les intercomparison of drizzling stratocumulus: dycoms-ii rf02€¦ · results from previous...

LES Intercomparison of Drizzling Stratocumulus: DYCOMS-I I RF02

Andy Ackerman, NASA Ames Research Centerhttp://sky.arc.nasa.gov:6996/ack/gcss9

AcknowledgmentsMagreet van Zanten, KNMI

Bjorn Stevens, UCLAMarkus Petters, CSUParticipating Groups

Outline

• Motivation

• Case specifications

• Some results (ensemble, then group by group)

◦ time series◦ profiles◦ trends within ensemble

• Summary

• Questions and issues

Scientific Focus

• How do increasing numbers of submicron aerosol affect stratocumulus

◦ cloud cover◦ liquid water path

• How does drizzle affect

◦ boundary layer dynamics◦ entrainment◦ bulk cloud properties

• How do predictions of drizzle in LES compare with observations?

• Does sedimentation of cloud droplets affect results?

• If so, is the response from different models consistent?

Results from Previous Workshop

• Case: DYCOMS-II RF01, with very dry inversion, droplet concentrations about100 cm−3, and no precipitation below cloud base

• Most LES entrained overlying air faster than measurements indicated, resulting in athin, cloud layer with LWP lower than observed

• Reduction of radiative cooling by thin clouds results in poorly mixed boundary layers⇒ negative feedback on further entrainment

• Limiting subgrid-scale mixing at inversion (ad hoc or by skill or luck of SGS model)reduces entrainment, resulting in well-mixed boundary layer with thick cloud layer

Drizzle and Entrainment in a Mixed Layer Model

precipitationsfc source

sfc source = entrainment drying + precipitation

Steady−state moisture budget:

entrainment dryinginversion

• Decreased drizzle leads to deeper boundary layer and thicker cloud(Pincus & Baker 1994)

• Considered a single meteorological scenario, with a moist inversion

• Whether entrainment deepens or thins a cloud layer depends on thermodynamic jumpsat top of BL (Randall 1984)

Large-Eddy Simulations of Strongly Precipitating, Shallo w,Stratocumulus-Topped Boundary Layers (Stevens et al. 1998)

• ASTEX case study (moist inversion) with CCN concentration of 25 cm−3, using binmicrophysics and 2-stream radiative transfer

• Drizzle dries updrafts ⇒ less evaporative cooling available to drive downdrafts

• Dry downdrafts ⇒ cumuliform convection (Bjerknes 1938)

• “Moreover, light drizzle – by reducing entrainment in PBLs with large jumps in moistureacross the inversion – might actually lessen entrainment drying thereby leading todeeper PBL clouds. Such scenarios are largely speculative and need to be consideredfurther.”

The Impact of Humidity above Stratiform Clouds onIndirect Aerosol Climate Forcing (Ackerman et al. 2004)

• LES with bin microphysics and 2-stream radiative transfer based on three case studies:ASTEX (A209, 4th GCSS WG1 Workshop), FIRE-I (EUROCS intercomparison), andDYCOMS-II (RF01, 8th GCSS WG1 Workshop)

• Droplet sedimentation and drizzle consistently decrease with increasing numbers ofsub-micron aerosol

• Entrainment consistently increases as water sedimentation decreases

• Response of LWP depends on humidity of air overlying boundary layer

Temperature and Moisture Jumps above Cloud Top

0 5 10 15 20∆θl (K)

-10

-8

-6

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-2

0

∆qt (

g/kg

)

100% 80% 60% 40%

20%

RF01

FIRE-I

ASTEX

Domain Averages

10 100 1000Droplet Concentration (cm-3)

50

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Liqu

id W

ater

Pat

h(g

m-2)

DYCOMS-II

FIRE-I

ASTEX

dry ASTEX

Response to Suppressing Water Sedimentation

0 1 2 3 4Precipitation (mm d-1)

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Alti

tude

(m

)

ASTEXASTEX

0.0 0.2 0.4 0.6 0.8 1.0Liquid Water (g kg-1)

0 1 2 3 4Precipitation (mm d-1)

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Alti

tude

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RF01RF01

0.0 0.1 0.2 0.3 0.4 0.5Liquid Water (g kg-1)

Temperature and Moisture Jumps above Cloud Top

0 5 10 15 20∆θl (K)

