We evaluate the climate response of the NASA Goddard Earth Observing System (GEOS) coupled Atmosphere-Ocean General Circulation Model (AOGCM) to idealized quadrupling of CO2 concentrations. A pre-industrial simulation and an abrupt 4 & times; CO2 simulation are conducted with the GEOS AOGCM following the Coupled Model Intercomparison Project (CMIP) approach. The 4 & times; CO2 run shows expected tropospheric and stratospheric climate response features, including Arctic amplification, intensification of the hydrologic cycle, Hadley Cell expansion, a poleward shift of the Southern Hemisphere jet stream, acceleration of the Brewer-Dobson circulation, and a weaker Quasi-Biennial Oscillation. At the ocean surface, we see a complete disappearance of Arctic sea ice in July-December, and in the Southern Hemisphere, there is a poleward shift of the surface wind stress that drives the Southern Ocean meridional overturning circulation poleward. In the ocean, there is enhanced heat content but reduced heat export to the high northern latitudes coincident with a slowing of the Atlantic Meridional Overturning Circulation. The Equilibrium Climate Sensitivity (ECS) of the GEOS AOGCM is estimated to be 2.55 K, in the lowest quartile of the CMIP5 and CMIP6 ranges. The low ECS is due to a strong negative feedback and a weak effective radiative forcing.

Since 1991, continuous, consistently calibrated and openly archived ground-based measurements from the Network for the Detection of Atmospheric Composition Change (NDACC) have been collected to investigate processes responsible for decadal-scale changes, anomalies in atmospheric composition, and to validate satellite observations and model simulations. These measurements, from nearly 120 stations, support fundamental research in the area of stratospheric and tropospheric processes impacting ozone chemistry, greenhouse gases, atmospheric radiative forcing, air quality, and interactions with solar radiation and the entire Earth system. NDACC data are supplemented by observations from eleven global Cooperating Networks. The operational principles of Cooperating Networks are well aligned with NDACC objectives and protocols, focusing on data that (a) are high-quality, uniformly processed and traceable to reference standards; and (b) capture short-term (daily to interannual) anomalies and long-term trends. This paper summarizes the NDACC organizational structure. We also review the major accomplishments of NDACC since De Mazi & egrave;re et al. (2018), collaborative research with Cooperating Networks, and interactions with the satellite and modeling communities. Ground-based atmospheric composition monitoring is at a crossroads. Challenges include sustainability of human and financial resources required for complex and intensive data collection, technical issues including aging instrumentation, requirements for FAIR (findable, accessible, interoperable, reusable) data, and lack of data over large parts of Asia, Africa and South America. NDACC is well-positioned to adopt a three-pronged strategy going forward: protecting and modernizing existing stations; promoting the growing use of NDACC data; expanding the number of measured species and network coverage in under-sampled or under-reporting regions.

Aerosols are a key climate forcer and harmful to human health at the surface. Accurately modeling aerosol optical properties, mass loading and their relationship is important for constraining aerosol-climate forcing and characterizing particulate matter pollution exposure. We investigate the drivers of uncertainties in the NASA Goddard Earth Observing System Chemistry Climate Model (GEOSCCM) in simulating aerosols by focusing on the link between aerosol optical properties and mass. We compare a GEOSCCM hindcast with long-term coincident observations including satellite AOD measurements, speciated PM2.5 datasets from observations-model data fusion, and ground-based measurements of aerosol mass and optical properties. We analyze regional trends and seasonal variations of AOD and PM2.5, and surface aerosol properties, including relative humidity's role in hygroscopic enhancement. This work also presents the first extensive assessment of GEOSCCM's aerosol component with observational data. Our findings show that biases in PM2.5 components and relative humidity significantly impact simulated aerosol scattering at the surface, while scattering efficiency assumptions align with observations. This indicates that errors in simulated scattering relate more to simulated aerosol speciated mass and relative humidity than optical properties and size distribution assumptions in GEOSCCM. Our work highlights the importance of relative humidity biases on aerosol scattering enhancement for climate models where meteorology is not prescribed. Findings suggest improvements in GEOSCCM aerosols mass and optical properties could be achieved through updating emission inventories, especially over biomass burning regions, reducing nitrate biases, and improving relative humidity simulation.

