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The hydroxyl radical (OH) plays a central role in tropospheric chemistry, as well as influencing the lifetimes of some greenhouse gases. Because of limitations in our ability to observe OH, we have historically relied on indirect methods to constrain its concentrations, trends, and variations but only as annual global or annual semi-hemispheric averages. Recent methods demonstrated the feasibility of indirectly constraining tropospheric OH on finer spatio-temporal scales using satellite observations as proxies for the photochemical drivers of OH (e.g., nitrogen dioxide, formaldehyde, isoprene, water vapor, ozone). We found that there are currently reasonable satellite proxies to constrain up to about 75 % of the global sources of tropospheric OH and up to about 50 % of the global sinks. With additional research and investment in observing various volatile organic compounds, there is potential to constrain an additional 10 % of the global sources and 30 % of the global sinks. We propose steps forward for the development of a comprehensive space-based observing strategy, which will improve our ability to indirectly constrain OH on much finer spatio-temporal scales than previously achieved. We discuss the strengths and limitations of such an observing strategy and potential improvements to current satellite instrument observing capabilities that would enable better constraint of OH. Suborbital observations (i.e., data collected from non-satellite platforms such as aircraft, balloons, and buildings) are required to collect information difficult to obtain from space and for validation of satellite-based OH estimates; therefore, they should be an integral part of a comprehensive observing strategy.

The tropospheric hydroxyl (TOH) radical is a key player in regulating oxidation of various compounds in Earth's atmosphere. Despite its pivotal role, the spatiotemporal distributions of OH are poorly constrained. Past modeling studies suggest that the main drivers of OH, including NO2, tropospheric ozone (TO3), and H2O(v), have increased TOH globally. However, these findings often offer a global average and may not include more recent changes in diverse compounds emitted on various spatiotemporal scales. Here, we aim to deepen our understanding of global TOH trends for more recent years (2005–2019) at 1×1°. To achieve this, we use satellite observations of HCHO and NO2 to constrain simulated TOH using a technique based on a Bayesian data fusion method, alongside a machine learning module named the Efficient CH4-CO-OH (ECCOH) configuration, which is integrated into NASA's Goddard Earth Observing System (GEOS) global model. This innovative module helps efficiently predict the convoluted response of TOH to its drivers and proxies in a statistical way. Aura Ozone Monitoring Instrument (OMI) NO2 observations suggest that the simulation has high biases for biomass burning activities in Africa and eastern Europe, resulting in a regional overestimation of up to 20 % in TOH. OMI HCHO primarily impacts the oceans, where TOH linearly correlates with this proxy. Five key parameters, i.e., TO3, H2O(v), NO2, HCHO, and stratospheric ozone, can collectively explain 65 % of the variance in TOH trends. The overall trend of TOH influenced by NO2 remains positive, but it varies greatly because of the differences in the signs of anthropogenic emissions. Over the oceans, TOH trends are primarily positive in the Northern Hemisphere, resulting from the upward trends in HCHO, TO3, and H2O(v). Using the present framework, we can tap the power of satellites to quickly gain a deeper understanding of simulated TOH trends and biases.

Despite its importance in controlling the abundance of methane (CH4) and a myriad of other tropospheric species, the hydroxyl radical (OH) is poorly constrained due to its large spatial heterogeneity and the inability to measure tropospheric OH with satellites. Here, we present a methodology to infer tropospheric column OH (TCOH) in the tropics over the open oceans using a combination of a machine learning model, output from a simulation of the GEOS model, and satellite observations. Our overall goals are to assess the feasibility of our methodology, to identify potential limitations, and to suggest areas of improvement in the current observational network. The methodology reproduces the variability of TCOH from independent 3D model output and of observations from the Atmospheric Tomography mission (ATom). While the methodology also reproduces the magnitude of the 3D model validation set, the accuracy of the magnitude when applied to observations is uncertain because current observations are insufficient to fully evaluate the machine learning model. Despite large uncertainties in some of the satellite retrievals necessary to infer OH, particularly for NO2 and formaldehyde (HCHO), current satellite observations are of sufficient quality to apply the machine learning methodology, resulting in an error comparable to that of in situ OH observations. Finally, the methodology is not limited to a specific suite of satellite retrievals. Comparison of TCOH determined from two sets of retrievals does show, however, that systematic biases in NO2, resulting both from retrieval algorithm and instrumental differences, lead to relative biases in the calculated TCOH. Further evaluation of NO2 retrievals in the remote atmosphere is needed to determine their accuracy. With slight modifications, a similar methodology could likely be expanded to the extratropics and over land, with the benefits of increasing our understanding of the atmospheric oxidation capacity and, for instance, informing understanding of recent CH4 trends.

We present a methodology that uses gradient-boosted regression trees (a machine learning technique) and a full-chemistry simulation (i.e., training dataset) from a chemistry–climate model (CCM) to efficiently generate a parameterization of tropospheric hydroxyl radical (OH) that is a function of chemical, dynamical, and solar irradiance variables. This surrogate model of OH is designed to be integrated into a CCM and allow for computationally efficient simulation of nonlinear feedbacks between OH and tropospheric constituents that have loss by reaction with OH as their primary sinks (e.g., carbon monoxide (CO), methane (CH4), volatile organic compounds (VOCs)). Such a model framework is advantageous for studies that require multi-decadal simulations of CH4 or multi-year sensitivity simulations to understand the causes of trends and variations of CO and CH4. To allow the user to easily target the training dataset towards a desired application, we are outlining a methodology to generate a parameterization of OH and not presenting an “off-the-shelf” version of a parameterization to be incorporated into a CCM. This provides for the relatively easy creation of a new parameterization in response to, for example, changes in research goals or the underlying CCM chemistry and/or dynamics schemes. We show that a sample parameterization of OH generated from a CCM simulation is able to reproduce OH concentrations with a normalized root-mean-square error of approximately 5 % and capture the global mean methane lifetime within approximately 1 %. Our calculated accuracy of the parameterization assumes inputs being within the bounds of the training dataset. Large excursions from these bounds will likely decrease the overall accuracy. However, we show that the sample parameterization predicts large deviations in OH for an El Niño event that was not part of the training dataset and that the spatial distribution and strength of these deviations are consistent with the event. This result gives confidence in the fidelity of a parameterization developed with our methodology to simulate the spatial and temporal responses of OH to perturbations from large variations in the chemical, dynamical, and solar irradiance drivers of OH. In addition, we discuss how two machine learning metrics, Gain feature importance and Shapley additive explanations values, indicate that the behavior of a parameterization of OH generally accords with our understanding of OH chemistry, even though there are no physics- or chemistry-based constraints on the parameterization.

