increased cloud droplet concentration leads to longer subtropical...

Increased cloud droplet concentration leads to longer subtropical stratocumulus lifetimes and a deeper PBL from a Lagrangian perspective Ryan Eastman, Robert Wood, University of Washington, Department of Atmospheric Sciences I. A Lagrangian perspective To better capture the cloud cover (CC) response to environmental perturbations we sample the same cloudy parcel as it evolves in time and space. We compute ~169,000 trajectories in four subtropical ocean basins at 925 hPa, allowing us to follow and compare parcels. Lagrangian sampling of cloud variables (from the A-Train) and environmental conditions (from ERA-Interim) is done at every A-Train overpass at 12-hour intervals 1 . Wind data are sourced from the ERA Interim reanalysis data at 0.75˚ resolution. II. Cloud cover in the subtropics Regions are characterized by low, thick stratocumulus (Sc) clouds forming near the coast and advecting offshore. Clouds eventually deepen and give way to trade cumulus. These thick, warm, bright clouds act to cool the subtropics, so it is of great value to know the controls on their extent. Cloud cover is estimated using the MODIS cloud mask product observed from the Aqua satellite in these four regions shown by the red boxes. Regions encompass Sc maxima and the declining cloud cover gradients offshore. Not only is the amount of cloud cover changing in time, but the depth of the planetary boundary layer (PBL) and cloud droplet concentration (N d ) are also evolving. We estimate the PBL depth using the T between cloud tops and the sea surface 2 . N d is measured using MODIS retrievals of effective cloud drop radius and liquid water path 1 .N d declines as the PBL deepens offshore. = Density of liquid water eff = ad × f ad ad = adiabatic rate of increase in liquid water content with respect to height f ad = estimate of the degree of adiabaticity LWP = Liquid Water Path (MODIS) r e = Cloud droplet effective radius (MODIS) h = estimate of cloud thickness N eff ≈ N d ×k k= 0.8 for marine Sc IV. Comparing cloud controlling variables V. Controlling for confounding variables, CC VI. Controlling for confounding variables, PBL Many other variables drive cloud cover change. We can do the analysis from part IV while holding these variables constant, reducing their confounding influence. Error bounds represent the spread in slopes after individually accounting for confounding variables. Main results: More N d precedes some cloud increase, though subsidence, column water vapor, and inversion strength (LTS, 700 - 1000 ) dominate. Main results: This analysis shows that anomalously high N d precedes PBL deepening. However, other variables show an even stronger relationship, including column water vapor, subsidence ( s, 700hPa), sea surface temperature (SST), and Inversion strength. References 1. Eastman, R, and R. Wood, 2016: Factors controlling low-cloud evolution over the eastern subtropical oceans: A Lagrangian perspective using the A-Train Satellites. J. Atmos. Sci., 73, 331-351. 2. Eastman, R., R. Wood, and K. T. O, 2017: The subtropical stratocumulus-topped planetary boundary layer: a climatology and the Lagrangian evolution. J. Atmos. Sci ., 74, 2633-2656. 3. Eastman, R., R. Wood, and C. S. Bretherton, 2016: Time scales of clouds and cloud-controlling variables in subtropical stratocumulus from a Lagrangian perspective. J. Atmos. Sci ., 73, 3079-3091. Supported by NASA grant NNXBAQ35G Trajectories are separated into groups based on initial conditions. The 24-hour evolution of each group is compared to a control group with an identical frequency distribution. The difference between the group and its control group is called the ‘residual’ change 1,3 . To compare the relative strengths of several variables, we compare changes in cloud cover and PBL depths for standard deviation ( ) bins 2 of our cloud controlling variables. Variables are converted to anomalies relative to their 100-day running mean. III. Evolving cloud decks = 2 3 4 Γ 1/2 1/2 (ℎ) 3

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Page 1: Increased cloud droplet concentration leads to longer subtropical ...rmeast/Eastman_Poster_AMS_2018_… · Increased cloud droplet concentration leads to longer subtropical stratocumulus

IncreasedclouddropletconcentrationleadstolongersubtropicalstratocumuluslifetimesandadeeperPBLfromaLagrangianperspective

RyanEastman,RobertWood,UniversityofWashington,DepartmentofAtmosphericSciences

I.ALagrangianperspective

Tobettercapturethecloudcover(CC)responsetoenvironmentalperturbationswesamplethesamecloudyparcelasitevolvesintimeandspace.Wecompute~169,000trajectoriesinfoursubtropicaloceanbasinsat925hPa,allowingustofollowandcompareparcels.Lagrangiansamplingofcloudvariables(fromtheA-Train)andenvironmentalconditions(fromERA-Interim)isdoneateveryA-Trainoverpassat12-hourintervals1. WinddataaresourcedfromtheERAInterimreanalysisdataat0.75˚resolution.

