Inverse modelling of cloud-aerosol interactions - Part 2: Sensitivity tests on liquid phase clouds using a Markov chain Monte Carlo based simulation approach

Atmospheric Chemistry and Physics

Volumn 12, Issue 6, 2012, Pages 2823-2847

  Partridge, D G ^a   Vrugt, J A ^b,c   Tunved, P ^a   Ekman, A M L ^a   Struthers, H ^a   Sorooshian, A ^d,e  

^a STOCKHOLM UNIVERSITY   (Sweden)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF AMSTERDAM   (Netherlands)

^d University of Arizona   (United States)

^e UNIVERSITY OF ARIZONA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AEROSOL; CLOUD DROPLET; COMPUTER SIMULATION; CONCENTRATION (COMPOSITION); INVERSE ANALYSIS; MARKOV CHAIN; MONTE CARLO ANALYSIS; NUMERICAL MODEL; PARTICULATE MATTER; PHYSICOCHEMICAL PROPERTY; PROBABILITY; SENSITIVITY ANALYSIS;

ARCTIC;

EID: 84868015071     PISSN: 16807316     EISSN: 16807324     Source Type: Journal    
DOI: 10.5194/acp-12-2823-2012     Document Type: Article

References (62)

1. Andreae, M. O. and Rosenfeld, D.: Aerosol-cloud-precipitation interactions. Part 1. The nature and sources of cloud-active aerosols, Earth Sci. Rev., 89, 13-41, 2008.
2. Anttila, T. and Kerminen, V.-M.: On the contribution of Aitken mode particles to cloud droplet populations at continental background areas - a parametric sensitivity study, Atmos. Chem. Phys., 7, 4625-4637, doi:10.5194/acp-7-4625-2007, 2007.
3. Ayers, G. P. and Larson, T. V.: Numerical study of droplet size dependent chemistry in oceanic, wintertime, stratus cloud at southern midlattitudes, J. Atmos. Chem., 11, 143-167, 1990.
4. Benke, K. K., and Lowell., K. E., and Hamilton, A. J.: Parameter uncertainty, sensitivity analysis and prediction error in a waterbalance hydrological model, Math. Comput. Model., 47, 1134-1149, 2008.
5. Bikowski, J., van der Kruk, J., Huisman, J. A., Vereecken, H., and Vrugt, J. A.: Inversion and sensitivity analysis of GPR data with waveguide dispersion using Markov Chain Monte Carlo simulation, Ground Penetrating Radar (GPR), 13th International Conference, 2010.
6. Birmili, W., Wiedensohler, A., Heintzenberg, J., and Lehmann, K.: Atmospheric particle number size distribution in Central Europe: statistical relations to air masses and meteorology, J. Geophys. Res., 106, 32005-32018, doi:10.1029/2000JD000220, 2001.
7. Brenguier, J. L. and Wood, R.: Observational strategies from the micro- to mesoscale, in: Clouds in the Perturbed Climate System: There relationship to Energy Balance, Atmospheric Dynamics and Precipitation, edited by: Heintzenberg, J. and Charlson, R. J., pp. 487-510, MIT Press, Cambridge, Mass, 2009.
8. Chuang, P. Y.: Sensitivity of cloud condensation nuclei activation processes to kinetic parameters, J. Geophys. Res., 111, D09201, doi:10.1029/2005JD006529, 2006.
9. Conant, W. C., and VanReken., T. M., Rissman, T. A., Varutbangkul, V., Jonsson, H. H., Nenes, A., and Jimenez., J. L., Delia, A. E., Bahreini, R., Roberts, G. C., and Flagan., R. C., and Seinfeld, J. H.: Aerosol, cloud drop concentration closure in warm cumulus, J. Geophys. Res., 109, D13204, doi:10.1029/2003JD004324, 2004.
10. Dekker, S. C., and Vrugt., J. A., and Elkington, R. J.: Significant variation in vegetation characteristics and dynamics from ecohydro-logic optimality of net carbon profit, Ecohydrology, 5, 1-18, doi:10.1002/eco.17, 2010.
11. Duan, Q. Y., Sorooshian, S., and Gupta, V.: Effective and Efficient Global Optimization for Conceptual Rainfall-Runoff Models, Water Resour. Res., 28, 1015-1031, 1992.
12. Dusek, U., and Frank., G. P., Hildebrandt, L., Curtius, J., Schneider, J., Walter, S., Chand, D., Drewnick, F., Hings, S., Jung, D., Borrmann, S., and Andreae, M. O.: Size matters more than chemistry for cloud-nucleating ability of aerosol particles, Science, 312, 1375-1378, 2006.
