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 to 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 a 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 grid cell. 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 – Carbon Monitoring System) to reproduce observations made at a site in northern China. The measurements are heavily influenced by the northern China administrative region. Modeled hourly time series 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 a 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.

PM2.5 during severe winter haze in Beijing, China, has reached levels as high as 880 μg/m3, with sulfur compounds contributing significantly to PM2.5 composition. This sulfur has been traditionally assumed to be sulfate, although atmospheric chemistry models are unable to account for such large sulfate enhancements under dim winter conditions. Using a 1-D model, we show that well-characterized but previously overlooked chemistry of aqueous-phase HCHO and S(IV) in cloud droplets to form a S(IV)-HCHO adduct, hydroxymethane sulfonate, may explain high particulate sulfur in wintertime Beijing. We also demonstrate in the laboratory that methods of ion chromatography typically used to measure ambient particulates easily misinterpret hydroxymethane sulfonate as sulfate. Our findings suggest that HCHO and not SO2 has been the limiting factor in many haze events in Beijing and that to reduce severe winter pollution in this region, policymakers may need to address HCHO sources such as transportation.

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 0.03–1.0 cm s−1. The stomatal O3 flux during the growing season (typically April–September) is 0.5–11.0 nmol O3 m−2 s−1 with a mean of 4.5 nmol O3 m−2 s−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 (R2=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.

With the pending withdrawal of the United States from the Paris Climate Accord, cities are now leading US actions toward reducing greenhouse gas emissions. Implementing effective mitigation strategies requires the ability to measure and track emissions over time and at various scales. We report CO2 emissions in the Boston, MA, urban region from September 2013 to December 2014 based on atmospheric observations in an inverse model framework. Continuous atmospheric measurements of CO2 from five sites in and around Boston were combined with a high-resolution bottom-up CO2 emission inventory and a Lagrangian particle dispersion model to determine regional emissions. Our model−measurement framework incorporates emissions estimates from submodels for both anthropogenic and biological CO2 fluxes, and development of a CO2 concentration curtain at the boundary of the study region based on a combination of tower measurements and modeled vertical concentration gradients. We demonstrate that an emission inventory with high spatial and temporal resolution and the inclusion of urban biological fluxes are both essential to accurately modeling annual CO2 fluxes using surface measurement networks. We calculated annual average emissions in the Boston region of 0.92 kg C·m−2·y−1 (95% confidence interval: 0.79 to 1.06), which is 14% higher than the Anthropogenic Carbon Emissions System inventory. Based on the capability of the model−measurement approach demonstrated here, our framework should be able to detect changes in CO2 emissions of greater than 18%, providing stakeholders with critical information to assess mitigation efforts in Boston and surrounding areas.

Methane emissions from the U.S. oil and natural gas supply chain were estimated by using ground-based, facility-scale measurements and validated with aircraft observations in areas accounting for ~30% of U.S. gas production. When scaled up nationally, our facility-based estimate of 2015 supply chain emissions is 13 ± 2 teragrams per year, equivalent to 2.3% of gross U.S. gas production. This value is ~60% higher than the U.S. Environmental Protection Agency inventory estimate, likely because existing inventory methods miss emissions released during abnormal operating conditions. Methane emissions of this magnitude, per unit of natural gas consumed, produce radiative forcing over a 20-year time horizon comparable to the CO2 from natural gas combustion. Substantial emission reductions are feasible through rapid detection of the root causes of high emissions and deployment of less failure-prone systems.

There are few whole-canopy or ecosystem scale assessments of the interplay between canopy temperature and photosynthesis across both spatial and temporal scales. The stable oxygen isotope ratio (δ18O) of plant cellulose can be used to resolve a photosynthesis-weighted estimate of canopy temperature, but the method requires independent confirmation. We compare isotope-resolved canopy temperatures derived from multi-year homogenization of tree cellulose δ18O to canopy-air temperatures weighted by gross primary productivity (GPP) at multiple sites, ranging from warm temperate to boreal and subalpine forests. We also perform a sensitivity analysis for isotope-resolved canopy temperatures that showed errors in plant source water δ18O lead to the largest errors in canopy temperature estimation. The relationship between isotope-resolved canopy temperatures and GPP-weighted air temperatures was highly significant across sites (p < 0.0001, R2 = 0.82), thus offering confirmation of the isotope approach. The previously observed temperature invariance from temperate to boreal biomes was confirmed, but the greater elevation of canopy temperature above air temperature in the boreal forest was not. Based on the current analysis, we conclude that canopy temperatures in the boreal forest are as warm as those in temperate systems because day-time-growing-season air temperatures are similarly warm.

