modeling stratocumulus clouds: from cloud droplet to the meso-scales stephan de roode clouds,...

Modeling Stratocumulus Clouds:

From Cloud Droplet to the Meso-scales

Stephan de Roode

Clouds, Climate & Air Quality

Multi-Scale Physics (MSP), Faculty of Applied Sciences, TU Delft

Clouds, Climate and Air Quality

atmospheric boundary layer in the laboratory

N2O CH4

new methods for measuring emission rates

cloud-climate feedback

detailed numerical simulation

Landsat satellite ~65 km

Large Eddy Simulation ~10 km

~mmviscous dissipation

~1 kmshallow cumulus

~1m-100mCloud droplets

Earth ~13000 km

slide by Harm Jonker

Contents

(1) Eddy diffusivity profiles in stratocumulusDoes its shape matter?When is a turbulent flux countergradient?

(2) Grid resolution in weather forecast modelsAre parameterizations independent of the grid size?

(3) Stratocumulus equilibrium statesHow will global warming affect cloud amount?

(4) “Cloud droplets” on the ground: dew formationCan we measure it?

(5) Summary and outlookStratocumulus equilibrium states, an interesting study case for WRF?

GEWEX Cloud Systems Study (GCSS)

Stratocumulus Intercomparison Cases

• Stratocumulus case based on observations (FIRE I)

• Use observations to prescribe

- initial state

- large-scale horizontal advection

- large-scale subsidence rate

• Simulation of diurnal cycle

- 1D versions of General Circulation Models

- Large-Eddy Simulation Models (LES)

GEWEX Cloud Systems Study (GCSS)

Stratocumulus Intercomparison Cases

• Stratocumulus case based on observations (FIRE I)

• Use observations to prescribe

- initial state

- large-scale horizontal advection

- large-scale subsidence rate

• Simulation of diurnal cycle

- 1D versions of General Circulation Models

- Large-Eddy Simulation Models (LES)

0 5 10 15-10

-5

0

Δθl [ ]K

Δq

t

[ / ]g kg

03 ( )ASTEX RF EUCREM

( )FIRE I EUROCS

01 ( )DYCOMS II RF GCSS

initial jumps for three

GCSS stratocumulus cases

3D results from Large-Eddy Simulation results -The cloud liquid water path

Local time [h] LWP [g/m2] SWnet,sfc [W/m2]

night-time 0100 ≤ t ≤ 0400 156 ± 11

daytime 1100 ≤ t ≤ 1400 69 ± 20 551 ± 104

0

50

100

150

200

250

0 8 16 24 32 40 48

MMobs

obs

IMAU

MPI

UKMO

INM

NCAR

WVU

LWP [ g m

-2 ]

local time [hours]

1D results from General Circulation Models -The cloud liquid water path (LWP)

0

50

100

150

200

250

0 8 16 24 32 40 48

MMobsobsKNMI RACMOINM MESO-NHINM HIRLAMCSU MassfluxLMD GCMMPI ECHAMARPEGE Clim.UKMOARPEGE NWPECMWF

LWP [ g m

-2 ]

local time [hours]

Single Column Model liquid water path results very sensitive to

• entrainment rate

• drizzle parameterization

• convection scheme (erroneous triggering of cumulus clouds)

Duynkerke, P. G., S. R. de Roode, M. C. van Zanten, J. Calvo, J. Cuxart, S. Cheinet, A. Chlond, H. Grenier, P. J. Jonker, M. Koehler, G. Lenderink, D. Lewellen, C.-L. Lappen, A. P. Lock, C.-H. Moeng, F. Mller, D. Olmeda, J.-M. Piriou, E. Sanchez, I. Sednev, 2004: Observations and numerical simulations of the diurnal cycle of the EUROCS stratocumulus case . Quart. J. R. Met. Soc., 130, 3269-3296.

Entrainment

Entrainment- mixing of relatively warm and dry air from above the inversion into the cloud layer- important for cloud evolution

Entrainment parameterizations -Implementation in K-diffusion schemes

• Turbulent flux at the top of the boundary layer due to entrainment with rate we:

("flux-jump" relation)

• Top-flux with K-diffusion:

w'ψ'T =−weΔψ

w'ψ'T =−KψΔψΔz

⇒ Kψ =weΔz

Diagnose eddy- diffusivity coefficients from LES results

Kψ =−w'ψ'

∂ψ / ∂z

288 292 296 300 3040

200

400

600

800

1000

Liquid water potential temperature θl [ ]K

-0.04 00

200

400

600

800

1000

<w'θl> [ / ]' mK s

Diagnose eddy- diffusivity coefficients from LES results

Kψ =−w'ψ'