13. Ervens, B., Feingold, G., and Kreidenweis, S. M.: The influence of water-soluble organic carbon on cloud drop number concentration, J. Geophys. Res., 110, D18211, doi:10.1029/2004JD005634, 2005.
14. Facchini, M. C., Mircea, M., Fuzzi, S., and Charlson, R. J.: Cloud albedo enhancement by surface-active organic solutes in growing droplets, Nature, 410, 257-259, 1999.
15. Feingold, G.: Modelling of the first indirect effect: Analysis of measurement requirements, Geophys. Res. Let., 30, 1997, doi:10.1029/ 2003GL017967, 2003.
16. Fitzgerald, J. W.: Effect of aerosol composition on cloud droplet size distribution - numerical study, J. Atmos. Sci., 31, 1358-1367, 1974.
17. Gautam, K. K. and Tyagi, V. K.: Microbial surfactants: a review, J Oleo Sci., 55, 155-166, 2006.
18. Gelfand, A. E. and Smith, A. F.: Sampling based approaches to calculating marginal densities, J. Amer. Stat. Assoc., 85, 398-409, 1990.
19. Gelman, A. and Rubin, D. B.: Inference from iterative simulation using multiple sequences, Statistical Science, 7, 457-511, 1992.
20. Hegg, D. A. and Larson, T. V.: The effects of microphysical param-eterisation on model predictions of sulfate production in clouds, Tellus, 42B, 272-284, 1990.
21. Heintzenberg, J., and Covert., D. C., and Van Dingenen, R.: Size distribution and chemical composition of marine aerosols: a compilation and review, Tellus B, 52, 1104-1122, 2000.
22. Hsieh, W. C., Nenes, A., Flagan, R. C., and Seinfeld., J. H., Buzorius, G., and Jonsson, H.: Parameterization of cloud droplet size distributions: Comparison with parcel models and observations, J. Geophys. Res., 114, D11205, doi:10.1029/2008JD011387, 2009.
23. Hudson, J. G.: Variability of the relationship between particle size and cloud nucleating ability, Geophys. Res. Lett., 34, L08801, doi:10.1029/ 2006GL028850, 2007.
24. Jackson, C., and Sen., M. K., and Stoffa, P. L.: An efficient stochastic Bayesian approach to optimal parameter and uncertainty estimation for climate model predictions, J. Climate, 17, 2828-2841, 2004.
25. Järvinen, H., Räisänen, P., Laine, M., Tamminen, J., Ilin, A., Oja, E., Solonen, A., and Haario, H.: Estimation of ECHAM5 climate model closure parameters with adaptive MCMC, Atmos. Chem. Phys., 10, 9993-10002, doi:10.5194/acp-10-9993-2010, 2010.
26. Kanakidou, M., and Seinfeld., J. H., Pandis, S. N., Barnes, I., Dentener, F. J., and Facchini., M. C., Van Dingenen, R., Ervens, B., Nenes, A., and Nielsen., C. J., Swietlicki, E., Putaud, J. P., Balkanski, Y., Fuzzi, S., Horth, J., Moortgat, G. K., Winterhalter, R., Myhre, C. E. L., Tsigaridis, K., Vignati, E., Stephanou, E. G., and Wilson, J.: Organic aerosol and global climate modelling: a review, Atmos. Chem. Phys., 5, 1053-1123, doi:10.5194/acp-5-1053-2005, 2005.
27. Kanso, A., Ghebbo, G., and Tassin, B.: Application of MCMC-GSA model calibration method to urban runoff quality modelling, Reliability Engineering and System Safety, 91, 1398-1405, 2006.
28. Kivekäs, N., Kerminen, V.-M., Anttila, T., Hakola, H., Komppula, M., and Lihavainen, H.: Using Aerosol Number to Volume Ratio in Predicting Cloud Droplet Number Concentration, in: Nucleation and Atmospheric Aerosols, edited by: O'Dowd, C. D. and Wagner, P. E., pp. 551-555, Springer Netherlands, 2007.
29. Koda, M. and Seinfeld, J. H.: Estimation of Urban Air-Pollution, Automatica, 14, 583-595, 1978.
30. Laaksonen, A., Korhonen, P., Kulmala, M., and Charlson, R. J.: Modification of the Köhler equation to include soluble trace gases and slightly soluble substances, J. Atmos. Sci., 55, 853-862, 1998.
