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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.

Long-term experimental stratospheric NO2 and NO partial columns measured by means of solar Fourier-transform infrared (FTIR) spectrometry at Zugspitze (47.42° N, 10.98° E; 2964 m a.s.l.), Germany, were used to create a set of experiment-based monthly scaling factors (SFexp). The underlying data set is published in a companion paper (Nürnberg et al., 2024) and comprises over 25 years of measurements depicting the daytime variability of stratospheric NO2 and NO partial columns with respect to local solar time (LST). In accordance with simulation-based scaling factors recently published by Strode et al. (2022), we created SFexp normalized to SZA =72° for NO2 and NO for every month of the year as a function of solar zenith angle (SZA). Apart from a boundary value problem at minimum SZA values originating from averaging over different times of the month, the obtained scaling factors SFexp(NO2) and SFexp(NO) as a function of SZA represent the daytime behavior already shown in model simulations and experiments in the literature very well. This shows a well-pronounced increase in the NO2 and NO stratospheric partial column with the time of the day and a flattening of this increase after noon. In addition to the discussion of SFexp, we validate the simulation-based scaling factors SFsim(NO2) (Strode et al., 2022) and present simulation-based scaling factors for NO SFsim(NO). The simulation-based scaling factors show excellent agreement with the experiment-based ones; i.e., for NO2 and NO the mean value of the modulus between the experiment and simulation over all SZAs and months is only 0.02 %. We show that recently used model simulations can describe the real behavior of nitrogen oxide (NOx) variability in the stratosphere very well. Furthermore, we conclude that ground-based FTIR measurements can be used for validation of the output of photochemistry models and for creating experiment-based data sets describing the daytime stratospheric NOx variability as a function of SZA. This is a contribution to improved satellite validation and a better understanding of stratospheric photochemistry.

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.

Satellite-based retrievals of tropospheric NO2 columns are widely used to infer NOx (≡ NO + NO2) emissions. These retrievals rely on model information for the vertical distribution of NO2. The free tropospheric background above 2 km is particularly important because the sensitivity of the retrievals increases with altitude. Free tropospheric NOx also has a strong effect on tropospheric OH and ozone concentrations. Here we use observations from three aircraft campaigns (SEAC4RS, DC3, and ATom) and four atmospheric chemistry models (GEOS-Chem, GMI, TM5, and CAMS) to evaluate the model capabilities for simulating NOx in the free troposphere and attribute it to sources. NO2 measurements during the Studies of Emissions and Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys (SEAC4RS) and Deep Convective Clouds and Chemistry (DC3) campaigns over the southeastern U.S. in summer show increasing concentrations in the upper troposphere above 10 km, which are not replicated by the GEOS-Chem, although the model is consistent with the NO measurements. Using concurrent NO, NO2, and ozone observations from a DC3 flight in a thunderstorm outflow, we show that the NO2 measurements in the upper troposphere are biased high, plausibly due to interference from thermally labile NO2 reservoirs such as peroxynitric acid (HNO4) and methyl peroxy nitrate (MPN). We find that NO2 concentrations calculated from the NO measurements and NO–NO2 photochemical steady state (PSS) are more reliable to evaluate the vertical profiles of NO2 in models. GEOS-Chem reproduces the shape of the PSS-inferred NO2 profiles throughout the troposphere for SEAC4RS and DC3 but overestimates NO2 concentrations by about a factor of 2. The model underestimates MPN and alkyl nitrate concentrations, suggesting missing organic NOx chemistry. On the other hand, the standard GEOS-Chem model underestimates NO observations from the Atmospheric Tomography Mission (ATom) campaigns over the Pacific and Atlantic oceans, indicating a missing NOx source over the oceans. We find that we can account for this missing source by including in the model the photolysis of particulate nitrate on sea salt aerosols at rates inferred from laboratory studies and field observations of nitrous acid (HONO) over the Atlantic. The median PSS-inferred tropospheric NO2 column density for the ATom campaign is 1.7 ± 0.44 × 1014 molec. cm−2, and the NO2 column density simulated by the four models is in the range of 1.4–2.4 × 1014 molec. cm−2, implying that the uncertainty from using modeled NO2 tropospheric columns over clean areas in the retrievals for stratosphere–troposphere separation is about 1 × 1014 molec. cm−2. We find from GEOS-Chem that lightning is the main primary NOx source in the free troposphere over the tropics and southern midlatitudes, but aircraft emissions dominate at northern midlatitudes in winter and in summer over the oceans. Particulate nitrate photolysis increases ozone concentrations by up to 5 ppbv (parts per billion by volume) in the free troposphere in the northern extratropics in the model, which would largely correct the low model bias relative to ozonesonde observations. Global tropospheric OH concentrations increase by 19 %. The contribution of the free tropospheric background to the tropospheric NO2 columns observed by satellites over the contiguous U.S. increases from 25 ± 11 % in winter to 65 ± 9 % in summer, according to the GEOS-Chem vertical profiles. This needs to be accounted for when deriving NOx emissions from satellite NO2 column measurements.

