Radiative forcing bias calculation based on COSMO (Core-Shell Mie model Optimization) and AERONET data

Direct radiative forcing (DRF) of aerosols is driven by aerosol concentration, size, and mixing state, and solar radiation. This work introduces Core-Shell Mie model optimization (COSMO) to compute top of the atmosphere (TOA) forcing based on inversely constrained black carbon (BC) size and mixing state from AERONET, over two rapidly developing areas: Lumbini and Taihu. COSMO has both, a less negative TOA than AERONET and a wider range of variability, with the mean and standard deviation difference between COSMO and AERONET being 13 +/- 8.1 W m(-2) at Lumbini and 16 +/- 12 W m(-2) at Taihu. These differences are driven by particle aging and size-resolved BC emissions, with up to 17.9% of cases warmer than the maximum AERONET TOA, and 1.9% of the total possible cases show a net-warming at TOA (TOA > 0). A linearized correction is deduced which can be immediately implemented by climate models, and suggested ranges of BC size and mixing observations are made for future campaigns. Given that the COSMO TOA bias and uncertainty are larger than the forcing of locally emitted GHGs, active consideration of BC is necessary to reduce climate uncertainty in developing areas.

Automated summary

This study introduces the Core-Shell Mie model optimization (COSMO) to compute top of the atmosphere (TOA) forcing based on inversely constrained black carbon (BC) size and mixing state. The results show differences between COSMO and AERONET, driven by particle aging and size-resolved BC emissions. A linearized correction is deduced and suggested ranges of BC size and mixing observations are made for future campaigns.