∂ψ / ∂z

0.005 0.008 0.010

200

400

600

800

1000

total water content [g/kg]

0 100

1.5 10-5

0

200

400

600

800

1000

<w'qt'> [(g/kg) m/s]

K-coefficients from FIRE I LES

Kψ =−w'ψ'

∂ψ / ∂z

0 100 200 300 400 500 6000

100

200

300

400

500

600

K_ θl

_K qt

[Eddy diffusivity coefficient m2 / ]s

Importance of eddy-diffusivity coefficients on internal boundary-layer structure

• Vary magnitude K profile

• Compute solutions θl and qt for given surface and entrainment fluxes

0 200 400 600 800 10000

100

200

300

400

500

600

Kref

x 0.2

Kref

x 0.5

Kref

Kref

x 2

Kref

x 5

Eddy diffusivity K [m2/s]

Total water content profiles for different K-profiles but

identical vertical fluxes

8 8.5 9 9.5 100

100

200

300

400

500

600

Kref

x 0.2

Kref

x 0.5

Kref

Kref

x 2

Kref

x 5

Kref

x inf

total water content [g/kg]

For weakly unstable conditions above sea : small values for the eddy diffusivity if it depends on the convective velocity scale w*

Liquid water content profiles for different K-profiles

Magnitude K-coefficient in interior BL important for liquid water content!

0 0.1 0.2 0.3 0.4 0.5 0.6 0.70

100

200

300

400

500

600

Kref

x 0.2

Kref

x 0.5

Kref

Kref

x 2

Kref

x 5

Kref

x inf

liquid water content [g/kg]

K factor LWP [g/m2]

0.2 2

0.5 52

1.0 79

2.0 94

5.0 103

109

De Roode, S. R., 2007: The role of eddy diffusivity profiles on stratocumulus liquid water path biases. Monthly Weather Rev., 135, 2786-2793.

Contents

(1) Eddy diffusivity profiles in stratocumulusDoes its shape matter?When is a turbulent flux countergradient?

(2) Grid resolution in weather forecast modelsAre parameterizations independent of the grid size?

(3) Stratocumulus equilibrium statesHow will global warming affect cloud amount?

(4) “Cloud droplets” on the ground: dew formationCan we measure it?

(5) Summary and outlookStratocumulus equilibrium states, an interesting study case for WRF?

Countergradient fluxes:

Clear Convective Boundary Layer (CBL)

285 290 2950

500

1000

1500

temperature [K]

height [m]

convective boundary layer

thermal inversion

free atmosphere

Flux profiles in the Clear Convective Boundary Layer

-0.04 -0.02 0 0.02 0.04 0.060

0.2

0.4

0.6

0.8

1

1.2"buoyancy" (θ

v)

(temperature θ)

0.61 θ ( )x moisture q

[Kms-1 ]

/z zi

buoyancy flux

w'θv' = w'θ' + 0.61 θ w'q'

virtual potential temperature (buoyancy) potential temperature

(temperature)

moisture

Countergradient fluxes in the CBL

temperature buoyancy moisture

rχ =w' χ'top

w' χ'bottom

No countergradient flux if vertical flux does not change sign in the mixed layer

De Roode, S. R., et al., 2004: Countergradient fluxes of conserved variables in the clear convective and stratocumulus-topped boundary layer. The role of the entrainment flux., Bound.-Lay. Meteor, 112, 179-196.

Contents

(1) Eddy diffusivity profiles in stratocumulusDoes its shape matter?When is a turbulent flux countergradient?

(2) Grid resolution in weather forecast modelsAre parameterizations independent of the grid size?

(3) Stratocumulus equilibrium statesHow will global warming affect cloud amount?

(4) “Cloud droplets” on the ground: dew formationCan we measure it?

(5) Summary and outlookStratocumulus equilibrium states, an interesting study case for WRF?

Cloud dynamics

10 m 100 m 1 km 10 km 100 km 1000 km 10000 km

turbulence Cumulus

clouds

Cumulonimbus

clouds

Mesoscale

Convective systems

Extratropical

Cyclones

Planetary

waves

Large Eddy Simulation (LES) Model

Cloud System Resolving Model (CSRM)

Numerical Weather Prediction (NWP) Model

Global Climate Model

The Zoo of Atmospheric Models

DNS

mm

Cloud microphysics

Countergradient fluxes in the CBL

Dx

= 2

5.6

km

Dy = 25.6 km

t=8h

Countergradient fluxes: destruction of variance

prohibiting growth of length scales

temperature buoyancy moisture

rχ =w' χ'top

w' χ'bottom

∂χ'2

∂t=−2w'χ'

∂χ∂z

−∂w'χ'χ'

∂z−εχ

De Roode, S. R., P. G. Duynkerke and H. J. J. Jonker, 2004: Large Eddy Simulation: How large is large enough? J. Atmos. Sci., 61, 403-421.