The NASA Atmospheric Tomography (ATom) mission built a photochemical climatology of air parcels based on in situ measurements with the NASA DC-8 aircraft along objectively planned profiling transects through the middle of the Pacific and Atlantic oceans. In this paper we present and analyze a data set of 10 s (2 km) merged and gap-filled observations of the key reactive species driving the chemical budgets of O3 and CH4 (O3, CH4, CO, H2O, HCHO, H2O2, CH3OOH, C2H6, higher alkanes, alkenes, aromatics, NOx, HNO3, HNO4, peroxyacetyl nitrate, and other organic nitrates), consisting of 146 494 distinct air parcels from ATom deployments 1 through 4. Six models calculated the O3 and CH4 photochemical tendencies from this modeling data stream for ATom 1. We find that 80 %–90 % of the total reactivity lies in the top 50 % of the parcels and 25 %–35 % in the top 10 %, supporting previous model-only studies that tropospheric chemistry is driven by a fraction of all the air. Surprisingly, the probability densities of species and reactivities averaged on a model scale (100 km) differ only slightly from the 2 km ATom 10 s data, indicating that much of the heterogeneity in tropospheric chemistry can be captured with current global chemistry models. Comparing the ATom reactivities over the tropical oceans with climatological statistics from six global chemistry models, we find generally good agreement with the reactivity rates for O3 and CH4. Models distinctly underestimate O3 production below 2 km relative to the mid-troposphere, and this can be traced to lower NOx levels than observed. Attaching photochemical reactivities to measurements of chemical species allows for a richer, yet more constrained-to-what-matters, set of metrics for model evaluation. This paper presents a corrected version of the paper published under the same authors and title (sans “corrected”) as https://doi.org/10.5194/acp-21-13729-2021.

We developed a set of solar zenith angle, latitude- and altitude-dependent scaling factors to account for the diurnal variability in ozone (O3) and nitrogen dioxide (NO2) when comparing Stratospheric Aerosol and Gas Experiment (SAGE) III/ISS observations to observations from other times of day. The scaling factors are calculated as a function of solar zenith angle from the four-dimensional output of a global atmospheric chemistry model simulation of 2017–2020 that shows good agreement with observed vertical profiles. Using a global atmospheric chemistry model allows us to account for both chemically and dynamically driven variability. Both year-specific scale factors and a multi-year monthly climatology are available to decrease the uncertainty in inter-instrument comparisons and allow consistent comparisons between observations from different times of day. We describe the variability in the diurnal scale factors as a function of space and time. The quasi-biennial oscillation (QBO) appears to be a contributing factor to interannual variability in the NO2 scaling factors, leading to differences between years that switch sign with altitude. We show that application of these scaling factors improves the comparison between SAGE III/ISS and OSIRIS NO2 and between SAGE III/ISS and OMPS LP, OSIRIS, and ACE-FTS O3 observations. The comparisons between SAGE III/ISS O3 for sunrise or sunset vs. Microwave Limb Sounder (MLS) daytime or nighttime observations are also more consistent when we apply the diurnal scaling factors. There is good agreement between SAGE III/ISS V5.2 ozone and correlative measurements, with differences within 5 % between 20 and 50 km when corrected for diurnal variability. Similarly, the SAGE III/ISS V5.2 NO2 agreement with correlative measurement is mostly within 10 %. While the scale factors were designed for use with SAGE III/ISS observations, they can easily be applied to other observation intercomparisons as well.

