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Volume 12, issue 6
Atmos. Chem. Phys., 12, 2823–2847, 2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: European Integrated Project on Aerosol-Cloud-Climate and Air...

Atmos. Chem. Phys., 12, 2823–2847, 2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 16 Mar 2012

Research article | 16 Mar 2012

Inverse modelling of cloud-aerosol interactions – Part 2: Sensitivity tests on liquid phase clouds using a Markov chain Monte Carlo based simulation approach

D. G. Partridge1,2, J. A. Vrugt3,4, P. Tunved1,2, A. M. L. Ekman2,5, H. Struthers1,2,5, and A. Sorooshian6,7 D. G. Partridge et al.
  • 1Department of Applied Environmental Science, Stockholm University, 10691 Stockholm, Sweden
  • 2Bert Bolin Centre for Climate Research, Stockholm University, 10691 Stockholm, Sweden
  • 3The Henry Samueli School of Engineering, Department of Civil and Environmental Engineering, University of California, Irvine, USA
  • 4Computational Geo-Ecology, Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, The Netherlands
  • 5Department of Meteorology, Stockholm University, Sweden
  • 6Department of Chemical and Environmental Engineering, The University of Arizona, Tucson, USA
  • 7Department of Atmospheric Sciences, The University of Arizona, Tucson, USA

Abstract. This paper presents a novel approach to investigate cloud-aerosol interactions by coupling a Markov chain Monte Carlo (MCMC) algorithm to an adiabatic cloud parcel model. Despite the number of numerical cloud-aerosol sensitivity studies previously conducted few have used statistical analysis tools to investigate the global sensitivity of a cloud model to input aerosol physiochemical parameters. Using numerically generated cloud droplet number concentration (CDNC) distributions (i.e. synthetic data) as cloud observations, this inverse modelling framework is shown to successfully estimate the correct calibration parameters, and their underlying posterior probability distribution.

The employed analysis method provides a new, integrative framework to evaluate the global sensitivity of the derived CDNC distribution to the input parameters describing the lognormal properties of the accumulation mode aerosol and the particle chemistry. To a large extent, results from prior studies are confirmed, but the present study also provides some additional insights. There is a transition in relative sensitivity from very clean marine Arctic conditions where the lognormal aerosol parameters representing the accumulation mode aerosol number concentration and mean radius and are found to be most important for determining the CDNC distribution to very polluted continental environments (aerosol concentration in the accumulation mode >1000 cm−3) where particle chemistry is more important than both number concentration and size of the accumulation mode.

The competition and compensation between the cloud model input parameters illustrates that if the soluble mass fraction is reduced, the aerosol number concentration, geometric standard deviation and mean radius of the accumulation mode must increase in order to achieve the same CDNC distribution.

This study demonstrates that inverse modelling provides a flexible, transparent and integrative method for efficiently exploring cloud-aerosol interactions with respect to parameter sensitivity and correlation.

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