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Intercomparison of model simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic Cloud Experiment. II: Multilayer cloud

  1. Hugh Morrison1,*,
  2. Renata B. McCoy2,
  3. Stephen A. Klein2,
  4. Shaocheng Xie2,
  5. Yali Luo7,
  6. Alexander Avramov3,
  7. Mingxuan Chen4,
  8. Jason N. S. Cole10,
  9. Michael Falk17,
  10. Michael J. Foster16,
  11. Anthony D. Del Genio13,†,
  12. Jerry Y. Harrington3,
  13. Corinna Hoose9,
  14. Marat F. Khairoutdinov5,
  15. Vincent E. Larson17,
  16. Xiaohong Liu6,
  17. Greg M. McFarquhar18,
  18. Michael R. Poellot22,
  19. Knut von Salzen23,
  20. Ben J. Shipway15,‡,
  21. Matthew D. Shupe19,
  22. Yogesh C. Sud12,†,
  23. David D. Turner20,
  24. Dana E. Veron14,
  25. Gregory K. Walker12,†,
  26. Zhien Wang21,
  27. Audrey B. Wolf13,
  28. Kuan-Man Xu8,†,
  29. Fanglin Yang11,
  30. Gong Zhang18

Article first published online: 5 MAY 2009

DOI: 10.1002/qj.415

Issue

Quarterly Journal of the Royal Meteorological Society

Quarterly Journal of the Royal Meteorological Society

Volume 135, Issue 641, pages 1003–1019, April 2009 Part B

Additional Information

How to Cite

Morrison, H., McCoy, R. B., Klein, S. A., Xie, S., Luo, Y., Avramov, A., Chen, M., Cole, J. N. S., Falk, M., Foster, M. J., Del Genio, A. D., Harrington, J. Y., Hoose, C., Khairoutdinov, M. F., Larson, V. E., Liu, X., McFarquhar, G. M., Poellot, M. R., von Salzen, K., Shipway, B. J., Shupe, M. D., Sud, Y. C., Turner, D. D., Veron, D. E., Walker, G. K., Wang, Z., Wolf, A. B., Xu, K.-M., Yang, F. and Zhang, G. (2009), Intercomparison of model simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic Cloud Experiment. II: Multilayer cloud. Q.J.R. Meteorol. Soc., 135: 1003–1019. doi: 10.1002/qj.415

Author Information

  1. 1

    National Center for Atmospheric Research, Boulder, Colorado, USA

  2. 2

    Lawrence Livermore National Laboratory, Livermore, California, USA

  3. 3

    The Pennsylvania State University, University Park, PA, USA

  4. 4

    Colorado State University, Fort Collins, CO, USA

  5. 5

    State University of New York at Stony Brook, Stony Brook, NY, USA

  6. 6

    Pacific Northwest National Laboratory, Richland, Washington, USA

  7. 7

    State Key Laboratory of Severe Weather, Chinese Academy of Meterological Sciences, Beijing, China

  8. 8

    NASA Langley Research Center, Hampton, Virginia, USA

  9. 9

    ETH Zurich, Institute for Atmospheric and Climate Science, Zurich, Switzerland

  10. 10

    University of British Columbia, Vancouver, BC, Canada

  11. 11

    National Centers for Environmental Prediction, Camp Springs, Maryland, USA

  12. 12

    NASA Goddard Space Flight Center, Greenbelt, Maryland, USA

  13. 13

    NASA Goddard Institute for Space Studies, New York, NY, USA

  14. 14

    University of Delaware, Newark, DE, USA

  15. 15

    Met Office, Exeter, United Kingdom

  16. 16

    Rutgers University, New Brunswick, New Jersey, USA

  17. 17

    University of Wisconsin—Milwaukee, Milwaukee, WI, USA

  18. 18

    University of Illinois, Urbana, IL, USA

  19. 19

    Cooperative Institute for Research in Environmental Sciences, University of Colorado/NOAA, Boulder, CO, USA

  20. 20

    University of Wisconsin—Madison, Madison, WI, USA

  21. 21

    University of Wyoming, Laramie, WY, USA

  22. 22

    University of North Dakota, Grand Forks, ND, USA

  23. 23

    Canadian Center for Climate, Vancouver, British Columbia, Canada

  1. †

    The contributions of Anthony D. Del Genio, Yogesh C. Sud, Gregory K. Walker, and Kuan-Man Xu to this article were prepared as part of their official duties as United States Federal Government employees.

  2. ‡

    The contribution of Ben J. Shipway was written in the course of his employment at the Met Office, UK and is published with the permission of the Controller of HMSO and the Queen's Printer for Scotland.

*National Center for Atmospheric Research, Boulder, Colorado, 80307, USA.

  1. †

    The contributions of Anthony D. Del Genio, Yogesh C. Sud, Gregory K. Walker, and Kuan-Man Xu to this article were prepared as part of their official duties as United States Federal Government employees.

  2. ‡

    The contribution of Ben J. Shipway was written in the course of his employment at the Met Office, UK and is published with the permission of the Controller of HMSO and the Queen's Printer for Scotland.

Publication History

  1. Issue published online: 8 JUN 2009
  2. Article first published online: 5 MAY 2009
  3. Manuscript Accepted: 2 MAR 2009
  4. Manuscript Revised: 23 FEB 2009
  5. Manuscript Received: 5 MAR 2008

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Keywords:

  • mixed-phase cloud;
  • Arctic clouds;
  • single-column models;
  • cloud-resolving models

Abstract

Results are presented from an intercomparison of single-column and cloud-resolving model simulations of a deep, multilayered, mixed-phase cloud system observed during the Atmospheric Radiation Measurement (ARM) Mixed-Phase Arctic Cloud Experiment. This cloud system was associated with strong surface turbulent sensible and latent heat fluxes as cold air flowed over the open Arctic Ocean, combined with a low pressure system that supplied moisture at mid-levels. The simulations, performed by 13 single-column and 4 cloud-resolving models, generally overestimate liquid water path and strongly underestimate ice water path, although there is a large spread among models. This finding is in contrast with results for the single-layer, low-level mixed-phase stratocumulus case in Part I, as well as previous studies of shallow mixed-phase Arctic clouds, that showed an underprediction of liquid water path. These results suggest important differences in the ability of models to simulate deeper Arctic mixed-phase clouds versus the shallow, single-layered mixed-phase clouds in Part I. The observed liquid-ice mass ratios were much smaller than in Part I, despite the similarity of cloud temperatures. Thus, models employing microphysics schemes with temperature-based partitioning of cloud liquid and ice masses are not able to produce results consistent with observations for both cases. Models with more sophisticated, two-moment treatment of cloud microphysics produce a somewhat smaller liquid water path closer to observations. Cloud-resolving models tend to produce a larger cloud fraction than single-column models. The liquid water path and cloud fraction have a large impact on the cloud radiative forcing at the surface, which is dominated by long-wave flux. Copyright © 2009 Royal Meteorological Society

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