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Inverse Modeling of Cloud – Aerosol Interactions
Stockholm University, Faculty of Science, Department of Applied Environmental Science (ITM). (Atmoshpheric Science)
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 [en]
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: urn:nbn:se:su:diva-60454ISBN: 978-91-7447-343-8 (print)OAI: oai:DiVA.org:su-60454DiVA: diva2:435608
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
List of papers
1. New trajectory-driven aerosol and chemical process model Chemical and Aerosol Lagrangian Model (CALM)
Open this publication in new window or tab >>New trajectory-driven aerosol and chemical process model Chemical and Aerosol Lagrangian Model (CALM)
2010 (English)In: Atmospheric Chemistry And Physics, ISSN 1680-7316, Vol. 10, no 21, 10161-10185 p.Article in journal (Refereed) Published
Abstract [en]

A new Chemical and Aerosol Lagrangian Model (CALM) has been developed and tested. The model incorporates all central aerosol dynamical processes, from nucleation, condensation, coagulation and deposition to cloud formation and in-cloud processing. The model is tested and evaluated against observations performed at the SMEAR II station located at Hyytiala (61 degrees 51'N, 24 degrees 17'E) over a time period of two years, 2000-2001. The model shows good agreement with measurements throughout most of the year, but fails in reproducing the aerosol properties during the winter season, resulting in poor agreement between model and measurements especially during December-January. Nevertheless, through the rest of the year both trends and magnitude of modal concentrations show good agreement with observation, as do the monthly average size distribution properties. The model is also shown to capture individual nucleation events to a certain degree. This indicates that nucleation largely is controlled by the availability of nucleating material (as prescribed by the [H2SO4]), availability of condensing material (in this model 15% of primary reactions of monoterpenes (MT) are assumed to produce low volatile species) and the properties of the size distribution (more specifically, the condensation sink). This is further demonstrated by the fact that the model captures the annual trend in nuclei mode concentration. The model is also used, alongside sensitivity tests, to examine which processes dominate the aerosol size distribution physical properties. It is shown, in agreement with previous studies, that nucleation governs the number concentration during transport from clean areas. It is also shown that primary number emissions almost exclusively govern the CN concentration when air from Central Europe is advected north over Scandinavia. We also show that biogenic emissions have a large influence on the amount of potential CCN observed over the boreal region, as shown by the agreement between observations and modeled results for the receptor SMEAR II, Hyytiala, during the studied period.

National Category
Earth and Related Environmental Sciences
Research subject
Applied Environmental Science
Identifiers
urn:nbn:se:su:diva-51326 (URN)10.5194/acp-10-10161-2010 (DOI)000284210400002 ()
Note
authorCount :3Available from: 2011-01-11 Created: 2011-01-10 Last updated: 2011-08-19Bibliographically approved
2. Inverse modeling of cloud-aerosol interactions: Part 1: Detailed response surface analysis
Open this publication in new window or tab >>Inverse modeling of cloud-aerosol interactions: Part 1: Detailed response surface analysis
Show others...
2011 (English)In: Atmospheric Chemistry And Physics, ISSN 1680-7316, E-ISSN 1680-7324, Vol. 11, no 14, 7269-7287 p.Article in journal (Refereed) Published
Abstract [en]

New methodologies are required to probe the sensitivity of parameters describing cloud droplet activation. This paper presents an inverse modeling-based method for exploring cloud-aerosol interactions via response surfaces. The objective function, containing the difference between the measured and model predicted cloud droplet size distribution is studied in a two-dimensional framework, and presented for pseudo-adiabatic cloud parcel model parameters that are pair-wise selected. From this response surface analysis it is shown that the susceptibility of cloud droplet size distribution to variations in different aerosol physiochemical parameters is highly dependent on the aerosol environment and meteorological conditions. In general the cloud droplet size distribution is most susceptible to changes in the updraft velocity. A shift towards an increase in the importance of chemistry for the cloud nucleating ability of particles is shown to exist somewhere between marine average and rural continental aerosol regimes. We also use these response surfaces to explore the feasibility of inverse modeling to determine cloud-aerosol interactions. It is shown that the "cloud-aerosol" inverse problem is particularly difficult to solve due to significant parameter interaction, presence of multiple regions of attraction, numerous local optima, and considerable parameter insensitivity. The identifiability of the model parameters will be dependent on the choice of the objective function. Sensitivity analysis is performed to investigate the location of the information content within the calibration data to confirm that our choice of objective function maximizes information retrieval from the cloud droplet size distribution. Cloud parcel models that employ a moving-centre based calculation of the cloud droplet size distribution pose additional difficulties when applying automatic search algorithms for studying cloud-aerosol interactions. To aid future studies, an increased resolution of the region of the size spectrum associated with droplet activation within cloud parcel models, or further development of fixed-sectional cloud models would be beneficial. Despite these improvements, it is demonstrated that powerful search algorithms remain necessary to efficiently explore the parameter space and successfully solve the cloud-aerosol inverse problem.

Keyword
RAINFALL-RUNOFF MODELS, SIZE DISTRIBUTION, PARAMETER-ESTIMATION, GLOBAL OPTIMIZATION, HYDRAULIC-PROPERTIES, DROPLET ACTIVATION, HYDROLOGIC-MODELS DATA ASSIMILATION, CALIBRATION DATA, CCN ACTIVATION
National Category
Earth and Related Environmental Sciences
Research subject
Applied Environmental Science
Identifiers
urn:nbn:se:su:diva-60002 (URN)10.5194/acp-11-7269-2011 (DOI)000293125100031 ()
Note
6Available from: 2011-08-04 Created: 2011-08-04 Last updated: 2017-12-08Bibliographically approved
3. Inverse modeling of cloud-aerosol interactions: Part 2: Sensitivity tests on liquid phase clouds using a Markov Chain Monte carlo based simulation approach
Open this publication in new window or tab >>Inverse modeling of cloud-aerosol interactions: Part 2: Sensitivity tests on liquid phase clouds using a Markov Chain Monte carlo based simulation approach
Show others...
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.

National Category
Earth and Related Environmental Sciences
Research subject
Applied Environmental Science
Identifiers
urn:nbn:se:su:diva-60003 (URN)10.5194/acp-12-2823-2012 (DOI)000302178000002 ()
Note

6

Available from: 2011-08-04 Created: 2011-08-04 Last updated: 2017-12-08Bibliographically approved
4. A study of marine stratocumulus clouds using an inverse modelling approach
Open this publication in new window or tab >>A study of marine stratocumulus clouds using an inverse modelling approach
Show others...
(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
Identifiers
urn:nbn:se:su:diva-60452 (URN)
Available from: 2011-08-17 Created: 2011-08-17 Last updated: 2011-08-23Bibliographically approved

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