Uncertainty in aerosol–cloud radiative forcing is driven by clean conditions

Gryspeerdt, E., A. C. Povey, R. G. Grainger, O. Hasekamp, N. C. Hsu, J. P. Mulcahy, A. M. Sayer, and A. Sorooshian (2023), Uncertainty in aerosol–cloud radiative forcing is driven by clean conditions, Atmos. Chem. Phys., doi:10.5194/acp-23-4115-2023.

Atmospheric aerosols and their impact on cloud properties remain the largest uncertainty in the human forcing of the climate system. By increasing the concentration of cloud droplets (Nd ), aerosols reduce droplet size and increase the reflectivity of clouds (a negative radiative forcing). Central to this climate impact is the susceptibility of cloud droplet number to aerosol (β), the diversity of which explains much of the variation in the radiative forcing from aerosol–cloud interactions (RFaci) in global climate models. This has made measuring β a key target for developing observational constraints of the aerosol forcing.

While the aerosol burden of the clean, pre-industrial atmosphere has been demonstrated as a key uncertainty for the aerosol forcing, here we show that the behaviour of clouds under these clean conditions is of equal importance for understanding the spread in radiative forcing estimates between models and observations. This means that the uncertainty in the aerosol impact on clouds is, counterintuitively, driven by situations with little aerosol. Discarding clean conditions produces a close agreement between different model and observational estimates of the cloud response to aerosol but does not provide a strong constraint on the RFaci. This makes constraining aerosol behaviour in clean conditions an important goal for future observational studies.

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Radiation Science Program (RSP)
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