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 NASA Atmospheric Tomography (ATom) mission built a photochemical climatology of air parcels based on in situ measurements with the NASA DC-8 aircraft along objectively planned profiling transects through the middle of the Pacific and Atlantic oceans. In this paper we present and analyze a data set of 10 s (2 km) merged and gap-filled observations of the key reactive species driving the chemical budgets of O3 and CH4 (O3, CH4, CO, H2O, HCHO, H2O2, CH3OOH, C2H6, higher alkanes, alkenes, aromatics, NOx, HNO3, HNO4, peroxyacetyl nitrate, other organic nitrates), consisting of 146 494 distinct air parcels from ATom deployments 1 through 4. Six models calculated the O3 and CH4 photochemical tendencies from this modeling data stream for ATom 1. We find that 80 %–90 % of the total reactivity lies in the top 50 % of the parcels and 25 %–35 % in the top 10 %, supporting previous model-only studies that tropospheric chemistry is driven by a fraction of all the air. In other words, accurate simulation of the least reactive 50 % of the troposphere is unimportant for global budgets. Surprisingly, the probability densities of species and reactivities averaged on a model scale (100 km) differ only slightly from the 2 km ATom data, indicating that much of the heterogeneity in tropospheric chemistry can be captured with current global chemistry models. Comparing the ATom reactivities over the tropical oceans with climatological statistics from six global chemistry models, we find excellent agreement with the loss of O3 and CH4 but sharp disagreement with production of O3. The models sharply underestimate O3 production below 4 km in both Pacific and Atlantic basins, and this can be traced to lower NOx levels than observed. Attaching photochemical reactivities to measurements of chemical species allows for a richer, yet more constrained-to-what-matters, set of metrics for model evaluation.

The 13C isotopic ratio of methane, δ13C of CH4, provides additional constraints on the CH4 budget to complement the constraints from CH4 observations. The interpretation of δ13C observations is complicated, however, by uncertainties in the methane sink. The reaction of CH4 with Cl is highly fractionating, increasing the relative abundance of 13CH4, but there is currently no consensus on the strength of the tropospheric Cl sink. Global model simulations of halogen chemistry differ strongly from one another in terms of both the magnitude of tropospheric Cl and its geographic distribution. This study explores the impact of the intermodel diversity in Cl fields on the simulated δ13C of CH4. We use a set of GEOS global model simulations with different predicted Cl fields to test the sensitivity of the δ13C of CH4 to the diversity of Cl output from chemical transport models. We find that δ13C is highly sensitive to both the amount and geographic distribution of Cl. Simulations with Cl providing 0.28 % or 0.66 % of the total CH4 loss bracket the δ13C observations for a fixed set of emissions. Thus, even when Cl provides only a small fraction of the total CH4 loss and has a small impact on total CH4, it provides a strong lever on δ13C. Consequently, it is possible to achieve a good representation of total CH4 using widely different Cl concentrations, but the partitioning of the CH4 loss between the OH and Cl reactions leads to strong differences in isotopic composition depending on which model's Cl field is used. Comparing multiple simulations, we find that altering the tropospheric Cl field leads to approximately a 0.5 ‰ increase in δ13CH4 for each percent increase in how much CH4 is oxidized by Cl. The geographic distribution and seasonal cycle of Cl also impacts the hemispheric gradient and seasonal cycle of δ13C. The large effect of Cl on δ13C compared to total CH4 broadens the range of CH4 source mixtures that can be reconciled with δ13C observations. Stronger constraints on tropospheric Cl are necessary to improve estimates of CH4 sources from δ13C observations.

