EGUsphere EGUsphere EGUsphere EGUsphere Copernicus Publications Göttingen, Germany 10.5194/egusphere-2026-1807 Development of the CCPP-Based GEFS-Aerosols Component in the Unified Forecast System for Subseasonal Prediction (UFS-Chem v1.0) Zhang Li https://orcid.org/0000-0001-9034-3338 1 2 Li Haiqin https://orcid.org/0000-0002-8105-9033 1 2 Grell Georg A. 9 2 10 Bhattacharjee Partha S. https://orcid.org/0000-0003-1117-0649 3 Ferrada Gonzalo A. https://orcid.org/0000-0001-7502-9439 1 2 Green Benjamin W. 1 2 Sun Shan 2 Bernardet Ligia 2 Jensen Anders 2 Baker Barry https://orcid.org/0000-0002-6431-2391 4 Pan Li https://orcid.org/0000-0002-1806-5414 3 He Jian https://orcid.org/0000-0002-1627-6859 1 5 Schnell Jordan 1 2 Ahmadov Ravan 2 Trahan Samuel 1 2 Swales Dustin 2 Cheng Anning 3 Yang Fanglin https://orcid.org/0000-0003-1042-7983 4 Schwantes Rebecca H. https://orcid.org/0000-0002-7095-3718 5 McDonald Brian C. https://orcid.org/0000-0001-8600-5096 5 Heinzeller Dominikus 6 7 Kondragunta Shobha 8 CIRES, University of Colorado Boulder, Boulder, CO, US Global Systems Laboratory, NOAA, Boulder, CO, US SAIC/Lynker at NOAA/NWS/MDC, College Park, MD, US NCEP/NWS/MDC, College Park, MD, US Chemical Sciences Laboratory, NOAA, Boulder, CO, US Marine Meteorology Division, Naval Research Laboratory, Monterey, CA, US Cooperative Programs for the Advancement of Earth System Sciences, University Corporation for Atmospheric Research, Boulder, CO, US NOAA/NESDIS Center for Satellite Applications and Research, College Park, MD, US retired visiting scientist at: National Science Foundation National Center for Atmospheric Research (NCAR) 20 04 2026 2026 1 43 Copyright: © 2026 Li Zhang et al. 2026 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://egusphere.copernicus.org/preprints/2026/egusphere-2026-1807/ The full text article is available as a PDF file from https://egusphere.copernicus.org/preprints/2026/egusphere-2026-1807/egusphere-2026-1807.pdf

The Global Ensemble Forecast System (GEFS) version 12 has been operational at the National Centers for Environmental Prediction since September 2020, with GEFS-Aerosols serving as its global aerosol forecasting member. In 2023, GEFS-Aerosols was upgraded to version 12.3 in operations, incorporating improvements to wet deposition, anthropogenic and biomass burning emissions, aerosol optical depth calculations, and the FENGSHA dust emission scheme. While GEFS-Aerosols provides valuable operational aerosol forecasts on medium-range timescales, its one-way coupling strategy restricts aerosol–atmosphere interactions to prescribed climatological fields, thereby precluding fully interactive aerosol feedbacks for extended-range prediction. To overcome this limitation, we develop the Unified Forecast System coupled with Chemistry (UFS-Chem), which incorporates the Configurable Atmospheric Chemistry (CATChem) library and modeling component to include both aerosol and gas-phase chemistry schemes. In the current UFS-Chem configuration, the aerosol component is based on the operational GEFS-Aerosols v12.3 and implemented using the Common Community Physics Package (CCPP) framework. Relative to GEFS-Aerosols v12.3, UFS-Chem incorporates several enhancements, including inline aerosol radiative feedback, inline large-scale wet deposition, and updated FENGSHA dust scheme. In particular, the integration of inline large-scale wet deposition and aerosol indirect effects through the Thompson aerosol-aware microphysics scheme improves the representation of aerosol–cloud interactions and supports weather and subseasonal-to-seasonal forecasting when fully coupled with ocean, sea ice, land, and wave components. The performance of UFS-Chem in weather and subseasonal prediction is evaluated against reanalysis data, ground-based measurements, and satellite observations. The relative impacts of aerosol radiative and indirect feedback on weather and subseasonal predictions are investigated and quantified, advancing our understanding of how aerosol–radiation and aerosol–cloud interactions influence forecast skill across timescales.

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