The loss of ozone to terrestrial and aquatic systems, known as dry deposition, is a highly uncertain process governed by turbulent transport, interfacial chemistry, and plant physiology. We demonstrate the value of using Deep Neural Networks (DNN) in predicting ozone dry deposition velocities. We find that a feedforward DNN trained on observations from a coniferous forest site (Hyytiälä, Finland) can predict hourly ozone dry deposition velocities at a mixed forest site (Harvard Forest, Massachusetts) more accurately than modern theoretical models, with a reduction in the normalized mean bias (0.05 versus ~0.1). The same DNN model, when driven by assimilated meteorology at 2° × 2.5° spatial resolution, outperforms the Wesely scheme as implemented in the GEOS-Chem model. With more available training data from other climate and ecological zones, this methodology could yield a generalizable DNN suitable for global models.

Measurements of atmospheric O2 have been used to quantify large-scale fluxes of carbon between the oceans, atmosphere and land since 1992 (Keeling and Shertz, 1992). With time, datasets have grown and estimates of fluxes have become more precise, but a key uncertainty in these calculations is the exchange ratio of O2 and CO2 associated with the net land carbon sink (αB). We present measurements of atmospheric O2 and CO2 collected over a 6-year period from a mixed deciduous forest in central Massachusetts, USA (42.537∘ N, 72.171∘ W). Using a differential fuel-cell-based instrument for O2 and a nondispersive infrared analyzer for CO2, we analyzed airstreams collected within and ∼5 m above the forest canopy. Averaged over the entire period of record, we find these two species covary with a slope of −1.081±0.007 mol of O2 per mole of CO2 (the mean and standard error of 6 h periods). If we limit the data to values collected on summer days within the canopy, the slope is −1.03±0.01. These are the conditions in which biotic influences are most likely to dominate. This result is significantly different from the value of −1.1 widely used in O2-based calculations of the global carbon budget, suggesting the need for a deeper understanding of the exchange ratios of the various fluxes and pools comprising the net sink.

Recent warming of the Arctic has motivated assessments of methane (CH4) release from the North Slope Region of Alaska (NSRA). This study examines the contributions of thermogenic emissions from the Prudhoe Bay Oil Field (PBOF) to the elevated concentrations of atmospheric CH4 observed across the NSRA. We report high precision atmospheric measurements of CH4 and ethane (C2H6) within and downwind of the PBOF. Biogenic CH4 emissions, due to methanogenic processes within the Arctic tundra, are not co-emitted with C2H6. We show that the thermogenic gas emanating from oil and gas extraction point sources contains on average 1 mol of C2H6 for every 16 mol of CH4. We use a mass balance approach to estimate total emissions of thermogenic CH4 from two days in the summer of 2016 and find 2–5 times greater emissions than the sum of all sources in the PBOF reported to the EPA Greenhouse Gas Reporting Program in 2016. Although higher than reported, these emissions are much smaller than estimates of CH4 emissions from other oil and natural gas production areas in the US, and they make a very small contribution to total CH4 emissions from the North Slope.

The Ecosystem Demography model version 2.2 (ED-2.2) is a terrestrial biosphere model that simulates the biophysical, ecological, and biogeochemical dynamics of vertically and horizontally heterogeneous terrestrial ecosystems. In a companion paper (Longo et al., 2019a), we described how the model solves the energy, water, and carbon cycles, and verified the high degree of conservation of these properties in long-term simulations that include long-term (multi-decadal) vegetation dynamics. Here, we present a detailed assessment of the model's ability to represent multiple processes associated with the biophysical and biogeochemical cycles in Amazon forests. We use multiple measurements from eddy covariance towers, forest inventory plots, and regional remote-sensing products to assess the model's ability to represent biophysical, physiological, and ecological processes at multiple timescales, ranging from subdaily to century long. The ED-2.2 model accurately describes the vertical distribution of light, water fluxes, and the storage of water, energy, and carbon in the canopy air space, the regional distribution of biomass in tropical South America, and the variability of biomass as a function of environmental drivers. In addition, ED-2.2 qualitatively captures several emergent properties of the ecosystem found in observations, specifically observed relationships between aboveground biomass, mortality rates, and wood density; however, the slopes of these relationships were not accurately captured. We also identified several limitations, including the model's tendency to overestimate the magnitude and seasonality of heterotrophic respiration and to overestimate growth rates in a nutrient-poor tropical site. The evaluation presented here highlights the potential of incorporating structural and functional heterogeneity within biomes in Earth system models (ESMs) and to realistically represent their impacts on energy, water, and carbon cycles. We also identify several priorities for further model development.