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-8

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0

∆qt (

g/kg

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100% 80% 60% 40%

20%

RF0135 cm-3

FIRE-I225 cm-3

ASTEX>350 cm-3

RF02

Model Domain

• Wider than past GCSS stratocumulus domains to allow for larger scales of convectiveorganization expected in drizzling regime:

6.4 x 6.4 x 1.5 km, ∆x = ∆y = 50 m, ∆z = 5 m near surface and initial inversion

• Those able to use a stretched grid requested to use specified grid, with 96 layers

Initial Conditions and Forcings

2 4 6 8 100.0

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Alti

tude

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)

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290 295 300 305

thetal [K]

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ql [g/kg]

0 20 40 60 80 100

rad_flx [W/m^2]

Initial Conditions and Forcings

• Radiation: Beer’s Law parameterization from previous workshop, which includesheating at cloud base, cooling at and above cloud top (no hook for radiative term indroplet condensational growth equation)

• Subsidence: fixed divergence of horizontal wind (3.76 x 10−6 s−1)

• Coriolis: geostrophic wind profiles specified (by Bjorn)

• Surface fluxes: fix friction velocity at 0.28 m/s, surface Prandtl number at unity, surfacetemperature at 292 K, and 100% RH at surface (should be 98% because of salinity)

• Sponge: above 1250 m with time constant of 100 s

Cloud Microphysics

• Leg averages of droplet number concentrations (N , cm−3) within cloudy air (definedby N > 20 cm−3):

Flight Leg Open Cells Closed CellsCloud Top 54 ± 14 60 ± 13Cloud Base 56 ± 16 80 ± 17

• Fix N at 65 cm−3, if possible

• If microphysics ignores sedimentation of cloud droplets, use integral over log-normalsize distribution assuming Stokes sedimentation (v ∼ r2):

F = c(3/(4πN))2/3q5/3l exp(5 ln2 σg)

where c is taken from Rogers and Yau (1989) and σg = 1.5

• If unable to fix N , use idealized CCN spectrum based on measurements

Cloud Condensation Nuclei

Above BLWithin BL

• Using non-prognostic aerosol, cannot handle vertical variation in context of a BLthat is deepening

• Dotted line is idealized bimodal fit for BL aerosol assuming ammonium bisulfate (log-normal, not a power law)

• Supersaturation for droplet activation specified to not to exceed 1% during first hour

Model Descriptions

Group/Model Precipitation Cloud DropletTeam SGS Model Microphysics Sedimentation

CSU/RAMS Deardorff 2 moment someJiang

CSU/SAM Deardorff Khairoutdinov yesKhairoutdinov and Kogan

(2 moment)

MetO Smag-Lilly 2 moment yesLock

MPI Deardorff 1 moment, noChlond 2 moment

NASA/DHARMA dynamic bin, yesAckerman Smag-Lilly Wyant et al.

(2 moment)

NCAR Deardorff Wyant et al. noMoeng

NRL/COAMPS Deardorff KhairoutdinovGolaz and Kogan

U Redding/LEM Smag-Lilly 1 moment noWeinbrecht

UCLA noneSavic-Jovcic, Stevens

U Utah Deardorff 1 moment? yesZulauf, Krueger

Utrecht-KNMI/DALES Deardorff none yesvan Zanten, de Roode

WVU Deardorff KhairoutdinovLewellen w/ partial cloudiness and Kogan yes

Ensemble Requirements

• One simulation from each group w/ and w/o precipitation

• Precipitation must include warm rain or drizzle, not just cloud droplet sedimentation,and no sedimentation permitted in run w/o precipitation

• Specification must be followed for both simulations

• Nine groups satisfied these constraints:

CSU (Khairoutdinov), MetO, MPI, NASA, NCAR, NRL, U Reading, U Utah, WVU

• Results from 13 groups shown here, just not included in ensemble

Ensemble Time Series

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lwp (g/m^2)

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zi (m)

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wstar (m/s)

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vhf (W/m^2)

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shf (W/m^2)

• A bit low on LWP and high on entrainment• Nowhere near enough drizzle, and vapor flux too large• Drizzle decreases entrainment, convective velocity scale (integral of buoyancy flux), and surface vapor

flux, but not LWP median

CSU (Jiang)