Past studies have suggested that ozone in the troposphere has increased globally throughout much of the 20th century due to increases in anthropogenic emissions and transport. We show, by combining satellite measurements with a chemical transport model, that during the last four decades tropospheric ozone does indeed indicate increases that are global in nature, yet still highly regional. Satellite ozone measurements from Nimbus-7 and Earth Probe Total Ozone Mapping Spectrometer (TOMS) are merged with ozone measurements from the Aura Ozone Monitoring Instrument/Microwave Limb Sounder (OMI/MLS) to determine trends in tropospheric ozone for 1979–2016. Both TOMS (1979–2005) and OMI/MLS (2005–2016) depict large increases in tropospheric ozone from the Near East to India and East Asia and further eastward over the Pacific Ocean. The 38-year merged satellite record shows total net change over this region of about +6 to +7 Dobson units (DU) (i.e., ∼15 %–20 % of average background ozone), with the largest increase (∼4 DU) occurring during the 2005–2016 Aura period. The Global Modeling Initiative (GMI) chemical transport model with time-varying emissions is used to aid in the interpretation of tropospheric ozone trends for 1980–2016. The GMI simulation for the combined record also depicts the greatest increases of +6 to +7 DU over India and East Asia, very similar to the satellite measurements. In regions of significant increases in tropospheric column ozone (TCO) the trends are a factor of 2–2.5 larger for the Aura record when compared to the earlier TOMS record; for India and East Asia the trends in TCO for both GMI and satellite measurements are ∼+3 DU decade−1 or greater during 2005–2016 compared to about +1.2 to +1.4 DU decade−1 for 1979–2005. The GMI simulation and satellite data also reveal a tropospheric ozone increases in ∼+4 to +5 DU for the 38-year record over central Africa and the tropical Atlantic Ocean. Both the GMI simulation and satellite-measured tropospheric ozone during the latter Aura time period show increases of ∼+3 DU decade−1 over the N Atlantic and NE Pacific.

Measurements from actinic flux spectroradiometers on board the NASA DC-8 during the Atmospheric Tomography (ATom) mission provide an extensive set of statistics on how clouds alter photolysis rates (J values) throughout the remote Pacific and Atlantic Ocean basins. J values control tropospheric ozone and methane abundances, and thus clouds have been included for more than three decades in tropospheric chemistry modeling. ATom made four profiling circumnavigations of the troposphere capturing each of the seasons during 2016–2018. This work examines J values from the Pacific Ocean flights of the first deployment, but publishes the complete Atom-1 data set (29 July to 23 August 2016). We compare the observed J values (every 3 s along flight track) with those calculated by nine global chemistry–climate/transport models (globally gridded, hourly, for a mid-August day). To compare these disparate data sets, we build a commensurate statistical picture of the impact of clouds on J values using the ratio of J-cloudy (standard, sometimes cloudy conditions) to J-clear (artificially cleared of clouds). The range of modeled cloud effects is inconsistently large but they fall into two distinct classes: (1) models with large cloud effects showing mostly enhanced J values aloft and or diminished at the surface and (2) models with small effects having nearly clear-sky J values much of the time. The ATom-1 measurements generally favor large cloud effects but are not precise or robust enough to point out the best cloud-modeling approach. The models here have resolutions of 50–200 km and thus reduce the occurrence of clear sky when averaging over grid cells. In situ measurements also average scattered sunlight over a mixed cloud field, but only out to scales of tens of kilometers. A primary uncertainty remains in the role of clouds in chemistry, in particular, how models average over cloud fields, and how such averages can simulate measurements.