Earth system models (ESMs) have been developed to represent the role of terrestrial ecosystems on the energy, water, and carbon cycles. However, many ESMs still lack representation of within-ecosystem heterogeneity and diversity. In this paper, we present the Ecosystem Demography model version 2.2 (ED-2.2). In ED-2.2, the biophysical and physiological processes account for the horizontal and vertical heterogeneity of the ecosystem: the energy, water, and carbon cycles are solved separately for a series of vegetation cohorts (groups of individual plants of similar size and plant functional type) distributed across a series of spatially implicit patches (representing collections of micro-environments that have a similar disturbance history). We define the equations that describe the energy, water, and carbon cycles in terms of total energy, water, and carbon, which simplifies the differential equations and guarantees excellent conservation of these quantities in long-term simulation (< 0.1 % error over 50 years). We also show examples of ED-2.2 simulation results at single sites and across tropical South America. These results demonstrate the model's ability to characterize the variability of ecosystem structure, composition, and functioning both at stand and continental scales. A detailed model evaluation was conducted and is presented in a companion paper (Longo et al., 2019a). Finally, we highlight some of the ongoing model developments designed to improve the model's accuracy and performance and to include processes hitherto not represented in the model.

Spatiotemporal variability in ozone dry deposition is often overlooked despite its implications for interpreting and modeling tropospheric ozone concentrations accurately. Understanding the influences of stomatal versus nonstomatal deposition processes on ozone deposition velocity is important for attributing observed changes in the ozone depositional sink and associated damage to ecosystems. Here, we aim to identify the stomatal versus nonstomatal deposition processes driving observed variability in ozone deposition velocity over the northeastern United States during June–September. We use ozone eddy covariance measurements from Harvard Forest in Massachusetts, which span a decade, and from Kane Experimental Forest in Pennsylvania and Sand Flats State Forest in New York, which span one growing season each, along with observation-driven modeling. Using a cumulative precipitation indicator of soil wetness, we infer that high soil uptake during dry years and low soil uptake during wet years may contribute to the twofold interannual variability in ozone deposition velocity at Harvard Forest. We link stomatal deposition and humidity to variability in ozone deposition velocity on daily timescales. The humidity dependence may reflect higher uptake by leaf cuticles under humid conditions, noted in previous work. Previous work also suggests that uptake by leaf cuticles may be enhanced after rain, but we find that increases in ozone deposition velocity on rainy days are instead mostly associated with increases in stomatal conductance. Our analysis highlights a need for constraints on subseasonal variability in ozone dry deposition to soil and fast in-canopy chemistry during ecosystem stress.

Multiple lines of evidence suggest that plant water-use efficiency (WUE)—the ratio of carbon assimilation to water loss—has increased in recent decades. Although rising atmospheric CO2 has been proposed as the principal cause, the underlying physiological mechanisms are still being debated, and implications for the global water cycle remain uncertain. Here, we addressed this gap using 30-y tree ring records of carbon and oxygen isotope measurements and basal area increment from 12 species in 8 North American mature temperate forests. Our goal was to separate the contributions of enhanced photosynthesis and reduced stomatal conductance to WUE trends and to assess consistency between multiple commonly used methods for estimating WUE. Our results show that tree ring-derived estimates of increases in WUE are consistent with estimates from atmospheric measurements and predictions based on an optimal balancing of carbon gains and water costs, but are lower than those based on ecosystem-scale flux observations. Although both physiological mechanisms contributed to rising WUE, enhanced photosynthesis was widespread, while reductions in stomatal conductance were modest and restricted to species that experienced moisture limitations. This finding challenges the hypothesis that rising WUE in forests is primarily the result of widespread, CO2-induced reductions in stomatal conductance.

Simulation of the planetary boundary layer (PBL) is key for forecasting air quality and estimating greenhouse gas (GHG) emissions in cities. Here we conducted the first long-term and continuous study of PBL heights (PBLHs) in Boston, MA, using a compact lidar instrument. We developed an image recognition algorithm to estimate PBLHs from the lidar measurements and evaluated simulations of the PBL from seven numerical weather prediction (NWP) model versions, which showed different systematic errors and variability in simulating the PBLHs (discrepancies from −2.5 to 4.0 km). The NWP model with the best overall agreement for the fully developed PBL had R2 = 0.72 and a bias of only 0.128 km. However, this model predicted a notable number of anomalously high carbon dioxide concentrations at ground stations, because it occasionally significantly underestimated the PBLH. We also developed a novel method that combines lidar data with footprints from a Lagrangian particle dispersion model to identify long-range transport of air pollution in the nocturnal residual layer. Our framework was powerful in evaluating the performance of models used to estimate air pollution and GHG emissions in cities, which is critical to track progress on emission reduction targets and guide effective policies.