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lwp (g/m^2)

precip off

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precip on

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precip off

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0.01

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ndrop_cld (/cc)

precip off

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ndrop_cld (/cc)

precip on

• Includes “giant” CCN, substantially suppressing droplet activation• LWP nearly triples in response to light drizzle, and cloud cover increases

CSU (Khairoutdinov)

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precip offno sedqc sed

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lwp (g/m^2)

precip on65/cc40/cc

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precip on65/cc40/cc

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precip on65/cc40/cc

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zb (m)

precip on65/cc40/cc

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0.01

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precip (mm/d)


0 1 2 3 4 5 6Time (h)

0.01

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precip (mm/d)

precip on65/cc40/cc

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ndrop_cld (/cc)


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ndrop_cld (/cc)

precip on65/cc40/cc

• LWP roughly doubles in response to cloud droplet sedimentation aloneslightly decreases when drizzle is then included, and then increases when droplet concentrations re-duced by 25%

MetO (Lock)

0

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lwp (g/m^2)

precip offl_0 varl_0 fixedmonotone

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lwp (g/m^2)

precip onl_0 varl_0 fixedmonotone

0.0

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cfrac


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750

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zi (m)


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zi (m)


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zb (m)


0 1 2 3 4 5 6Time (h)

0.01

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1.00

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1.00

precip (mm/d)

precip offl_0 varl_0 fixed

monotone

0 1 2 3 4 5 6Time (h)

0.01

0.10

1.00

0.01

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precip (mm/d)

precip onl_0 varl_0 fixed

monotone

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ndrop_cld (/cc)


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• Variable mixing length in SGS model diminishes entrainment and doubles LWP; monotone advection ofscalars furthers both trends

MPI (Chlond)

0

50

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lwp (g/m^2)

precip off

0

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lwp (g/m^2)

precip onKesslerLuepkes

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0 1 2 3 4 5 6Time (h)

0.01

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1.00

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precip (mm/d)

precip off

0 1 2 3 4 5 6Time (h)

0.01

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precip (mm/d)


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ndrop_cld (/cc)

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• Thick, overcast cloud is not maintained (w/ and w/o drizzle)• Entrainment slows as radiative cooling diminishes• One-parameter (Kessler) drizzle scheme has little effect; two-parameter scheme further diminishes LWP

and cloud cover

NASA (Ackerman)

0

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lwp (g/m^2)

precip off

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lwp (g/m^2)

precip onn90_100n65_125Wyant+sed

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zi (m)


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precip off

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0 1 2 3 4 5 6Time (h)

0.01

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1.00

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precip (mm/d)

precip off

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0.01

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precip onn90_100n65_125

Wyant+sed

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ndrop_cld (/cc)

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• LWP increases (too much) with bin microphysics (lack radiative effect on droplet growth)• Precipitation (brackets measurements when parameterized) reduces entrainment too much• CCN in boundary layer not enough to maintain measured droplet concentration

NCAR (Moeng)

0

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lwp (g/m^2)

precip off

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0 1 2 3 4 5 6Time (h)

0.01

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precip off

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• Precipitation nearly as great as measured, substantially reduces LWP and cloud cover

NRL (Golaz)

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precip off

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0.01

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precip off

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• Precipitation reduces LWP• Precipitating simulation is archetypical ensemble member

UCLA (Savic-Jovcic and Stevens)

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lwp (g/m^2)

precip off

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precip off

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• Precipitation limited to cloud droplet sedimentation, which increases entrainment and decreases LWP

U Reading (Weinbrecht)

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lwp (g/m^2)

precip off

0

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lwp (g/m^2)

precip onbackscatno backscat

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0 1 2 3 4 5 6Time (h)

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precip off

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precip (mm/d)


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• Precipitation has little effect• Turning of stochastic backscatter (negative viscosity) increases LWP and cloud cover

U Utah (Zulauf and Krueger)

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precip off

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precip off

0 1 2 3 4 5 6Time (h)

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• Precipitation w/o cloud droplet sedimentation has little effect• Precipitation w/ cloud droplet sedimentation decreases entrainment and increases LWP

Utrecht-KNMI (van Zanten and de Roode)

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ndrop_cld (/cc)

precip on

• Thick, overcast cloud is not maintained (w/ and w/o drizzle)• Cloud droplet sedimentation (not drizzle) decreases LWP and cloud cover

