We use observations from dual high-resolution mass spectrometers to characterize ecosystem-atmosphere fluxes of reactive carbon across an extensive range of volatile organic compounds (VOCs) and test how well that exchange is represented in current chemical transport models. Measurements combined proton-transfer reaction mass spectrometry (PTRMS) and iodide chemical ionization mass spectrometry (ICIMS) over a Colorado pine forest; together, these techniques have been shown to capture the majority of ambient VOC abundance and reactivity. Total VOC mass and associated OH reactivity fluxes were dominated by emissions of 2-methyl-3-buten-2-ol, monoterpenes, and small oxygenated VOCs, with a small number of compounds detected by PTRMS driving the majority of both net and upward exchanges. Most of these dominant species are explicitly included in chemical models, and we find here that GEOS-Chem accurately simulates the net and upward VOC mass and OH reactivity fluxes under clear sky conditions. However, large upward terpene fluxes occurred during sustained rainfall, and these are not captured by the model. Far more species contributed to the downward fluxes than are explicitly modeled, leading to a major underestimation of this key sink of atmospheric reactive carbon. This model bias mainly reflects missing and underestimated concentrations of depositing species, though inaccurate deposition velocities also contribute. The deposition underestimate is particularly large for assumed isoprene oxidation products, organic acids, and nitrates—species that are primarily detected by ICIMS. Ecosystem-atmosphere fluxes of ozone reactivity were dominated by sesquiterpenes and monoterpenes, highlighting the importance of these species for predicting near-surface ozone, oxidants, and aerosols.
We synthesized N2O emissions over North America using 17 bottom-up (BU) estimates from 1980-2016 and five top-down (TD) estimates from 1998-2016. The BU-based total emission shows a slight increase owing to U.S. agriculture, while no consistent trend is shown in TD estimates. During 2007-2016, North American N2O emissions are estimated at 1.7 (1.0-3.0) Tg N yr-1 (BU) and 1.3 (0.9-1.5) Tg N yr-1 (TD). Anthropogenic emissions were twice larger than natural fluxes from soil and water. Direct agricultural and industrial activities accounted for 68% of total anthropogenic emissions, 71% of which was contributed by the U.S. Our estimates of U.S. agricultural emissions are comparable to the EPA greenhouse gas (GHG) inventory, which includes estimates from IPCC tier 1 (emission factor) and tier 3 (process-based modeling) approaches. Conversely, our estimated agricultural emissions for Canada and Mexico are twice as large as the respective national GHG inventories based on tier 1 approaches.
We perform Observation System Simulation Experiments (OSSEs) with the GEOS-Chem adjoint model to test how well methane emissions over North America can be resolved using measurements from the TROPOspheric Monitoring Instrument (TROPOMI) and similar high-resolution satellite sensors. We focus analysis on the impacts of i) spatial errors in the prior emissions, and ii) model transport errors. Along with a standard scale-factor (SF) optimization we conduct a set of inversions using alternative formalisms that aim to overcome limitations in the SF-based approach that arise for missing sources. We show that 4D-Var analysis of the TROPOMI data can improve monthly emission estimates at 25 km even with a spatially biased prior or model transport errors (42–93% domain-wide bias reduction; R increases from 0.51 up to 0.73). However, when both errors are present, no single inversion framework can successfully improve both the overall bias and spatial distribution of fluxes relative to the prior on the 25 km model grid. In that case, the ensemble-mean optimized fluxes have a domain-wide bias of 77 Gg/d (comparable to that in the prior), with spurious source adjustments compensating for the transport errors. Increasing observational coverage through longer-timeframe inversions does not significantly change this picture. An inversion formalism that optimizes emission enhancements rather than scale factors exhibits the best performance for identifying missing sources, while an approach combining a uniform background emission with the prior inventory yields the best performance in terms of overall spatial fidelity—even in the presence of model transport errors. However, the standard SF optimization outperforms both of these for the magnitude of the domain-wide flux. For the common scenario in which prior errors are non-random, approximate posterior error reduction calculations for the inversions reflect the sensitivity to observations but have no spatial correlation with the actual emission improvements. This demonstrates that such information content analysis can be used for general observing system characterization but does not describe the spatial accuracy of the posterior emissions or of the actual emission improvements. Findings here highlight the need for careful evaluation of potential missing sources in prior emission datasets and for robust accounting of model transport errors in inverse analyses of the methane budget.
