Articles | Volume 12, issue 6
https://doi.org/10.5194/acp-12-2823-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-12-2823-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
16 Mar 2012
Research article |
| 16 Mar 2012
Inverse modelling of cloud-aerosol interactions – Part 2: Sensitivity tests on liquid phase clouds using a Markov chain Monte Carlo based simulation approach
D. G. Partridge
Department of Applied Environmental Science, Stockholm University, 10691 Stockholm, Sweden
Bert Bolin Centre for Climate Research, Stockholm University, 10691 Stockholm, Sweden
J. A. Vrugt
The Henry Samueli School of Engineering, Department of Civil and Environmental Engineering, University of California, Irvine, USA
Computational Geo-Ecology, Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, The Netherlands
P. Tunved
Department of Applied Environmental Science, Stockholm University, 10691 Stockholm, Sweden
Bert Bolin Centre for Climate Research, Stockholm University, 10691 Stockholm, Sweden
A. M. L. Ekman
Bert Bolin Centre for Climate Research, Stockholm University, 10691 Stockholm, Sweden
Department of Meteorology, Stockholm University, Sweden
H. Struthers
Department of Applied Environmental Science, Stockholm University, 10691 Stockholm, Sweden
Bert Bolin Centre for Climate Research, Stockholm University, 10691 Stockholm, Sweden
Department of Meteorology, Stockholm University, Sweden
A. Sorooshian
Department of Chemical and Environmental Engineering, The University of Arizona, Tucson, USA
Department of Atmospheric Sciences, The University of Arizona, Tucson, USA
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20 citations as recorded by crossref.
- Technical note: Monte Carlo genetic algorithm (MCGA) for model analysis of multiphase chemical kinetics to determine transport and reaction rate coefficients using multiple experimental data sets T. Berkemeier et al. 10.5194/acp-17-8021-2017
- The importance of vertical velocity variability for estimates of the indirect aerosol effects R. West et al. 10.5194/acp-14-6369-2014
- Key drivers of cloud response to surface-active organics S. Lowe et al. 10.1038/s41467-019-12982-0
- Sensitivity estimations for cloud droplet formation in the vicinity of the high-alpine research station Jungfraujoch (3580 m a.s.l.) E. Hammer et al. 10.5194/acp-15-10309-2015
- The effect of clouds and precipitation on the aerosol concentrations and composition in a boreal forest environment S. Isokääntä et al. 10.5194/acp-22-11823-2022
- Embracing equifinality with efficiency: Limits of Acceptability sampling using the DREAM(LOA) algorithm J. Vrugt & K. Beven 10.1016/j.jhydrol.2018.02.026
- The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei L. Lee et al. 10.5194/acp-13-8879-2013
- Entrepreneurship, Income Inequality and Public Spending: A Spatial Analysis into Regional Determinants of Growing Firms in Greece C. Agiropoulos et al. 10.1007/s11294-021-09832-5
- The potential of an observational data set for calibration of a computationally expensive computer model D. McNeall et al. 10.5194/gmd-6-1715-2013
- Cloud response to co-condensation of water and organic vapors over the boreal forest L. Heikkinen et al. 10.5194/acp-24-5117-2024
- On the competition among aerosol number, size and composition in predicting CCN variability: a multi-annual field study in an urbanized desert E. Crosbie et al. 10.5194/acp-15-6943-2015
- Ozonolysis of Oleic Acid Aerosol Revisited: Multiphase Chemical Kinetics and Reaction Mechanisms T. Berkemeier et al. 10.1021/acsearthspacechem.1c00232
- Inverse modelling of Köhler theory – Part 1: A response surface analysis of CCN spectra with respect to surface-active organic species S. Lowe et al. 10.5194/acp-16-10941-2016
- Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation J. Vrugt 10.1016/j.envsoft.2015.08.013
- On the relationship between cloud water composition and cloud droplet number concentration A. MacDonald et al. 10.5194/acp-20-7645-2020
- Physical and Chemical Properties of Cloud Droplet Residuals and Aerosol Particles During the Arctic Ocean 2018 Expedition L. Karlsson et al. 10.1029/2021JD036383
- Challenges in constraining anthropogenic aerosol effects on cloud radiative forcing using present-day spatiotemporal variability S. Ghan et al. 10.1073/pnas.1514036113
- Mapping the uncertainty in global CCN using emulation L. Lee et al. 10.5194/acp-12-9739-2012
- Bayesian calibration and uncertainty analysis of an agroecosystem model under different N management practices H. Liang et al. 10.1016/j.eja.2021.126429
- Chemical and physical influences on aerosol activation in liquid clouds: a study based on observations from the Jungfraujoch, Switzerland C. Hoyle et al. 10.5194/acp-16-4043-2016
1 citations as recorded by crossref.
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