Inverse modelling of cloud-aerosol interactions - Part 2: Sensitivity tests on liquid phase clouds using a Markov chain Monte Carlo based simulation approach.
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Partridge, D. G.
Vrugt, J. A.
Ekman, A. M. L.
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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 confirm.....
JournalAtmospheric Chemistry and Physics
Maturity LevelPilot or Demonstrated
Spatial CoverageArctic Ocean
CitationPartridge, D. G., Vrugt, J. A., Tunved, P., Ekman, A. M. L., Struthers, H., and Sorooshian, A. (2012) Inverse modelling of cloud-aerosol interactions – Part 2: Sensitivity tests on liquid phase clouds using a Markov chain Monte Carlo based simulation approach. Atmospheric Chemistry and Physics, 12:2823, pp.2823–2847. DOI: https://doi.org/10.5194/acp-12-2823-2012
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