Lower stratospheric ozone between 60°S and 60°N has continued to decline since 1998, despite the reduction of ozone‐depleting substances following the Montreal Protocol. Previous studies have shown that, while chemistry‐climate models reproduce the negative ozone trend in the tropical lower stratosphere as a response to increased upwelling, they fail to capture the ozone decline in northern midlatitudes. This study revisits recent lower stratospheric ozone trends over the period 1998–2018 using two types of simulations from the new Chemistry Climate Model Initiative 2022 (CCMI‐2022): REF‐D1, with observed sea surface temperatures, and REF‐D2, with simulated ocean. The observed negative trend in midlatitudes falls within the range of model trends, especially when considering simulations with observed boundary conditions. There is a large spread in the simulated midlatitudes ozone trends, with some simulations showing positive and others negative trends. A multiple linear regression analysis shows that the spread in the trends is not explained by the different linear response to external forcings (solar cycle, global warming, and ozone‐depleting substances) or to the main variability modes (El Niño‐Southern Oscillation and the quasi‐biennial oscillation) but is instead attributed to internal atmospheric variability. Moreover, the fact that some models show very different trends across members, while other models show similar trends in all members, suggests fundamental differences in the representation of the internal variability of ozone transport across models. Indeed, we report substantial intermodel differences in the ozone‐transport connection on interannual timescales and we find that ozone trends are closely coupled to transport trends.

The accurate representation of tropospheric hydroxyl radical (TOH) is crucial for reasonably modeling methane concentrations — a potent greenhouse gas. We use an improved parameterization of TOH using an interpretable and agile machine learning module named ECCOH (pronounced "echo") in NASA's GEOS global model to unravel the intricacies of TOH to its key inputs. However, the accuracy of this model is hampered by the accurate representation of its critical inputs. Fortunately, retrieving trace gases like nitrogen dioxide (NO2) and formaldehyde (HCHO) from space-borne sensors, like the Aura Ozone Monitoring Instrument (OMI), has seen remarkable progress. Consequently, we leverage these observations to assess how they can effectively alleviate some biases in TOH and can help better reproduce its long-term trends. In contrast to the earlier investigations, the refined representation of TOH archives a finer spatial resolution (1x1 degrees), and it is more up to date (2005-2019), allowing for elucidating the impact of recent emission regulations, such as those imposed in China, on TOH. OMI NO2 yields valuable insights over biomass-burning areas in Eastern Europe and central Africa, where our prior emission estimates possess significant biases, mitigating regional TOH biases up to 20%. Oceanic HCHO concentrations, serving as a proxy for TOH due to the predominant chemical pathway of VOC oxidation through OH, are only moderately altered by OMI HCHO, attributed to low signal-to-noise ratios and satisfactory representation of HCHO in the a priori simulations. Ultimately, we disentangle the convoluted map of TOH linear trends by isolating five pivotal inputs to the TOH parameterization, including stratospheric ozone, tropospheric ozone, water vapor, HCHO, and NO2. Our results demonstrate that these five parameters can collectively explain 65% of the variability in TOH trends alone. With the deployment of new satellites with enhanced sensor configurations and better temporal resolutions, our mission at NASA is to exploit those observations to improve the representation of many variables highly linked to TOH.

A too cold and too strong Antarctic stratospheric polar vortex (ASPV) in spring (the so-called cold-pole bias) is a common model problem. This study investigates the impact of the cold-pole bias on Southern Hemisphere springtime stratosphere-troposphere coupling and how this impact is affected by interactive ozone using a pair of Goddard Earth Observing System (GEOS) simulations with and without interactive chemistry. The cold-pole bias in the GEOS simulations delays the poleward and downward progression of the ASPV and stratosphere-troposphere coupling in spring by 1-2 months, causing severe underestimation of stratosphere-troposphere coupling in October-November. Consequently, the simulations poorly capture or completely miss the observed springtime tropospheric predictability from ASPV conditions in late winter/early spring. Compared to the prescribed ozone simulation, interactive ozone exacerbates the cold-pole bias by overpredicting Antarctic ozone loss, leading to degradation of springtime stratosphere-troposphere coupling and loss of tropospheric predictability.