The hydroxyl radical (OH) is the primary atmospheric oxidant responsible for removing many important trace gases, including methane, from the atmosphere. Although robust relationships between OH drivers and modes of climate variability have been shown, the underlying mechanisms between OH and these climate modes, such as the El Niño–Southern Oscillation (ENSO), have not been thoroughly investigated. Here, we use a chemical transport model to perform a 38 year simulation of atmospheric chemistry, in conjunction with satellite observations, to understand the relationship between tropospheric OH and ENSO, Northern Hemispheric modes of variability, the Indian Ocean Dipole, and monsoons. Empirical orthogonal function (EOF) and regression analyses show that ENSO is the dominant mode of global OH variability in the tropospheric column and upper troposphere, responsible for approximately 30 % of the total variance in boreal winter. Reductions in OH due to El Niño are centered over the tropical Pacific and Australia and can be as high as 10 %–15 % in the tropospheric column. The relationship between ENSO and OH is driven by changes in nitrogen oxides in the upper troposphere and changes in water vapor and O1D in the lower troposphere. While the correlations between monsoons or other modes of variability and OH span smaller spatial scales than for ENSO, regional changes in OH can be significantly larger than those caused by ENSO. Similar relationships occur in multiple models that participated in the Chemistry–Climate Model Initiative (CCMI), suggesting that the dependence of OH interannual variability on these well-known modes of climate variability is robust. Finally, the spatial pattern and r2 values of correlation between ENSO and modeled OH drivers – such as carbon monoxide, water vapor, lightning, and, to a lesser extent, NO2 – closely agree with satellite observations. The ability of satellite products to capture the relationship between OH drivers and ENSO provides an avenue to an indirect OH observation strategy and new constraints on OH variability.

The hydroxyl radical (OH) plays critical roles within the troposphere, such as determining the lifetime of methane (CH4), yet is challenging to model due to its fast cycling and dependence on a multitude of sources and sinks. As a result, the reasons for variations in OH and the resulting methane lifetime (τCH4), both between models and in time, are difficult to diagnose. We apply a neural network (NN) approach to address this issue within a group of models that participated in the Chemistry-Climate Model Initiative (CCMI). Analysis of the historical specified dynamics simulations performed for CCMI indicates that the primary drivers of τCH4 differences among 10 models are the flux of UV light to the troposphere (indicated by the photolysis frequency JO1D), the mixing ratio of tropospheric ozone (O3), the abundance of nitrogen oxides (NOx≡NO+NO2), and details of the various chemical mechanisms that drive OH. Water vapour, carbon monoxide (CO), the ratio of NO:NOx, and formaldehyde (HCHO) explain moderate differences in τCH4, while isoprene, methane, the photolysis frequency of NO2 by visible light (JNO2), overhead ozone column, and temperature account for little to no model variation in τCH4. We also apply the NNs to analysis of temporal trends in OH from 1980 to 2015. All models that participated in the specified dynamics historical simulation for CCMI demonstrate a decline in τCH4 during the analysed timeframe. The significant contributors to this trend, in order of importance, are tropospheric O3, JO1D, NOx, and H2O, with CO also causing substantial interannual variability in OH burden. Finally, the identified trends in τCH4 are compared to calculated trends in the tropospheric mean OH concentration from previous work, based on analysis of observations. The comparison reveals a robust result for the effect of rising water vapour on OH and τCH4, imparting an increasing and decreasing trend of about 0.5 % decade−1, respectively. The responses due to NOx, ozone column, and temperature are also in reasonably good agreement between the two studies.

We present a method to infer CO2 emissions from individual power plants based on satellite observations of co-emitted nitrogen dioxide (NO2), which could serve as complementary verification of bottom-up inventories or be used to supplement these inventories. We demonstrate its utility on eight large and isolated US power plants, where accurate stack emission estimates of both gases are available for comparison. In the first step of our methodology, we infer nitrogen oxides (NOx) emissions from US power plants using Ozone Monitoring Instrument (OMI) NO2 tropospheric vertical column densities (VCDs) averaged over the ozone season (May–September) and a “top-down” approach that we previously developed. Second, we determine the relationship between NOx and CO2 emissions based on the direct stack emissions measurements reported by continuous emissions monitoring system (CEMS) programs, accounting for coal quality, boiler firing technology, NOx emission control device type, and any change in operating conditions. Third, we estimate CO2 emissions for power plants using the OMI-estimated NOx emissions and the CEMS NOx∕CO2 emission ratio. We find that the CO2 emissions estimated by our satellite-based method during 2005–2017 are in reasonable agreement with the US CEMS measurements, with a relative difference of 8 %±41 % (mean ± standard deviation). The broader implication of our methodology is that it has the potential to provide an additional constraint on CO2 emissions from power plants in regions of the world without reliable emissions accounting. We explore the feasibility by comparing the derived NOx∕CO2 emission ratios for the US with those from a bottom-up emission inventory for other countries and applying our methodology to a power plant in South Africa, where the satellite-based emission estimates show reasonable consistency with other independent estimates. Though our analysis is limited to a few power plants, we expect to be able to apply our method to more US (and world) power plants when multi-year data records become available from new OMI-like sensors with improved capabilities, such as the TROPOspheric Monitoring Instrument (TROPOMI), and upcoming geostationary satellites, such as the Tropospheric Emissions: Monitoring Pollution (TEMPO) instrument.

Abundance-based model evaluations with observations provide critical tests for the simulated mean state in models of intercontinental pollution transport, and under certain conditions may also offer constraints on model responses to emission changes. We compile multiyear measurements of peroxy acetyl nitrate (PAN) available from five mountaintop sites and apply them in a proof-of-concept approach that exploits an ensemble of global chemical transport models (HTAP1) to identify an observational emergent constraint. In April, when the signal from anthropogenic emissions on PAN is strongest, simulated PAN at northern midlatitude mountaintops correlates strongly with PAN source–receptor relationships (the response to 20 % reductions in precursor emissions within northern midlatitude continents; hereafter, SRRs). This finding implies that PAN measurements can provide constraints on PAN SRRs by limiting the SRR range to that spanned by the subset of models simulating PAN within the observed range. In some cases, regional anthropogenic volatile organic compound (AVOC) emissions, tracers of transport from different source regions, and SRRs for ozone also correlate with PAN SRRs. Given the large observed interannual variability in the limited available datasets, establishing strong constraints will require matching meteorology in the models to the PAN measurements. Application of this evaluation approach to the chemistry–climate models used to project changes in atmospheric composition will require routine, long-term mountaintop PAN measurements to discern both the climatological SRR signal and its interannual variability.

