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The Goddard Earth Observing System composition forecast (GEOS-CF) system is a high-resolution (0.25 degree) global constituent prediction system from NASA’s Global Modeling and Assimilation Office (GMAO). GEOS-CF offers a new tool for atmospheric chemistry research, with the goal to supplement NASA’s broad range of space-based and in-situ observation sand to support flight campaign planning, support of satellite observations, and air quality research. GEOS-CF expands on the GEOS weather and aerosol modeling system by introducing the GEOS-Chem chemistry module to provide analyses and 5-day forecasts of atmospheric constituents including ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), and fine particulate matter (PM2.5). The chemistry module integrated in GEOS-CF is identical to the offline GEOS-Chem model and readily benefits from the innovations provided by the GEOS-Chem community.Evaluation of GEOS-CF against satellite, ozone sonde and surface observations show realistic simulated concentrations of O3, NO2, and CO, with normalized mean biases of -0.1 to -0.3, normalized root mean square errors (NRMSE) between 0.1-0.4, and correlations between 0.3-0.8. Comparisons against surface observations highlight the successful representation of air pollutants under a variety of meteorological conditions, yet also highlight current limitations, such as an over prediction of summertime ozone over the Southeast United States. GEOS-CFv1.0 generally overestimates aerosols by 20-50% due to known issues in GEOS-Chem v12.0.1 that have been addressed in later versions.The 5-day hourly forecasts have skill scores comparable to the analysis. Model skills can be improved significantly by applying a bias-correction to the surface model output using a machine-learning approach.

Despite 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. Plain Language Summary Hydroxyl is a chemical that removes many gases from the atmosphere, including methane, an important greenhouse gas. To understand recent trends in methane, we must also understand recent trends in hydroxyl. Because of various limitations, we unfortunately do not have long‐term, direct observations of hydroxyl. To address this problem, we have developed a machine learning model that uses satellite observations of variables relevant to hydroxyl chemistry and variability to calculate hydroxyl. We demonstrate that this product can be used to understand trends and variability of hydroxyl over the tropical oceans. While, on average, hydroxyl increases from 2005–2019, we show that this is not a universal trend and that hydroxyl actually decreases in multiple regions over the same time period. Using satellite observations of various chemicals, we demonstrate that changes in emissions due to human activity and increases in temperature cause many of these trends. This product is potentially a significant advance in understanding changes in hydroxyl and could be a useful complement to more traditional methods in understanding atmospheric methane. Key Points Satellite proxies can constrain the spatial distribution of trends and variability of the hydroxyl radical in the tropics Changes in biomass burning, temperature, and other emissions lead to large spatial heterogeneity in hydroxyl radical trends More direct observations of the hydroxyl radical and its drivers are needed to further validate the fidelity of our methodology

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 combination of air quality (AQ) data from satellites and low‐cost sensor systems, along with output from AQ models, have the potential to augment high‐quality, regulatory‐grade data in countries with in situ monitoring networks and provide much needed AQ information in countries without them, including Low and Moderate Income Countries (LMICs). We demonstrate the potential of free and publicly available USA National Aeronautics and Space Administration (NASA) resources, which include capacity building activities, satellite data, and global AQ forecasts, to provide cost‐effective, and reliable AQ information to health and AQ professionals around the world. We provide illustrative case studies that highlight how global AQ forecasts along with satellite data may be used to characterize AQ on urban to regional scales, including to quantify pollution concentrations, identify pollution sources, and track the long‐range transport of pollution. We also provide recommendations to data product developers to facilitate and broaden usage of NASA resources by health and AQ stakeholders. Plain Language Summary We demonstrate that free and publicly available USA National Aeronautics and Space Administration (NASA) resources, which include capacity building activities, satellite data, and global air quality (AQ) forecasts, have the potential to augment air pollution data from surface monitors and low‐cost sensors. These resources are valuable for health and air quality stakeholders who have no or little access to air pollution data for their communities. For instance, they are a viable and cost‐effective option for stakeholders in Low and Moderate Income Countries (LMICs) to acquire the data necessary for monitoring air quality trends, assessing the efficacy of emission controls, daily risk communication and local health studies. Such an approach to air quality monitoring combines the strengths of these monitoring technologies (i.e., regulatory‐grade stationary monitors, satellite data, low‐cost sensors, and air quality forecast models). We also provide recommendations to data product developers to facilitate and broaden usage of NASA resources by health and AQ stakeholders. Key Points Satellite data and global air quality models can augment data from surface monitors for health and air quality applications These resources provide much needed, cost‐effective air quality information for many Low and Moderate Income Countries With case studies, we show that an effective approach to monitoring combines the strengths of these resources with surface monitors

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

Lymphoid tissue immunopathology is a characteristic feature of chronic HIV/SIV infection in AIDS-susceptible species, but is absent in SIV-infected natural hosts. To investigate factors contributing to this difference, we compared germinal center development and SIV RNA distribution in peripheral lymph nodes during primary SIV infection of the natural host sooty mangabey and the non-natural host pig-tailed macaque. Although SIV-infected cells were detected in the lymph node of both species at two weeks post infection, they were confined to the lymph node paracortex in immune-competent mangabeys but were seen in both the paracortex and the germinal center of SIV-infected macaques. By six weeks post infection, SIV-infected cells were no longer detected in the lymph node of sooty mangabeys. The difference in localization and rate of disappearance of SIV-infected cells between the two species was associated with trapping of cell-free virus on follicular dendritic cells and higher numbers of germinal center CD4(+) T lymphocytes in macaques post SIV infection. Our data suggests that fundamental differences in the germinal center microenvironment prevent productive SIV infection within the lymph node germinal centers of natural hosts contributing to sustained immune competency.