The first phase of the Atmospheric Tomography Mission (ATom-1) took place in July–August 2016 and included flights above the remote Pacific and Atlantic oceans. Sampling of atmospheric constituents during these flights is designed to provide new insights into the chemical reactivity and processes of the remote atmosphere and how these processes are affected by anthropogenic emissions. Model simulations provide a valuable tool for interpreting these measurements and understanding the origin of the observed trace gases and aerosols, so it is important to quantify model performance. Goddard Earth Observing System Model version 5 (GEOS-5) forecasts and analyses show considerable skill in predicting and simulating the CO distribution and the timing of CO enhancements observed during the ATom-1 aircraft mission. We use GEOS-5's tagged tracers for CO to assess the contribution of different emission sources to the regions sampled by ATom-1 to elucidate the dominant anthropogenic influences on different parts of the remote atmosphere. We find a dominant contribution from non-biomass-burning sources along the ATom transects except over the tropical Atlantic, where African biomass burning makes a large contribution to the CO concentration. One of the goals of ATom is to provide a chemical climatology over the oceans, so it is important to consider whether August 2016 was representative of typical boreal summer conditions. Using satellite observations of 700 hPa and column CO from the Measurement of Pollution in the Troposphere (MOPITT) instrument, 215 hPa CO from the Microwave Limb Sounder (MLS), and aerosol optical thickness from the Moderate Resolution Imaging Spectroradiometer (MODIS), we find that CO concentrations and aerosol optical thickness in August 2016 were within the observed range of the satellite observations but below the decadal median for many of the regions sampled. This suggests that the ATom-1 measurements may represent relatively clean but not exceptional conditions for lower-tropospheric CO.

We develop a new protocol for merging in situ measurements with 3-D model simulations of atmospheric chemistry with the goal of integrating these data to identify the most reactive air parcels in terms of tropospheric production and loss of the greenhouse gases ozone and methane. Presupposing that we can accurately measure atmospheric composition, we examine whether models constrained by such measurements agree on the chemical budgets for ozone and methane. In applying our technique to a synthetic data stream of 14 880 parcels along 180° W, we are able to isolate the performance of the photochemical modules operating within their global chemistry-climate and chemistry-transport models, removing the effects of modules controlling tracer transport, emissions, and scavenging. Differences in reactivity across models are driven only by the chemical mechanism and the diurnal cycle of photolysis rates, which are driven in turn by temperature, water vapor, solar zenith angle, clouds, and possibly aerosols and overhead ozone, which are calculated in each model. We evaluate six global models and identify their differences and similarities in simulating the chemistry through a range of innovative diagnostics. All models agree that the more highly reactive parcels dominate the chemistry (e.g., the hottest 10 % of parcels control 25–30 % of the total reactivities), but do not fully agree on which parcels comprise the top 10 %. Distinct differences in specific features occur, including the spatial regions of maximum ozone production and methane loss, as well as in the relationship between photolysis and these reactivities. Unique, possibly aberrant, features are identified for each model, providing a benchmark for photochemical module development. Among the six models tested here, three are almost indistinguishable based on the inherent variability caused by clouds, and thus we identify four, effectively distinct, chemical models. Based on this work, we suggest that water vapor differences in model simulations of past and future atmospheres may be a cause of the different evolution of tropospheric O3 and CH4, and lead to different chemistry-climate feedbacks across the models.

Ozone within deep convective clouds is controlled by several factors involving photochemical reactions and transport. Gas-phase photochemical reactions and heterogeneous surface chemical reactions involving ice, water particles, and aerosols inside the clouds all contribute to the distribution and net production and loss of ozone. Ozone in clouds is also dependent on convective transport that carries low-troposphere/boundary-layer ozone and ozone precursors upward into the clouds. Characterizing ozone in thick clouds is an important step for quantifying relationships of ozone with tropospheric H2O, OH production, and cloud microphysics/transport properties. Although measuring ozone in deep convective clouds from either aircraft or balloon ozonesondes is largely impossible due to extreme meteorological conditions associated with these clouds, it is possible to estimate ozone in thick clouds using backscattered solar UV radiation measured by satellite instruments. Our study combines Aura Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) satellite measurements to generate a new research product of monthly-mean ozone concentrations in deep convective clouds between 30° S and 30° N for October 2004–April 2016. These measurements represent mean ozone concentration primarily in the upper levels of thick clouds and reveal key features of cloud ozone including: persistent low ozone concentrations in the tropical Pacific of ∼ 10 ppbv or less; concentrations of up to 60 pphv or greater over landmass regions of South America, southern Africa, Australia, and India/east Asia; connections with tropical ENSO events; and intraseasonal/Madden–Julian oscillation variability. Analysis of OMI aerosol measurements suggests a cause and effect relation between boundary-layer pollution and elevated ozone inside thick clouds over landmass regions including southern Africa and India/east Asia.

