Fire Aerosol Prediction in NOAA’s Global Aerosol Systems and its Impact on Subseasonal to Seasonal (S2S) Forecasting
Conference Proceeding
Overview
abstract
There are two global aerosol forecast systems currently under development at NOAA, both of which are coupled online with the Unified Forecast System (UFS), encompassing ocean, sea ice, wave and land surface components for Subseasonal to Seasonal (S2S) forecasting: UFS-Aerosols and UFS-Chem.  The UFS-Aerosols model is planned to be implemented into Global Ensemble Forecast System (GEFS) v13.0 in 2026, which incorporates NASA’s 2nd-generation GOCART model within a National Unified Operational Prediction Capability (NUOPC) infrastructure. The UFS-Chem is an innovative community model of chemistry online coupled with UFS. It is an extensive collaboration between NOAA Oceanic and Atmospheric Research (OAR) laboratories and NCAR. The aerosol component currently implemented in UFS-Chem is based on the operational GEFS-Aerosols v12.3, and it is implemented to the UFS model using the Common Community Physics Package (CCPP) infrastructure. Significant updates have been made to the aerosol component in UFS-Chem compared to GEFS-Aerosols, including inline aerosol radiative forcing, large-scale wet deposition, fire emission and the incorporation of indirect feedback via the Thompson aerosol-aware microphysics scheme. Both UFS-Aerosols and UFS-Chem have the capability to account for aerosol direct and semi-direct radiative feedback through online aerosol predictions for S2S forecasting. A range of global fire emission datasets and their ensemble products are used to quantify the uncertainties associated with fire aerosol predictions. Additionally, statistical and machine learning methods have been developed to enhance fire emission predictions for S2S forecasts. The performance of both UFS-Aerosols and UFS-Chem in predicting fire aerosols and their impacts on S2S forecasts is evaluated and compared using observations from reanalysis data, ground-based measurements, and satellite data.