31. Laine, M. and Tamminen, J.: Aerosol model selection and uncertainty modelling by adaptive MCMC technique, Atmos. Chem. Phys., 8, 7697-7707, doi:10.5194/acp-8-7697-2008, 2008.
32. Lance, S., Nenes, A., and Rissman T. A.: Chemical and dynamical effects on cloud droplet number: Implications for estimates of the aerosol indirect effect, J. Geophys. Res., 109, D22208, doi:10.1029/2004JD004596, 2004.
33. Loridan, T., Grimmond, C. S. B., Grossman-Clarke, S., Chen, F., Tewari, M., Manning, K., Martilli, A., Kusaka, H., and Best, M.: Trade-offs and responsiveness of the single-layer urban canopy parameterization in WRF: an offline evaluation using the MOSCEM optimization algorithm and field observations, Q. J. Roy. Meteorol. Soc., 136, 997-1019. doi:10.1002/qj.614, 2010.
34. McFiggans, G., Artaxo, P., Baltensperger, U., Coe, H., and Facchini., M. C., Feingold, G., Fuzzi, S., Gysel, M., Laaksonen, A., Lohmann, U., Mentel, T. F., Murphy, D. M., O'Dowd, C. D., Snider, J. R., and Weingartner, E.: The effect of physical and chemical aerosol properties on warm cloud droplet activation, Atmos. Chem. Phys., 6, 2593-2649, doi:10.5194/acp-6-2593-2006, 2006.
35. Metropolis, N., and Rosenbluth., A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E.: Equation of state calculations by fast computing machines, J. Chem. Phys., 21, 1087-1092, 1953.
36. Nenes, A., and Charlson., R. J., Facchini, M. C., Kulmala, M., Laaksonen, A., and Seinfeld, J. H.: Can chemical effects on cloud droplet number rival the first indirect effect?, Geophys. Res. Lett., 29, 1848, doi:10.1029/ 2002GL015295, 2002.
37. Partridge, D. G., and Vrugt., J. A., Tunved, P., Ekman, A. M. L., Gorea, D., and Sorooshian, A.: Inverse modelling of cloud-aerosol interactions - Part 1: Detailed response surface analysis, Atmos. Chem. Phys., 11, 7269-7287, doi:10.5194/acp-11-7269-2011, 2011.
38. Pérez, C. J., Martín, J., and Rufo, M. F.: Sensitivity estimations for Bayesian inference models solved by MCMC methods, Reliability Engineering and System Safety, 91, 1310-1314, 2006.
39. Platnick, S. and Twomey, S.: Determining the susceptibility of cloud albedo to changes in droplet concentration with the Advanced Very High Resolution Radiometer. J. Appl. Meteor., 33, 334-347, 1994.
40. Quinn, P. K., and Bates., T. S., Coffman, D. J., and Covert, D. S.: Influence of particle size and chemistry on the cloud nucleating properties of aerosols, Atmos. Chem. Phys., 8, 1029-1042, doi:10.5194/acp-8-1029-2008, 2008.
41. Rissman, T., Nenes, A., and Seinfeld, J. H.: Chemical amplification (or dampening) of the Twomey effect: Conditions derived from droplet activation theory, J. Atmos. Sci., 61, 919-930, 2004.
42. Roelofs, G. J. and Jongen, S.: A model study of the influence of aerosol size and chemical properties on precipitation formation in warm clouds, J. Geophys. Res., 109, D22201, doi:10.1029/2004JD004779, 2004.
43. San Martini, F. M., Dunlea, E. J., Grutter, M., and Onasch., T. B., Jayne, J. T., Canagaratna, M. R., and Worsnop., D. R., Kolb, C. E., Shorter, J. H., Herndon, S. C., Zahniser, M. S., Ortega, J. M., McRae, G. J., Molina, L. T., and Molina, M. J.: Implementation of a Markov Chain Monte Carlo method to inorganic aerosol modelling of observations from the MCMA-2003 campaign - Part I: Model description and application to the La Merced site, Atmos. Chem. Phys., 6, 4867-4888, doi:10.5194/acp-6-4867-2006, 2006.
44. Schoups, G. and Vrugt, J. A.: A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic and non-Gaussian errors, Water Resour. Res., 46, W10531, doi:10.1029/ 2009WR008933, 2010.
45. Snider, J. R. and Brenguier, J.-L.: Cloud condensation nuclei and cloud droplet measurements during ACE-2, Tellus B, 53, 828-842, 2000.