Tropospheric ozone results from in situ chemical formation and stratosphere-troposphere exchange (STE), with the latter being more important in the middle and upper troposphere than in the lower troposphere. Ozone photochemical formation is nonlinear and results from the oxidation of methane and non-methane hydrocarbons (NMHCs) in the presence of nitrogen oxide (NOx=NO+NO2). Previous studies showed that O3 short- and long-term trends are nonlinearly controlled by near-surface anthropogenic emissions of carbon monoxide (CO), volatile organic compounds (VOCs), and nitrogen oxides, which may also be impacted by the long-range transport (LRT) of O3 and its precursors. In addition, several studies have demonstrated the important role of STE in enhancing ozone levels, especially in the midlatitudes. In this article, we investigate tropospheric ozone spatial variability and trends from 2005 to 2019 and relate those to ozone precursors on global and regional scales. We also investigate the spatiotemporal characteristics of the ozone formation regime in relation to ozone chemical sources and sinks. Our analysis is based on remote sensing products of the tropospheric column of ozone (TrC-O3) and its precursors, nitrogen dioxide (TrC-NO2), formaldehyde (TrC-HCHO), and total column CO (TC-CO), as well as ozonesonde data and model simulations. Our results indicate a complex relationship between tropospheric ozone column levels, surface ozone levels, and ozone precursors. While the increasing trends of near-surface ozone concentrations can largely be explained by variations in VOC and NOx concentration under different regimes, TrC-O3 may also be affected by other variables such as tropopause height and STE as well as LRT. Decreasing or increasing trends in TrC-NO2 have varying effects on TrC-O3, which is related to the different local chemistry in each region. We also shed light on the contribution of NOx lightning and soil NO and nitrous acid (HONO) emissions to trends of tropospheric ozone on regional and global scales.

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.

Abstract. Ozone formation is nonlinear, and results from the photochemical oxidation of methane and non-methane hydrocarbons (NMHCs) in the presence of nitrogen oxide (NOx=NO+NO2). Previous studies showed that O3 short- and long-term trends are nonlinearly controlled by near-surface anthropogenic emissions of carbon monoxide (CO), volatile organic compounds (VOCs), and nitrogen oxides. In this review article, we investigate tropospheric ozone spatial variability and trends from 2005 to 2019 and relate those to ozone precursors on global and regional scales. We also investigate the spatiotemporal characteristics of the ozone formation regime in relation to ozone chemical sources and sinks. Our analysis is based on remote sensing products of the Tropospheric Column of Ozone (TrC-O3) and its precursors, nitrogen dioxide (TrC-NO2), formaldehyde (TrC-HCHO), and total column of CO (TC-CO) as well as ozonesonde data and model simulations. Our results indicate a complex relationship between tropospheric ozone column levels, surface ozone levels, and ozone precursors. While the increasing trends of near-surface ozone concentrations can largely be explained by variations in VOC and NOx concentration under different regimes, TrC-O3 may also be affected by other variables such as tropopause height. Decreasing trends in TrC-NO2 have varying effects on the TrC-O3, which is related to the different local chemistry in each region. The concomitant increase or decrease in TrC-O3 and TrC-NO2 over the eastern US, and central Europe is due to dominant NO-sensitive conditions resulting from the strict measures to control NOx emissions over the last two decades. The decreasing trends of TrC-NO2 but increasing trends of TrC-O3 in some regions in the central US and parts of eastern Asia are due to high NOx conditions leading to VOC sensitivity in these regions. We also shed light on the contribution of NOx lightning and soil NO and nitrous acid (HONO) emissions to trends of tropospheric ozone on regional and global scales.

AbstractDespite its importance for the global oxidative capacity, spatially resolved trends and variability of the hydroxyl radical (OH) are poorly constrained. We demonstrate the utility of a tropospheric column OH (TCOH) product, created from machine learning and satellite proxy data, in determining the spatial variability in trends of tropical OH over the oceans during September through November. While OH increases domain‐wide by 2.1%/decade from 2005–2019, we find significant spatial heterogeneity in regional trends, with decreases in some areas of 2.5%/decade. Our analysis of the trends in the proxy data indicate anthropogenic‐driven changes in emissions of OH drivers as well as increasing temperatures cause these trends. This OH product is potentially a significant advance in constraining OH spatial variability and serves as a useful complement to existing tools in understanding the atmospheric oxidative capacity. Comprehensive observations of TCOH are required to assess the fidelity of this method.