Although the O2a1Δg band has long been used in ground-based greenhouse gas remote sensing to constrain the light path, it is challenging for nadir spaceborne sensors due to strong mesosphere/stratosphere airglow. Spectroscopic simulations using upper state populations successfully reconstruct the airglow spectra with excellent agreement with SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY limb observations (residual root-mean-square <0.7%). The accurate knowledge of airglow spectrum enables retrieval of O2(a1Δg) number density, volume emission rate, and temperature. For nadir spaceborne observations, the a1Δg airglow will lead to a negative bias of ∼10% to O2 column, if not considered. However, when properly included, the airglow spectral feature can be adequately separated from O2 absorption (mean bias <0.1%) at the spectral resolution of modern spaceborne spectrometers.

The impact of increases in drought frequency on the Amazon forest's composition, structure and functioning remain uncertain. We used a process- and individual-based ecosystem model (ED2) to quantify the forest's vulnerability to increased drought recurrence. We generated meteorologically realistic, drier-than-observed rainfall scenarios for two Amazon forest sites, Paracou (wetter) and Tapajós (drier), to evaluate the impacts of more frequent droughts on forest biomass, structure and composition. The wet site was insensitive to the tested scenarios, whereas at the dry site biomass declined when average rainfall reduction exceeded 15%, due to high mortality of large-sized evergreen trees. Biomass losses persisted when year-long drought recurrence was shorter than 2-7 yr, depending upon soil texture and leaf phenology. From the site-level scenario results, we developed regionally applicable metrics to quantify the Amazon forest's climatological proximity to rainfall regimes likely to cause biomass loss > 20% in 50 yr according to ED2 predictions. Nearly 25% (1.8 million km2 ) of the Amazon forests could experience frequent droughts and biomass loss if mean annual rainfall or interannual variability changed by 2σ. At least 10% of the high-emission climate projections (CMIP5/RCP8.5 models) predict critically dry regimes over 25% of the Amazon forest area by 2100.

Net ecosystem exchange (NEE) measurements using the eddy covariance technique have been widely used for calibration and evaluation of carbon flux estimates from terrestrial ecosystem models as well as for remote sensing-based estimates across various spatial and temporal scales. Therefore, it is vital to fully understand the land surface characteristics within the area contributing to these flux measurements (i.e. source area) when upscaling plot-scale tower measurements to regional-scale ecosystem estimates, especially in heterogeneous landscapes, such as mixed forests. We estimated the source area of a flux tower at a mixed forest (Harvard Forest in US) using a footprint model, and analyzed the spatial representativeness of the source area for the vegetation characteristics (density variation and magnitude) within the surrounding 1- and 1.5-km grid cells during two decades (1993–2011). Semi-variogram and window size analyses using 19 years of Landsat-retrieved enhanced vegetation index (EVI) confirmed that spatial heterogeneity within the 1-km grid cell has been gradually increasing for leaf-on periods. The overall prevailing source areas lay toward the southwest, yet there were considerable variations in the extents and the directions of the source areas. The source areas generally cover a large enough area to adequately represent the vegetation density magnitude and variation during both daytime and nighttime. We show that the variation in the daytime NEE during peak growing season should be more attributed to variations in the deciduous forest contribution within the source areas rather than the vegetation density. This study highlights the importance of taking account of the land cover variation within the source areas into gap-filling and upscaling procedures.

