Cirrus Clouds and Their Representation in Models

  • Ulrike Burkhardt
  • Ingo Sölch
Part of the Research Topics in Aerospace book series (RTA)


This article gives a short summary of the physical processes relevant to cirrus and their representation in cloud-resolving models and in global general circulation models. Cloud-resolving models are used to study the evolution of single clouds or cloud systems. With global models the role of clouds in the atmosphere and their interaction with large scale dynamics can be studied. Applications of such models to study cirrus processes and the global contrail cirrus climate impact are discussed. Future research towards a prognostic cloud scheme to include nonequilibrium cirrus cloud physics is laid out.


Probability Density Function Microphysical Process Cirrus Cloud Cloud Scheme Warm Cloud 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Bony, S., Emanuel, K.A.: A parameterization of the cloudiness associated with cumulus convection; evaluation using TOGA COARE data. J. Atmos. Sci. 58, 3158–3183 (2001)ADSCrossRefGoogle Scholar
  2. Burkhardt, U., Kärcher, B., Ponater, M., Gierens, K., Gettelman, A.: Contrail cirrus supporting areas in model and observations. Geophys. Res. Lett. 35, L16808 (2008). doi: 10.1029/2008GL034056 ADSCrossRefGoogle Scholar
  3. Burkhardt, U., Kärcher, B.: Process-based parameterization of contrail cirrus in a global climate model. J. Geophys. Res. 114 (2009). doi: 10.1029/2008JD011491
  4. Burkhardt, U., Kärcher, B.: Global radiative forcing from contrail cirrus. Nature Clim. Change 1, 54–58 (2011)ADSCrossRefGoogle Scholar
  5. Cess, R.D., et al.: Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models. J. Geophys. Res. 95, 16601–16615 (1990)ADSCrossRefGoogle Scholar
  6. DeMott, P.J., Cziczo, D.J., Prenni, A.J., Murphy, D.M., Kreidenweis, S.M., Thomson, D.S., Borys, R., Rogers, D.C.: Measurements of the concentration and composition of nuclei for cirrus formation. Proc. Nat. Acad. Sci. U.S.A. 100, 14655–14660 (2003)ADSCrossRefGoogle Scholar
  7. Gettelman, A., Kinnison, D.E.: The global impact of supersaturation in a coupled chemistry climate model. Atmos. Chem. Phys. 7, 1629–1643 (2007)ADSCrossRefGoogle Scholar
  8. Gierens, K., Schumann, U., Helten, M., Smit, H., Marenco, A.: A distribution law for relative humidity in the upper troposphere and lower stratosphere derived from three years of MOZAIC measurements. Ann. Geophys. 17, 1218–1226 (1999)ADSCrossRefGoogle Scholar
  9. Hendricks, J., Kärcher, B., Lohmann,U.: Effects of ice nuclei on cirrus clouds in a global climate model. J. Geophys. Res. 116(D18206), 1–24 (2011). doi: 10.1029/2010JD015302 Google Scholar
  10. Herzegh, P.H., Hobbs, P.V.: The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. II: Warm frontal clouds. J. Atmos. Sci. 37, 597–611 (1980)ADSCrossRefGoogle Scholar
  11. Holton, J.R., Gettelman, A.: Horizontal transport and the dehydration of the stratosphere. Geophys. Res. Lett. 28, 2799–2802 (2001)ADSCrossRefGoogle Scholar
  12. IPCC: Climate Change 2007: The physical basis, Contribution of working group I to the 4th assessment report of the Intergovernmental Panel on Climate Change. In: Solomon, S., et al. (eds.) Cambridge University Press, Cambridge (2007)Google Scholar
  13. Jensen, E.J., Toon, O.B., Westphal, D.L., Kinne, S., Heymsfield, A.J.: Microphysical modeling of cirrus. 1. Comparison with 1986 FIRE IFO measurements. J. Geophys. Res. 99, 10421–10442 (1994)ADSCrossRefGoogle Scholar
  14. Jensen, E.J., Toon, O.B., Vay, S.A., Ovarlez, J., May, R., Bui, P., Twohy, C.H., Gandrud, B., Pueschel, R.F., Schumann, U.: Prevalence of ice-supersaturated regions in the upper troposphere: implications for optically thin ice cloud formation. J. Geophys. Res. 106, 17253–17266 (2001)ADSCrossRefGoogle Scholar
  15. Kärcher, B., Burkhardt, U.: A cirrus cloud scheme for general circulation models. Quart. J. R. Meteorol. Soc. 134, 1439–1461 (2008). doi: 10.1002/qj.301 ADSCrossRefGoogle Scholar
  16. Koop, T.: Homogeneous ice nucleation in water and aqueous solutions. Z. Phys. Chem. 218, 1231–1258 (2004)CrossRefGoogle Scholar