An approach for analysis and modeling of global atmospheric chemistry is developed for application to measurements that provide a tropospheric climatology of those heterogeneously distributed, reactive species that control the loss of methane and the production and loss of ozone. We identify key species (e.g., O3, NOx, HNO3, HNO4, C2H3NO5, H2O, HOOH, CH3OOH, HCHO, CO, CH4, C2H6, acetaldehyde, acetone) and presume that they can be measured simultaneously in air parcels on the scale of a few km horizontally and a few tenths of a km vertically. As a first step, six global models have prepared such climatologies sampled at the modeled resolution for August with emphasis on the vast central Pacific Ocean basin. Objectives of this paper are to identify and characterize differences in model-generated reactivities as well as species covariances that could readily be discriminated with an unbiased climatology. A primary tool is comparison of multidimensional probability densities of key species weighted by the mass of such parcels or frequency of occurrence as well as by the reactivity of the parcels with respect to methane and ozone. The reactivity-weighted probabilities tell us which parcels matter in this case, and this method shows skill in differentiating among the models' chemistry. Testing 100 km scale models with 2 km measurements using these tools also addresses a core question about model resolution and whether fine-scale atmospheric structures matter to the overall ozone and methane budget. A new method enabling these six global chemistry–climate models to ingest an externally sourced climatology and then compute air parcel reactivity is demonstrated. Such an objective climatology containing these key species is anticipated from the NASA Atmospheric Tomography (ATom) aircraft mission (2015–2020), executing profiles over the Pacific and Atlantic Ocean basins. This modeling study addresses a core part of the design of ATom.

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.

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.

We test the current generation of global chemistry–climate models in their ability to simulate observed, present-day surface ozone. Models are evaluated against hourly surface ozone from 4217 stations in North America and Europe that are averaged over 1° × 1° grid cells, allowing commensurate model–measurement comparison. Models are generally biased high during all hours of the day and in all regions. Most models simulate the shape of regional summertime diurnal and annual cycles well, correctly matching the timing of hourly (~ 15:00 local time (LT)) and monthly (mid-June) peak surface ozone abundance. The amplitude of these cycles is less successfully matched. The observed summertime diurnal range (~ 25 ppb) is underestimated in all regions by about 7 ppb, and the observed seasonal range (~ 21 ppb) is underestimated by about 5 ppb except in the most polluted regions, where it is overestimated by about 5 ppb. The models generally match the pattern of the observed summertime ozone enhancement, but they overestimate its magnitude in most regions. Most models capture the observed distribution of extreme episode sizes, correctly showing that about 80 % of individual extreme events occur in large-scale, multi-day episodes of more than 100 grid cells. The models also match the observed linear relationship between episode size and a measure of episode intensity, which shows increases in ozone abundance by up to 6 ppb for larger-sized episodes. We conclude that the skill of the models evaluated here provides confidence in their projections of future surface ozone.

Aerosol variations and trends over different land and ocean regions from 1980 to 2009 are analyzed with the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model and observations from multiple satellite sensors and available ground-based networks. Excluding time periods with large volcanic influence, aerosol optical depth (AOD) and surface concentration over polluted land regions generally vary with anthropogenic emissions, but the magnitude of this association can be dampened by the presence of natural aerosols, especially dust. Over the 30-year period in this study, the largest reduction in aerosol levels occurs over Europe, where AOD has decreased by 40–60% on average and surface sulfate concentrations have declined by a factor of up to 3–4. In contrast, East Asia and South Asia show AOD increases, but the relatively high level of dust aerosols in Asia reduces the correlation between AOD and pollutant emission trends. Over major dust source regions, model analysis indicates that the change of dust emissions over the Sahara and Sahel has been predominantly driven by the change of near-surface wind speed, but over Central Asia it has been largely influenced by the change of the surface wetness. The decreasing dust trend in the North African dust outflow region of the tropical North Atlantic and the receptor sites of Barbados and Miami is closely associated with an increase of the sea surface temperature in the North Atlantic. This temperature increase may drive the decrease of the wind velocity over North Africa, which reduces the dust emission, and the increase of precipitation over the tropical North Atlantic, which enhances dust removal during transport. Despite significant trends over some major continental source regions, the model-calculated global annual average AOD shows little change over land and ocean in the past three decades, because opposite trends in different land regions cancel each other out in the global average, and changes over large open oceans are negligible. This highlights the necessity for regional-scale assessment of aerosols and their climate impacts, as global-scale average values can obscure important regional changes.

