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A study of marine stratocumulus clouds using an inverse modelling approach
Stockholm University, Faculty of Science, Department of Applied Environmental Science (ITM). (Atmoshpheric Science: ITM-L)
Stockholm University, Faculty of Science, Department of Applied Environmental Science (ITM). (Atmoshpheric Science: ITM-L)
Stockholm University, Faculty of Science, Department of Meteorology .
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

This paper presents a Bayesian inverse modelling approach to simultaneously assess the ability of a pseudo-adiabatic cloud parcel model to match in-situ measurements of the droplet size distribution in a cloud as well as model parameters describing the updraft and different aerosol microphysical properties (herein termed calibration parameters). Our methodology is tested using observations from two clean (average accumulation mode number concentration < 60 cm-3) and two polluted clouds (average accumulation mode number concentration > 100 cm-3) observed during the Marine Stratus/Stratocumulus Experiment (MASE II) campaign. Our framework capitalizes on recent developments in Markov Chain Monte Carlo (MCMC) simulation and retrieves the most likely parameter values and their underlying posterior probability density function. This distribution provides necessary information to efficiently and in a statistically robust manner, assess both the global sensitivity of aerosol physiochemical and meteorological parameters, and the suitability of cloud parcel models to comprehensively describe the evolution of cloud droplet size distributions in stratocumulus clouds.

We demonstrate that the updraft velocity is the most important calibration parameter for describing the observed droplet distribution for each cloud case, corroborating previous findings. The accumulation mode number, shape and size are found to be more important than chemistry except for the most polluted conditions (average accumulation mode number concentration ~455 cm-3). This highlights that conditions exist for marine stratocumulus clouds in which an accurate description of the aerosol chemistry is a pre-requisite for the accurate representation of cloud microphysical properties.

Overall, the MCMC algorithm successfully matches the observed droplet size distribution for each cloud case. In doing so, however, the subsequent agreement between the derived and measured calibration parameters is generally poor. An important result from this analysis is that for certain calibration parameters, consistent patterns of deviation were found in the posterior distributions for all the clouds included in this study. This finding indicates that either there is systematic sampling or averaging artefacts in our observations, or our pseudo-adiabatic cloud parcel model omits or consistently misrepresents processes and/or parameter(s) required to accurately simulate the droplet size distributions of the observed marine stratocumulus. By repeating our inverse methodology with more calibration parameters of which current measurements are uncertain (surface tension, mass accommodation coefficient), we find that it is likely that the process description within the current formulation of the pseudo-adiabatic cloud model used in this study misses a dynamical process rather than parameter(s).

National Category
Meteorology and Atmospheric Sciences Meteorology and Atmospheric Sciences Computational Mathematics
Research subject
Applied Environmental Science
URN: urn:nbn:se:su:diva-60452OAI: diva2:435026
Available from: 2011-08-17 Created: 2011-08-17 Last updated: 2011-08-23Bibliographically 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.
stratocumulus, marine, cloud, aerosol, interactions, MCMC, inverse modeling, droplet closure, global sensitivity
National Category
Meteorology and Atmospheric Sciences
Research subject
Applied Environmental Science
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)
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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