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

Keller, Christoph A.; Knowland, K. Emma; Duncan, Bryan N.; Liu, Junhua; Anderson, Daniel C.; Das, Sampa; Lucchesi, Robert A.; Lundgren, Elizabeth W.; Nicely, Julie M.; Nielsen, Eric; Ott, Lesley E.; Saunders, Emily; Strode, Sarah A.; Wales, Pamela A.; Jacob, Daniel J.; Pawson, Steven

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 (O(3)), carbon monoxide (CO), nitrogen dioxide (NO(2)), sulfur dioxide (SO(2)), 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 O(3), NO(2), 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 SO(2) and an overprediction of summertime O(3) 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.