We use simultaneous observations of tropospheric ozone and outgoing longwave radiation (OLR) sensitivity to tropospheric ozone from the Tropospheric Emission Spectrometer (TES) to evaluate model tropospheric ozone and its effect on OLR simulated by a suite of chemistry-climate models that participated in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). The ensemble mean of ACCMIP models show a persistent but modest tropospheric ozone low bias (5–20 ppb) in the Southern Hemisphere (SH) and modest high bias (5–10 ppb) in the Northern Hemisphere (NH) relative to TES ozone for 2005–2010. These ozone biases have a significant impact on the OLR. Using TES instantaneous radiative kernels (IRK), we show that the ACCMIP ensemble mean tropospheric ozone low bias leads up to 120 mW m−2 OLR high bias locally but zonally compensating errors reduce the global OLR high bias to 39 ± 41 m Wm−2 relative to TES data. We show that there is a correlation (R2 = 0.59) between the magnitude of the ACCMIP OLR bias and the deviation of the ACCMIP preindustrial to present day (1750–2010) ozone radiative forcing (RF) from the ensemble ozone RF mean. However, this correlation is driven primarily by models whose absolute OLR bias from tropospheric ozone exceeds 100 m Wm−2. Removing these models leads to a mean ozone radiative forcing of 394 ± 42 m Wm−2. The mean is about the same and the standard deviation is about 30% lower than an ensemble ozone RF of 384 ± 60 m Wm−2 derived from 14 of the 16 ACCMIP models reported in a companion ACCMIP study. These results point towards a profitable direction of combining satellite observations and chemistry-climate model simulations to reduce uncertainty in ozone radiative forcing.

Ozone (O3) from 17 atmospheric chemistry models taking part in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) has been used to calculate tropospheric ozone radiative forcings (RFs). All models applied a common set of anthropogenic emissions, which are better constrained for the present-day than the past. Future anthropogenic emissions follow the four Representative Concentration Pathway (RCP) scenarios, which define a relatively narrow range of possible air pollution emissions. We calculate a value for the pre-industrial (1750) to present-day (2010) tropospheric ozone RF of 410 mW m−2. The model range of pre-industrial to present-day changes in O3 produces a spread (±1 standard deviation) in RFs of ±17%. Three different radiation schemes were used – we find differences in RFs between schemes (for the same ozone fields) of ±10%. Applying two different tropopause definitions gives differences in RFs of ±3%. Given additional (unquantified) uncertainties associated with emissions, climate-chemistry interactions and land-use change, we estimate an overall uncertainty of ±30% for the tropospheric ozone RF. Experiments carried out by a subset of six models attribute tropospheric ozone RF to increased emissions of methane (44±12%), nitrogen oxides (31 ± 9%), carbon monoxide (15 ± 3%) and non-methane volatile organic compounds (9 ± 2%); earlier studies attributed more of the tropospheric ozone RF to methane and less to nitrogen oxides. Normalising RFs to changes in tropospheric column ozone, we find a global mean normalised RF of 42 mW m−2 DU−1, a value similar to previous work. Using normalised RFs and future tropospheric column ozone projections we calculate future tropospheric ozone RFs (mW m−2; relative to 1750) for the four future scenarios (RCP2.6, RCP4.5, RCP6.0 and RCP8.5) of 350, 420, 370 and 460 (in 2030), and 200, 300, 280 and 600 (in 2100). Models show some coherent responses of ozone to climate change: decreases in the tropical lower troposphere, associated with increases in water vapour; and increases in the sub-tropical to mid-latitude upper troposphere, associated with increases in lightning and stratosphere-to-troposphere transport. Climate change has relatively small impacts on global mean tropospheric ozone RF.

