William R. Emanuel

A global land primary productivity and phytogeography model

A global primary productivity and phytogeography model is described. The model represents the biochemical processes of photosynthesis and the dependence of gas exchange on stomatal conductance, which in turn depends on temperature and soil moisture. Canopy conductance controls soil water loss by evapotranspiration. The assignment of nitrogen uptake to leaf layers is proportional to irradiance, and respiration and maximum assimilation rates depend on nitrogen uptake and temperature. Total nitrogen uptake is derived from soil carbon and nitrogen and depends on temperature. The long-term average annual carbon and hydrological budgets dictate canopy leaf area. Although observations constrain soil carbon and nitrogen, the distribution of vegetation types is not specified by an underlying map. Variables simulated by the model are compared to experimental results. These comparisons extend from biochemical processes to the whole canopy, and the comparisons are favorable for both current and elevated CO2 atmospheres. The model is used to simulate the global distributions of leaf area index and annual net primary productivity. These distributions are sufficiently realistic to demonstrate that the model is useful for analyzing vegetation responses to global environmental change.

Carbon cycling in soil

As yet, nobody knows what effects climate change will have on soil carbon reserves, or how those changes will affect the global carbon cycle. Soils are the primary terrestrial repository for carbon, so minor changes in the balance between belowground carbon storage and release could have major impacts on greenhouse gases. Soil fauna, roots, fungi, and microbes interact with mineral and organic matter to process soil carbon. Studies have been hampered by the difficulty of observing processes beneath the earths surface, but advances in science and technology are improving our ability to understand belowground ecosystems.

Patterns and Dynamics of Forests: An Application of Simulation Models

Studies of forest succession characteristically rely heavily on inference to order the spatial pattern of forest stands (usually on a regularly disturbed landscape) into a temporal pattern. The inferential structure used to arrange these data is, in a sense, a “theory of succession,” and the relation between the artificially ordered successional pattern and the ordering theory are, of necessity, tautological. This circularity persists regardless of the particular theory of succession (monoclimax, polyclimax, multiple paths, etc.) to which an ecologist happens to subscribe. The fact that both the observation data sets and the theory may both be quite elaborate makes it difficult to test the theories of succession. In this chapter, we shall take an alternate approach to studying successional dynamics. Instead of a direct and long-term observation of vegetation plots or a synthetic ordering of several plots, we shall use a set of reasonably detailed computer models of forest dynamics to explore system-level responses of different ecosystems. This simulation technique does not rely upon inferences made regarding temporal and spatial scales as do empirical studies of vegetation dynamics.

Projecting future concentrations of atmospheric CO2 with global carbon cycle models: The importance of simulating historical changes

Projections of future atmospheric CO2 concentrations using global carbon cycle models and assumed time series of future anthropogenic CO2 emissions are only useful if simulations agree reasonably well with the observed history of past changes in atmospheric CO2. In this article we compare simulations from a set of eight global carbon cycle models with observations of atmospheric CO2 from the Siple Station, Antarctica, ice core and the monitoring station at Mauna Loa Observatory, Hawaii, USA. Our comparisons reinforce previous assessments that early estimates of biospheric CO2 emissions derived by reconstruction of historical land-use change are incompatible with the understanding of atmosphere-ocean CO2 exchange codified in conventional carbon cycle models and the observed history of changes in atmospheric CO2. More recent estimates of the history of CO2 emissions associated with land-use change do not significantly resolve this incompatibility. Terrestrial biospheric emissions estimated by deconvolution of atmospheric CO2 observations provide reasonable correspondence between simulation and observation, but the deconvolution estimates differ dramatically from the estimates by land-use reconstruction. Resolution of this difference is a challenge for modelers of the global terrestrial biosphere. In the interim, caution is required in interpreting atmospheric CO2 projections from models that have not yet resolved the basic inconsistencies among emission estimates, models of oceanic uptake, and observations of atmospheric CO2.

