1. Impacts of Large-scale Controls and Internal Processes on Low Clouds
Observational and Numerical Studies
Xue Zheng
RSMAS, University of Miami
Acknowledgement:
Bruce Albrecht, Virendra Ghate, and coauthors
Amy Clement, Ping Zhu, and Paquita Zuidema

2. Low clouds over the world
Cumulus
Cumulus
Stratocumulus
From VOCLAS, RICO website

3. The climatological importance
The annual mean stratus cloud amount over the world reveals several Sc regions, such as the mid-latitudes and the eastern ocean basins, all of which are also areas with strong negative cloud radiative forcing. Therefore, Sc and stratus are found to be the most radiatively important cloud type for the current climate. Furthermore, stratocumulus and cumulus
Hahn and Warren 2007; Ackerman et al. 1993; Warren et al. 1988; Hartmann et al. 1992; Slingo 1990; Stephens and Greenwald 1991; etc.

4. Cloud-controlling factors
Large-scale controls
lower tropospheric static stability(LTS), SST and SST advection, large scale subsidence, free-troposphere humidity
(Albrecht et al. 1995)

5. precipitation and cool pool entrainment decoupling
Internal processes
precipitation and cool pool
entrainment
decoupling
aerosol-induced processes
(Rauber et al. 2007)
(NASA)

6. The uncertainty of low-cloud feedback
Low clouds
“…, process studies leading to a better assessment of the behaviour of MBL clouds … will have the potential to reduce substantially the uncertainty in model predictions of tropical cloud feedbacks and climate sensitivity. ”- Bony and Dufresne 2005

7. Motivation
Better understand the cloud-controlling factors and related mechanisms
Hopefully, provide ideas to improve the low-cloud simulation in climate models
The significant climatological importance of low clouds and their large feedback uncertainty in the future climate motivated a number of researchers, including me, to study and better understand low clouds, particularly the mechanisms underline the cloud-controlling factors. Hopefully, the finding can eventually provide some ideas to improve the low-cloud simulation in climate models and reduce the cloud feedback uncertainty.

8. Data and methodology
ARM Nauru cumulus observation and VOCALS stratocumulus observation
Important factors for cloud variations
Large eddy simulations with observed large-scale forcing
Process-oriented simulations
Nested WRF simulations for stratocumulus cases
Further test in more realistic simulations
Here is the general data and methodology of this study. I used cumulus observation from ARM TWP Nauru site and aircraft observations during VOCALS for stratocumulus to identify the important factors for observed cloud variations. And then, I used LES to do process-oriented simulations based on observed large-scale frocing.
For stratocumulus study, I did more realistic simulations with WRF model to further prove the finding from LES study.

9. Cumulus clouds

10. Seasonal variability of low-cloud amount
Spring Case
Summer Case
From Bruce Albrecht
My research about cumulus is mainly motived by this figure.
This is the monthly cloud amount during the nighttime from January 1999 to December You can see the seasonal variability is very large. The cloud amount changes between 10% and 20%.
During January 1999 and December 2000, a period of La Niña event, Nauru region was dominated with shallow cumuli and lack of deep convections cloud. This study only includes nighttime observations with the prevalent easterly winds in order to minimize the island effect. Therefore, it’s interesting to figure out what control this seasonal variability. Addressing this question would potentially help us to constrain the cloud sensitivities to related factors in climate models.
Therefore, I used LES model to study these two time periods. Just for convenience, I will call March-April, 2000 Spring case and Aug-Spt, 2000 case summer case. I will analysis observations from Nauru site during these two periods first and then use these results to evaluate the LES results.
Spring Case: Mar-Apr, %
Summer Case: Aug-Spt, %

11. Composite large-scale profiles
Summer
Spring
These are composite profiles from nighttime soundings. The dash lines indicate the range of observed variation and the solid lines are the mean value.
May I ask you to make a guess, which is the spring case: black or red?
For convenience, I will use black to represent spring case and red to represent summer case from now on.
Can LES model reproduce such substantial difference in cloud amount with such subtle difference in large scale profiles?
Spring Case
Colder SST (300.4 K)
Stronger surface wind (5.6 m/s)
Summer Case
Warmer SST (302.4 K)
Weaker surface wind (4.1 m/s)

12. Remote sensing measurements
Radar reflectivity (dBz)
3/30/2000
Vertical Velocity (m/s)

13. More stratiform More active Spring Case 19% cloudiness Color contours:
Negative buoyancy (m2/s1)
Summer Case
More active
9% cloudiness

14. Observation-simulation comparison
LES
Summer
Spring
Simulations based on the two observed states capture the cloudiness and cloud structure differences

