Phenology, by controlling the seasonal activity of vegetation on the land surface, plays a fundamental role in regulating photosynthesis and other ecosystem processes, as well as competitive interactions and feedbacks to the climate system. We conducted an analysis to evaluate the representation of phenology, and the associated seasonality of ecosystem-scale CO 2 exchange, in 14 models participating in the North American Carbon Program Site Synthesis. Model predictions were evaluated using long-term measurements (emphasizing the period 2000-2006) from 10 forested sites within the AmeriFlux and Fluxnet-Canada networks. In deciduous forests, almost all models consistently predicted that the growing season started earlier, and ended later, than was actually observed; biases of 2 weeks or more were 566-584, doi: 10.1111/j.1365-2486.2011.02562.x This article is a U.S. government work, and is not subject to copyright in the United States.Global Change Biology (2012) 18,typical. For these sites, most models were also unable to explain more than a small fraction of the observed interannual variability in phenological transition dates. Finally, for deciduous forests, misrepresentation of the seasonal cycle resulted in over-prediction of gross ecosystem photosynthesis by +160 ± 145 g C m À2 yr À1 during the spring transition period and +75 ± 130 g C m À2 yr À1 during the autumn transition period (13% and 8% annual productivity, respectively) compensating for the tendency of most models to under-predict the magnitude of peak summertime photosynthetic rates. Models did a better job of predicting the seasonality of CO 2 exchange for evergreen forests. These results highlight the need for improved understanding of the environmental controls on vegetation phenology and incorporation of this knowledge into better phenological models. Existing models are unlikely to predict future responses of phenology to climate change accurately and therefore will misrepresent the seasonality and interannual variability of key biosphere-atmosphere feedbacks and interactions in coupled global climate models.

Abstract. Leaf area index (LAI) is a key structural characteristic of forest ecosystems because of the role of green leaves in controlling many biological and physical processes in plant canopies. Accurate LAI estimates are required in studies of ecophysiology, atmosphere-ecosystem interactions, and global change. The objective of this paper is to evaluate LAI values obtained by several research teams using different methods for a broad spectrum of boreal forest types in support of the international Boreal EcosystemAtmosphere Study (BOREAS). These methods include destructive sampling and optical instruments: the tracing radiation and architecture of canopies (TRAC), the LAI-2000 plant canopy analyzer, hemispherical photography, and the Sunfleck Ceptometer. The latter three calculate LAI from measured radiation transmittance (gap fraction) using inversion models that assume a random spatial distribution of leaves. It is shown that these instruments underestimate LAI of boreal forest stands where the foliage is clumped. The TRAC quantifies the clumping effect by measuring the canopy gap size distribution. For deciduous stands the clumping index measured from TRAC includes the clumping effect at all scales, but for conifer stands it only resolves the clumping effect at scales larger than the shoot (the basic collection of needles). To determine foliage clumping within conifer shoots, a video camera and rotational light table system was used. The major difficulties in determining the surface area of small conifer needles have been largely overcome by the use of an accurate volume displacement method. Hemispherical photography has the advantage that it also provides a permanent image record of the canopies. Typically, LAI falls in the range from 1 to 4 for jack pine and aspen forests and from 1 to 6 for black spruce. Our comparative studies provide the most comprehensive set of LAI estimates available for boreal forests and demonstrate that optical techniques, combined with limited direct foliage sampling, can be used to obtain quick and accurate LAI measurements. IntroductionThe boreal forest is the second largest biome, and there is increasing interest in this biome for its role in the terrestial carbon cycle. One key to characterizing forest stands is obtaining accurate and meaningful measures of the forest canopy.

[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).