WVU (Lewellen)

0

50

100

150

200

lwp (g/m^2)

precip off

0

50

100

150

200

lwp (g/m^2)


0.0

0.2

0.4

0.6

0.8

1.0

cfrac

precip off

0.0

0.2

0.4

0.6

0.8

1.0

cfrac


750

800

850

900

950

zi (m)

precip off

750

800

850

900

950

zi (m)


300

400

500

600

700

800

900

zb (m)

precip off

300

400

500

600

700

800

900

zb (m)


0 1 2 3 4 5 6Time (h)

0.01

0.10

1.00

0.01

0.10

1.00

precip (mm/d)

precip off

0 1 2 3 4 5 6Time (h)

0.01

0.10

1.00

0.01

0.10

1.00

precip (mm/d)


0 1 2 3 4 5 6Time (h)

20

40

60

80

100

ndrop_cld (/cc)

precip off

0 1 2 3 4 5 6Time (h)

20

40

60

80

100

ndrop_cld (/cc)


• Precipitation w/o cloud droplet sedimentation has little effect• Precipitation w/ cloud droplet sedimentation decreases entrainment and increases LWP

CSU (Jiang)

0 1 2 3 4 5 6Time (h)

0.01

0.10

1.00

0.01

0.10

1.00

precip (mm/d)

precip on

0 1 2 3 4 5 6Time (h)

0.1

1.0

10.0

100.0

0.1

1.0

10.0

100.0

precip sd/mean

precip on

1

10

100

1

10

100

precip (max-mean)/sd

precip on

• Particularly narrow dispersion and range of precipitation

NCAR (Moeng)

0 1 2 3 4 5 6Time (h)

0.01

0.10

1.00

0.01

0.10

1.00

precip (mm/d)

precip on

0 1 2 3 4 5 6Time (h)

0.1

1.0

10.0

100.0

0.1

1.0

10.0

100.0

precip sd/mean

precip on

1

10

100

1

10

100

precip (max-mean)/sd

precip on

• Dispersion low• Peak values more than 50 standard deviations from mean

⇒ Precipitation limited to very small area

Ensemble Profiles

2 4 6 8 100.0

0.2

0.4

0.6

0.8

1.0

1.2A

ltitu

de (

km)

u [m/s]

-8 -6 -4 -2 0

v [m/s]

290 295 300 305

thetal [K]

2 4 6 8 10

qt [g/kg]

0.0 0.2 0.4 0.6 0.80.0

0.2

0.4

0.6

0.8

1.0

1.2

Alti

tude

(km

)

ql [g/kg]

0.00 0.01 0.02 0.03 0.04 0.05

qr [g/kg]

0.0 0.2 0.4 0.6 0.8 1.0

cfrac

0 20 40 60 80

ndrop_cld [cm^-3]

0.1 1.0 10.0 100.00.0

0.2

0.4

0.6

0.8

1.0

1.2

Alti

tude

(km

)

precip [W/m^2]

0 20 40 60 80 100

rad_flx [W/m^2]

-100 -50 0 50

tot_tw [W/m^2]

-50 0 50 100 150

tot_qw [W/m^2]

• Geostrophic wind speeds too high, and total fluxes far from measurements• Median precipitation remarkably similar to average in closed cells• Precipitation induced changes in total moisture flux seems inconsistent with other results, suggesting

possible internal inconsistencies in ensemble member(s)

Ensemble Profiles

0.0 0.1 0.2 0.3 0.4 0.5 0.60.0

0.2

0.4

0.6

0.8

1.0

1.2A

ltitu

de (

km)

w_var [(m/s)^2]

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

tke [m^2/s^2]

0 1 2 3 4 5

thetal_var [K^2]

0.0 0.2 0.4 0.6 0.8 1.0

qt_var [(g/kg)^2]

-0.20 -0.10 0.00 0.10 0.200.0

0.2

0.4

0.6

0.8

1.0

1.2

Alti

tude

(km

)

w_skw [(m/s)^3]

-10 -5 0 5 10 15

tot_boy [cm^2/s^3]

