Geoscientific Model Development (Jan 2024)

Analysis of the GEFS-Aerosols annual budget to better understand aerosol predictions simulated in the model

  • L. Pan,
  • P. S. Bhattacharjee,
  • L. Zhang,
  • L. Zhang,
  • R. Montuoro,
  • B. Baker,
  • J. McQueen,
  • G. A. Grell,
  • S. A. McKeen,
  • S. A. McKeen,
  • S. A. McKeen,
  • S. Kondragunta,
  • X. Zhang,
  • G. J. Frost,
  • F. Yang,
  • I. Stajner

DOI
https://doi.org/10.5194/gmd-17-431-2024
Journal volume & issue
Vol. 17
pp. 431 – 447

Abstract

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In September 2020, a global aerosol forecasting model was implemented as an ensemble member of the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) Global Ensemble Forecasting System (GEFS) v12.0.1 (hereafter referred to as “GEFS-Aerosols”). In this study, GEFS-Aerosols simulation results from 1 September 2019 to 30 September 2020 were evaluated using an aerosol budget analysis. These results were compared with results from other global models as well as reanalysis data. From this analysis, the global average lifetimes of black carbon (BC), organic carbon (OC), dust, sea salt, and sulfate are 4.06, 4.29, 4.59, 0.34, and 3.3 d, respectively, with the annual average loads of 0.14, 1.29, 4.52, 6.80, and 0.51 Tg. Compared with the National Aeronautics and Space Administration (NASA) Goddard Earth Observing System–Goddard Chemistry Aerosol and Radiation Transport (GEOS4-GOCART) model, the aerosols in GEFS-Aerosols have a relatively short lifetime because of the faster removal processes in GEFS-Aerosols. Meanwhile, in GEFS-Aerosols, aerosol emissions are the determining factor for the mass and composition of aerosols in the atmosphere. The size (bin) distribution of aerosol emissions is as important as its total emissions, especially in simulations of dust and sea salt. Moreover, most importantly, the strong monthly and interannual variations in natural sources of aerosols in GEFS-Aerosols suggest that improving the accuracy of the prognostic concentrations of aerosols is important for applying aerosol feedback to weather and climate predictions.