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Improving the accuracy of estimates of forest carbon exchange is a central priority for understanding ecosystem response to increased atmospheric CO levels and improving carbon cycle modelling. However, the spatially continuous parameterization of photosynthetic capacity (Vcmax) at global scales and appropriate temporal intervals within terrestrial biosphere models (TBMs) remains unresolved. This research investigates the use of biochemical parameters for modelling leaf photosynthetic capacity within a deciduous forest. Particular attention is given to the impacts of seasonality on both leaf biophysical variables and physiological processes, and their interdependent relationships. Four deciduous tree species were sampled across three growing seasons (2013-2015), approximately every 10 days for leaf chlorophyll content (Chl ) and canopy structure. Leaf nitrogen (N ) was also measured during 2014. Leaf photosynthesis was measured during 2014-2015 using a Li-6400 gas-exchange system, with A-Ci curves to model Vcmax. Results showed that seasonality and variations between species resulted in weak relationships between Vcmax normalized to 25°C (Vcmax25) and N (R = 0.62, P < 0.001), whereas Chl demonstrated a much stronger correlation with Vcmax25 (R = 0.78, P < 0.001). The relationship between Chl and N was also weak (R = 0.47, P < 0.001), possibly due to the dynamic partitioning of nitrogen, between and within photosynthetic and nonphotosynthetic fractions. The spatial and temporal variability of Vcmax25 was mapped using Landsat TM/ETM satellite data across the forest site, using physical models to derive Chl . TBMs largely treat photosynthetic parameters as either fixed constants or varying according to leaf nitrogen content. This research challenges assumptions that simple N -Vcmax25 relationships can reliably be used to constrain photosynthetic capacity in TBMs, even within the same plant functional type. It is suggested that Chl provides a more accurate, direct proxy for Vcmax25 and is also more easily retrievable from satellite data. These results have important implications for carbon modelling within deciduous ecosystems.

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The retrieval of canopy architectural parameters using off-the-shelf digital cameras with fish-eye lens is investigated. The technique used takes advantage of the sensor's linear response to light of these cameras to improve the estimation of gap fraction using:(1) the digital numbers of mixed sky-canopy pixels to estimate the within-pixel gap fraction; and (2) this process is done considering the variation in view zenith angle to take into account the sky radiance distribution and the canopy multiple scattering effects.The foliage element clumping index is retrieved over a wide range of view zenith angles using:(1) the accumulated gap size distribution theory developed for the TRAC by Chen and Cihlar (1995a); (2) This article is a U.S. government work, and is not subject to copyright in the United States.Using data from Canadian and Russian boreal forests, comparisons of gap fraction, clumping index and plant area index measured with the tracing radiation and architecture of canopies (TRAC) and digital hemispherical photography are presented. Evaluation of the LAI estimated from digital hemispherical photography with allometric LAI of two boreal forest stands suggest that that the clumping index combined method may be more accurate. # 2005 Published by Elsevier B.V.

[1] Sunlit and shaded leaf separation proposed by Norman (1982) is an effective way to upscale from leaf to canopy in modeling vegetation photosynthesis. The Boreal Ecosystem Productivity Simulator (BEPS) makes use of this methodology, and has been shown to be reliable in modeling the gross primary productivity (GPP) derived from CO 2 flux and tree ring measurements. In this study, we use BEPS to investigate the effect of canopy architecture on the global distribution of GPP. For this purpose, we use not only leaf area index (LAI) but also the first ever global map of the foliage clumping index derived from the multiangle satellite sensor POLDER at 6 km resolution. The clumping index, which characterizes the degree of the deviation of 3-dimensional leaf spatial distributions from the random case, is used to separate sunlit and shaded LAI values for a given LAI. Our model results show that global GPP in 2003 was 132 AE 22 Pg C. Relative to this baseline case, our results also show: (1) global GPP is overestimated by 12% when accurate LAI is available but clumping is ignored, and (2) global GPP is underestimated by 9% when the effective LAI is available and clumping is ignored. The clumping effects in both cases are statistically significant (p < 0.001). The effective LAI is often derived from remote sensing by inverting the measured canopy gap fraction to LAI without considering the clumping. Global GPP would therefore be generally underestimated when remotely sensed LAI (actually effective LAI by our definition) is used. This is due to the underestimation of the shaded LAI and therefore the contribution of shaded leaves to GPP. We found that shaded leaves contribute 50%, 38%, 37%, 39%, 26%, 29% and 21% to the total GPP for broadleaf evergreen forest, broadleaf deciduous forest, evergreen conifer forest, deciduous conifer forest, shrub, C4 vegetation, and other vegetation, respectively. The global average of this ratio is 35%.