Stratocumulus cloud albedo: example

cloud layer depth = 400 m

effective cloud droplet radius = 10 m

optical depth = 25

0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50 60

Cloud albedo

Cloud optical depth

homogeneous stratocumuluscloud layer

€

=32

LWPρ liqreff

, LWP = ρ air

zbase

ztop

∫ q ldz

Real clouds are inhomogeneous

Stratocumulus albedo from satellite

Albedo for an inhomgeneous cloud layer

27

Redistribute liquid water:

optical depths = 5 and 45

0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50 60

Cloud albedo

Cloud optical depth

inhomogeneous stratocumuluscloud layer

mean albedo = 0.65 < 0.79

Cloud albedo in a weather forecast or climate model

Decrease optical thickness:

Cahalan et al (1994): = 0.7 (FIRE I observations)

0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50 60

Cloud albedo

Cloud optical depth

effective mean

inhomogeneous albedo homogeneous

albedo

€

effective = χτ mean

Analytical results for the inhomogeneity factor

Assumption: Gaussian optical depth distribution

Value of correction factor depends on grid size

De Roode, S. R., and A. Los, 2008: The effect of temperature and humidity fluctuations on the liquid water path of non-precipitating closed cell stratocumulus clouds. Quart. J. Roy. Meteor. Soc., 134, 403-416.

Contents

(1) Eddy diffusivity profiles in stratocumulusDoes its shape matter?When is a turbulent flux countergradient?

(2) Grid resolution in weather forecast modelsAre parameterizations independent of the grid size?

(3) Stratocumulus equilibrium statesHow will global warming affect cloud amount?

(4) “Cloud droplets” on the ground: dew formationCan we measure it?

(5) Summary and outlookStratocumulus equilibrium states, an interesting study case for WRF?

Feedback effects in a changing climate

Dufresne & Bony, Journal of Climate 2008

Radiative effects only

Water vapor feedback

Surface albedo feedback

Cloud feedback

The playground for cloud physicists: Hadley circulation

deep convection shallow cumulus stratocumulus

EU Cloud Intercomparison,

Process Study and

Evaluation Project

(EUCLIPSE)

Future

Sea water temperature: T+ΔT

enhanced surface evaporation

Present

Sea water temperature: T

Positive Feedback?

Entrainment drying dominates moisture

tendency

Negative Feedback?

CGILS: CFMIP-GCSS Intercomparison of Large-Eddy and Single-Column Models

CGILS –

Simulation details

Simulation time

10 daysadaptive time step, dtmax = 10 secs

radiation time step = 60 secs

Domain size4.8 x 4.8 x 4 km3, 96 x 96 x 128 grid points (Δz = 25 m in lower part)

Total CPU hours on 32 processors2700 hours

CGILS

Hourly-averaged vertical mean profiles during the last 5

hours

CGILS

Cloud liquid water path (LWP)

Top

Of

Atmosphere

Net

Radiative

Fluxes

Contents

(1) Eddy diffusivity profiles in stratocumulusDoes its shape matter?When is a turbulent flux countergradient?

(2) Grid resolution in weather forecast modelsAre parameterizations independent of the grid size?

(3) Stratocumulus equilibrium statesHow will global warming affect cloud amount?

(4) “Cloud droplets” on the ground: dew formationCan we measure it?

(5) Summary and outlookStratocumulus equilibrium states, an interesting study case for WRF?

Dew formation

at Cabauw

Mean surface energy balance at Cabauw during the

night

De Roode, S. R., F. C. Bosveld and P. S. Kroon, 2010: Dew formation, eddy-correlation latent heat fluxes, and the surface energy imbalance at Cabauw during stable conditions. In press, Bound.-Layer Meteorology.

Summary and outlook

Equilibrium states Good approach to investigate model representation of stratocumulus

NWP future Scale dependency of paramaterizations (variances, mass flux approach)

Stable boundary layers and dew formation Dew formation can occus for very stable conditions (RiB>1) Difficult to measure

ReferencesCGILS case http://atmgcm.msrc.sunysb.edu/cfmip_figs/Case_specification.html

Papers can be downloaded from www.srderoode.nl/ -> publications