II.Cloudcoverinthesubtropics

Regionsarecharacterizedbylow,thickstratocumulus(Sc)cloudsformingnearthecoastandadvecting offshore.Cloudseventuallydeepenandgivewaytotradecumulus.Thesethick,warm,brightcloudsacttocoolthesubtropics,soitisofgreatvaluetoknowthecontrolsontheirextent.CloudcoverisestimatedusingtheMODIScloudmaskproductobservedfromtheAquasatelliteinthesefourregionsshownbytheredboxes. RegionsencompassSc maximaandthedecliningcloudcover gradientsoffshore.

Notonlyistheamountofcloudcoverchangingintime,butthedepthoftheplanetaryboundarylayer(PBL)andclouddropletconcentration(Nd)arealsoevolving.WeestimatethePBLdepthusingthe𝚫Tbetweencloudtopsandtheseasurface2.

Nd ismeasuredusingMODISretrievalsofeffectiveclouddropradiusandliquidwaterpath1.Nd declinesasthePBLdeepensoffshore.

𝜌𝜔 =Densityofliquidwater𝛤eff =𝛤ad × fad𝛤ad =adiabaticrateofincreaseinliquid

watercontentwithrespecttoheightfad =estimateofthedegreeofadiabaticityLWP =LiquidWaterPath(MODIS)re =Clouddropleteffectiveradius(MODIS)h =estimateofcloudthicknessNeff ≈Nd × kk=0.8formarineSc

IV.Comparingcloudcontrollingvariables

V.Controllingforconfoundingvariables,𝚫CC

VI.Controllingforconfoundingvariables,𝚫PBL

Manyothervariablesdrivecloudcoverchange.WecandotheanalysisfrompartIVwhileholdingthesevariablesconstant,reducingtheirconfoundinginfluence.Errorboundsrepresentthespreadinslopesafterindividuallyaccountingforconfoundingvariables.

Mainresults:MoreNdprecedessomecloudincrease,though𝚫subsidence,columnwatervapor,andinversionstrength(LTS,𝜽700- 𝜽1000)dominate.

Mainresults:ThisanalysisshowsthatanomalouslyhighNd precedesPBLdeepening.However,othervariablesshowanevenstrongerrelationship,includingcolumnwatervapor,𝚫subsidence(𝜔s,700hPa),𝚫seasurfacetemperature(SST),andInversionstrength.

References

1.Eastman,R,andR.Wood,2016:Factorscontrollinglow-cloudevolutionovertheeasternsubtropicaloceans:ALagrangianperspectiveusingtheA-TrainSatellites.J.Atmos.Sci., 73,331-351.2.Eastman,R.,R.Wood,andK.T.O,2017:Thesubtropicalstratocumulus-topped planetaryboundary layer:aclimatologyandtheLagrangianevolution.J.Atmos.Sci.,74,2633-2656.3.Eastman,R.,R.Wood,andC.S.Bretherton,2016:Timescalesofcloudsandcloud-controllingvariablesinsubtropicalstratocumulusfromaLagrangianperspective.J.Atmos.Sci.,73,3079-3091.

SupportedbyNASAgrantNNXBAQ35G

Trajectoriesareseparatedintogroupsbasedoninitialconditions.The24-hourevolutionofeachgroupiscomparedtoacontrolgroupwithanidenticalfrequencydistribution.Thedifferencebetweenthegroupanditscontrolgroupiscalledthe‘residual’change1,3.Tocomparetherelativestrengthsofseveralvariables,wecomparechangesincloudcoverandPBLdepthsforstandarddeviation(𝜎)bins2ofourcloudcontrollingvariables.Variablesareconvertedtoanomaliesrelativetotheir100-dayrunningmean.

III.Evolvingclouddecks

𝑁𝑒𝑓𝑓 = 234𝜋𝜌𝜔Γ𝑒𝑓𝑓

1/2 𝐿𝑊𝑃1/2

𝑟𝑒(ℎ)3

water droplet

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