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Stephen Klein and Renata McCoy Lawrence Livermore National Laboratory Hugh Morrison National Center for Atmospheric Rese

Intercomparison of model simulations of mixed-phase clouds during M-PACE. Part I: Single-layer cloud. Stephen Klein and Renata McCoy Lawrence Livermore National Laboratory Hugh Morrison National Center for Atmospheric Research +38 additional scientists GEWEX Cloud Systems Study Meeting

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Stephen Klein and Renata McCoy Lawrence Livermore National Laboratory Hugh Morrison National Center for Atmospheric Rese

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  1. Intercomparison of model simulations of mixed-phase clouds during M-PACE. Part I: Single-layer cloud Stephen Klein and Renata McCoy Lawrence Livermore National Laboratory Hugh Morrison National Center for Atmospheric Research +38 additional scientists GEWEX Cloud Systems Study Meeting Toulouse, France June 4, 2008 LLNL-PRES-403710

  2. What You Will See • This paper, plus the following paper, presents results of the first intercomparison of single-column and cloud-resolving models performed by the GCSS Polar Cloud Working Group • In the present paper, simulations from seventeen SCMs and nine CRMs were compared to observations of cold-air outbreak stratocumulus that occurred during the ARM Mixed-Phase Arctic Cloud Experiment (M-PACE) • Most models underestimate the amount of super-cooled water

  3. Outline • The Case Study • The Models and Their Set-Up • Results • Sensitivity Studies • Conclusions

  4. Mixed-Phase Arctic Cloud Experiment Cloud Fraction @ Barrow A B C Day in October 2004 • M-PACE took place at ARM’s Barrow site in October 2004 (Verlinde et al. 2007) • M-PACE featured numerous aircraft flights that measured clouds and aerosols among other increased observations ARM’s Barrow site • A variety of cloud types were observed A – multi-layer stratus B – boundary layer stratocumulus C – frontal clouds

  5. A Case Study: October 9 – 10, 2004 sea ice Barrow Oliktok Point MODIS Visible Satellite Composite • Boundary layer stratocumulus formed when cold air from above the sea ice flowed over the ocean upstream from Alaska • The sensible and latent heat fluxes created a convective cloud-topped boundary layer organized into rolls or ‘cloud-streets’ common to cold air outbreaks • Observations were collected in the clouds when they arrived at the Alaskan coast

  6. Stratocumulus Basics 600 m 700 m Sensible heat flux ~ 140 W m-2 LW cooling ~ 70 W m-2 Latent heat flux ~ 110 W m-2 • LWP ~ 150 g m-2 (LWPad ~ 230 g m-2) IWP ~ 15 g m-2 • Barrow sounding indicates a well-mixed boundary layer with a cloud-top temperature of – 15C

  7. Observations Aircraft Observations • Two aircraft flights during the period took 32 vertical profiles over Oliktok Point and Barrow • Liquid and ice water contents, effective radii, number concentrations were computed from the data (McFarquhar et al. 2007) • CDFC-measured ice nuclei concentrations were very low (~0.1 L-1) (Prenni et al. 2007) Radar Observations • Liquid and ice water contents were retrieved from the remote sensing instruments @ Barrow (Shupe et al. 2006 and Turner et al. 2007; Wang 2007)

  8. Participating Models • Seventeen SCMs and nine CRMs • SCMs include • five operational climate models (CCCMA, ECHAM, GFDL, GISS, CAM3) • two weather models (ECMWF, NCEP) • four research models (ARCSCM, MCRAS, SCRIPPS, UWM) • six models which include single modifications to the base set (ECMWF-DUALM, GISS-LBL, MCRASI, SCAM3-LIU, SCAM3-MG, and SCAM3-UW). (The modifications include cloud microphysics and boundary layer turbulence)

  9. Participating Models • CRMs include • four 3-dimensional models (COAMPS, DHARMA, METO, SAM). These models have horizontal and vertical resolutions of ~50 m and total domain of ~5 km x 5 km. • five 2-dimensional models (NMS-BULK, NMS-SHIPS, RAMS-CSU, UCLA-LARC, UCLA-LARC-LIN). These models have horizontal resolutions of ~1000 m and vertical resolutions of ~100 m with a total domain length of ~100 km

  10. Cloud Microphysics • There is a broad distribution of microphysical complexity among the models

  11. Model Configurations • Models are initialized with the observed sounding and begin with a pure liquid cloud with an adiabatic water content • Models are forced with horizontal cooling and drying advective tendencies, a prescribed subsidence rate, and surface fluxes based on ECMWF analyses • Model aerosols are fixed in time except for 2 models with prognostic ice nuclei • Models simulate a 12 hour period and results from the last 9 hours are presented

  12. Results: Liquid Water Content cloud top Adiabatic LWC normalized height cloud base Aircraft surface normalized height SCMs CRMs g m-3 g m-3 0. 0.7 0. 0.7 • Models generally underestimate liquid water content

  13. Results: Ice Water Content • Models generally simulate ice water content within the observational uncertainties cloud top cloud base Radar – Lidar Aircraft (Wang) surface normalized height SCMs CRMs 0. 0.2 0. 0.2 g m-3 g m-3

