The Moderate Resolution Spectroradiometer (MODIS) sensor has provided near real-time estimates of gross primary production (GPP) since March 2000. We compare four years (2000 to 2003) of satellite-based calculations of GPP with tower eddy CO 2 flux-based estimates across diverse land cover types and climate regimes. We examine the potential error contributions from meteorology, leaf area index (LAI)/fPAR, and land cover. The error between annual GPP computed from NASA's Data Assimilation Office's (DAO) and tower-based meteorology is 28%, indicating that NASA's DAO global meteorology plays an important role in the accuracy of the GPP algorithm. Approximately 62% of MOD15-based estimates of LAI were within the estimates based on field optical measurements, although remaining values

[1] In the southeastern United States (SE), the conversion of abandoned agricultural land to forests is the dominant feature of land-cover change. However, few attempts have been made to quantify the impact of such conversion on surface temperature. Here, this issue is explored experimentally and analytically in three adjacent ecosystems (a grass-covered old-field, OF, a planted pine forest, PP, and a hardwood forest, HW) representing a successional chronosequence in the SE. The results showed that changes in albedo alone can warm the surface by 0.9°C for the OF-to-PP conversion, and 0.7°C for the OF-to-HW conversion on annual time scales. However, changes in eco-physiological and aerodynamic attributes alone can cool the surface by 2.9 and 2.1°C, respectively. Both model and measurements consistently suggest a stronger over-all surface cooling for the OF-to-PP conversion, and the reason is attributed to leaf area variations and its impacts on boundary layer conductance. Citation: Juang, J.-Y., G. Katul, M. Siqueira, P. Stoy, and K. Novick (2007), Separating the effects of albedo from eco-physiological changes on surface temperature along a successional chronosequence in the southeastern United States,

We combined Eddy-covariance measurements with a linear perturbation analysis to isolate the relative contribution of physical and biological drivers on evapotranspiration (ET) in three ecosystems representing two end-members and an intermediate stage of a successional gradient in the southeastern US (SE). The study ecosystems, an abandoned agricultural field [old field (OF)], an early successional planted pine forest (PP), and a late-successional hardwood forest (HW), exhibited differential sensitivity to the wide range of climatic and hydrologic conditions encountered over the 4-year measurement period, which included mild and severe droughts and an ice storm. ET and modeled transpiration differed by as much as 190 and 270 mm yr À1 , respectively, between years for a given ecosystem. Soil water supply, rather than atmospheric demand, was the principal external driver of interannual ET differences. ET at OF was sensitive to climatic variability, and results showed that decreased leaf area index (L) under mild and severe drought conditions reduced growing season (GS) ET (ET GS ) by ca. 80 mm compared with a year with normal precipitation. Under wet conditions, higher intrinsic stomatal conductance (g s ) increased ET GS by 50 mm. ET at PP was generally larger than the other ecosystems and was highly sensitive to climate; a 50 mm decrease in ET GS due to the loss of L from an ice storm equaled the increase in ET from high precipitation during a wet year. In contrast, ET at HW was relatively insensitive to climatic variability. Results suggest that recent management trends toward increasing the land-cover area of PP-type ecosystems in the SE may increase the sensitivity of ET to climatic variability.

We measured net ecosystem CO 2 exchange (NEE) using the eddy covariance (EC) technique for 4 years at adjoining old field (OF), planted pine (PP) and hardwood forest (HW) ecosystems in the Duke Forest, NC. To compute annual sums of NEE and its components-gross ecosystem productivity (GEP) and ecosystem respiration (RE)-different 'flux partitioning' models (FPMs) were tested and the resulting C flux estimates were compared against published estimates from C budgeting approaches, inverse models, physiology-based forward models, chamber respiration measurements, and constraints on assimilation based on sapflux and evapotranspiration measurements. Our analyses demonstrate that the more complex FPMs, particularly the 'non-rectangular hyperbolic method', consistently produced the most reasonable C flux estimates. Of the FPMs that use nighttime data to estimate RE, one that parameterized an exponential model over short time periods generated predictions that were closer to expected flux values. To explore how much 'new information' was injected into the data by the FPMs, we used formal information theory methods and computed the Shannon entropy for: (1) the probability density, to assess alterations to the flux measurement distributions, and (2) the wavelet energy spectra, to assess alterations to the internal autocorrelation within the NEE time series. Based on this joint analysis, gap-filling had little impact on the IC of daytime data, but gap-filling significantly altered nighttime data in both the probability and wavelet spectral domains.