Results from simulations performed for the Atmospheric Chemistry and Climate Modeling Intercomparison Project (ACCMIP) are analysed to examine how OH and methane lifetime may change from present day to the future, under different climate and emissions scenarios. Present day (2000) mean tropospheric chemical lifetime derived from the ACCMIP multi-model mean is 9.8 ± 1.6 yr (9.3 ± 0.9 yr when only including selected models), lower than a recent observationally-based estimate, but with a similar range to previous multi-model estimates. Future model projections are based on the four Representative Concentration Pathways (RCPs), and the results also exhibit a large range. Decreases in global methane lifetime of 4.5 ± 9.1% are simulated for the scenario with lowest radiative forcing by 2100 (RCP 2.6), while increases of 8.5 ± 10.4% are simulated for the scenario with highest radiative forcing (RCP 8.5). In this scenario, the key driver of the evolution of OH and methane lifetime is methane itself, since its concentration more than doubles by 2100 and it consumes much of the OH that exists in the troposphere. Stratospheric ozone recovery, which drives tropospheric OH decreases through photolysis modifications, also plays a partial role. In the other scenarios, where methane changes are less drastic, the interplay between various competing drivers leads to smaller and more diverse OH and methane lifetime responses, which are difficult to attribute. For all scenarios, regional OH changes are even more variable, with the most robust feature being the large decreases over the remote oceans in RCP8.5. Through a regression analysis, we suggest that differences in emissions of non-methane volatile organic compounds and in the simulation of photolysis rates may be the main factors causing the differences in simulated present day OH and methane lifetime. Diversity in predicted changes between present day and future OH was found to be associated more strongly with differences in modelled temperature and stratospheric ozone changes. Finally, through perturbation experiments we calculated an OH feedback factor (F) of 1.24 from present day conditions (1.50 from 2100 RCP8.5 conditions) and a climate feedback on methane lifetime of 0.33 ± 0.13 yr K−1, on average. Models that did not include interactive stratospheric ozone effects on photolysis showed a stronger sensitivity to climate, as they did not account for negative effects of climate-driven stratospheric ozone recovery on tropospheric OH, which would have partly offset the overall OH/methane lifetime response to climate change.

Present day tropospheric ozone and its changes between 1850 and 2100 are considered, analysing 15 global models that participated in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). The ensemble mean compares well against present day observations. The seasonal cycle correlates well, except for some locations in the tropical upper troposphere. Most (75 %) of the models are encompassed with a range of global mean tropospheric ozone column estimates from satellite data, but there is a suggestion of a high bias in the Northern Hemisphere and a low bias in the Southern Hemisphere, which could indicate deficiencies with the ozone precursor emissions. Compared to the present day ensemble mean tropospheric ozone burden of 337 ± 23 Tg, the ensemble mean burden for 1850 time slice is ~30% lower. Future changes were modelled using emissions and climate projections from four Representative Concentration Pathways (RCPs). Compared to 2000, the relative changes in the ensemble mean tropospheric ozone burden in 2030 (2100) for the different RCPs are: −4% (−16%) for RCP2.6, 2% (−7%) for RCP4.5, 1% (−9%) for RCP6.0, and 7% (18%) for RCP8.5. Model agreement on the magnitude of the change is greatest for larger changes. Reductions in most precursor emissions are common across the RCPs and drive ozone decreases in all but RCP8.5, where doubled methane and a 40–150% greater stratospheric influx (estimated from a subset of models) increase ozone. While models with a high ozone burden for the present day also have high ozone burdens for the other time slices, no model consistently predicts large or small ozone changes; i.e. the magnitudes of the burdens and burden changes do not appear to be related simply, and the models are sensitive to emissions and climate changes in different ways. Spatial patterns of ozone changes are well correlated across most models, but are notably different for models without time evolving stratospheric ozone concentrations. A unified approach to ozone budget specifications and a rigorous investigation of the factors that drive tropospheric ozone is recommended to help future studies attribute ozone changes and inter-model differences more clearly.

No abstract available.