The phenological response of vegetation to ongoing climate change may have great implications for hydrological regimes in the eastern United States. However, there have been few studies that analyze its resultant effect on catchment discharge dynamics, separating from dominant climatic controls. In this study, we examined the net effect of phenological variations on the long-term and interannual gross primary production (GPP) and evapotranspiration (ET) fluxes in a temperate deciduous forest, as well as on the catchment discharge behavior in a mixed deciduous-conifer forest catchment. First, we calibrated the spring and autumn leaf phenology models for the Harvard Forest in the northeastern United States, where the onsets of greenup and senescence have been significantly advanced and delayed, 10.3 and 6.0 days respectively, over the past two decades (1992–2011). We then integrated the phenology models into a mechanistic watershed ecohydrological model (RHESSys), which improved the interannual and long-term simulations of both the plot-scale daily GPP and ET fluxes and the catchment discharge dynamics. We found that the phenological changes amplified the long-term increases in GPP and ET driven by the climatic controls. In particular, the earlier greenup onsets resulted in increases in annual ET significantly, while the delayed senescence onsets had less influence. Consequently, the earlier greenup onsets reduced stream discharge not only during the growing season but also during the following dormant season due to soil water depletion. This study highlights the importance of understanding vegetation response to ongoing climate change in order to predict the future hydrological nonstationarity in this region.

Understanding, quantifying, and tracking atmospheric methane and emissions is essential for addressing concerns and informing decisions that affect the climate, economy, and human health and safety. Atmospheric methane is a potent greenhouse gas (GHG) that contributes to global warming. While carbon dioxide is by far the dominant cause of the rise in global average temperatures, methane also plays a significant role because it absorbs more energy per unit mass than carbon dioxide does, giving it a disproportionately large effect on global radiative forcing. In addition to contributing to climate change, methane also affects human health as a precursor to ozone pollution in the lower atmosphere.

The East Asian Summer Monsoon driven by temperature and moisture gradients between the Asian continent and the Pacific Ocean, leads to approximately 50% of the annual rainfall in the region across 20–40°N. Due to its increasing scientific and social importance, there have been several previous studies on identification of moisture sources for summer monsoon rainfall over East Asia mainly using Lagrangian or Eulerian atmospheric water vapor models. The major source regions for EASM previously proposed include the North Indian Ocean, South China Sea and North western Pacific. Based on high-precision and high-frequency 6-year measurement records of hydrofluorocarbons (HFCs), here we report a direct evidence of rapid intrusion of warm and moist tropical air mass from the Southern Hemisphere (SH) reaching within a couple of days up to 33°N into East Asia. We further suggest that the combination of direct chemical tracer record and a back-trajectory model with physical meteorological variables helps pave the way to identify moisture sources for monsoon rainfall. A case study for Gosan station (33.25°N, 126.19°E) indicates that the meridional transport of precipitable water from the SH accompanying the southerly/southwesterly flow contributes most significantly to its summer rainfall.

Systematic biases in eddy covariance measurements of net ecosystem-atmosphere carbon dioxide exchange (NEE) are ubiquitous in forests when turbulence is low at night. We propose an alternative to the conventional bias correction, the friction velocity (u*) filter, by hypothesizing that these biases have two separate, concurrent causes: (1) a subcanopy CO2 storage pool that eludes typical storage measurements, creating a turbulence-dependent bias, and (2) advective divergence loss of CO2, creating a turbulence-independent bias. We correct for (1) using a simple parametric model of missing storage (MS). Prior experiments have inferred (2) directly from atmospheric measurements (DRAINO). For sites at which DRAINO experiments have not been performed or are infeasible, we estimate (2) empirically using a PAR-extrapolated advective respiration loss (PEARL) approach. We compare u* filter estimates of advection and NEE to MS-PEARL estimates at one temperate forest and two tropical forest sites. We find that for tropical forests, u* filters can produce a range of extreme NEE estimates, from long-term forest carbon emission to sequestration, that diverge from independent assessments and are not physically sustainable. Our MS model eliminates the dependence of nighttime NEE on u*, consistent with findings from DRAINO studies that nighttime advective losses of CO2 are often not dependent on the strength of turbulence. Our PEARL estimates of mean advective loss agree with available DRAINO measurements. The MS-PEARL correction to long-term NEE produces better agreement with forest inventories at all three sites. Moreover, the correction retains all nighttime eddy covariance data and is therefore more widely applicable than the u* filter approach, which rejects substantial nighttime data—up to 93% at one of the tropical sites. The full MS-PEARL NEE correction is therefore an equally defensible and more practical alternative to the u* filter, but leads to different conclusions about the resulting carbon balance. Our results therefore highlight the need to investigate which approach's underlying hypotheses are more physically realistic.