Grasslands cover about 40% of the ice-free global terrestrial surface, but their contribution to local and regional water and carbon fluxes and sensitivity to climatic perturbations such as drought remains uncertain. Here, we assess the direction and magnitude of net ecosystem carbon exchange (NEE) and its components, ecosystem carbon assimilation ( A(c)) and ecosystem respiration ( R(E)), in a southeastern United States grassland ecosystem subject to periodic drought and harvest using a combination of eddy-covariance measurements and model calculations. We modeled A(c) and evapotranspiration (ET) using a big-leaf canopy scheme in conjunction with ecophysiological and radiative transfer principles, and applied the model to assess the sensitivity of NEE and ET to soil moisture dynamics and rapid excursions in leaf area index (LAI) following grass harvesting. Model results closely match eddy-covariance flux estimations on daily, and longer, time steps. Both model calculations and eddy-covariance estimates suggest that the grassland became a net source of carbon to the atmosphere immediately following the harvest, but a rapid recovery in LAI maintained a marginal carbon sink during summer. However, when integrated over the year, this grassland ecosystem was a net C source (97 g C m(-2) a(-1)) due to a minor imbalance between large A(c) (-1,202 g C m(-2) a(-1)) and R(E) (1,299 g C m(-2) a(-1)) fluxes. Mild drought conditions during the measurement period resulted in many instances of low soil moisture (theta<0.2 m(3)m(-3)), which influenced A(c) and thereby NEE by decreasing stomatal conductance. For this experiment, low theta had minor impact on R(E). Thus, stomatal limitations to A(c) were the primary reason that this grassland was a net C source. In the absence of soil moisture limitations, model calculations suggest a net C sink of -65 g C m(-2) a(-1) assuming the LAI dynamics and physiological properties are unaltered. These results, and the results of other studies, suggest that perturbations to the hydrologic cycle are key determinants of C cycling in grassland ecosystems.

We introduce an analytical model, the Wald analytical long-distance dispersal (WALD) model, for estimating dispersal kernels of wind-dispersed seeds and their escape probability from the canopy. The model is based on simplifications to well-established three-dimensional Lagrangian stochastic approaches for turbulent scalar transport resulting in a two-parameter Wald (or inverse Gaussian) distribution. Unlike commonly used phenomenological models, WALD's parameters can be estimated from the key factors affecting wind dispersal--wind statistics, seed release height, and seed terminal velocity--determined independently of dispersal data. WALD's asymptotic power-law tail has an exponent of -3/2, a limiting value verified by a meta-analysis for a wide variety of measured dispersal kernels and larger than the exponent of the bivariate Student t-test (2Dt). We tested WALD using three dispersal data sets on forest trees, heathland shrubs, and grassland forbs and compared WALD's performance with that of other analytical mechanistic models (revised versions of the tilted Gaussian Plume model and the advection-diffusion equation), revealing fairest agreement between WALD predictions and measurements. Analytical mechanistic models, such as WALD, combine the advantages of simplicity and mechanistic understanding and are valuable tools for modeling large-scale, long-term plant population dynamics.

Orthonormal wavelet transformation (OWT) is a computationally efficient technique for quantifying underlying frequencies in nonstationary and gap-infested time series, such as eddy-covariance-measured net ecosystem exchange of CO2 (NEE). We employed OWT to analyze the frequency characteristics of synchronously measured and modeled NEE at adjacent pine (PP) and hardwood (HW) ecosystems. Wavelet cospectral analysis showed that NEE at PP was more correlated to light and vapor pressure deficit at the daily time scale, and NEE at HW was more correlated to leaf area index (LAI) and temperature, especially soil temperature, at seasonal time scales. Models were required to disentangle the impacts of environmental drivers on the components of NEE, ecosystem carbon assimilation (Ac) and ecosystem respiration (RE). Sensitivity analyses revealed that using air temperature rather than soil temperature in RE models improved the modeled wavelet spectral frequency response on time scales longer than 1 day at both ecosystems. Including LAI improved RE model fit on seasonal time scales at HW, and incorporating parameter variability improved the RE model response at annual time scales at both ecosystems. Resolving variability in canopy conductance, rather than leaf-internal CO2, was more important for modeling Ac at both ecosystems. The PP ecosystem was more sensitive to hydrologic variables that regulate canopy conductance: vapor pressure deficit on weekly time scales and soil moisture on seasonal to interannual time scales. The HW ecosystem was sensitive to water limitation on weekly time scales. A combination of intrinsic drought sensitivity and non-conservative water use at PP was the basis for this response. At both ecosystems, incorporating variability in LAI was required for an accurate spectral representation of modeled NEE. However, nonlinearities imposed by canopy light attenuation were of little importance to spectral fit. The OWT revealed similarities and differences in the scale-wise control of NEE by vegetation with implications for model simplification and improvement.

Understanding the relationships between climate and carbon exchange by terrestrial ecosystems is critical to predict future levels of atmospheric carbon dioxide because of the potential accelerating effects of positive climate-carbon cycle feedbacks. However, directly observed relationships between climate and terrestrial CO 2 exchange with the atmosphere across biomes and continents are lacking. Here we present data describing the relationships between net ecosystem exchange of carbon (NEE) and climate factors as measured using the eddy covariance method at 125 unique sites in various ecosystems over six continents with a total of 559 site-years. We find that NEE observed at eddy covariance sites is (1) a strong function of mean annual temperature at mid-and high-latitudes, (2) a strong function of dryness at mid-and low-latitudes, and (3) a function of both temperature and dryness around the mid-latitudinal belt (45 • N). The sensitivity of NEE to mean annual temperature breaks down at ∼16 • C (a threshold value of mean annual temperature), above which no further increase of CO 2 uptake with temperature was observed and dryness influence overrules temperature influence.