We report airborne measurements of acetaldehyde (CH3CHO) during the first and second deployments of the National Aeronautics and Space Administration Atmospheric Tomography Mission (ATom). The budget of CH3CHO is examined using the Community Atmospheric Model with chemistry (CAM-chem), with a newly developed online air-sea exchange module. The upper limit of the global ocean net emission of CH3CHO is estimated to be 34 Tg/a (42 Tg/a if considering bubble-mediated transfer), and the ocean impacts on tropospheric CH3CHO are mostly confined to the marine boundary layer. Our analysis suggests that there is an unaccounted CH3CHO source in the remote troposphere and that organic aerosols can only provide a fraction of this missing source. We propose that peroxyacetic acid is an ideal indicator of the rapid CH3CHO production in the remote troposphere. The higher-than-expected CH3CHO measurements represent a missing sink of hydroxyl radicals (and halogen radical) in current chemistry-climate models.

The chemical mechanisms responsible for rapid sulfate production, an important driver of winter haze formation in northern China, remain unclear. Here, we propose a potentially important heterogeneous hydroxymethanesulfonate (HMS) chemical mechanism. Through analyzing field measurements with aerosol mass spectrometry, we show evidence for a possible significant existence in haze aerosols of organosulfur primarily as HMS, misidentified as sulfate in previous observations. We estimate that HMS can account for up to about one-third of the sulfate concentrations unexplained by current air quality models. Heterogeneous production of HMS by SO2 and formaldehyde is favored under northern China winter haze conditions due to high aerosol water content, moderately acidic pH values, high gaseous precursor levels, and low temperature. These analyses identify an unappreciated importance of formaldehyde in secondary aerosol formation and call for more research on sources and on the chemistry of formaldehyde in northern China winter.

We have compared a suite of recent global CO2 atmospheric inversion results to independent airborne observations and to each other, to assess their dependence on differences in northern extratropical (NET) vertical transport and to identify some of the drivers of model spread. We evaluate posterior CO2 concentration profiles against observations from the High-Performance Instrumented Airborne Platform for Environmental Research (HIAPER) Pole-to-Pole Observations (HIPPO) aircraft campaigns over the mid-Pacific in 2009–2011. Although the models differ in inverse approaches, assimilated observations, prior fluxes, and transport models, their broad latitudinal separation of land fluxes has converged significantly since the Atmospheric Carbon Cycle Inversion Intercomparison (TransCom 3) and the REgional Carbon Cycle Assessment and Processes (RECCAP) projects, with model spread reduced by 80 % since TransCom 3 and 70 % since RECCAP. Most modeled CO2 fields agree reasonably well with the HIPPO observations, specifically for the annual mean vertical gradients in the Northern Hemisphere. Northern Hemisphere vertical mixing no longer appears to be a dominant driver of northern versus tropical (T) annual flux differences. Our newer suite of models still gives northern extratropical land uptake that is modest relative to previous estimates (Gurney et al., 2002; Peylin et al., 2013) and near-neutral tropical land uptake for 2009–2011. Given estimates of emissions from deforestation, this implies a continued uptake in intact tropical forests that is strong relative to historical estimates (Gurney et al., 2002; Peylin et al., 2013). The results from these models for other time periods (2004–2014, 2001–2004, 1992–1996) and re-evaluation of the TransCom 3 Level 2 and RECCAP results confirm that tropical land carbon fluxes including deforestation have been near neutral for several decades. However, models still have large disagreements on ocean–land partitioning. The fossil fuel (FF) and the atmospheric growth rate terms have been thought to be the best-known terms in the global carbon budget, but we show that they currently limit our ability to assess regional-scale terrestrial fluxes and ocean–land partitioning from the model ensemble.

2 inexorable rise propels anthropogenic climate change. Modeling and mechanistically understanding C uptake by the terrestrial biosphere are thus of broad societal concerns. Yet despite considerable progress, scaling up point observations to landscape and larger scales continues to frustrate analyses of the anthropogenically perturbed global C cycle. While that up-scaling is our overarching motivation, here we focus on one of its elements, modeling C uptake at a given site. We devise a novel artificial neural network (ANN)-based model of C uptake at Harvard Forest that combines locally observed and remotely sensed variables. Most of our model predictors are those used by an established ecosystem C uptake model, the Vegetation Photosynthesis and Respiration Model (VPRM), easing comparisons. To those, we add observed cumulative antecedent precipitation and soil temperature. We find that model errors are much larger in winter, indicating that better understanding and modeling of respiration will likely discernibly improve model performance. Comparing the ANN and VPRM results reveals errors attributed to unrealistic treatment of temperature in the VPRM formulation, indicating that better representation of temperature dependencies is also likely to enhance model skill. By judiciously comparing VPRM and ANN errors we thus overcome ANNs' notoriety for concealing the mechanisms underlying their predictive skills. We demonstrate their ability to identify outstanding ecosystem science knowledge gaps and particularly fruitful corresponding model development directions, improving site specific and up-scaling flux modeling and understanding of the climate impacts of the northern forest.