Boundary layer depth, entrainment, decoupling, and clouds over the eastern Pacific Ocean Robert Wood, Atmospheric Sciences, University of Washington

Motivation Processes in the subtropical MBL have enormous impact upon the structure and coverage of clouds Subtropical MBL clouds strongly impact the earth’s radiation budget These MBLs and the clouds therein are poorly represented in large scale numerical models

MBL depth, entrainment and decoupling Integrative approach to estimate MBL and cloud properties in regions of low cloud Combines observations from MODIS and TMI with reanalysis from NCEP and climatology from COADS Results in estimates of MBL depth and decoupling (and climatology of entrainment)

MBL depth, entrainment, and decoupling

Methodology Independent observables: LWP, Ttop, SST Unknowns: zi, q (= ) Use COADS climatological surface RH and air-sea temperature difference Use NCEP reanalysis free-tropospheric temperature and moisture Iterative solution employed to resulting non-linear equation for zi

Mean MBL depth (Sep/Oct 2000) NE Pacific SE Pacific

Mean decoupling parameter q Decoupling scales well with MBL depth

q vs zi-zLCL

Deriving mean entrainment rates Use equation: we=uzi+ws Estimate ws from NCEP reanalysis Estimate uzi from NCEP winds and two month mean zi

Mean entrainment rates Entrainment rate [mm/s] ◄ NE Pacific SE Pacific ► Subsidence rate [mm/s]

Summary of MBL depth work Scene-by-scene estimation of MBL depth and decoupling Climatology of entrainment rates over the subtropical cloud regions derived using MBL depth and subsidence from reanalysis Decoupling strong function of MBL depth Next step: deriving links between turbulence, inversion strength and entrainment by coupling to simple model forced with realistic boundary conditions

IPCC estimates of radiative forcing changes (present-preindustrial) Tropospheric aerosol “indirect” effect upon cloud reflectivity

….cloud feedbacks remain the largest uncertainty in the prediction of future climate change from Cess et al. 1989, 1996

Second aerosol indirect effect AEROSOL INDEX LIQUID WATER PATH [ g m-2] GCM simulations show systematic increases of cloud liquid water content with increasing aerosol Simulations with prescribed cloud microphysical properties (for use in the precipitation scheme) do not Aerosol Index = 3.915t865ln(t670/t865) from Lohmann and Lesins, 2002, Science, 298, 1012-1015

Aerosol-cloud microphysical observations ….first measurements (1967) Aerosol-cloud microphysical observations + wind from land  wind from sea ….recent measurements (1994) Observed cloud droplet concentration [cm-3] Cloud droplet concentration [cm-3] Predicted droplet concentration from aerosol spectrum [cm-3] From Twomey and Warner, J. Atmos. Sci., 24, 704-706, 1967 Aerosol concentration [cm-3] From Martin et al., J. Atmos. Sci., 51, 1823-1842, 1994

Aerosol loading and cloud droplet radius…. ….35 years on 0.0 0.1 0.2 0.3 0.4 Aerosol index from Breon et al. 2002, Science, 295, 834-838 6 8 10 12 14 Cloud droplet radius [micron]

Droplet concentration and drizzle “maritime” cloud base drizzle rate [mm day-1] “continental” cloud droplet concentration [cm-3] From Wood, 2005 J. Atmos. Sci., in press

EPIC 2001 – drizzle, cloud LWP, cloud droplet concentration Nd

Fundamental difficulties Reflected SW for cloudy regions is a function of cloud optical depth t, itself a function of both cloud droplet size (re) and liquid water path (LWP): Effect upon reflected SW depends upon both cloud optical depth and cloud cover C: R=C RCLD + (1-C) RCLEAR LWP re t  RCLD = f(t)

} SHIPTRACKS natural laboratories for the study of aerosol-cloud interactions SHIP TRACK From Durkee et al., J. Atmos. Sci., 57, 2554-2569, 2000 ALBEDO DROPLET RADIUS [mm] DROPLET CONC. [cm-3] AEROSOL CONC. [cm-3]

t/t = -re/re + LWP/LWP implying: -lnt/lnre = 1 (for constant LWP) -lnt/lnre < 1 (for decreasing LWP) -lnt/lnre > 1 (for increasing LWP) From Coakley and Walsh, J. Atmos. Sci., 59, 668-680, 2002 -lnt/lnre

Confounding results? In shiptracks…. increased aerosols  smaller droplets  decreased drizzle (e.g. Ferek et al. 2000)  reduced LWP? Important to remember that LWP is controlled not only by precipitation but also by radiation, entrainment, and surface fluxes….feedbacks that are poorly represented in many climate models

Bistable CCN concentrations? Baker and Charlson (1990) suggest two regimes in the MBL: R1: low CCN conc., drizzle is major CCN sink R2: high CCN conc., aerosol coagulation is major CCN sink

stable equilibria unstable equilibrium reproduced from Baker and Charlson, Nature, 345, 142-145, 1990

particle diameter [m] 400 300 200 100 Na (D>0.1 mm) [cm-3] 0.7 0.3 0.1 0.05 0.03 0.01 dN/dlogR [cm-3] accumulation mode particle diameter [m] Aitken mode day in November 2003

Coalescence Probability that two drops, radii R and r, will coalesce in unit time is: KR,r  (R+r)2 × [vt(R)-vt(r)] × HR,r × CR,r KR,r  R5 × f(r/R) (R2-r2) R × f(r/R) Area swept out Relative fall speed of droplets Collision efficiency Coalescence efficiency Coalescence probability increases rapidly with droplet size

Remote sensing of cloud droplet concentration [85W, 20S]

Effect of assuming uniform droplet size on TOA reflected SW flux W m-2

Sea surface temperature Southeast Pacific very poorly sampled Net cloud forcing

Scanning C-band radar (EPIC 2001) drizzle is intermittent with cellular structure max reflectivity of 20-30 dBZ 0 90 180 270 15 km 30 km -24 -12 0 12 24 dBZ

Can drizzle affect MBL dynamics?

Cloud structural properties Pockets Of Open Cells (POCs) are frequently observed in otherwise unbroken Sc. These may be formed as a cloud response to drizzle-induced decoupling POCs

Microphysical-macrophysical links? MODIS brightness temperature difference, GOES thermal IR, scanning C-band radar

POCs and aerosols POC CCN [cm-3] solid cloud -50 0 50 0 400 800 1200 Figures from Sharon et al. (J. Atmos. Sci., in revision) CCN [cm-3] Ultrafine particles [3-12 nm diameter] solid cloud -50 0 50 DISTANCE from POC edge [km] 0 400 800 1200 Altitude [m] Figure from Petters et al. (ICCP conf. 2004) 0 400 800 1200 Concentration [cm-3]

DIURNAL CYCLE

IMET Buoy (85W, 20S) drizzle index (data courtesy of Robert Weller, WHOI)

Fraction of MODIS 256x256 km regions containing open cells Open cellular convection frequency of occurrence Fraction of MODIS 256x256 km regions containing open cells Northeast Pacific Southeast Pacific …more likely to find open cells closer to land in SEP

Can precipitation affect MBL dynamics? Mixed layer budget analysis using observationally-derived forcings (EPIC 2001) and entrainment, shows subcloud negative buoyancy fluxes ( decoupling) only when drizzle is included