[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).

Abstract. We analyzed half-hourly tower-based flux measurements of carbon dioxide (CO2) from a boreal aspen forest and a temperate mixed deciduous forest in Canada to examine the influences of clouds on forest carbon uptake. We showed that the presence of clouds consistently and significantly increased the net ecosystem exchanges (NEE) of CO2 of both forests from the level under clear skies. The enhancement varied with cloudiness, solar elevation angles, and differed between the two forests. For the aspen forest the enhancement at the peak ranged from about 30% for the 200-25 ø interval of solar elevation angles to about 55% for the 550-60 ø interval. For the mixed forest the enhancement at the peak ranged from more than 60% for the 300-35 ø interval of solar elevation angles to about 30% for the 650-70 ø interval. Averaged over solar elevation angles >20 ø , the aspen and mixed forests had the maximal NEE at the irradiance equivalent to 78 and 71% of the clear-sky radiation, respectively. The general patterns of current sky conditions at both sites permit further increases in cloudiness to enhance their carbon uptake. We found that both forests can tolerate exceedingly large reductions of solar radiation (53% for the aspen forest and 46% for the mixed forest) caused by increases in cloudiness without lowering their capacities of carbon uptake. We suggest that the enhancement of carbon uptake under cloudy conditions results from the interactions of multiple environmental factors associated with the presence of clouds. IntroductionClouds, as a natural weather element at a given location, strongly influence environmental conditions on the ground surface via radiative transfer, latent heating, and precipitation [Benner and Curry, 1998]. Therefore it is expected that clouds can have important ramifications on CO2 exchanges between terrestrial ecosystems and the overlying atmosphere.

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Evaporation, drainage, and changes in storage for a bare Plainfield sand were measured with a lysimeter during June, July, and August 1967, under natural rainfall conditions. Cumulative evaporation at any stage was proportional to the square root of time following each heavy rainfall. The drainage rate was found to be an exponential function of water storage. Both relations can be predicted from flow theory with knowledge of soil capillary conductivity, diffusivity, and moisture retention characteristics. Using these two relations and daily rainfall data, the water storage in the top 150 cm was predicted over the season to within 0.3 cm.

Energy balance measurements of evapotranspiration from a young Douglas fir forest are reported for a period of 18 days in July 1õ70 when soil water was not limiting. Peak daily evapotranspiration rates characteristically occurred 2-3 hours after solar noon, and evapotranspiration showed a short-term independence from net radiation. This behavior is interpreted as being a consequence of the large forest roughnes.s. Daily evapotranspiration and net radiation were, however, well correlated. Values of surface diffusion resistance calculated from Monteith's combination formula are presented. Daytime values showed significant day-to-day diff•renc. es, and an attempt to define g potential evapotmnspiration rate by assuming a constant daytime surface resistance was not successful. Comparison of evapotransp{ration measurements with a potential evaporation formula for wet surfaces developed by Priestley and Taylor suggests that evaporation of intercepted water proceeds 20% more rapidly than evgpotranspiration from the nonwetted canopy. In July and August, 1970, energy balance/ Bowen ratio measurements of evapotranspiration from a. young Douglas fir forest were made as part o]• a hydrologic balance experimen.t on a site in the University of British Columbia (UBC) Research Forest. Examination of the energy balances compu•ted from the measurements reveals several clear patterns in the results. This paper presents the results for the initial 18-day period , of the experiment and discusses these patterns and some of the implications of the results. As a basis for interpreting the results, Monteith's [1965] canopy transpiration model has been used for part of the analysis.

covariance towers as part of the North American Carbon Program's site-level intercomparison. This study expands upon previous single-site and single-model analyses to determine what patterns of model error are consistent across a diverse range of models and sites. To assess the significance of model error at different time scales, a novel Monte Carlo approach was developed to incorporate flux observation error. Failing to account for observation error leads to a misidentification of the time scales that dominate model error. These analyses show that model error (1) is largest at the annual and 20-120 day scales, (2) has a clear peak at the diurnal scale, and (3) shows large variability among models in the 2-20 day scales. Errors at the annual scale were consistent across time, diurnal errors were predominantly during the growing season, and intermediate-scale errors were largely event driven. Breaking spectra into discrete temporal bands revealed a significant model-by-band effect but also a nonsignificant model-by-site effect, which together suggest that individual models show consistency in their error patterns. Differences among models were related to model time step, soil hydrology, and the representation of photosynthesis and phenology but not the soil carbon or nitrogen cycles. These factors had the greatest impact on diurnal errors, were less important at annual scales, and had the least impact at intermediate time scales.

Light use efficiency (LUE) models are widely used to simulate gross primary production (GPP).However, the treatment of the plant canopy as a big leaf by these models can introduce large uncertainties in simulated GPP. Recently, a two-leaf light use efficiency (TL-LUE) model was developed to simulate GPP separately for sunlit and shaded leaves and has been shown to outperform the big-leaf MOD17 model at six FLUX sites in China. In this study we investigated the performance of the TL-LUE model for a wider range of biomes. For this we optimized the parameters and tested the TL-LUE model using data from 98 FLUXNET sites which are distributed across the globe. The results showed that the TL-LUE model performed in general better than the MOD17 model in simulating 8 day GPP. Optimized maximum light use efficiency of shaded leaves (ε msh ) was 2.63 to 4.59 times that of sunlit leaves (ε msu ). Generally, the relationships of ε msh and ε msu with ε max ZHOU ET AL. were well described by linear equations, indicating the existence of general patterns across biomes. GPP simulated by the TL-LUE model was much less sensitive to biases in the photosynthetically active radiation (PAR) input than the MOD17 model. The results of this study suggest that the proposed TL-LUE model has the potential for simulating regional and global GPP of terrestrial ecosystems, and it is more robust with regard to usual biases in input data than existing approaches which neglect the bimodal within-canopy distribution of PAR.