Most major global satellite surveyors of climate-relevant trace gases have relatively coarse spatial resolution or temporal sampling. While these data can be supplemented by fine-pointing satellites and aircraft, the spatial and temporal resolutions available from crewed aircraft is not sufficient to observe stochastic, ephemeral events that take place between observations. Emerging High Altitude Long Endurance (HALE) Uncrewed Aerial Systems (UAS) can operate for months at a time and loiter over targets to provide continuous daylight geostationary-like observations, allowing these new platforms to be integrated with existing satellites as part of a New Observing Strategy (NOS). To aid in the planning of future NOS missions, NASA is developing the Intelligent Long Endurance Observing System (ILEOS), a science activity planning system. ILEOS will help scientists build plans to improve spatio-temporal resolution of climate-relevant gases by fusing coarse-grained sensor data from satellites and other sources (e.g., terrain, forecasts), and plan HALE UAS flights to obtain finer-grain (high spatio-temporal) data. ILEOS will also enable observations for longer periods and of environments not accessible through in-situ observations and crewed aircraft field campaigns.

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.

Behind the scenes of a remote sensing mission there are complex decision making and planning operations. Streamlining these operations, with a quantitative scientific value framework, aids efficient and optimized science data collection. While there have been previous efforts to quantify the science value for specific science scenarios, our work aims to develop a general framework which can be applied across different scenarios. We describe a pipeline of processes which combines model forecast and observation data, in computational forms, as dictated by the mission objectives set forth by subject matter experts. The framework is described with use cases involving the monitoring of nitrogen dioxide (NO2) concentrations over the Gulf of Mexico and methane concentrations over interior Alaska.

Estimates of ground-level ozone concentrations have been improved through data fusion of observations and atmospheric chemistry models. Our previous global ozone estimates for the Global Burden of Disease study corrected for bias uniformly across continents and then corrected near monitoring stations using the Bayesian Maximum Entropy (BME) framework for data fusion. Here, we use the Regionalized Air Quality Model Performance (RAMP) framework to correct model bias over a much larger spatial range than BME can, accounting for the spatial inhomogeneity of bias and nonlinearity as a function of modeled ozone. RAMP bias correction is applied to a composite of 9 global chemistry-climate models, based on the nearest set of monitors. These estimates are then fused with observations using BME, which matches observations at measurement stations, with the influence of observations declining with distance in space and time. We create global ozone maps for each year from 1990 to 2017 at fine spatial resolution. RAMP is shown to create unrealistic discontinuities due to the spatial clustering of ozone monitors, which we overcome by applying a weighting for RAMP based on the number of monitors nearby. Incorporating RAMP before BME has little effect on model performance near stations, but strongly increases R2 by 0.15 at locations farther from stations, shown through a checkerboard cross-validation. Corrections to estimates differ based on location in space and time, confirming heterogeneity. We quantify the likelihood of exceeding selected ozone levels, finding that parts of the Middle East, India, and China are most likely to exceed 55 parts per billion (ppb) in 2017. About 96% of the global population was exposed to ozone levels above the World Health Organization guideline of 60 µg m−3 (30 ppb) in 2017. Our annual fine-resolution ozone estimates may be useful for several applications including epidemiology and assessments of impacts on health, agriculture, and ecosystems.

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.

Water vapor plays an important role in many aspects of the climate system, by affecting radiation, cloud formation, atmospheric chemistry and dynamics. Even the low stratospheric water vapor content provides an important climate feedback, but current climate models show a substantial moist bias in the lowermost stratosphere. Here we report crucial sensitivity of the atmospheric circulation in the stratosphere and troposphere to the abundance of water vapor in the lowermost stratosphere. We show from a mechanistic climate model experiment and inter-model variability that lowermost stratospheric water vapor decreases local temperatures, and thereby causes an upward and poleward shift of subtropical jets, a strengthening of the stratospheric circulation, a poleward shift of the tropospheric eddy-driven jet and regional climate impacts. The mechanistic model experiment in combination with atmospheric observations further shows that the prevailing moist bias in current models is likely caused by the transport scheme, and can be alleviated by employing a less diffusive Lagrangian scheme. The related effects on atmospheric circulation are of similar magnitude as climate change effects. Hence, lowermost stratospheric water vapor exerts a first order effect on atmospheric circulation and improving its representation in models offers promising prospects for future research.

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.