This overview paper highlights the successes of the Ozone Monitoring Instrument (OMI) on board the Aura satellite spanning a period of nearly 14 years. Data from OMI has been used in a wide range of applications and research resulting in many new findings. Due to its unprecedented spatial resolution, in combination with daily global coverage, OMI plays a unique role in measuring trace gases important for the ozone layer, air quality, and climate change. With the operational very fast delivery (VFD; direct readout) and near real-time (NRT) availability of the data, OMI also plays an important role in the development of operational services in the atmospheric chemistry domain.

We examine the capability of the Global Modeling Initiative (GMI) chemistry and transport model to reproduce global mid-tropospheric (618 hPa) ozone–carbon monoxide (O3–CO) correlations determined by the measurements from the Tropospheric Emission Spectrometer (TES) aboard NASA's Aura satellite during boreal summer (July–August). The model is driven by three meteorological data sets (finite-volume General Circulation Model (fvGCM) with sea surface temperature for 1995, Goddard Earth Observing System Data Assimilation System Version 4 (GEOS-4 DAS) for 2005, and Modern-Era Retrospective Analysis for Research and Applications (MERRA) for 2005), allowing us to examine the sensitivity of model O3–CO correlations to input meteorological data. Model simulations of radionuclide tracers (222Rn, 210Pb, and 7Be) are used to illustrate the differences in transport-related processes among the meteorological data sets. Simulated O3 values are evaluated with climatological profiles from ozonesonde measurements and satellite tropospheric O3 columns. Despite the fact that the three simulations show significantly different global and regional distributions of O3 and CO concentrations, they show similar patterns of O3–CO correlations on a global scale. All model simulations sampled along the TES orbit track capture the observed positive O3–CO correlations in the Northern Hemisphere midlatitude continental outflow and the Southern Hemisphere subtropics. While all simulations show strong negative correlations over the Tibetan Plateau, northern Africa, the subtropical eastern North Pacific, and the Caribbean, TES O3 and CO concentrations at 618 hPa only show weak negative correlations over much narrower areas (i.e., the Tibetan Plateau and northern Africa). Discrepancies in regional O3–CO correlation patterns in the three simulations may be attributed to differences in convective transport, stratospheric influence, and subsidence, among other processes. To understand how various emissions drive global O3–CO correlation patterns, we examine the sensitivity of GMI/MERRA model-calculated O3 and CO concentrations and their correlations to emission types (fossil fuel, biomass burning, biogenic, and lightning NOx emissions). Fossil fuel and biomass burning emissions are mainly responsible for the strong positive O3–CO correlations over continental outflow regions in both hemispheres. Biogenic emissions have a relatively smaller impact on O3–CO correlations than other emissions but are largely responsible for the negative correlations over the tropical eastern Pacific, reflecting the fact that O3 is consumed and CO generated during the atmospheric oxidation process of isoprene under low-NOx conditions. We find that lightning NOx emissions degrade both positive correlations at mid- and high latitudes and negative correlations in the tropics because ozone production downwind of lightning NOx emissions is not directly related to the emission and transport of CO. Our study concludes that O3–CO correlations may be used effectively to constrain the sources of regional tropospheric O3 in global 3-D models, especially for those regions where convective transport of pollution plays an important role.

We use a series of chemical transport model and chemistry climate model simulations to investigate the observed negative trends in MOPITT CO over several regions of the world, and to examine the consistency of time-dependent emission inventories with observations. We find that simulations driven by the MACCity inventory, used for the Chemistry Climate Modeling Initiative (CCMI), reproduce the negative trends in the CO column observed by MOPITT for 2000–2010 over the eastern United States and Europe. However, the simulations have positive trends over eastern China, in contrast to the negative trends observed by MOPITT. The model bias in CO, after applying MOPITT averaging kernels, contributes to the model–observation discrepancy in the trend over eastern China. This demonstrates that biases in a model's average concentrations can influence the interpretation of the temporal trend compared to satellite observations. The total ozone column plays a role in determining the simulated tropospheric CO trends. A large positive anomaly in the simulated total ozone column in 2010 leads to a negative anomaly in OH and hence a positive anomaly in CO, contributing to the positive trend in simulated CO. These results demonstrate that accurately simulating variability in the ozone column is important for simulating and interpreting trends in CO.

The Ozone Monitoring Instrument (OMI) onboard NASA's Aura satellite has been providing global observations of the ozone layer and key atmospheric pollutant gases, such as nitrogen dioxide (NO2) and sulfur dioxide (SO2), since October 2004. The data products from the same instrument provide consistent spatial and temporal coverage and permit the study of anthropogenic and natural emissions on local-to-global scales. In this paper, we examine changes in SO2 and NO2 over some of the world's most polluted industrialized regions during the first decade of OMI observations. In terms of regional pollution changes, we see both upward and downward trends, sometimes in opposite directions for NO2 and SO2, for different study areas. The trends are, for the most part, associated with economic and/or technological changes in energy use, as well as regional regulatory policies. Over the eastern US, both NO2 and SO2 levels decreased dramatically from 2005 to 2015, by more than 40 and 80 %, respectively, as a result of both technological improvements and stricter regulations of emissions. OMI confirmed large reductions in SO2 over eastern Europe's largest coal-fired power plants after installation of flue gas desulfurization devices. The North China Plain has the world's most severe SO2 pollution, but a decreasing trend has been observed since 2011, with about a 50 % reduction in 2012–2015, due to an economic slowdown and government efforts to restrain emissions from the power and industrial sectors. In contrast, India's SO2 and NO2 levels from coal power plants and smelters are growing at a fast pace, increasing by more than 100 and 50 %, respectively, from 2005 to 2015. Several SO2 hot spots observed over the Persian Gulf are probably related to oil and gas operations and indicate a possible underestimation of emissions from these sources in bottom-up emission inventories. Overall, OMI observations have proved valuable in documenting rapid changes in air quality over different parts of the world during last decade. The baseline established during the first 11 years of OMI is indispensable for the interpretation of air quality measurements from current and future satellite atmospheric composition missions.

