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Impacts of Large-scale Controls and Internal Processes on Low Clouds Observational and Numerical Studies Xue Zheng RSMAS, University of Miami Acknowledgement:

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Presentation on theme: "Impacts of Large-scale Controls and Internal Processes on Low Clouds Observational and Numerical Studies Xue Zheng RSMAS, University of Miami Acknowledgement:"— Presentation transcript:

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 From VOCLAS, RICO website Stratocumulus Cumulus

3 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.

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

6 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

7 Better understand the cloud-controlling factors and related mechanisms Hopefully, provide ideas to improve the low-cloud simulation in climate models Motivation

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

9 Cumulus clouds

10 Seasonal variability of low-cloud amount Spring Case: Mar-Apr, % Summer Case: Aug-Spt, % Spring Case Summer Case From Bruce Albrecht

11 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) Summer Spring Composite large-scale profiles

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

13 Spring Case Summer Case Color contours: Negative buoyancy (m 2 /s 1 ) More active More stratiform 19% cloudiness 9% cloudiness

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

15 NudgingSST (  =1K) Rain scheme WindLTS Mar-Apr +  Aug-Spt -  No rainExchange U profiles Exchange the inversion strength Same as above The impact of large-scale forcing 16 sensitivity cases: 8 cases for Spring Case, 8 cases for Summer Case

16 Control SST Wind Strong constrains Weak 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

17 The impacts of precipitation and cold pools Summer Spring

18 Summary for cumulus study High Cloudiness Low Cloudiness Shallow cumulus, very weak cool pool

19 Stratocumulus clouds

20 CIRPAS Twin Otter Instrumentation Oct 16 – Nov 13, out of 18 flights were around 8 AM local time

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

22 Observed CCN and LWP relationships Precip. suppression

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

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

25 CaseCCN (cm -3 ) z i0 (m) LWP 0 (g m -2 ) Comments A Constant BL with thinning cloud layer A12000 B Deepening BL with deepening cloud layer B12000 C Deepening BL with constant cloud layer C C12000 The impact of CCN on non-drizzling stratocumulus

26 Entrainment instability index k The cloud top interface of polluted cloud is not unstable compared with clean clouds k = (Lilly 2002) CaseA0A1B0B1C0C0.5C1 LWP (g m -2 ) k The LWP of the polluted clouds ↓ <10% (5%) after 12h (Zheng 2012)

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

29 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.

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!


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