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Impacts of Large-scale Controls and Internal Processes on Low Clouds

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Presentation on theme: "Impacts of Large-scale Controls and Internal Processes on Low Clouds"— 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
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!

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