We present the Efficient CH4–CO–OH (ECCOH) chemistry module that allows for the simulation of the methane, carbon monoxide, and hydroxyl radical (CH4–CO–OH) system, within a chemistry climate model, carbon cycle model, or Earth system model. The computational efficiency of the module allows many multi-decadal sensitivity simulations of the CH4–CO–OH system, which primarily determines the global atmospheric oxidizing capacity. This capability is important for capturing the nonlinear feedbacks of the CH4–CO–OH system and understanding the perturbations to methane, CO, and OH, and the concomitant impacts on climate. We implemented the ECCOH chemistry module in the NASA GEOS-5 atmospheric global circulation model (AGCM), performed multiple sensitivity simulations of the CH4–CO–OH system over 2 decades, and evaluated the model output with surface and satellite data sets of methane and CO. The favorable comparison of output from the ECCOH chemistry module (as configured in the GEOS-5 AGCM) with observations demonstrates the fidelity of the module for use in scientific research.

A low bias in carbon monoxide (CO) at northern high and mid-latitudes is a common feature of chemistry climate models (CCMs) that may indicate or contribute to a high bias in simulated OH and corresponding low bias in methane lifetime. We use simulations with CO tagged by source type to investigate the sensitivity of the CO bias to CO emissions, transport, global mean OH, and the hemispheric asymmetry of OH. We also investigate how each of these possible contributors to the CO bias affects the methane lifetime. We find that the use of specified meteorology alters the distribution of CO compared to a free-running CCM simulation, improving the comparison with surface observations in summer. Our results also show that reducing the hemispheric asymmetry of OH improves the agreement of simulated CO with observations. We use simulations with parameterized OH to quantify the impact of known model biases on simulated OH. Removing biases in ozone and water vapor as well as reducing Northern Hemisphere NOx does not remove the hemispheric asymmetry in OH, but it reduces global mean OH by 18 %, bringing the simulated methane lifetime into agreement with observation-based estimates.

Satellite remote sensing of tropospheric nitrogen dioxide (NO2) can provide valuable information for estimating surface nitrogen oxides (NOx) emissions. Using an exponentially modified Gaussian (EMG) method and taking into account the effect of wind on observed NO2 distributions, we estimate 3-year moving-average emissions of summertime NOx from 35 US (United States) urban areas directly from NO2 retrievals of the Ozone Monitoring Instrument (OMI) during 2005–2014. Following conclusions of previous studies that the EMG method provides robust and accurate emission estimates under strong-wind conditions, we derive top-down NOx emissions from each urban area by applying the EMG method to OMI data with wind speeds greater than 3–5 m s−1. Meanwhile, we find that OMI NO2 observations under weak-wind conditions (i.e., < 3 m s−1) are qualitatively better correlated to the surface NOx source strength in comparison to all-wind OMI maps; therefore, we use them to calculate the satellite-observed NO2 burdens of urban areas and compare with NOx emission estimates. The EMG results show that OMI-derived NOx emissions are highly correlated (R > 0.93) with weak-wind OMI NO2 burdens as well as with bottom-up NOx emission estimates over 35 urban areas, implying a linear response of the OMI observations to surface emissions under weak-wind conditions. The simultaneous EMG-obtained effective NO2 lifetimes (~ 3.5 ± 1.3 h), however, are biased low in comparison to the summertime NO2 chemical lifetimes. In general, isolated urban areas with NOx emission intensities greater than ~ 2 Mg h−1 produce statistically significant weak-wind signals in 3-year average OMI data. From 2005 to 2014, we estimate that total OMI-derived NOx emissions over all selected US urban areas decreased by 49 %, consistent with reductions of 43, 47, 49, and 44 % in the total bottom-up NOx emissions, the sum of weak-wind OMI NO2 columns, the total weak-wind OMI NO2 burdens, and the averaged NO2 concentrations, respectively, reflecting the success of NOx control programs for both mobile sources and power plants. The decrease rates of these NOx-related quantities are found to be faster (i.e., −6.8 to −9.3 % yr−1) before 2010 and slower (i.e., −3.4 to −4.9 % yr−1) after 2010. For individual urban areas, we calculate the R values of pair-wise trends among the OMI-derived and bottom-up NOx emissions, the weak-wind OMI NO2 burdens, and ground-based NO2 measurements, and high correlations are found for all urban areas (median R= 0.8), particularly large ones (R up to 0.97). The results of the current work indicate that using the EMG method and considering the wind effect, the OMI data allow for the estimation of NOx emissions from urban areas and the direct constraint of emission trends with reasonable accuracy.

Aura OMI and MLS measurements are combined to produce daily maps of tropospheric ozone beginning October 2004. We show that El Niño-Southern Oscillation (ENSO) related inter-annual change in tropospheric ozone in the tropics is small in relation to combined intra-seasonal/Madden–Julian Oscillation (MJO) and shorter timescale variability by a factor of ~ 3–10 (largest in the Atlantic). Outgoing longwave radiation (OLR), taken as a proxy for convection, suggests that convection is a dominant driver of large-scale variability of tropospheric ozone in the Pacific from inter-annual (e.g., ENSO) to weekly periods. We compare tropospheric ozone and OLR satellite observations with two simulations: (1) the Goddard Earth Observing System (GEOS) chemistry-climate model (CCM) that uses observed sea surface temperatures and is otherwise free-running, and (2) the NASA Global Modeling Initiative (GMI) chemical transport model (CTM) that is driven by Modern Era Retrospective-Analysis for Research and Applications (MERRA) analyses. It is shown that the CTM-simulated ozone accurately matches measurements for timescales from ENSO to intra-seasonal/MJO and even 1–2-week periods. The CCM simulation reproduces ENSO variability but not shorter timescales. These analyses suggest that a model used to delineate temporal and/or spatial properties of tropospheric ozone and convection in the tropics must reproduce both ENSO and non-ENSO variability.