  17. Lamquin, N., Stubenrauch, C., Cros, S., Smit, H., Gierens, K., Burkhardt, U.: A 6-year global climatology of occurrence of upper tropospheric ice supersaturation inferred from the Atmospheric Infrared Sounder and its synergy with MOZAIC. Atmos. Chem. Phys. 12, 381–405 (2012)CrossRefGoogle Scholar
  18. Liou, K.-N.: Influence of cirrus clouds on weather and climate processes: a global perspective. Mon. Weather Rev. 114, 1167–1199 (1986)ADSCrossRefGoogle Scholar
  19. Lohmann, U., Kärcher, B.: First interactive simulations of cirrus clouds formed by homogeneous freezing in the ECHAM GCM. J. Geophys. Res. 107, 4105 (2002). doi: 10.1029/2001JD000767 CrossRefGoogle Scholar
  20. Luo, Z., Rossow, W.B.: Characterizing tropical cirrus life cycle, evolution and interaction with upper-tropospheric water vapor using lagrangian trajectory analysis of satellite observations. J. Clim. 17, 4541–4563 (2004)ADSCrossRefGoogle Scholar
  21. Miloshevich, L.M., Heymsfield, A.J.: A balloon-borne continuous cloud particle replicator for measuring vertical profiles of cloud microphysical properties: instrument design, performance, and collection efficiency analysis. J. Atmos. Oceanic Technol. 14, 753–768 (1997)ADSCrossRefGoogle Scholar
  22. Ovarlez, J., van Velthoven, P., Sachse, G., Vay, S., Schlager, H., Ovarlez, H.: Comparison of water vapor measurements from POLINAT 2 with ECMWF analyses in high humidity conditions. J. Geophys. Res. 105, 3737–3744 (2000)ADSCrossRefGoogle Scholar
  23. Pincus, R., Klein, S.A.: Unresolved spatial variability and microphysical process rates in large-scale models. J. Geophys. Res. 105, 27059–27065 (2000)ADSCrossRefGoogle Scholar
  24. Ponater, M., Marquart, S., Sausen, R.: Contrails in a comprehensive global climate model: parameterization and radiative forcing results. J. Geophys. Res. 107(D13), 4164 (2002). doi:  10.1029/2001JD000429 Google Scholar
  25. Sölch, I., Kärcher, B.: A large eddy model for cirrus clouds with explicit aerosol and ice microphysics and Lagrangian ice particle tracking. Quart. J. R. Meteorol. Soc. 136, 2074–2093 (2010)ADSCrossRefGoogle Scholar
  26. Sölch, I., Kärcher, B.: Process-oriented large-eddy simulations of a midlatitude cirrus cloud system based on observations. Quart. J. R. Meteorol. Soc. 137(655), 374–393 (2011)ADSCrossRefGoogle Scholar
  27. Spichtinger, P., Gierens, K.: Modelling of cirrus clouds—Part 1a: Model description and validation. Atmos. Chem. Phys. 9, 685–706 (2009)ADSCrossRefGoogle Scholar
  28. Starr, D.O.C., Cox, S.K.: Cirrus clouds. Part I: A cirrus cloud model. J. Atmos. Sci. 42, 2663–2681 (1985)ADSCrossRefGoogle Scholar
  29. Stephens, G.L., Vane, D.G., Boain, R.J., Mace, G.G., Sassen, K., Wang, Z., Illingworth, A.J., O’Connor, E.J., Rossow, W.B., Durden, S.L., et al.: The CloudSat mission and the A-train: a new dimension of space-based observations of clouds and precipitation. Bull. Am. Meteorol. Soc. 83, 1771–1790 (2002). doi: 10.1175/BAMS-83-12-1771 ADSCrossRefGoogle Scholar
  30. Stephens, G.L.: Cloud feedbacks in the climate system: a critical review. J. Clim. 18, 237–273 (2005)ADSCrossRefGoogle Scholar
  31. Ström, J., Seifert, M., Kärcher, B., Ovarlez, J., Minikin, A., Gayet, J.-F., Krejci, R., Petzold, A., Auriol, F., Haag, W., et al.: Cirrus cloud occurrence as function of ambient relative humidity: a comparison of observations obtained during the INCA experiment. Atmos. Chem. Phys. 3, 1807–1816 (2003)ADSCrossRefGoogle Scholar
  32. Thorsen, T.J., Fu, Q., Comstock, J.: Comparison of the CALIPSO satellite and ground-based observations of cirrus clouds at the ARM TWP sites. J. Geophys. Res. 116, D21203 (2011). doi: 10.1029/2011JD015970 ADSCrossRefGoogle Scholar
  33. Tompkins, A.: A prognostic parameterization for the subgrid-scale variability of water vapor and clouds in large-scale models and its use to diagnose cloud cover. J. Atmos. Sci. 59, 1917–1942 (2002)ADSCrossRefGoogle Scholar
  34. Tompkins, A., Gierens, K., Rädel, G.: Ice supersaturation in the ECMWF integrated forecast system. Quart. J. R. Meteorol. Soc. 133, 53–63 (2007)ADSCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.DLR, Institute of Atmospheric Physics (IPA)OberpfaffenhofenGermany

Personalised recommendations