  14. Liquid Water Path vs. Ice Water Path 171 ice water path (g m-2) liquid water path (g m-2) • Models simulate a wide range of results Radar – Lidar Aircraft

  15. Liquid Water Path vs. Ice Water Path • Median LWP is ~56 g m-2 for both SCMs and CRMs, whereas observed LWP is 150 50 g m-2 • Median IWP is 29 g m-2 for SCMs and 17 g m-2 for CRMs, whereas observed IWP is 15 g m-2 a factor of two • Three-quarters of the models simulate LWP > IWP but two-thirds of models simulate LWP < LWPobserved

  16. Do microphysics matter? M-PACE Period B liquid water path (g m-2) Observations double moment bin microphysics 1 mom. with T-dep. part. 1 mom. with ind. liq. & ice

  17. Do microphysics matter? M-PACE Period B 171 ice water path (g m-2) Observations double moment bin microphysics 1 mom. with T-dep. part. 1 mom. with ind. liq. & ice

  18. Is the Relationship Significant? • The trend towards improved simulations with increased microphysical sophistication is intriguing but... • the scatter is large • the physical reason(s) for the improved simulation are not known • perhaps models with more sophisticated microphysics are run by scientists who have spent more time studying Arctic clouds

  19. Is the Relationship Significant? • Furthermore, cloud properties depend on many other things including the representation of boundary layer turbulence • For example, the same double moment microphysics (Morrison et al. 2005) in an SCM (ARCSCM) produces 290 g m-2 but in a CRM (UCLA-LARC) produces 170 g m-2

  20. Sensitivity Studies: No Ice • A sensitivity study was performed in which ice microphysics was disabled so that the simulated cloud would be of pure liquid phase no-ice simulation liquid water path (g m-2) control condensate water path (g m-2)

  21. Sensitivity Studies: No Ice • In many of the models with total condensate water path (IWP+LWP) < 150 g m-2 in the control experiment, the LWP increases strongly • The relative increase is greater in models with higher relative amounts of ice in the control experiment 19 25 LWP (no-ice simulation) WP (control simulation) ice fraction in the control simulation

  22. Sensitivity Studies: No Ice • This suggests that the interaction between ice and liquid microphysics is responsible for the significant underestimate of LWP in some models • The range among models in the simulated LWP is from 60 g m-2 to 360 g m-2 (omitting one outlier). This large range must be due to differences in the representation of processes such as boundary layer turbulence and liquid microphysics

  23. Is Ice Crystal Number Important? liquid water path (g m-2) Ice crystal number concentration (L-1) • Many studies have shown that if ice crystal numbers are elevated that it is difficult to maintain high amounts of LWP (Pinto 1998, Harrington et al. 1999, Jiang et al. 2000, Morrison and Pinto 2006, Prenni et al. 2007) • Intercomparison results do not show a simple relationship between LWP and Ni

  24. Sensitivity Studies: Vertical Resolution • Models performed another sensitivity studied with increased vertical resolution • The median number of vertical levels in the boundary layer increased from 7 to 19 for the SCMs and from 17 to 29 for the CRMs • Model sensitivity to vertical resolution is small high resolution condensate water path (g m-2) control condensate water path (g m-2)

  25. Conclusions • For this stratocumulus cloud, models generally underestimate LWP and but are within the uncertainties for IWP. Previous studies have also found that models underestimate the LWP in thin Arctic mixed-phase clouds (Inoue et al. 2006, Morrison and Pinto 2006, Prenni et al. 2007) • The sensitivity study with no ice microphysics suggests that the interaction between liquid and ice microphysics is responsible for the underestimate of LWP in many models. For these models, it may be too easy to form ice and or that ice diffusional growth (the Bergeron process) is too rapid

  26. Conclusions • A weak association between increased microphysical complexity and improved simulation has been found. However, it is not clear how significant this is, and a good cloud simulation does not depend solely on the microphysical model • Model simulations do not seem to be greatly sensitive to the vertical resolution • The relative simplicity of the present case as well as the availability of a good set of observations may make this a suitable benchmark case for mixed-phase clouds

  27. The End

  28. Boundary Layer is Well-Mixed qv q Tct ~ – 15C pressure (hPa) well-mixed sounding well-mixed sounding SST g kg-1 K Red lines indicate the profiles of qv and q for a well-mixed boundary layer with qt = 1.95 g kg-1 and ql= 269.2 K

  29. Radiation • Since models underestimate LWP, the amount of solar radiation transmitted to the surface is greater than observed • Models with LWP > 100 g m-2 simulate the longwave radiation downward at the surface correctly surface downward longwave radiation (W m-2) solar trans-mission condensate water path (g m-2) condensate water path (g m-2)

  30. Cloud and Hydrometeor Fraction SCMs CRMs cloud fraction height (km) Radar/Lidar hydro-meteor fraction height (km) • Models generally simulate an overcast precipitating cloud 2.0 0. 2.0 0. 0. 1.0 0. 1.0

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