We have evaluated tropospheric ozone enhancement in air dominated by biomass burning emissions at high latitudes (> 50° N) in July 2008, using 10 global chemical transport model simulations from the POLMIP multi-model comparison exercise. In model air masses dominated by fire emissions, ΔO3/ΔCO values ranged between 0.039 and 0.196 ppbv ppbv−1 (mean: 0.113 ppbv ppbv−1) in freshly fire-influenced air, and between 0.140 and 0.261 ppbv ppbv−1 (mean: 0.193 ppbv) in more aged fire-influenced air. These values are in broad agreement with the range of observational estimates from the literature. Model ΔPAN/ΔCO enhancement ratios show distinct groupings according to the meteorological data used to drive the models. ECMWF-forced models produce larger ΔPAN/ΔCO values (4.47 to 7.00 pptv ppbv−1) than GEOS5-forced models (1.87 to 3.28 pptv ppbv−1), which we show is likely linked to differences in efficiency of vertical transport during poleward export from mid-latitude source regions. Simulations of a large plume of biomass burning and anthropogenic emissions exported from towards the Arctic using a Lagrangian chemical transport model show that 4-day net ozone change in the plume is sensitive to differences in plume chemical composition and plume vertical position among the POLMIP models. In particular, Arctic ozone evolution in the plume is highly sensitive to initial concentrations of PAN, as well as oxygenated VOCs (acetone, acetaldehyde), due to their role in producing the peroxyacetyl radical PAN precursor. Vertical displacement is also important due to its effects on the stability of PAN, and subsequent effect on NOx abundance. In plumes where net ozone production is limited, we find that the lifetime of ozone in the plume is sensitive to hydrogen peroxide loading, due to the production of HOx from peroxide photolysis, and the key role of HO2 + O3 in controlling ozone loss. Overall, our results suggest that emissions from biomass burning lead to large-scale photochemical enhancement in high-latitude tropospheric ozone during summer.

Using observations from aircraft, surface stations and a satellite instrument, we comprehensively evaluate multi-model simulations of carbon monoxide (CO) and ozone (O3) in the Arctic and over lower latitude emission regions, as part of the POLARCAT Model Inter-comparison Project (POLMIP). Evaluation of 11- atmospheric models with chemistry shows that they generally underestimate CO throughout the Arctic troposphere, with the largest biases found during winter and spring. Negative CO biases are also found throughout the Northern Hemisphere, with multi-model mean gross errors (9–12%) suggesting models perform similarly over Asia, North America and Europe. A multi-model annual mean tropospheric OH (10.8 ± 0.6 × 105 molec cm−3) is found to be slightly higher than previous estimates of OH constrained by methyl chloroform, suggesting negative CO biases in models may be improved through better constraints on OH. Models that have lower Arctic OH do not always show a substantial improvement in their negative CO biases, suggesting that Arctic OH is not the dominant factor controlling the Arctic CO burden in these models. In addition to these general biases, models do not capture the magnitude of CO enhancements observed in the Arctic free troposphere in summer, suggesting model errors in the simulation of plumes that are transported from anthropogenic and biomass burning sources at lower latitudes. O3 in the Arctic is also generally underestimated, particularly at the surface and in the upper troposphere. Summer O3 comparisons over lower latitudes show several models overestimate upper tropospheric concentrations. Simulated CO, O3 and OH all demonstrate a substantial degree of inter-model variability. Idealised CO-like tracers are used to quantitatively compare the impact of inter-model differences in transport and OH on CO in the Arctic troposphere. The tracers show that model differences in transport from Europe in winter and from Asia throughout the year are important sources of model variability at Barrow. Unlike transport, inter-model variability in OH similarly affects all regional tracers at Barrow. Comparisons of fixed-lifetime and OH-loss idealised CO-like tracers throughout the Arctic troposphere show that OH differences are a much larger source of inter-model variability than transport differences. Model OH concentrations are correlated with H2O concentrations, suggesting water vapour concentrations are linked to differences in simulated concentrations of CO and OH at high latitudes in these simulations. Despite inter-model differences in transport and OH, the relative contributions from the different source regions (North America, Europe and Asia) and different source types (anthropogenic and biomass burning) are comparable across the models. Fire emissions from the boreal regions in 2008 contribute 33, 43 and 19% to the total Arctic CO-like tracer in spring, summer and autumn, respectively, highlighting the importance of boreal fire emissions in controlling pollutant burdens in the Arctic.

We derive free-tropospheric NO2 volume mixing ratios (VMRs) by applying a cloud-slicing technique to data from the Ozone Monitoring Instrument (OMI) on the Aura satellite. In the cloud-slicing approach, the slope of the above-cloud NO2 column versus the cloud scene pressure is proportional to the NO2 VMR. In this work, we use a sample of nearby OMI pixel data from a single orbit for the linear fit. The OMI data include cloud scene pressures from the rotational-Raman algorithm and above-cloud NO2 vertical column density (VCD) (defined as the NO2 column from the cloud scene pressure to the top of the atmosphere) from a differential optical absorption spectroscopy (DOAS) algorithm. We compare OMI-derived NO2 VMRs with in situ aircraft profiles measured during the NASA Intercontinental Chemical Transport Experiment Phase B (INTEX-B) campaign in 2006. The agreement is generally within the estimated uncertainties when appropriate data screening is applied. We then derive a global seasonal climatology of free-tropospheric NO2 VMR in cloudy conditions. Enhanced NO2 in the free troposphere commonly appears near polluted urban locations where NO2 produced in the boundary layer may be transported vertically out of the boundary layer and then horizontally away from the source. Signatures of lightning NO2 are also shown throughout low and middle latitude regions in summer months. A profile analysis of our cloud-slicing data indicates signatures of lightning-generated NO2 in the upper troposphere. Comparison of the climatology with simulations from the global modeling initiative (GMI) for cloudy conditions (cloud optical depth > 10) shows similarities in the spatial patterns of continental pollution outflow. However, there are also some differences in the seasonal variation of free-tropospheric NO2 VMRs near highly populated regions and in areas affected by lightning-generated NOx.

