Presentation on theme: "Impacts of Large-scale Controls and Internal Processes on Low Clouds"— Presentation transcript:
1Impacts of Large-scale Controls and Internal Processes on Low Clouds Observational and Numerical StudiesXue ZhengRSMAS, University of MiamiAcknowledgement:Bruce Albrecht, Virendra Ghate, and coauthorsAmy Clement, Ping Zhu, and Paquita Zuidema
2Low clouds over the world CumulusCumulusStratocumulusFrom VOCLAS, RICO website
3The 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 cumulusHahn and Warren 2007; Ackerman et al. 1993; Warren et al. 1988; Hartmann et al. 1992; Slingo 1990; Stephens and Greenwald 1991; etc.
4Cloud-controlling factors Large-scale controlslower tropospheric static stability(LTS), SST and SST advection, large scale subsidence, free-troposphere humidity(Albrecht et al. 1995)
5precipitation and cool pool entrainment decoupling Internal processesprecipitation and cool poolentrainmentdecouplingaerosol-induced processes(Rauber et al. 2007)(NASA)
6The 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
7MotivationBetter understand the cloud-controlling factors and related mechanismsHopefully, provide ideas to improve the low-cloud simulation in climate modelsThe 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.
8Data and methodologyARM Nauru cumulus observation and VOCALS stratocumulus observationImportant factors for cloud variationsLarge eddy simulations with observed large-scale forcingProcess-oriented simulationsNested WRF simulations for stratocumulus casesFurther test in more realistic simulationsHere 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.
10Seasonal variability of low-cloud amount Spring CaseSummer CaseFrom Bruce AlbrechtMy 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, %
11Composite large-scale profiles SummerSpringThese 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 CaseColder SST (300.4 K)Stronger surface wind (5.6 m/s)Summer CaseWarmer SST (302.4 K)Weaker surface wind (4.1 m/s)
13More stratiform More active Spring Case 19% cloudiness Color contours: Negative buoyancy (m2/s1)Summer CaseMore active9% cloudiness
14Observation-simulation comparison LESSummerSpringSimulations based on the two observed states capture the cloudiness and cloud structure differences
15The impact of large-scale forcing NudgingSST (=1K)Rain schemeWindLTS, qt𝜏=4 ℎ𝑜𝑢𝑟𝑠U,V𝜏=10 𝑚𝑖𝑛Mar-Apr+Aug-Spt-No rainExchange U profilesExchange the inversion strength𝜏=2 ℎ𝑜𝑢𝑟𝑠Same as above16 sensitivity cases: 8 cases for Spring Case, 8 cases for Summer Case
16Summary of all 20 casesSpring Case is more sensitive to large-scale forcingMost sensitive to lower tropospheric static stability (inversion)If the inversion strength is constrained, both cases are insensitive to SSTWind profiles also have impacts on cloudinessWeak constrainsStrong constrainsLTSControlWindSST(Zheng et al. 2013a)
17The impacts of precipitation and cold pools SummerSpring
18Summary for cumulus study High CloudinessLow CloudinessInversion constrains cloud depth; wind shear tilts cloudWeak negative buoyance area induced by weak precipitation stabilizes the surrounding area, but when the cool pool strength increases the negative feedback fades awayShallow cumulus, very weak cool pool
20CIRPAS 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, 200815 out of 18 flights were around 8 AM local time
21Observed CCN and LWP relationships Cause and effect(Zheng et al. ACP 2011)
22Observed CCN and LWP relationships Precip. suppressionCause and effect
23Observed CCN and LWP relationships Precip. suppressionLarge-scale controlsCause and effect
24? Observed CCN and LWP relationships Large-scale controls Precip. suppressionA 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
25The impact of CCN on non-drizzling stratocumulus CaseCCN (cm-3)zi0(m)LWP0(g m-2)CommentsA02001055117Constant BL with thinning cloud layerA12000B018Deepening BL with deepening cloud layerB1C090047Deepening BL with constant cloud layerC0.5400C1
26Entrainment instability index k The cloud top interface of polluted cloud is not unstable compared with clean cloudsk =(Lilly 2002)CaseA0A1B0B1C0C0.5C1LWP (g m-2)534742384543k1.031.021.241.188.8.131.52Basically, 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
27WRF simulationsInitialized with the Naval Research Laboratory’s COAMPS real-time forecasts4 nested domains:4km, 1.4km, 450m, 150m91 (/128) levels < 850hPa10/19/2008 Case: High LWP10/27/2008 Case: Low LWPNo aerosol indirect effects60-hour simulationDiurnal cycle(Zheng et al. 2013b)
28Maximum cloud liquid water mixing ratio GOES visible images during the similar time
29Summary 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.
30ConclusionsLarge-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 circulationsInternal 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