Methane (CH4) is the second most important greenhouse gas but its emissions from northern regions are still poorly constrained. In this study, we analyze a subset of in situ CH4 aircraft observations made over Alaska during the growing seasons of 2012-2014 as part of the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). Net surface CH4 fluxes are estimated using a Lagrangian particle dispersion model which quantitatively links surface emissions from Alaska and the western Yukon with observations of enhanced CH4 in the mixed layer. We estimate that between May and September, net CH4 emissions from the region of interest were 2.2 ± 0.5 Tg, 1.9 ± 0.4 Tg, and 2.3 ± 0.6 Tg of CH4 for 2012, 2013, and 2014, respectively. If emissions are only attributed to two biogenic eco-regions within our domain, then tundra regions were the predominant source, accounting for over half of the overall budget despite only representing 18 % of the total surface area. Boreal regions, which cover a large part of the study region, accounted for the remainder of the emissions. Simple multiple linear regression analysis revealed that, overall, CH4 fluxes were largely driven by soil temperature and elevation. In regions specifically dominated by wetlands, soil temperature and moisture at 10 cm depth were important explanatory variables while in regions that were not wetlands, soil temperature and moisture at 40 cm depth were more important, suggesting deeper methanogenesis in drier soils. Although similar environmental drivers have been found in the past to control CH4 emissions at local scales, this study shows that they can be used to generate a statistical model to estimate the regional-scale net CH4 budget.

Development of greenhouse gas (GHG) emission inventories is necessary for estimating the relative significance of emissions from various sources and evaluating the effects of mitigation efforts. Inventories are developed for specific purposes and can provide emission estimates at many scales, from facility level, to urban, regional, national, and global scales.

This report outlines actions to improve estimates of the amounts of anthropogenic methane emitted in the United States and to improve the utility and usability of methane emission inventories to governments, industries, academia, nongovernmental organizations, and the general public. Atmospheric observations are fed into atmospheric inverse models to compute emission fluxes, called the “top-down” approach to emission estimation. The more familiar “bottom-up” approach is based on scaling up of data for emissions by individual components or facilities.Top-down and bottom-up approaches can be tested against each other to improve the application of both approaches.

In the United States, methane emission data are generated by various entities including the U.S. Environmental Protection Agency (EPA), state and local governments, industry, and researchers from academia, national laboratories, and nongovernmental organizations (NGOs). The stakeholder community using these emission data is even broader and includes policymakers at various levels of government, industry, scientific communities, and the general public. The needs of these stakeholders are diverse, as are their academic backgrounds and their understanding of the generation and reporting of methane emission information. Careful consideration of the audience for any published methane emission data is a key step in generating products that will be scientifically valid and properly used. For the scientific community, presentation of results in peer-reviewed literature would be expected to facilitate dissemination of key technical findings to fellow researchers. Similarly, various governmental agencies, research institutes, industries, and NGOs also publish methane results and reports, but they may not have undergone the peer-review process. As a broader audience attempts to understand and apply the research findings, there is increasing potential for misinterpreting and incorrectly using the results.

Measurements of emissions and monitoring of methane are essential for the development of robust emission inventories as described in Chapter 2. Field measurement of emissions from various sectoral sources can provide improved understanding of processes that lead to emissions, which contributes to the development of process-based emission models as well as regional- and urban-scale mitigation strategies. Furthermore, atmospheric monitoring of methane concentrations is also needed to detect regional trends in emissions and enable rigorous comparisons to bottom-up approaches.