Date post: | 14-Dec-2015 |
Category: |
Documents |
Upload: | kailyn-corson |
View: | 213 times |
Download: | 0 times |
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 coauthorsAmy Clement, Ping Zhu, and Paquita Zuidema
The climatological importance
Hahn and Warren 2007; Ackerman et al. 1993; Warren et al. 1988; Hartmann et al. 1992; Slingo 1990; Stephens and Greenwald 1991; etc.
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)
• Internal processes– precipitation and cool pool– entrainment– decoupling– aerosol-induced processes
(Rauber et al. 2007) (NASA)
The uncertainty of low-cloud feedback
“…, 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
Low clouds
Better understand the cloud-controlling factors and related mechanisms
Hopefully, provide ideas to improve the low-cloud simulation in climate models
Motivation
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
Seasonal variability of low-cloud amount
Spring Case: Mar-Apr, 2000 20%Summer Case: Aug-Spt, 2000 10%
Spring Case
Summer Case
From Bruce Albrecht
Summer Case• Warmer SST (302.4 K)• Weaker surface wind (4.1 m/s)
Spring Case• Colder SST (300.4 K)• Stronger surface wind (5.6 m/s)
SummerSpring
Composite large-scale profiles
Spring Case
Summer Case
Color contours:Negative buoyancy (m2/s1)
More active
More stratiform
19% cloudiness
9% cloudiness
Observation-simulation comparison
Obs. Obs.
LES LES
SummerSpring
SummerSpring
Simulations based on the two observed states capture the cloudiness and cloud structure differences
Nudging SST (=1K)
Rain scheme
Wind LTS
, qt
U,V
Mar-Apr +Aug-Spt-
No rain Exchange U profiles
Exchange the inversion strength
, qt
U,V
Same as above
Same as above
Same as above
Same as above
The impact of large-scale forcing
16 sensitivity cases: 8 cases for Spring Case, 8 cases for Summer Case
Control
SST
Wind
Strong constrainsWeak constrains
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
(Zheng et al. 2013a)
LTS
CIRPAS Twin Otter Instrumentation
Oct 16 – Nov 13, 200815 out of 18 flights were around 8 AM local time
Observed CCN and LWP relationships
• 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?
Precip. suppression
Large-scale controls
?
Case CCN (cm-3)zi0
(m)
LWP0
(g m-2)Comments
A0 2001055 117
Constant BL with thinning cloud layerA1 2000
B0 2001055 18
Deepening BL with deepening cloud layer
B1 2000
C0 200
900 47Deepening BL with constant cloud layerC0.5 400
C1 2000
The impact of CCN on non-drizzling stratocumulus
• 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 47 45 43
k 1.03 1.02 1.24 1.23 1.14 1.13 1.12
The LWP of the polluted clouds ↓ <10% (5%) after 12h
(Zheng 2012)
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)
Summary for stratocumulus study
• 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.
• 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.
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