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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.

Deforestation in mid- to high latitudes is hypothesized to have the potential to cool the Earth's surface by altering biophysical processes. In climate models of continental-scale land clearing, the cooling is triggered by increases in surface albedo and is reinforced by a land albedo-sea ice feedback. This feedback is crucial in the model predictions; without it other biophysical processes may overwhelm the albedo effect to generate warming instead. Ongoing land-use activities, such as land management for climate mitigation, are occurring at local scales (hectares) presumably too small to generate the feedback, and it is not known whether the intrinsic biophysical mechanism on its own can change the surface temperature in a consistent manner. Nor has the effect of deforestation on climate been demonstrated over large areas from direct observations. Here we show that surface air temperature is lower in open land than in nearby forested land. The effect is 0.85 ± 0.44 K (mean ± one standard deviation) northwards of 45° N and 0.21 ± 0.53 K southwards. Below 35° N there is weak evidence that deforestation leads to warming. Results are based on comparisons of temperature at forested eddy covariance towers in the USA and Canada and, as a proxy for small areas of cleared land, nearby surface weather stations. Night-time temperature changes unrelated to changes in surface albedo are an important contributor to the overall cooling effect. The observed latitudinal dependence is consistent with theoretical expectation of changes in energy loss from convection and radiation across latitudes in both the daytime and night-time phase of the diurnal cycle, the latter of which remains uncertain in climate models.

Author(s): Kengo F Kusano [1]; Roberto Pola [2]; Toshinori Murayama [1]; Cynthia Curry [1]; Atsuhiko Kawamoto [1]; Atsushi Iwakura [1]; Satoshi Shintani [1]; Masaaki Ii [1]; Jun Asai [1]; Tengiz [...]

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]

Uncertainties in model projections of carbon cycling in terrestrial ecosystems stem from inaccurate parameterization of incorporated processes (endogenous uncertainties) and processes or drivers that are not accounted for by the model (exogenous uncertainties). Here, we assess endogenous and exogenous uncertainties using a model-data fusion framework benchmarked with an artificial neural network (ANN). We used 18years of eddy-covariance carbon flux data from the Harvard forest, where ecosystem carbon uptake has doubled over the measurement period, along with 15 ancillary ecological data sets relative to the carbon cycle. We test the ability of combinations of diverse data to constrain projections of a process-based carbon cycle model, both against the measured decadal trend and under future long-term climate change. The use of high-frequency eddy-covariance data alone is shown to be insufficient to constrain model projections at the annual or longer time step. Future projections of carbon cycling under climate change in particular are shown to be highly dependent on the data used to constrain the model. Endogenous uncertainties in long-term model projections of future carbon stocks and fluxes were greatly reduced by the use of aggregated flux budgets in conjunction with ancillary data sets. The data-informed model, however, poorly reproduced interannual variability in net ecosystem carbon exchange and biomass increments and did not reproduce the long-term trend. Furthermore, we use the model-data fusion framework, and the ANN, to show that the long-term doubling of the rate of carbon uptake at Harvard forest cannot be explained by meteorological drivers, and is driven by changes during the growing season. By integrating all available data with the model-data fusion framework, we show that the observed trend can only be reproduced with temporal changes in model parameters. Together, the results show that exogenous uncertainty dominates uncertainty in future projections from a data-informed process-based model., Organismic and Evolutionary Biology