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Ozone (O3 ) damage to leaves can reduce plant photosynthesis, which suggests that declines in ambient O3 concentrations ([O3 ]) in the United States may have helped increase gross primary production (GPP) in recent decades. Here, we assess the effect of long-term changes in ambient [O3 ] using 20 years of observations at Harvard forest. Using artificial neural networks, we found that the effect of the inclusion of [O3 ] as a predictor was slight, and independent of O3 concentrations, which suggests limited high-frequency O3 inhibition of GPP at this site. Simulations with a terrestrial biosphere model, however, suggest an average long-term O3 inhibition of 10.4% for 1992-2011. A decline of [O3 ] over the measurement period resulted in moderate predicted GPP trends of 0.02-0.04 μmol C m-2  s-1  yr-1 , which is negligible relative to the total observed GPP trend of 0.41 μmol C m-2  s-1  yr-1 . A similar conclusion is achieved with the widely used AOT40 metric. Combined, our results suggest that ozone reductions at Harvard forest are unlikely to have had a large impact on the photosynthesis trend over the past 20 years. Such limited effects are mainly related to the slow responses of photosynthesis to changes in [O3 ]. Furthermore, we estimate that 40% of photosynthesis happens in the shade, where stomatal conductance and thus [O3 ] deposition is lower than for sunlit leaves. This portion of GPP remains unaffected by [O3 ], thus helping to buffer the changes of total photosynthesis due to varied [O3 ]. Our analyses suggest that current ozone reductions, although significant, cannot substantially alleviate the damages to forest ecosystems.

Accurately quantifying the spatiotemporal distribution of the biological component of CO2 surface-atmosphere exchange is necessary to improve top-down constraints on China's anthropogenic CO2 emissions. We provide hourly fluxes of CO2 as Net Ecosystem Exchange (NEE; µmol CO2 m-2 s-1) on a 0.25°×0.25° grid by adapting the Vegetation, Photosynthesis, and Respiration Model (VPRM) to the eastern half of China for the time period from 2005-2009; the minimal empirical parameterization of the VPRM-CHINA makes it well-suited for inverse modeling approaches. This study diverges from previous VPRM applications in that it is applied at large scale to China's ecosystems for the first time, incorporating a novel processing framework not previously applied to existing VPRM versions. In addition, the VPRM-CHINA model prescribes methods for addressing dual-cropping regions that have two separate growing season modes applied to the same model gridcell. We evaluate the VPRM-CHINA performance during the growing season and compare to other biospheric models. We calibrate the VPRM-CHINA with ChinaFlux and FluxNet data and scale up regionally using Weather Research and Forecasting (WRF) Model v3.6.1 meteorology and MODIS surface reflectances. When combined with an anthropogenic emissions model in a Lagrangian particle transport framework, we compare the ability of VPRM-CHINA relative to an ensemble mean of global hourly flux models (NASA CMS) to reproduce observations made at a site in Northern China. The measurements are heavily influenced by the Northern China administrative region. Modeled hourly timeseries using vegetation fluxes prescribed by VPRM-CHINA exhibit low bias relative to measurements during the May-September growing season. Compared to NASA CMS subset over the study region, VPRM-CHINA agrees significantly better with measurements. NASA CMS consistently underestimates regional uptake in the growing season. We find that during the peak growing season, when the heavily cropped North China Plain significantly influences measurements, VPRM-CHINA models an CO2 uptake signal comparable in magnitude to the modeled anthropogenic signal. In addition to demonstrating efficacy as a low-bias prior for top-down CO2 inventory optimization studies using ground-based measurements, high spatiotemporal resolution models such as the VPRM are critical for interpreting retrievals from global CO2 remote sensing platforms such as OCO-2 and OCO-3 (planned). Depending on the satellite time-of-day and season of crossover, efforts to interpret the relative contribution of the vegetation and anthropogenic components to the measured signal are critical in key emitting regions such as Northern China -where the magnitude of the vegetation CO2 signal is shown to be equivalent to the anthropogenic signal. [ABSTRACT FROM AUTHOR]