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We develop and evaluate a method to estimate O3 deposition and stomatal O3 uptake across networks of eddy covariance flux tower sites where O3 concentrations and O3 fluxes have not been measured. The method combines standard micrometeorological flux measurements, which constrain O3 deposition velocity and stomatal conductance, with a gridded dataset of observed surface O3 concentrations. Measurement errors are propagated through all calculations to quantify O3 flux uncertainties. We evaluate the method at three sites with O3 flux measurements: Harvard Forest, Blodgett Forest, and Hyytiälä Forest. The method reproduces 83% or more of the variability in daily stomatal uptake at these sites with modest mean bias (21% or less). At least 95% of daily average values agree with measurements within a factor of 2 and, according to the error analysis, the residual differences from measured O3 fluxes are consistent with the uncertainty in the underlying measurements. The product, called synthetic O3 flux or SynFlux, includes 43 FLUXNET sites in the United States and 60 sites in Europe, totaling 926 site years of data. This dataset, which is now public, dramatically expands the number and types of sites where O3 fluxes can be used for ecosystem impact studies and evaluation of air quality and climate models. Across these sites, the mean stomatal conductance and O3 deposition velocity is 0.03-1.0cms-1. The stomatal O3 flux during the growing season (typically April-September) is 0.5-11.0nmol O3m-2s-1 with a mean of 4.5nmol O3m-2s-1 and the largest fluxes generally occur where stomatal conductance is high, rather than where O3 concentrations are high. The conductance differences across sites can be explained by atmospheric humidity, soil moisture, vegetation type, irrigation, and land management. These stomatal fluxes suggest that ambient O3 degrades biomass production and CO2 sequestration by 20%-24% at crop sites, 6%-29% at deciduous broadleaf forests, and 4%-20% at evergreen needleleaf forests in the United States and Europe.

In Amazon forests, the relative contributions of climate, phenology, and disturbance to net ecosystem exchange of carbon (NEE) are not well understood. To partition influences across various timescales, we use a statistical model to represent eddy-covariance-derived NEE in an evergreen eastern Amazon forest as a constant response to changing meteorology and phenology throughout a decade. Our best fit model represented hourly NEE variations as changes due to sunlight, while seasonal variations arose from phenology influencing photosynthesis and from rainfall influencing ecosystem respiration, where phenology was asynchronous with dry-season onset. We compared annual model residuals with biometric forest surveys to estimate impacts of drought disturbance. We found that our simple model represented hourly and monthly variations in NEE well (R 2 Combining double low line 0.81 and 0.59, respectively). Modeled phenology explained 1 % of hourly and 26 % of monthly variations in observed NEE, whereas the remaining modeled variability was due to changes in meteorology. We did not find evidence to support the common assumption that the forest phenology was seasonally light- or water-triggered. Our model simulated annual NEE well, with the exception of 2002, the first year of our data record, which contained 1.2 MgC ha'1 of residual net emissions, because photosynthesis was anomalously low. Because a severe drought occurred in 1998, we hypothesized that this drought caused a persistent, multi-year depression of photosynthesis. Our results suggest drought can have lasting impacts on photosynthesis, possibly via partial damage to still-living trees.