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Vertical dependence of black carbon, sulphate and biomass burning aerosol radiative forcing
Author(s) -
Samset Bjørn H.,
Myhre Gunnar
Publication year - 2011
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2011gl049697
Subject(s) - radiative forcing , atmospheric sciences , aerosol , radiative transfer , environmental science , shortwave , forcing (mathematics) , altitude (triangle) , stratosphere , carbon black , climatology , cloud forcing , biomass burning , atmospheric radiative transfer codes , meteorology , physics , chemistry , geology , natural rubber , organic chemistry , geometry , mathematics , quantum mechanics
A global radiative transfer model is used to calculate the vertical profile of shortwave radiative forcing from a prescribed amount of aerosols. We study black carbon (BC), sulphate (SO 4 ) and a black and organic carbon mixture typical of biomass burning (BIO), by prescribing aerosol burdens in layers between 1000 hPa and 20 hPa and calculating the resulting direct radiative forcing divided by the burden (NDRF). We find a strong sensitivity in the NDRF for BC with altitude, with a tenfold increase between BC close to the surface and the lower part of the stratosphere. Clouds are a major contributor to this dependence with altitude, but other factors also contribute. We break down and explain the different physical contributors to this strong sensitivity. The results show a modest regional dependence of the altitudinal dependence of BC NDRF between industrial regions, while for regions with properties deviating from the global mean NDRF variability is significant. Variations due to seasons and interannual changes in cloud conditions are found to be small. We explore the effect that large altitudinal variation in NDRF may have on model estimates of BC radiative forcing when vertical aerosol distributions are insufficiently constrained, and discuss possible applications of the present results for reducing inter‐model differences.