15. The impact of large-scale forcing
Nudging
SST (=1K)
Rain scheme
Wind
LTS
, qt
𝜏=4 ℎ𝑜𝑢𝑟𝑠
U,V
𝜏=10 𝑚𝑖𝑛
Mar-Apr +
Aug-Spt -
No rain
Exchange U profiles
Exchange the inversion strength
𝜏=2 ℎ𝑜𝑢𝑟𝑠
Same as above
16 sensitivity cases: 8 cases for Spring Case, 8 cases for Summer Case

16. Summary of all 20 cases
Spring Case is more sensitive to large-scale forcing
Most sensitive to lower tropospheric static stability (inversion)
If the inversion strength is constrained, both cases are insensitive to SST
Wind profiles also have impacts on cloudiness
Weak constrains
Strong constrains
LTS
Control
Wind
SST
(Zheng et al. 2013a)

17. The impacts of precipitation and cold pools
Summer
Spring

18. Summary for cumulus study
High Cloudiness
Low Cloudiness
Inversion constrains cloud depth; wind shear tilts cloud
Weak negative buoyance area induced by weak precipitation stabilizes the surrounding area, but when the cool pool strength increases the negative feedback fades away
Shallow cumulus, very weak cool pool

19. Stratocumulus clouds

20. CIRPAS Twin Otter Instrumentation
As one component of the five aircraft and two research vessels in the VOCALS experiment, the Center for Interdisciplinary Remotely-Piloted Aircraft Studies Twin Otter aircraft focused on making observations off the coast of Northern Chile. Comprehensive in-situ observations of aerosol, turbulence, cloud properties, and drizzle were also collected. These offered a unique opportunity to acquire first-hand evidence of cloud-aerosol-turbulence interactions in the near-coastal marine Sc over the SE Pacific ocean.
The CIRPAS Twin Otter aircraft completed 19 flights in the vicinity of Point Alpha (20° S, 72° W) off the coast of Northern Chile from Oct. 16 to Nov during VOCALS-REx. Because cloud and aerosol probe data failed on one of those flights (Nov. 5), we only include observations from the other 18 flights in this paper.
Oct 16 – Nov 13, 2008
15 out of 18 flights were around 8 AM local time

21. Observed CCN and LWP relationships
Cause and effect
(Zheng et al. ACP 2011)

22. Observed CCN and LWP relationships
Precip. suppression
Cause and effect

23. Observed CCN and LWP relationships
Precip. suppression
Large-scale controls
Cause and effect

24. ? Observed CCN and LWP relationships Large-scale controls
Precip. suppression
A strong positive correlation between the LWP and the BL CCN
(Zheng et al. GRL 2010)
What about sedimentation/entrainment feedback?
Could be caused by earlier cloud history?
Large-scale controls ?
Cause and effect

25. The impact of CCN on non-drizzling stratocumulus
Case
CCN (cm-3)
zi0
(m)
LWP0
(g m-2)
Comments A0
200
1055
117
Constant BL with thinning cloud layer A1
2000 B0 18
Deepening BL with deepening cloud layer B1 C0
900 47
Deepening BL with constant cloud layer
C0.5
400 C1

26. Entrainment instability index k
The cloud top interface of polluted cloud is not unstable compared with clean clouds
k =
(Lilly 2002)
Case A0 A1 B0 B1 C0
C0.5 C1
LWP (g m-2) 53 47 42 38 45 43 k
1.03
1.02
1.24
1.23
1.14
1.13
1.12
Basically, k is the ratio of the available evaporation cooling and the stable stratification the parcel needs to overcome.
(Zheng 2012)
The LWP of the polluted clouds ↓ <10% (5%) after 12h

27. WRF simulations
Initialized with the Naval Research Laboratory’s COAMPS real-time forecasts
4 nested domains:
4km, 1.4km, 450m, 150m
91 (/128) levels < 850hPa
10/19/2008 Case: High LWP
10/27/2008 Case: Low LWP
No aerosol indirect effects
60-hour simulation
Diurnal cycle
(Zheng et al. 2013b)

28. Maximum cloud liquid water mixing ratio
GOES visible images during the similar time

29. Summary for stratocumulus study
The large-scale factors and internal processes can have large impacts on the cloud LWP variability.
The LWP increases with the CCN concentrations in spite of lack of precipitation.
The aerosol indirect effect on non-drizzling stratocumulus is limited.
The observed LWP difference is captured by WRF simulation in the absence of aerosol indirect effect.

30. Conclusions
Large-scale atmospheric pattern, including large-scale wind pattern might play the lead role in the low-cloud variability: low cloud is closely tied to large-scale circulations
Internal processes (e.g. precipitation) responding to the large-scale forcing can also play an important role in the low-cloud variability depending on the cloud regime

31. Thank you!