functions (PDFs) of carbon pool sizes. The null knowledge without a model or data was defined by the uniform PDF within a prior range. The relative model contribution was information content in the PDF of modeled carbon pools minus that in the uniform PDF, while the relative data contribution was the information content in the PDF of modeled carbon pools after data was assimilated minus that before data assimilation. Our results showed that the information contribution of the model to constrain carbon dynamics increased with time whereas the data contribution declined. The eight data sets contributed more than the model to constrain C dynamics in foliage and fine root pools over the 100-year forecasts. The model, however, contributed more than the data sets to constrain the litter, fast soil organic matter (SOM), and passive SOM pools. For the two major C pools, woody biomass and slow SOM, the model contributed less information in the first few decades and then more in the following decades than the data. Knowledge of relative information contributions of model vs. data is useful for model development, uncertainty analysis, future data collection, and evaluation of ecological forecasting.

MODELING PATTERNS OF NONLINEARITY IN ECOSYSTEM RESPONSES TO TEMPERATURE, CO2, AND PRECIPITATION CHANGES

It is commonly acknowledged that ecosystem responses to global climate change are nonlinear. However, patterns of the nonlinearity have not been well characterized on ecosystem carbon and water processes. We used a terrestrial ecosystem (TECO) model to examine nonlinear patterns of ecosystem responses to changes in temperature, CO2, and precipitation individually or in combination. The TECO model was calibrated against experimental data obtained from a grassland ecosystem in the central United States and ran for 100 years with gradual change at 252 different scenarios. We primarily used the 100th-year results to explore nonlinearity of ecosystem responses. Variables examined in this study are net primary production (NPP), heterotrophic respiration (R(h)), net ecosystem carbon exchange (NEE), runoff, and evapotranspiration (ET). Our modeling results show that nonlinear patterns were parabolic, asymptotic, and threshold-like in response to temperature, CO2, and precipitation anomalies, respectively, for NPP, NEE, and R(h). Runoff and ET exhibited threshold-like pattern in response to both temperature and precipitation anomalies but were less sensitive to CO2 changes. Ecosystem responses to combined temperature, CO2, and precipitation anomalies differed considerably from the responses to individual factors in terms of response patterns and/or critical points of nonlinearity. Our results suggest that nonlinear patterns in response to multiple global-change factors were diverse and were considerably affected by combined climate anomalies on ecosystem carbon and water processes. The diverse response patterns in nonlinearity have profound implications for both experimental design and theoretical development.

Thermal optimality of net ecosystem exchange of carbon dioxide and underlying mechanisms.

• It is well established that individual organisms can acclimate and adapt to temperature to optimize their functioning. However, thermal optimization of ecosystems, as an assemblage of organisms, has not been examined at broad spatial and temporal scales. • Here, we compiled data from 169 globally distributed sites of eddy covariance and quantified the temperature response functions of net ecosystem exchange (NEE), an ecosystem-level property, to determine whether NEE shows thermal optimality and to explore the underlying mechanisms. • We found that the temperature response of NEE followed a peak curve, with the optimum temperature (corresponding to the maximum magnitude of NEE) being positively correlated with annual mean temperature over years and across sites. Shifts of the optimum temperature of NEE were mostly a result of temperature acclimation of gross primary productivity (upward shift of optimum temperature) rather than changes in the temperature sensitivity of ecosystem respiration. • Ecosystem-level thermal optimality is a newly revealed ecosystem property, presumably reflecting associated evolutionary adaptation of organisms within ecosystems, and has the potential to significantly regulate ecosystem-climate change feedbacks. The thermal optimality of NEE has implications for understanding fundamental properties of ecosystems in changing environments and benchmarking global models.