This study describes a simple parameterization to estimate regionally averaged changes in surface ozone due to past or future changes in anthropogenic precursor emissions based on results from 14 global chemistry transport models. The method successfully reproduces the results of full simulations with these models. For a given emission scenario it provides the ensemble mean surface ozone change, a regional source attribution for each change, and an estimate of the associated uncertainty as represented by the variation between models. Using the Representative Concentration Pathway (RCP) emission scenarios as an example, we show how regional surface ozone is likely to respond to emission changes by 2050 and how changes in precursor emissions and atmospheric methane contribute to this. Surface ozone changes are substantially smaller than expected with the SRES A1B, A2 and B2 scenarios, with annual global mean reductions of as much as 2 ppb by 2050 vs. increases of 4–6 ppb under SRES, and this reflects the assumptions of more stringent precursor emission controls under the RCP scenarios. We find an average difference of around 5 ppb between the outlying RCP 2.6 and RCP 8.5 scenarios, about 75% of which can be attributed to differences in methane abundance. The study reveals the increasing importance of limiting atmospheric methane growth as emissions of other precursors are controlled, but highlights differences in modelled ozone responses to methane changes of as much as a factor of two, indicating that this remains a major uncertainty in current models.

We use aircraft observations obtained during the Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) mission to examine the distributions and source attributions of O3 and NOy in the Arctic and sub-Arctic region. Using a number of marker tracers, we distinguish various air masses from the background troposphere and examine their contributions to NOx, O3, and O3 production in the Arctic troposphere. The background Arctic troposphere has a mean O3 of ~60 ppbv and NOx of ~25 pptv throughout spring and summer with CO decreasing from ~145 ppbv in spring to ~100 ppbv in summer. These observed mixing ratios are not notably different from the values measured during the 1988 ABLE-3A and the 2002 TOPSE field campaigns despite the significant changes in emissions and stratospheric ozone layer in the past two decades that influence Arctic tropospheric composition. Air masses associated with stratosphere-troposphere exchange are present throughout the mid and upper troposphere during spring and summer. These air masses, with mean O3 concentrations of 140–160 ppbv, are significant direct sources of O3 in the Arctic troposphere. In addition, air of stratospheric origin displays net O3 formation in the Arctic due to its sustainable, high NOx (75 pptv in spring and 110 pptv in summer) and NOy (~800 pptv in spring and ~1100 pptv in summer). The air masses influenced by the stratosphere sampled during ARCTAS-B also show conversion of HNO3 to PAN. This active production of PAN is the result of increased degradation of ethane in the stratosphere-troposphere mixed air mass to form CH3CHO, followed by subsequent formation of PAN under high NOx conditions. These findings imply that an adequate representation of stratospheric NOy input, in addition to stratospheric O3 influx, is essential to accurately simulate tropospheric Arctic O3, NOx and PAN in chemistry transport models. Plumes influenced by recent anthropogenic and biomass burning emissions observed during ARCTAS show highly elevated levels of hydrocarbons and NOy (mostly in the form of NOx and PAN), but do not contain O3 higher than that in the Arctic tropospheric background except some aged biomass burning plumes sampled during spring. Convection and/or lightning influences are negligible sources of O3 in the Arctic troposphere but can have significant impacts in the upper troposphere in the continental sub-Arctic during summer.

Wildfires raged throughout western Russia and parts of Eastern Europe during a persistent heat wave in the summer of 2010. Anomalously high surface temperatures (35–41 °C) and low relative humidity (9–25 %) from mid-June to mid-August 2010 shown by analysis of radiosonde data from multiple sites in western Russia were ideal conditions for the wildfires to thrive. Measurements of outgoing longwave radiation (OLR) from the Atmospheric Infrared Sounder (AIRS) over western Russian indicate persistent subsidence during the heat wave. Daily three-day back-trajectories initiated over Moscow reveal a persistent anti-cyclonic circulation for 18 days in August, coincident with the most intense period of fire activity observed by Moderate Resolution Imaging Spectroradiometer (MODIS). This unfortunate meteorological coincidence allowed transport of polluted air from the region of intense fires to Moscow and the surrounding area. We demonstrate that the 2010 Russian wildfires are unique in the record of observations obtained by remote-sensing instruments on-board NASA satellites: Aura and Aqua (part of the A-Train Constellation) and Terra. Analysis of the distribution of MODIS fire products and aerosol optical thickness (AOT), UV aerosol index (AI) and single-scattering albedo (SSA) from Aura's Ozone Monitoring Instrument (OMI), and total column carbon monoxide (CO) from Aqua's Atmospheric Infrared Sounder (AIRS) show that the region in the center of western Russia surrounding Moscow (52°–58° N, 33°–43° E) is most severely impacted by wildfire emissions. Over this area, AIRS CO, OMI AI, and MODIS AOT are significantly enhanced relative to the historical satellite record during the first 18 days in August when the anti-cyclonic circulation persisted. By mid-August, the anti-cyclonic circulation was replaced with westerly transport over Moscow and vicinity. The heat wave ended as anomalies of surface temperature and relative humidity, and OLR disappeared. After 18 August the fire activity greatly diminished over western Russia and levels of the satellite smoke tracers returned to values typical of previous years.

A multi-model study of the long-range transport of ozone and its precursors from major anthropogenic source regions was coordinated by the Task Force on Hemispheric Transport of Air Pollution (TF HTAP) under the Convention on Long-range Transboundary Air Pollution (LRTAP). Vertical profiles of ozone at 12-h intervals from 2001 are available from twelve of the models contributing to this study and are compared here with observed profiles from ozonesondes. The contributions from each major source region are analysed for selected sondes, and this analysis is supplemented by retroplume calculations using the FLEXPART Lagrangian particle dispersion model to provide insight into the origin of ozone transport events and the cause of differences between the models and observations. In the boundary layer ozone levels are in general strongly affected by regional sources and sinks. With a considerably longer lifetime in the free troposphere, ozone here is to a much larger extent affected by processes on a larger scale such as intercontinental transport and exchange with the stratosphere. Such individual events are difficult to trace over several days or weeks of transport. This may explain why statistical relationships between models and ozonesonde measurements are far less satisfactory than shown in previous studies for surface measurements at all seasons. The lowest bias between model-calculated ozone profiles and the ozonesonde measurements is seen in the winter and autumn months. Following the increase in photochemical activity in the spring and summer months, the spread in model results increases, and the agreement between ozonesonde measurements and the individual models deteriorates further. At selected sites calculated contributions to ozone levels in the free troposphere from intercontinental transport are shown. Intercontinental transport is identified based on differences in model calculations with unperturbed emissions and emissions reduced by 20% by region. Intercontinental transport of ozone is finally determined based on differences in model ensemble calculations. With emissions perturbed by 20% per region, calculated intercontinental contributions to ozone in the free troposphere range from less than 1 ppb to 3 ppb, with small contributions in winter. The results are corroborated by the retroplume calculations. At several locations the seasonal contributions to ozone in the free troposphere from intercontinental transport differ from what was shown earlier at the surface using the same dataset. The large spread in model results points to a need of further evaluation of the chemical and physical processes in order to improve the credibility of global model results.