46. Sorooshian, A., and Murphy., S. M., Hersey, S., Bahreini, R., Jonsson, H., Flagan, R. C., and Seinfeld, J. H.: Constraining the contribution of organic acids and AMS m/z 44 to the organic aerosol budget: On the importance of meteorology, aerosol hygroscopicity, and region, Geophys. Res. Lett., 37, L21807, doi:10.1029/2010GL044951, 2010.
47. Stevens, B. and Feingold, G.: Untangling aerosol effects on clouds and precipitation in a buffered system, Nature, 461, 607-613, 2009.
48. Tamminen, J. and Kyrölä, E.: Bayesian solution for nonlinear and non-Gaussian inverse problems by Markov chain Monte Carlo method, J. Geophys. Res., 106, 14377-14390, doi:10.1029/2001JD900007, 2001.
49. Tomassini, L., Reichert, P., Knutti, R., Stocker, T. F., and Borsuk, M. E.: Robust Bayesian uncertainty analysis of climate system properties using Markov chain Monte Carlo methods, J. Climate, 20, 1239-1254, 2007.
50. Tunved, P., and Nilsson., E. D., Hansson, H. C., Strom, J., Kulmala, M., Aalto, P., and Viisanen, Y.: Aerosol characteristics of air masses in northern Europe: Influences of location, transport, sinks, and sources, J. Geophys. Res.-Atmos., 110, D07201, doi:10.1029/2004JD005085, 2005.
51. Twohy, C. H. and Anderson, J. R.: Droplet nuclei in non-precipitating clouds: composition and size matter, Environ. Res. Lett., 3, 045002, doi:10.1088/1748-9326/3/4/045002, 2008.
52. Villagran, A., Huerta, G., Jackson, C. S., and Sen, M. K.: Computational Methods for Parameter Estimation in Climate Models, Bayesian Anal, 3, 823-850, 2008.
53. Voutilainen, A. and Kaipio, J. P.: Sequential Monte Carlo estimation of aerosol size distributions, Comput. Stat Data An, 48, 887-908, 2005.
54. Vrugt, J. A., and Gupta., H. V., Bouten, W., and Sorooshian, S.: A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters, Water Resour. Res., 39, 1201, doi:10.1029/2002WR001642, 2003.
55. Vrugt, J. A., and Clark., M. P., Diks, C. G. H., Duan, Q., and Robinson, B. A.: Multi-objective calibration of forecast ensembles using Bayesian model averaging, Geophys. Res. Lett., 33, L19817, doi:10.1029/2006GL027126, 2006.
56. Vrugt, J.A., Braak, C. J. F. Ter., Clark, M. P., Hyman, J. M., and Robinson, B. A.: Treatment of input uncertainty in hydrologic modelling: Doing hydrology backward with Markov chain Monte Carlo simulation, Water Resour. Res., 44, W00B09, doi:10.1029/2007WR006720, 2008a.
57. Vrugt, J. A., and Stauffer., P. H., Wohling, T., Robinson, B. A., and Vesselinov, V. V.: Inverse modelling of subsurface flow and transport properties: A review with new developments, Vadose Zone J., 7, 843-864, 2008b.
58. Vrugt, J. A., and Robinson., B. A., and Hyman, J. M.: Self-Adaptive Multimethod Search for Global Optimization in Real-Parameter Spaces, IEEE Transactions on Evolutionary Computation, 13, 243-259, 2009a.
59. Vrugt, J. A., Ter Braak, C. J. F., Diks, C. G. H., Robinson, B. A., Hyman, J. M., and Higdon, D.: Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling, Int. J. Nonlin. Sci. Num., 10, 273-290, 2009b.
60. Vuollekoski, H., Boy, M., Kerminen, V. M., Lehtinen, K. E. J., and Kulmala, M.: MECCO: A method to estimate concentrations of condensing organics - Description and evaluation of a Markov chain Monte Carlo application, J. Aerosol Sci., 41, 1080-1089, 2010.
61. Wraith, D., Alston, C., Mengersen, K., and Hussein, T.: Bayesian mixture model estimation of aerosol particle size distributions, Environmetrics, 22, 23-34, doi:10.1002/env.1020, 2009.
62. Xue, H. and Feingold, G.: A modelling study of the effect of nitric acid on cloud properties, J. Geophys. Res., 109, D18204, doi:10.1029/2004JD004750, 2004.