The Goddard Earth Observing System composition forecast (GEOS‐CF) system is a high‐resolution (0.25°) 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 observations. GEOS‐CF expands on the GEOS weather and aerosol modeling system by introducing the GEOS‐Chem chemistry module to provide hindcasts and 5‐days forecasts of atmospheric constituents including ozone (O₃), carbon monoxide (CO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and fine particulate matter (PM_2.5). 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, ozonesonde and surface observations for years 2018–2019 show realistic simulated concentrations of O₃, NO₂, and CO, with normalized mean biases of −0.1 to 0.3, normalized root mean square errors between 0.1–0.4, and correlations between 0.3–0.8. Comparisons against surface observations highlight the successful representation of air pollutants in many regions of the world and during all seasons, yet also highlight current limitations, such as a global high bias in SO₂ and an overprediction of summertime O₃ over the Southeast United States. GEOS‐CF v1.0 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‐days forecasts have skill scores comparable to the 1‐day hindcast. Model skills can be improved significantly by applying a bias‐correction to the surface model output using a machine‐learning approach.

Social distancing to combat the COVID-19 pandemic has led to widespread reductions in air pollutant emissions. Quantifying these changes requires a business-as-usual counterfactual that accounts for the synoptic and seasonal variability of air pollutants. We use a machine learning algorithm driven by information from the NASA GEOS-CF model to assess changes in nitrogen dioxide (NO₂) and ozone (O₃) at 5756 observation sites in 46 countries from January through June 2020. Reductions in NO₂ coincide with the timing and intensity of COVID-19 restrictions, ranging from 60 % in severely affected cities (e.g., Wuhan, Milan) to little change (e.g., Rio de Janeiro, Taipei). On average, NO₂ concentrations were 18 (13–23) % lower than business as usual from February 2020 onward. China experienced the earliest and steepest decline, but concentrations since April have mostly recovered and remained within 5 % of the business-as-usual estimate. NO₂ reductions in Europe and the US have been more gradual, with a halting recovery starting in late March. We estimate that the global NO_x (NO + NO₂) emission reduction during the first 6 months of 2020 amounted to 3.1 (2.6–3.6) TgN, equivalent to 5.5 (4.7–6.4) % of the annual anthropogenic total. The response of surface O₃ is complicated by competing influences of nonlinear atmospheric chemistry. While surface O₃ increased by up to 50 % in some locations, we find the overall net impact on daily average O₃ between February–June 2020 to be small. However, our analysis indicates a flattening of the O₃ diurnal cycle with an increase in nighttime ozone due to reduced titration and a decrease in daytime ozone, reflecting a reduction in photochemical production. The O₃ response is dependent on season, timescale, and environment, with declines in surface O₃ forecasted if NO_x emission reductions continue.

Recent measurements over the Northern Hemisphere indicate that the long‐term decline in the atmospheric burden of ethane (C₂H₆) has ended and the abundance increased dramatically between 2010 and 2014. The rise in C₂H₆atmospheric abundances has been attributed to oil and natural gas extraction in North America. Existing global C₂H₆emission inventories are based on outdated activity maps that do not account for current oil and natural gas exploitation regions. We present an updated global C₂H₆emission inventory based on 2010 satellite‐derived CH₄fluxes with adjusted C₂H₆emissions over the U.S. from the National Emission Inventory (NEI 2011). We contrast our global 2010 C₂H₆emission inventory with one developed for 2001. The C₂H₆difference between global anthropogenic emissions is subtle (7.9 versus 7.2 Tg yr⁻¹), but the spatial distribution of the emissions is distinct. In the 2010 C₂H₆inventory, fossil fuel sources in the Northern Hemisphere represent half of global C₂H₆emissions and 95% of global fossil fuel emissions. Over the U.S., unadjusted NEI 2011 C₂H₆emissions produce mixing ratios that are 14–50% of those observed by aircraft observations (2008–2014). When the NEI 2011 C₂H₆emission totals are scaled by a factor of 1.4, the Goddard Earth Observing System Chem model largely reproduces a regional suite of observations, with the exception of the central U.S., where it continues to underpredict observed mixing ratios in the lower troposphere. We estimate monthly mean contributions of fossil fuel C₂H₆emissions to ozone and peroxyacetyl nitrate surface mixing ratios over North America of ~1% and ~8%, respectively.