Isoprene is the dominant non-methane organic compound emitted to the atmosphere, where it drives ozone and aerosol production, modulates atmospheric oxidation, and interacts with the global nitrogen cycle. Isoprene emissions are highly variable and uncertain, as is the non-linear chemistry coupling isoprene and its primary sink, the hydroxyl radical (OH). Space-based isoprene measurements can help close the gap on these uncertainties, and when combined with concurrent formaldehyde data provide a new constraint on atmospheric oxidation regimes. Here we present a next-generation machine-learning isoprene retrieval for the Cross-track Infrared Sounder (CrIS) that provides improved sensitivity, lower noise, and thus higher space-time resolution than earlier approaches. The Retrieval of Organics with CrIS Radiances (ROCR) isoprene measurements compare well with previous space-based retrievals as well as with the first-ever ground-based isoprene column measurements, with 20-50% discrepancies that reflect differing sources of systematic uncertainty. An ensemble of sensitivity tests points to the spectral background and isoprene profile specification as the most relevant uncertainty sources in the ROCR framework. We apply the ROCR isoprene algorithm to the full CrIS record from 2012-2020, showing that it can resolve fine-scale spatial gradients at daily resolution over the world’s isoprene hotspots. Results over North America and Amazonia highlight emergent connections between isoprene abundance and daily-to-interannual variations in temperature, nitrogen oxides, and drought stress.
The rate of increase in atmospheric methane (CH4) has accelerated in recent years, reaching 15 ppb/yr in 2020, with causes that are not well understood. Given methane’s potent global warming potential (85x that of CO2 on a 20-year timescale), this indicates a crucial need to better understand its current budget. Near-global high-precision methane column observations from the TROPOMI satellite sensor offer a major advance for mapping methane fluxes. Here we combine two years of TROPOMI data with the GEOS-Chem adjoint model in a 4D-Var framework to optimize global methane emissions at high spatial resolution. The inversions converge on distinct sets of solutions depending on whether methane loss rates are also simultaneously optimized or not. Findings thus show that even with the dense TROPOMI coverage, methane budget inferences remain sensitive to the prior assumptions for OH. The ensemble of solutions adheres to a close linear relationship between the derived global source and sink terms, with each distinct result successfully improving the simulation of globally-available in-situ data. Solutions with methane loss rates treated as a hard constraint exhibit the best consistency with remote OH and CO measurements and with the background seasonal cycle in methane. We further employ multiple inversion formalisms to test the solution sensitivity to the assumed prior emissions. This presentation will explore the derived emission adjustments in terms of their implications for methane flux drivers and potential missing sources.
Carbon monoxide (CO) is an ozone precursor, oxidant sink, and widely-used pollution tracer. The importance of anthropogenic versus other CO sources in the US is uncertain. Here we interpret extensive airborne measurements with an atmospheric model to constrain US fossil and non-fossil CO sources. Measurements reveal a low bias in the simulated CO background and a 30% overestimate of US fossil CO emissions in the 2016 National Emissions Inventory. After optimization we apply the model for source partitioning. During summer, regional fossil sources account for just 9-16% of the sampled boundary layer CO, and 32-38% of the North American enhancement-complicating use of CO as a fossil fuel tracer. The remainder predominantly reflects biogenic hydrocarbon oxidation plus fires. Fossil sources account for less domain-wide spatial variability at this time than non-fossil and background contributions. The regional fossil contribution rises in other seasons, and drives ambient variability downwind of urban areas.