As part of the Hemispheric Transport of Air Pollution (HTAP; http:// www.htap.org) project, we analyze results from 15 global and 1 hemispheric chemical transport models and compare these to Clean Air Status and Trends Network (CASTNet) observations in the United States (US) for 2001. Using the policy-relevant maximum daily 8-h average ozone (MDA8 O3) statistic, the multi-model ensemble represents the observations well (mean r2=0.57, ensemble bias = +4.1 ppbv for all US regions and all seasons) despite a wide range in the individual model results. Correlations are strongest in the northeastern US during spring and fall (r2=0.68); and weakest in the midwestern US in summer (r2=0.46). However, large positive mean biases exist during summer for all eastern US regions, ranging from 10–20 ppbv, and a smaller negative bias is present in the western US during spring (~3 ppbv). In nearly all other regions and seasons, the biases of the model ensemble simulations are ≤5 ppbv. Sensitivity simulations in which anthropogenic O3-precursor emissions (NOx + NMVOC + CO + aerosols) were decreased by 20% in four source regions: East Asia (EA), South Asia (SA), Europe (EU) and North America (NA) show that the greatest response of MDA8 O3 to the summed foreign emissions reductions occurs during spring in the West (0.9 ppbv reduction due to 20% emissions reductions from EA + SA + EU). East Asia is the largest contributor to MDA8 O3 at all ranges of the O3 distribution for most regions (typically ~0.45 ppbv) followed closely by Europe. The exception is in the northeastern US where emissions reductions in EU had a slightly greater influence than EA emissions, particularly in the middle of the MDA8 O3 distribution (response of ~0.35 ppbv between 35–55 ppbv). EA and EU influences are both far greater (about 4x) than that from SA in all regions and seasons. In all regions and seasons O3-precursor emissions reductions of 20% in the NA source region decrease MDA8 O3 the most – by a factor of 2 to nearly 10 relative to foreign emissions reductions. The O3 response to anthropogenic NA emissions is greatest in the eastern US during summer at the high end of the O3 distribution (5–6 ppbv for 20% reductions). While the impact of foreign emissions on surface O3 in the US is not negligible – and is of increasing concern given the recent growth in Asian emissions – domestic emissions reductions remain a far more effective means of decreasing MDA8 O3 values, particularly those above 75 ppb (the current US standard).

We have studied the effects of the 2006 El Niño on tropospheric O3 and CO at tropical and sub-tropical latitudes measured from the OMI and MLS instruments on the Aura satellite. The 2006 El Niño-induced drought caused forest fires (largely set to clear land) to burn out of control during October and November in the Indonesian region. The effects of these fires are clearly seen in the enhancement of CO concentration measured from the MLS instrument. We have used a global model of atmospheric chemistry and transport (GMI CTM) to quantify the relative importance of biomass burning and large scale transport in producing observed changes in tropospheric O3 and CO. The model results show that during October and November biomass burning and meteorological changes contributed almost equally to the observed increase in tropospheric O3 in the Indonesian region. The biomass component was 4–6 DU but it was limited to the Indonesian region where the fires were most intense. The dynamical component was 4–8 DU but it covered a much larger area in the Indian Ocean extending from South East Asia in the north to western Australia in the south. By December 2006, the effect of biomass burning was reduced to zero and the observed changes in tropospheric O3 were mostly due to dynamical effects. The model results show an increase of 2–3% in the global burden of tropospheric ozone. In comparison, the global burden of CO increased by 8–12%.

We used the seasonality of a combination of atmospheric trace gases and idealized tracers to examine stratosphere-to-troposphere transport and its influence on tropospheric composition in the Arctic. Maximum stratosphere-to-troposphere transport of CFCs and O3 occurs in April as driven by the Brewer-Dobson circulation. Stratosphere-troposphere exchange (STE) occurs predominantly between 40° N to 80° N with stratospheric influx in the mid-latitudes (30–70° N) accounting for 67–81% of the air of stratospheric origin in the Northern Hemisphere extratropical troposphere. Transport from the lower stratosphere to the lower troposphere (LT) takes three months on average, one month to cross the tropopause, the second month to travel from the upper troposphere (UT) to the middle troposphere (MT), and the third month to reach the LT. During downward transport, the seasonality of a trace gas can be greatly impacted by wet removal and chemistry. A comparison of idealized tracers with varying lifetimes suggests that when initialized with the same concentrations and seasonal cycles at the tropopause, trace gases that have shorter lifetimes display lower concentrations, smaller amplitudes, and earlier seasonal maxima during transport to the LT. STE contributes to O3 in the Arctic troposphere directly from the transport of O3 and indirectly from the transport of NOy. Direct transport of O3 from the stratosphere accounts for 78% of O3 in the Arctic UT with maximum contributions occurring from March to May. The stratospheric contribution decreases significantly in the MT/LT (20–25% of total O3) and shows a very weak March–April maximum. Our NOx budget analysis in the Arctic UT shows that during spring and summer, the stratospheric injection of NOy-rich air increases NOx concentrations above the 20 pptv threshold level, thereby shifting the Arctic UT from a regime of net photochemical ozone loss to one of net production with rates as high as +16 ppbv/month.

We used a 3-D model of chemistry and transport to investigate trends and variability in tropospheric carbon monoxide (CO) for 1988–1997 caused by changes in the overhead ozone column, fossil fuel emissions, biomass burning emissions, methane, and transport. We found that the decreasing CO burden in the northern extra-tropics (−0.85%/y) was more heavily influenced by the decrease in European emissions during our study period than by the similar increase in Asian emissions, as transport pathways from Europe favored accumulation at higher latitudes in winter and spring. However, the opposite trends in the CO burdens from these two source regions counterbalanced at lower latitudes. Elsewhere, the factors influencing CO often compete, diminishing their cumulative impact, and trends in model CO were small or insignificant for our study period, except in the tropics in boreal fall (1.1%/y), a result of emissions from major fires in Indonesia late in 1997. There was a decrease in the ozone column during the study period as a result of the phase of the solar cycle and the eruption of Pinatubo in 1991. This decrease contributed negatively to the trend in model CO by increasing the hydroxyl radical (OH). The impact of this negative contribution was diminished by a positive contribution of similar magnitude from increasing methane. However, the trends in these two factors did not cancel for tropospheric OH, which responded primarily to changes in the ozone column.