Background. Worldwide, ∼90% of all human immunodeficiency virus (HIV) transmissions occur mucosally; almost all involve R5 strains. Risks of sexual HIV acquisition are highest for rectal, then vaginal, and finally oral exposures. Methods. Mucosal lacerations may affect the rank order of susceptibility to HIV but cannot be assessed in humans. We measured relative virus transmissibility across intact mucosae in macaques using a single stock of SHIV-1157ipd3N4, a simian-human immunodeficiency virus encoding a primary R5 HIV clade C env (SHIV-C). Results. The penetrability of rhesus macaque mucosae differed significantly, with rectal challenge requiring the least virus, followed by vaginal and then oral routes ( P = .031 oral vs vaginal; rectal P < .001 vs vaginal). These findings imply that intrinsic mucosal properties are responsible for the differential mucosal permeability. The latter paralleled the rank order reported for humans, with relative risk estimates within the range of epidemiological human studies. To test whether inflammation facilitates virus transmission—as predicted from human studies— we established a macaque model of localized buccal inflammation. Systemic infection occurred across inflamed but not normal buccal mucosa. Conclusion. Our primate data recapitulate virus transmission risks observed in humans, thus establishing R5 SHIV-1157ipd3N4 in macaques as a robust model system to study cofactors involved in human mucosal HIV transmission and its prevention.

Because milk-borne transmission of human immunodeficiency virus (HIV) diminishes the benefits of perinatal antiviral drug therapy in developing countries, we have developed a new strategy to prevent postnatal and, possibly, intrapartum virus transmission in a primate model. Eight neonatal rhesus macaques were exposed orally to pathogenic simian-human immunodeficiency virus (SHIV); 4 neonates were then given intramuscular postexposure prophylaxis with 3 anti-HIV human neutralizing monoclonal antibodies (nMAbs) with potent cross-clade and cross-group neutralization activity. Untreated infants experienced high viral RNA levels and CD4+ T-cell losses and died (median survival time, 5.5 weeks). In contrast, all 4 nMAb-treated neonates were protected from infection (P =.028); their plasma, peripheral blood mononuclear cells, and lymph nodes remained virus negative for >1 year. These data are important for designing clinical trials in human neonates and have general implications for AIDS vaccine development, as the epitopes recognized by the 3 nMAbs are conserved among diverse primary isolates.

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.

The hydroxyl (OH), hydroperoxy (HO2), and organic peroxy (RO2) radicals play important roles in atmospheric chemistry. In the presence of nitrogen oxides (NOx), reactions between OH and volatile organic compounds (VOCs) can initiate a radical propagation cycle that leads to the production of ozone and secondary organic aerosols. Previous measurements of these radicals under low-NOx conditions in forested environments characterized by emissions of biogenic VOCs, including isoprene and monoterpenes, have shown discrepancies with modeled concentrations.During the summer of 2016, OH, HO2, and RO2 radical concentrations were measured as part of the Program for Research on Oxidants: Photochemistry, Emissions, and Transport – Atmospheric Measurements of Oxidants in Summer (PROPHET-AMOS) campaign in a midlatitude deciduous broadleaf forest. Measurements of OH and HO2 were made by laser-induced fluorescence–fluorescence assay by gas expansion (LIF-FAGE) techniques, and total peroxy radical (XO2) mixing ratios were measured by the Ethane CHemical AMPlifier (ECHAMP) instrument. Supporting measurements of photolysis frequencies, VOCs, NOx, O3, and meteorological data were used to constrain a zero-dimensional box model utilizing either the Regional Atmospheric Chemical Mechanism (RACM2) or the Master Chemical Mechanism (MCM). Model simulations tested the influence of HOx regeneration reactions within the isoprene oxidation scheme from the Leuven Isoprene Mechanism (LIM1). On average, the LIM1 models overestimated daytime maximum measurements by approximately 40 % for OH, 65 % for HO2, and more than a factor of 2 for XO2. Modeled XO2 mixing ratios were also significantly higher than measured at night. Addition of RO2 + RO2 accretion reactions for terpene-derived RO2 radicals to the model can partially explain the discrepancy between measurements and modeled peroxy radical concentrations at night but cannot explain the daytime discrepancies when OH reactivity is dominated by isoprene. The models also overestimated measured concentrations of isoprene-derived hydroxyhydroperoxides (ISOPOOH) by a factor of 10 during the daytime, consistent with the model overestimation of peroxy radical concentrations. Constraining the model to the measured concentration of peroxy radicals improves the agreement with the measured ISOPOOH concentrations, suggesting that the measured radical concentrations are more consistent with the measured ISOPOOH concentrations. These results suggest that the models may be missing an important daytime radical sink and could be overestimating the rate of ozone and secondary product formation in this forest.