-1.0-0.8-0.6-0.4-0.2 0.0 0.2 0.4

tot_uw

-0.4-0.2 0.0 0.2 0.4 0.6 0.8 1.0

tot_vw

• Precipitation diminishes buoyancy flux and decreases w′2, and increases w′3 (away from observations)• Precipitation diminishes buoyancy flux and decreases w′2, allow for more vigorous convection by de-

creasing entrainment through diminished surface fluxes and kinetic energy (?)• Momentum flux disagreement suggests scales beyond extent of model domain

Response to Droplet Sedimentation

0

50

100

150

200

lwp (g/m^2)

precip offw/o sedw/ sed

0

50

100

150

200

lwp (g/m^2)

precip onw/o sed

750

800

850

900

950

zi (m)

precip off

750

800

850

900

950

zi (m)

precip on

CSU_Marat

0

50

100

150

200

lwp (g/m^2)


0

50

100

150

200

lwp (g/m^2)

precip onw/o sedw/ sed

750

800

850

900

950

zi (m)

precip off

750

800

850

900

950

zi (m)

precip on

DHARMA

0

50

100

150

200

lwp (g/m^2)

precip offw/o sed

0

50

100

150

200

lwp (g/m^2)


750

800

850

900

950

zi (m)

precip off

750

800

850

900

950

zi (m)

precip on

Utah

Response to Droplet Sedimentation

0

50

100

150

200

lwp (g/m^2)


0

50

100

150

200

lwp (g/m^2)

precip on

750

800

850

900

950

zi (m)

precip off

750

800

850

900

950

zi (m)

precip on

UCLA

0 1 2 3 4 5 6Time (h)

0

50

100

150

200

lwp (g/m^2)

precip offw/o sed

0 1 2 3 4 5 6Time (h)

0

50

100

150

200

lwp (g/m^2)


0 1 2 3 4 5 6Time (h)

750

800

850

900

950

zi (m)

precip off

0 1 2 3 4 5 6Time (h)

750

800

850

900

950

zi (m)

precip on

WVU

• For all but UCLA, droplet sedimentation results in reduced entrainment and increasedLWP, consistent with Ackerman et al. (2004)

Trends within Ensemble

0 50 100 150 200LWP (g/m^2)

0.001

0.010

0.100

1.000

Sur

face

Pre

cipi

tatio

n (m

m/d

)

• Precipitation generally increases with LWP, as expected• NCAR is exception to trend (LWP low and precipitation high)• Should compare cloud base precipitation trend to H3N scaling found by Pawloska and Brenguier (2003)

and van Zanten and Stevens (2005)


0 50 100 150 200LWP (g/m^2)

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

Ent

rain

men

t Rat

e (c

m/s

)

• At low LWP, entrainment tends to increase with LWP (radiative cooling)• Tendency reverses at higher LWP (entrainment drying)• Should consider more sophisticated analysis along the lines done by Bjorn for previous workshop


0.001 0.010 0.100 1.000Surface Precipitation (mm/d)

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Ent

rain

men

t Rat

e (c

m/s

)

• Entrainment tends to decreases as precipitation increases


0 20 40 60 80 100120140LWP w/o Precip (g/m^2)

-60

-40

-20

0

20

40

60

Cha

nge

in L

WP

(g/

m^2

)

• LWP increases for more simulations than it decreases when precipitation is turned on.

Summary

• Precipitation generally reduces w′θv, w′2, and entrainment, and increases w′3

• Precipitation leads to increases in LWP and cloud cover in some, and decreases inother simulations; ensemble medians of both are unchanged

• Cloud droplet sedimentation generally decreases entrainment and increases LWP

• Tendencies within ensemble hold promise and require deeper thought and analysis

• Any robustness of tendencies should not be considered universal to stratocumulus,since response of BL dynamics and cloud properties to precipitation depends stronglyon thermodynamic jumps above BL

• I am deeply grateful for the efforts of all the participants and those providing measure-ment analyses

Questions and Issues

• Fix geostrophic winds

• For models that don’t fix droplet number, scale accumulation-mode numberconcentration to give average cloud droplet number concentration of ∼ 65 cm−3?

• While (if) changing the specification, might as well set RH at surface to 98%

• Any disagreement regarding 3-h averaging period?

• Should variations on grid stretching be permitted?

• If not, should we use WVU’s grid above initial inversion?

• Assess significance of neglecting radiative term in droplet condensational growth

les intercomparison of drizzling stratocumulus: dycoms-ii rf02€¦ · results from previous...

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