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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
Atmos. Chem. Phys., 17, 3687-3698, 2017
http://www.atmos-chem-phys.net/17/3687/2017/
doi:10.5194/acp-17-3687-2017
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
16 Mar 2017
A new statistical approach to improve the satellite-based estimation of the radiative forcing by aerosol–cloud interactions
Piyushkumar N. Patel1, Johannes Quaas2, and Raj Kumar1 1Space Applications Centre, ISRO, Ahmedabad, India
2Institute for Meteorology, Universität Leipzig, Leipzig, Germany
Abstract. In a previous study of Quaas et al. (2008) the radiative forcing by anthropogenic aerosol due to aerosol–cloud interactions, RFaci, was obtained by a statistical analysis of satellite retrievals using a multilinear regression. Here we employ a new statistical approach to obtain the fitting parameters, determined using a nonlinear least square statistical approach for the relationship between planetary albedo and cloud properties and, further, for the relationship between cloud properties and aerosol optical depth. In order to verify the performance, the results from both statistical approaches (previous and present) were compared to the results from radiative transfer simulations over three regions for different seasons. We find that the results of the new statistical approach agree well with the simulated results both over land and ocean. The new statistical approach increases the correlation by 21–23 % and reduces the error compared to the previous approach.

Citation: Patel, P. N., Quaas, J., and Kumar, R.: A new statistical approach to improve the satellite-based estimation of the radiative forcing by aerosol–cloud interactions, Atmos. Chem. Phys., 17, 3687-3698, doi:10.5194/acp-17-3687-2017, 2017.
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Short summary
Radiative forcing by aerosol–cloud interactions (RFaci) remains highly uncertain and difficult to quantify on the basis of current knowledge. The present study reassesses the estimated RFaci by using a new statistical fitting approach, which improves the quantification of RFaci with less uncertainty. The present work helps to improve the parameterisation of RFaci in the present climate model.
Radiative forcing by aerosol–cloud interactions (RFaci) remains highly uncertain and difficult...
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