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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.

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 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.

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.

Ozone exposure is associated with negative health impacts, including premature mortality. Observations and modeling studies demonstrate that emissions from one continent influence ozone air quality over other continents. We estimate the premature mortalities avoided from surface ozone decreases obtained via combined 20% reductions of anthropogenic nitrogen oxide, nonmethane volatile organic compound, and carbon monoxide emissions in North America (NA), East Asia (EA), South Asia (SA), and Europe (EU). We use estimates of ozone responses to these emission changes from several atmospheric chemical transport models combined with a health impact function. Foreign emission reductions contribute approximately 30%, 30%, 20%, and >50% of the mortalities avoided by reducing precursor emissions in all regions together in NA, EA, SA, and EU, respectively. Reducing emissions in NA and EU avoids more mortalities outside the source region than within, owing in part to larger populations in foreign regions. Lowering the global methane abundance by 20% reduces mortality most in SA, followed by EU, EA, and NA. For some source−receptor pairs, there is greater uncertainty in our estimated avoided mortalities associated with the modeled ozone responses to emission changes than with the health impact function parameters.

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.