Inverse modeling of cloud-aerosol interactions -- Part 2: Sensitivity tests on liquid phase clouds using a Markov Chain Monte Carlo based simulation approach
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 t...
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ftunivamstpubl:oai:uvapub:360258 2023-05-15T15:12:26+02:00 Inverse modeling of cloud-aerosol interactions -- Part 2: Sensitivity tests on liquid phase clouds using a Markov Chain Monte Carlo based simulation approach D.G. Partridge J.A. Vrugt P. Tunved A.M.L. Ekman H. Struthers A. Sooroshian 2012 http://hdl.handle.net/11245/1.360258 en eng 10.5194/acp-12-2823-2012 It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content licence (like Creative Commons). Atmospheric Chemistry and Physics (16807316) vol.12 (2012) nr.6 p.2823-2847 article 2012 ftunivamstpubl 2016-02-03T23:13:04Z 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. Article in Journal/Newspaper Arctic Universiteit van Amsterdam: Digital Academic Repository (UvA DARE) Arctic |
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Open Polar |
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Universiteit van Amsterdam: Digital Academic Repository (UvA DARE) |
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ftunivamstpubl |
language |
English |
description |
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. |
format |
Article in Journal/Newspaper |
author |
D.G. Partridge J.A. Vrugt P. Tunved A.M.L. Ekman H. Struthers A. Sooroshian |
spellingShingle |
D.G. Partridge J.A. Vrugt P. Tunved A.M.L. Ekman H. Struthers A. Sooroshian Inverse modeling of cloud-aerosol interactions -- Part 2: Sensitivity tests on liquid phase clouds using a Markov Chain Monte Carlo based simulation approach |
author_facet |
D.G. Partridge J.A. Vrugt P. Tunved A.M.L. Ekman H. Struthers A. Sooroshian |
author_sort |
D.G. Partridge |
title |
Inverse modeling of cloud-aerosol interactions -- Part 2: Sensitivity tests on liquid phase clouds using a Markov Chain Monte Carlo based simulation approach |
title_short |
Inverse modeling of cloud-aerosol interactions -- Part 2: Sensitivity tests on liquid phase clouds using a Markov Chain Monte Carlo based simulation approach |
title_full |
Inverse modeling of cloud-aerosol interactions -- Part 2: Sensitivity tests on liquid phase clouds using a Markov Chain Monte Carlo based simulation approach |
title_fullStr |
Inverse modeling of cloud-aerosol interactions -- Part 2: Sensitivity tests on liquid phase clouds using a Markov Chain Monte Carlo based simulation approach |
title_full_unstemmed |
Inverse modeling of cloud-aerosol interactions -- Part 2: Sensitivity tests on liquid phase clouds using a Markov Chain Monte Carlo based simulation approach |
title_sort |
inverse modeling of cloud-aerosol interactions -- part 2: sensitivity tests on liquid phase clouds using a markov chain monte carlo based simulation approach |
publishDate |
2012 |
url |
http://hdl.handle.net/11245/1.360258 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Atmospheric Chemistry and Physics (16807316) vol.12 (2012) nr.6 p.2823-2847 |
op_relation |
10.5194/acp-12-2823-2012 |
op_rights |
It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content licence (like Creative Commons). |
_version_ |
1766343116471664640 |