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SUMMARY:Comparison of methods to estimate aerosol effective radiative forc
 ings in climate models - Mark Zelinka\;  PCMDI | Lawrence Livermore Nation
 al Laboratory
DTSTART:20231024T150000Z
DTEND:20231024T160000Z
UID:TALK205777@talks.cam.ac.uk
CONTACT:Vichawan (Print) Sakulsupich
DESCRIPTION:Uncertainty in the effective radiative forcing (ERF) of climat
 e primarily arises from the unknown contribution of aerosols\, which impac
 t radiative fluxes directly and through modifying cloud properties. Climat
 e model simulations with fixed sea surface temperatures but perturbed atmo
 spheric aerosol loadings allow for an estimate of how strongly the planet'
 s radiative energy budget has been perturbed by the increase in aerosols s
 ince pre-industrial times. The approximate partial radiative perturbation 
 (APRP) technique further decomposes the contributions to the direct forcin
 g due to aerosol scattering and absorption and to the indirect forcing due
  to aerosol-induced changes in cloud scattering\, amount\, and absorption\
 , as well as the effects of aerosols on surface albedo. In this talk I wil
 l show that previously published APRP-derived estimates of aerosol effecti
 ve radiative forcings in CMIP6 models are biased as a result of two large 
 coding errors that – in most cases – fortuitously compensate. The most
  notable exception is the direct radiative forcing from absorbing aerosols
 \, which is more than 40 % larger averaged across CMIP6 models in the pr
 esent study. Correcting these biases eliminates the residuals and leads to
  better agreement with benchmark estimates derived from double calls to th
 e radiation code. The APRP method – when properly implemented – remain
 s a highly accurate and efficient technique for diagnosing aerosol ERF in 
 cases where double radiation calls are not available\, and in all cases it
  provides quantification of the individual contributors to the ERF that ar
 e highly useful but not otherwise available.
LOCATION:U202 Meeting room\, Department of Chemistry and Zoom
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