Aspects of the Interaction Between Vegetation and Soil Under Global Change

Responses of terrestrial ecosystems to a world undergoing a change in atmospheric CO2 concentration presents a formidable challenge to terrestrial ecosystem scientists. Strong relationships among climate, atmosphere, soils and biota at many different temporal and spatial scales make the understanding and prediction of changes in net ecosystem production (NEP) at a global scale difficult. Global C cycle models have implicitly attempted to account for some of this complexity by adapting lower pool sizes and smaller flux rates representing large regions and long temporal averages than values appropriate for a small area. However, it is becoming increasingly evident that terrestrial ecosystems may be experiencing a strong transient forcing as a result of increasing levels of atmospheric CO2 that will require a finer temporal and spatial representation of terrestrial systems than the parameters for current global C cycle models allow. To adequately represent terrestrial systems in the global C cycle it is necessary to explicitly model the response of terrestrial systems to primary environmental factors. While considerable progress has been made experimentally and conceptually in aspects of photosynthetic responses, and gross and net primary production, the application of this understanding to NEP at individual sites is not well developed. This is an essential step in determining effects of plant physiological responses on the global C cycle. We use a forest stand succession model to explore the effects of several possible plant responses to elevated atmospheric CO2 concentration. These simulations show that ecosystem C storage can be increased by increases in individual tree growth rate, reduced transpiration, or increases in fine root production commensurate with experimental observations.

Environmental gradients in a simulation model of a beech-yellow-poplar stand☆

Abstract A simulation model for growth and succession of a hypothetical American-beech–yellow-poplar forest has been developed to study changes in response of this simple community under conditions of a slowly varying climate. As the temperature, through a model parameter equal to the number of growing degree days (GDD), is increased, a sharp discontinuity in response of the model is noted at approximately 4500 GDD. A similar discontinuity is observed at about 4800 GDD as the temperature is slowly decreased. This hysteretic response with width of 300 GDD can be compared for consistency against the rather sharp boundaries which occur between forest communities along smooth environmental gradients. Although many environmental gradients are responsible for transitions is natural systems, the model calculations described indicate that changes in a single environmental variable, temperature, can account for transition zones by affecting competitive ability.

Modeling Large-Scale Vegetation Dynamics

The foregoing discussions have focused on information and processes needed to model global vegetation change. In this chapter, we discuss the models and modeling approaches that will be useful in analyzing responses of vegetation to large-scale environmental changes. We emphasize changing climate, but the concepts and approaches are pertinent to other global changes as well. First, we summarize the natural processes that dictate vegetation dynamics under changing environmental conditions. Models that relate natural vegetation distribution to climate are then described. These can be purely correlative—based on observed relationships between vegetation and climate—or more mechanistic, based on the physiological limits of different types of plants. To simulate transient responses, we need dynamic models that mechanistically represent community processes including competition. We summarize a class of such models that are suited to small landscape or patch analysis. We conclude by outlining a scheme for applying these models in large-scale studies by a Monte Carlo method that incorporates stochastic processes within the landscape and samples spatial variability in the environment.

Spectral analysis of forest model time series

Abstract The use of spectral analysis to elucidate the cyclic behavior in time series generated by a forest stand growth simulation model is discussed. A stand-level simulator, FORET, for an Appalachian deciduous forest is described. An estimate of the power spectral density of the total biomass time series is calculated. The power spectral density estimate indicates a dominant cyclic behavior with a period of about 200 years. In addition the spectral density is approximately bandlimited. This characteristic makes possible the application of the sampling theorem for analysis of sampling rates.

An optimization-based multi-resolution simulation methodology

The need for new approaches to the consistent simulation of related phenomena at multiple levels of resolution is great. While many fields of application would benefit from a complete and approachable solution to this problem, such solutions have proven extremely difficult. We present a multi-resolution simulation methodology which uses numerical optimization as a tool for maintaining external consistency between models of the same phenomena operating at different levels of temporal and/or spatial resolution. Our approach follows from previous work in the disparate fields of inverse modeling and spacetime constraint-based animation. As a case study, our methodology is applied to two environmental models of forest canopy processes that make overlapping predictions under unique sets of operating assumptions, and which execute at different temporal resolutions. Experimental results are presented and future directions are addressed.

A dynamic model of terrestrial carbon cycling

Many human activities tend to reduce the amount of carbon in plants and soils (Woodwell et al 1983, Houghton et al 1983, Houghton and Skole 1990). Houghton et al (1983) estimate that forest clearing and harvest released 180 Pg (1 Pg = 1 x 1015g) of carbon into the atmosphere between 1860 and 1980 compared to about 163 Pg from fossil fuel emissions over the same period (Marland et al 1989)–terrestrial releases exceeded fossil fuel emissions until about 1959, according to these estimates. The 1990 net flux of carbon into the atmosphere, mostly due to tropical deforestation, may have approached 3 Pg/year (Houghton, this volume). Thus the use and manipulation of terrestrial ecosystems probably caused a significant part of the observed increases in atmospheric CO2 concentration (Bolin 1986, Post et al 1990).