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Inverse modeling of cloud-aerosol interactions: Part 2: Sensitivity tests on liquid phase clouds using a Markov Chain Monte carlo based simulation approach
Stockholm University, Faculty of Science, Department of Applied Environmental Science (ITM).
Univ Calif Irvine, Dept Civil & Environm Engn, Henry Samueli Sch Engn, Irvine, CA USA .
Stockholm University, Faculty of Science, Department of Applied Environmental Science (ITM).
Stockholm University, Faculty of Science, Department of Meteorology .
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2012 (English)In: Atmospheric Chemistry And Physics, ISSN 1680-7316, E-ISSN 1680-7324, Vol. 12, no 6, 2823-2847 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents a novel approach to investigate cloud-aerosol interactions by coupling a Markov Chain Monte Carlo (MCMC) algorithm to a pseudo-adiabatic cloud parcel model. Despite the number of numerical cloud-aerosol sensitivity studies previously conducted few have used statistical analysis tools to investigate the sensitivity of a cloud model to input aerosol physiochemical parameters. Using synthetic data as observed values of cloud droplet number concentration (CDNC) distribution, this inverse modelling framework is shown to successfully converge to the correct calibration parameters. The employed analysis method provides a new, integrative framework to evaluate the sensitivity of the derived CDNC distribution to the input parameters describing the lognormal properties of the accumulation mode and the particle chemistry. To a large extent, results from prior studies are confirmed, but the present study also provides some additional insightful findings. There is a clear transition from very clean marine Arctic conditions where the aerosol parameters representing the mean radius and geometric standard deviation of the accumulation mode 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 illustrate that if the soluble mass fraction is reduced, both the number of particles and geometric standard deviation must increase and the mean radius of the accumulation mode must increase in order to achieve the same CDNC distribution. For more polluted aerosol conditions, with a reduction in soluble mass fraction the parameter correlation becomes weaker and more non-linear over the range of possible solutions (indicative of the sensitivity). This indicates that for the cloud parcel model used herein, the relative importance of the soluble mass fraction appears to decrease if the number or geometric standard deviation of the accumulation mode is increased. This study demonstrates that inverse modelling provides a flexible, transparent and integrative method for efficiently exploring cloud-aerosol interactions efficiently with respect to parameter sensitivity and correlation.

Place, publisher, year, edition, pages
2012. Vol. 12, no 6, 2823-2847 p.
National Category
Earth and Related Environmental Sciences
Research subject
Applied Environmental Science
Identifiers
URN: urn:nbn:se:su:diva-60003DOI: 10.5194/acp-12-2823-2012ISI: 000302178000002OAI: oai:DiVA.org:su-60003DiVA: diva2:432572
Note

6

Available from: 2011-08-04 Created: 2011-08-04 Last updated: 2017-12-08Bibliographically approved
In thesis
1. Inverse Modeling of Cloud – Aerosol Interactions
Open this publication in new window or tab >>Inverse Modeling of Cloud – Aerosol Interactions
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The role of aerosols and clouds is one of the largest sources of uncertainty in understanding climate change. The primary scientific goal of this thesis is to improve the understanding of cloud-aerosol interactions by applying inverse modeling using Markov Chain Monte Carlo (MCMC) simulation.

Through a set of synthetic tests using a pseudo-adiabatic cloud parcel model, it is shown that a self adaptive MCMC algorithm can efficiently find the correct optimal values of meteorological and aerosol physiochemical parameters for a specified droplet size distribution and determine the global sensitivity of these parameters. For an updraft velocity of 0.3 m s-1, a shift towards an increase in the relative importance of chemistry compared to the accumulation mode number concentration is shown to exist somewhere between marine (~75 cm-3) and rural continental (~450 cm-3) aerosol regimes.

Examination of in-situ measurements from the Marine Stratus/Stratocumulus Experiment (MASE II) shows that for air masses with higher number concentrations of accumulation mode (Dp = 60-120 nm) particles (~450 cm-3), an accurate simulation of the measured droplet size distribution requires an accurate representation of the particle chemistry. The chemistry is relatively more important than the accumulation mode particle number concentration, and similar in importance to the particle mean radius. This result is somewhat at odds with current theory that suggests chemistry can be ignored in all except for the most polluted environments. Under anthropogenic influence, we must consider particle chemistry also in marine environments that may be deemed relatively clean.

The MCMC algorithm can successfully reproduce the observed marine stratocumulus droplet size distributions. However, optimising towards the broadness of the measured droplet size distribution resulted in a discrepancy between the updraft velocity, and mean radius/geometric standard deviation of the accumulation mode. This suggests that we are missing a dynamical process in the pseudo-adiabatic cloud parcel model.  

Place, publisher, year, edition, pages
Stockholm: Department of Applied Environmental Science (ITM), Stockholm University, 2011. 64 p.
Keyword
stratocumulus, marine, cloud, aerosol, interactions, MCMC, inverse modeling, droplet closure, global sensitivity
National Category
Meteorology and Atmospheric Sciences
Research subject
Applied Environmental Science
Identifiers
urn:nbn:se:su:diva-60454 (URN)978-91-7447-343-8 (ISBN)
Public defence
2011-09-23, Nordenskiöldsalen, Geovetenskapens hus, Svante Arrhenius väg 12, Stockholm, 13:00 (English)
Opponent
Supervisors
Note
At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Submitted. Paper 4: Manuscript.Available from: 2011-09-01 Created: 2011-08-17 Last updated: 2011-08-30Bibliographically approved

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