Computational models of the Earth System are critical tools for modern scientific inquiry. Efforts toward evaluating and improving errors in representations of physical and chemical processes in these large computational systems are commonly stymied by highly nonlinear and complex error behavior. Recent work has shown that these errors can be effectively predicted using modern Artificial Intelligence (A.I.) techniques. In this work, we go beyond these previous studies to apply an explainable A.I. technique to not only predict model errors but also move toward understanding the underlying reasons for successful error prediction. We use XGBoost classification trees and SHapley Additive exPlanations analysis to explore the errors in the prediction of lightning occurrence in the NASA Goddard Earth Observing System model, a widely used Earth System Model. This explainable error prediction system can effectively predict the model error and indicates that the errors are strongly related to convective processes and the characteristics of the land surface.

The NASA Goddard Earth Observing System (GEOS) Composition Forecast (GEOS‐CF) provides recent estimates and 5‐day forecasts of atmospheric composition to the public in near‐real time. To do this, the GEOS Earth system model is coupled with the GEOS‐Chem tropospheric‐stratospheric unified chemistry extension (UCX) to represent composition from the surface to the top of the GEOS atmosphere (0.01 hPa). The GEOS‐CF system is described, including updates made to the GEOS‐Chem UCX mechanism within GEOS‐CF for improved representation of stratospheric chemistry. Comparisons are made against balloon, lidar, and satellite observations for stratospheric composition, including measurements of ozone (O₃) and important nitrogen and chlorine species related to stratospheric O₃ recovery. The GEOS‐CF nudges the stratospheric O₃ toward the GEOS Forward Processing (GEOS FP) assimilated O₃ product; as a result the stratospheric O₃ in the GEOS‐CF historical estimate agrees well with observations. During abnormal dynamical and chemical environments such as the 2020 polar vortexes, the GEOS‐CF O₃ forecasts are more realistic than GEOS FP O₃ forecasts because of the inclusion of the complex GEOS‐Chem UCX stratospheric chemistry. Overall, the spatial patterns of the GEOS‐CF simulated concentrations of stratospheric composition agree well with satellite observations. However, there are notable biases—such as low NO_x and HNO₃ in the polar regions and generally low HCl throughout the stratosphere—and future improvements to the chemistry mechanism and emissions are discussed. GEOS‐CF is a new tool for the research community and instrument teams observing trace gases in the stratosphere and troposphere, providing near‐real‐time three‐dimensional gridded information on atmospheric composition.

Tropospheric ozone is an air pollutant and a greenhouse gas whose anthropogenic production is limited principally by the supply of nitrogen oxides (NO _x ) from combustion. Tropospheric ozone in the northern hemisphere has been rising despite the flattening of NO _x emissions in recent decades. Here we propose that this sustained increase could result from the photolysis of nitrate particles (pNO ₃ ⁻ ) to regenerate NO _x . Including pNO ₃ ⁻ photolysis in the GEOS‐Chem atmospheric chemistry model improves the consistency with ozone observations. Our simulations show that pNO ₃ ⁻ concentrations have increased since the 1960s because of rising ammonia and falling SO ₂ emissions, augmenting the increase in ozone in the northern extratropics by about 50% to better match the observed ozone trend. pNO ₃ ⁻ will likely continue to increase through 2050, which would drive a continued increase in ozone even as NO _x emissions decrease. More work is needed to better understand the mechanism and rates of pNO ₃ ⁻ photolysis.