Updating and Evaluating Anthropogenic Emissions for NOAA’s Global Ensemble Forecast Systems for Aerosols (GEFS-Aerosols): Application of an SO2 Bias-Scaling Method

We updated the anthropogenic emissions inventory in NOAA’s operational Global Ensemble Forecast for Aerosols (GEFS-Aerosols) to improve model’s prediction of aerosol optical depth (AOD). used a methodology quickly update pivotal global sulfur dioxide (SO2) using speciated AOD bias-scaling method. The method is based on latest model predictions compared NASA’s Modern-Era Retrospective analysis Research and Applications, version 2 (MERRA2). bias was subsequently applied CEDS 2019 SO2 adjustment. monthly mean GEFS-Aerosols were evaluated against suite satellite observations (e.g., MISR, VIIRS, MODIS), ground-based AERONET observations, International Cooperative Aerosol Prediction (ICAP) ensemble results. results show that transitioning from 2014 data led significant improvement performance, applying bias-scaled could further distributions. biases simulated AODs observed varied with observation type seasons by factor 3~13 2~10, respectively. distributions showed differences simulations ICAP, MODIS largest March–May (MAM) smallest December–February (DJF). When evaluating ground-truth data, methods improved seasonal correlation (r), Index Agreement (IOA), biases, except MAM season, when negative regional exacerbated positive biases. effect had most beneficial impact performance regions dominated emissions, such as East Asia. However, it less other areas impacted greater relative transport natural sources, India. accuracies reference or assimilation adjusted inputs physics outputs, selection aerosols determine success methods. A companion study emission scaling absorbing needs prediction.