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 (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, 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.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2020MS002413

Description of the NASA GEOS Composition Forecast Modeling System GEOS-CF v1.0.

Long-range transport of Siberian biomass burning emissions to North America during FIREX-AQ

Observations of bay-breeze and ozone events over a marine site during the OWLETS-2 campaign

Long-term (2000–2019) assessment of aerosol loads and dominant aerosol types at spatiotemporal scales using multi-source datasets can provide a strong impetus to the investigation of aerosol loads and to the targeted prevention control of atmospheric pollution in densely populated regions with frequent anthropogenic activities and heavy aerosol emissions. This study uses multi-source aerosol datasets, including Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), Moderate Resolution Imaging Spectroradiometer (MODIS), and Aerosol Robotic Network (AERONET), to conduct a long-term variation assessment of aerosol load, high aerosol load frequency, and dominant aerosol types over Asia. The results indicate that regional aerosol type information with adequate spatial resolution can be combined with aerosol optical depth (AOD) values and heavy aerosol load frequency characterization results to explore the key contributors to air pollution. During the study period, the aerosol load over the North China Plain, Central China, Yangtze River Delta, Red River Delta, Sichuan Basin, and Pearl River Delta exhibited an increasing trend from 2000–2009 due to a sharp rise in aerosol emissions with economic development and a declining trend from 2010–2019 under stricter energy conservation controls and emissions reductions. The growth of urban/industrial (UI) type and biomass burning (BB) type aerosol emissions hindered the improvement of the atmospheric environment. Therefore, in future pollution mitigation efforts, focus should be on the control of UI-type and BB-type aerosol emissions. The Indus–Ganges River Plain, Deccan Plateau, and Eastern Ghats show a continuously increasing trend; however, the aerosol load growth rate of the last decade was lower than that of the first decade, which was mainly due to the decrease in the proportion of the mixed type aerosols.

https://www.mdpi.com/2072-4292/13/16/3116/pdf

Long-Term Variation Assessment of Aerosol Load and Dominant Types over Asia for Air Quality Studies Using Multi-Sources Aerosol Datasets

Case study of stratospheric intrusion above Hampton, Virginia: Lidar-observation and modeling analysis

Evaluation of NASA's high-resolution global composition simulations: Understanding a pollution event in the Chesapeake Bay during the summer 2017 OWLETS campaign.

International conferences such as the United Nations Framework Convention on Climate Change's (UNFCCC) Conference of the Parties (COP) attract over 25,000 attendees from around the world and have an alarming carbon footprint. Carbon neutral events have increased in popularity, and help to curb large amounts of emissions and slow climate change. We discuss techniques to reduce carbon emissions without settling for offsets from an individual and conference scale. The UNFCCC COP can reduce its overall emissions significantly by implementing a hybrid conference model and more sustainable conference choices. COP can lead the way in establishing a sustainable model to advance the climate agenda without exacerbating the global climate crisis.

https://www.sciencepolicyjournal.org/uploads/5/4/3/4/5434385/dacic_etal_jspg_20-1.pdf

How UNFCCC's COP Can Achieve Carbon Neutrality

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Evaluation of NASA's high-resolution global composition simulations: Understanding a pollution event in the Chesapeake Bay during the summer 2017 OWLETS campaign.

How UNFCCC's COP Can Achieve Carbon Neutrality