We examine the response of Arctic gas and aerosol concentrations to perturbations in pollutant emissions from Europe, East and South Asia, and North America using results from a coordinated model intercomparison. These sensitivities to regional emissions (mixing ratio change per unit emission) vary widely across models and species. Intermodel differences are systematic, however, so that the relative importance of different regions is robust. North America contributes the most to Arctic ozone pollution. For aerosols and CO, European emissions dominate at the Arctic surface but East Asian emissions become progressively more important with altitude, and are dominant in the upper troposphere. Sensitivities show strong seasonality: surface sensitivities typically maximize during boreal winter for European and during spring for East Asian and North American emissions. Mid-tropospheric sensitivities, however, nearly always maximize during spring or summer for all regions. Deposition of black carbon (BC) onto Greenland is most sensitive to North American emissions. North America and Europe each contribute ~40% of total BC deposition to Greenland, with ~20% from East Asia. Elsewhere in the Arctic, both sensitivity and total BC deposition are dominated by European emissions. Model diversity for aerosols is especially large, resulting primarily from differences in aerosol physical and chemical processing (including removal). Comparison of modeled aerosol concentrations with observations indicates problems in the models, and perhaps, interpretation of the measurements. For gas phase pollutants such as CO and O3, which are relatively well-simulated, the processes contributing most to uncertainties depend on the source region and altitude examined. Uncertainties in the Arctic surface CO response to emissions perturbations are dominated by emissions for East Asian sources, while uncertainties in transport, emissions, and oxidation are comparable for European and North American sources. At higher levels, model-to-model variations in transport and oxidation are most important. Differences in photochemistry appear to play the largest role in the intermodel variations in Arctic ozone sensitivity, though transport also contributes substantially in the mid-troposphere.

We present a modeling study of the long-range transport of pollution from Europe, showing that European emissions regularly elevate surface ozone by as much as 20 ppbv in summer in northern Africa and the Near East. European emissions cause 50–150 additional violations per year (i.e. above those that would occur without European pollution) of the European health standard for ozone (8-h average >120 μg/m3 or ~60 ppbv) in northern Africa and the Near East. We estimate that European ozone pollution is responsible for 50 000 premature mortalities globally each year, of which the majority occurs outside of Europe itself, including 37% (19 000) in northern Africa and the Near East. Much of the pollution from Europe is exported southward at low altitudes in summer to the Mediterranean Sea, northern Africa and the Near East, regions with favorable photochemical environments for ozone production. Our results suggest that assessments of the human health benefits of reducing ozone precursor emissions in Europe should include effects outside of Europe, and that comprehensive planning to improve air quality in northern Africa and the Near East likely needs to address European emissions.

We present a modeling study of the troposphere-to-stratosphere transport (TST) of pollution from major biomass burning regions to the tropical upper troposphere and lower stratosphere (UT/LS). TST occurs predominately through 1) slow ascent in the tropical tropopause layer (TTL) to the LS and 2) quasi-horizontal exchange to the lowermost stratosphere (LMS). We show that biomass burning pollution regularly and significantly impacts the composition of the TTL, LS, and LMS. Carbon monoxide (CO) in the LS in our simulation and data from the Aura Microwave Limb Sounder (MLS) shows an annual oscillation in its composition that results from the interaction of an annual oscillation in slow ascent from the TTL to the LS and seasonal variations in sources, including a semi-annual oscillation in CO from biomass burning. The impacts of CO sources that peak when ascent is seasonally low are damped (e.g. Southern Hemisphere biomass burning) and vice-versa for sources that peak when ascent is seasonally high (e.g. extra-tropical fossil fuels). Interannual variation of CO in the UT/LS is caused primarily by year-to-year variations in biomass burning and the locations of deep convection. During our study period, 1994–1998, we find that the highest concentrations of CO in the UT/LS occurred during the strong 1997–1998 El Niño event for two reasons: i. tropical deep convection shifted to the eastern Pacific Ocean, closer to South American and African CO sources, and ii. emissions from Indonesian biomass burning were higher. This extreme event can be seen as an upper bound on the impact of biomass burning pollution on the UT/LS. We estimate that the 1997 Indonesian wildfires increased CO in the entire TTL and tropical LS (>60 mb) by more than 40% and 10%, respectively, for several months. Zonal mean ozone increased and the hydroxyl radical decreased by as much as 20%, increasing the lifetimes and, subsequently TST, of trace gases. Our results indicate that the impact of biomass burning pollution on the UT/LS is likely greatest during an El Niño event due to favorable dynamics and historically higher burning rates.

Transport from the surface to the lowermost stratosphere (LMS) can occur on timescales of a few months or less, making it possible for short-lived tropospheric pollutants to influence stratospheric composition and chemistry. Models used to study this influence must demonstrate the credibility of their chemistry and transport in the upper troposphere and lower stratosphere (UT/LS). Data sets from satellite and aircraft instruments measuring CO, O3, N2O, and CO2 in the UT/LS are used to create a suite of diagnostics for the seasonally-varying transport into and within the lowermost stratosphere, and of the coupling between the troposphere and stratosphere in the extratropics. The diagnostics are used to evaluate a version of the Global Modeling Initiative (GMI) Chemistry and Transport Model (CTM) that uses a combined tropospheric and stratospheric chemical mechanism and meteorological fields from the GEOS-4 general circulation model. The diagnostics derived from N2O and O3 show that the model lowermost stratosphere has realistic input from the overlying high latitude stratosphere in all seasons. Diagnostics for the LMS show two distinct layers. The upper layer begins ~30 K potential temperature above the tropopause and has a strong annual cycle in its composition. The lower layer is a mixed region ~30 K thick near the tropopause that shows no clear seasonal variation in the degree of tropospheric coupling. Diagnostics applied to the GMI CTM show credible seasonally-varying transport in the LMS and a tropopause layer that is realistically coupled to the UT in all seasons. The vertical resolution of the GMI CTM in the UT/LS, ~1 km, is sufficient to realistically represent the extratropical tropopause layer. This study demonstrates that the GMI CTM has the transport credibility required to study the impact of tropospheric emissions on the stratosphere.