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

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
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
Cloud Barrow
Day in October 2004 A B C

5. MODIS Visible Satellite Composite
A Case Study: October 9 – 10, 2004
Barrow
Oliktok Point
MODIS Visible Satellite Composite
sea ice
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 LWP ~ 150 g m-2 (LWPad ~ 230 g m-2)
LW cooling ~ 70 W m-2
Sensible heat flux ~ 140 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 Barrow (Shupe et al 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
Microphysics
# of SCMs
# of CRMs
Single moment with T-dependent liquid and ice 6 1
Single moment with independent liquid and ice 5
Double moment
Bin Microphysics 2

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
SCMs
CRMs
Aircraft
normalized height
g m-3
cloud base
surface
cloud top
Adiabatic LWC 0.
0.7
Models generally underestimate liquid water content

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

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

15. Liquid Water Path vs. Ice Water Path
Median LWP is ~56 g m-2 for both SCMs and CRMs, whereas observed LWP is 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
Perhaps add a figure to show that some models (ARCSCM, DHARMA, UCLA-LARC, UWM) do a good LWC/IWC profile?

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

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

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 25 19
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?
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
Ice crystal number concentration (L-1)
liquid water path
(g m-2)

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
g kg-1 K
well-mixed
sounding
pressure
(hPa) qv q
SST
Tct ~ – 15C
Red lines indicate the profiles of qv and q for a well-mixed boundary layer with qt = 1.95 g kg-1 and ql = 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
cloud fraction
SCMs
CRMs
height (km)
hydro-meteor fraction
Radar/Lidar
2.0 0.
2.0 0. 0.
1.0 0.
1.0
Models generally simulate an overcast precipitating cloud