Stephan de Roode (KNMI) Entrainment in stratocumulus clouds.
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Transcript of Stephan de Roode (KNMI) Entrainment in stratocumulus clouds.
![Page 1: Stephan de Roode (KNMI) Entrainment in stratocumulus clouds.](https://reader036.fdocuments.us/reader036/viewer/2022081506/56649efe5503460f94c1342e/html5/thumbnails/1.jpg)
Stephan de Roode (KNMI)
Entrainment in stratocumulus clouds
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4 6 8 10
total specific humidity [g/kg]
0 0.5
liquid water content [g/kg]
284 288 292 2960
100
200
300
400
500
600
700
800
temperature [K]
cloud top
cloud base
stratocumulus vertical structure
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290 295 300
virtual potential temperature [K]
θv =θ 1+0.61q−ql( ) θl ≈θ−Lvcp
ql
liquid water potential temperature [K]
284 288 292 2960
100
200
300
400
500
600
700
800
temperature [K]
cloud top
cloud base
stratocumulus: vertical structure
![Page 4: Stephan de Roode (KNMI) Entrainment in stratocumulus clouds.](https://reader036.fdocuments.us/reader036/viewer/2022081506/56649efe5503460f94c1342e/html5/thumbnails/4.jpg)
Key questions
• How well is stratocumulus represented in models?
• Entrainment
- what is it?
- why important?
- how parameterized?
• Boundary-layer mixing and cloud liquid water path
- perfect boundary-conditions, perfect cloud structure?
• FIRE I observations revisited
- a different view on entrainment
![Page 5: Stephan de Roode (KNMI) Entrainment in stratocumulus clouds.](https://reader036.fdocuments.us/reader036/viewer/2022081506/56649efe5503460f94c1342e/html5/thumbnails/5.jpg)
ISCCP stratocumulus cloud climatology
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ECMWF RE-ANALYSIS shortwave radiation errors
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GCSS intercomparison cases
• Stratocumulus case based on observations (FIRE I)
• 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)
![Page 8: Stephan de Roode (KNMI) Entrainment in stratocumulus clouds.](https://reader036.fdocuments.us/reader036/viewer/2022081506/56649efe5503460f94c1342e/html5/thumbnails/8.jpg)
GCSS intercomparison cases
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• Stratocumulus case based on observations (FIRE I)
• 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)
![Page 9: Stephan de Roode (KNMI) Entrainment in stratocumulus clouds.](https://reader036.fdocuments.us/reader036/viewer/2022081506/56649efe5503460f94c1342e/html5/thumbnails/9.jpg)
GCSS FIRE I intercomparison participants
Fine-scale turbulence models [Large-Eddy Simulation Models (LES)] : Δx=Δy=50m, Δz=10m1. IMAU - Peter G. Duynkerke, Stephan de Roode, M. C. van Zanten and P. Jonker2. MPI - Andreas Chlond, Frank Müller, and Igor Sednev3. WVU - David Lewellen 4. INM - Javier Calvo, Joan Cuxart, Dolores Olmeda, Enrique Sanchez 5. UKMO - Adrian P. Lock 6. NCAR - Chin-Hoh Moeng (NCAR)
1D versions of General Circulation Models [Single-Column Models (SCM)]1. LMD - Sylvain Cheinet 2. MPI - Andreas Chlond, Frank Müller, and Igor Sednev3. Meteo France I - Hervé Grenier4. Meteo France II - Jean-Marcel Piriou5. ECMWF - Martin Köhler6. CSU - Cara-Lyn Lappen7. KNMI - Geert Lenderink8. UKMO - Adrian P. Lock9. INM - Javier Calvo, Joan Cuxart, Dolores Olmeda, Enrique Sanchez
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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]
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What is entrainment?Why is entrainment important?
Entrainment- mixing of relatively warm and dry air from above the inversion into the cloud layer- important for cloud evolution
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3D results from Large-Eddy Simulation results -Entrainment rates
Local time [h] LWP [g/m2] SWnet,sfc [W/m2] entrainment rate [cm/s]
night-time 0100 ≤ t ≤ 0400 156 ± 11 0.58 ± 0.08
daytime 1100 ≤ t ≤ 1400 69 ± 20 551 ± 104 0.36 ± 0.03
0.2
0.4
0.6
0.8
0 8 16 24 32 40 48
IMAUMPIUKMOINMNCARWVUmean
local time [hours]
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Boundary-layer representation
w'ψ' =−Kψ∂ψ∂z
w'ψ' =Mc ψc −ψ( )
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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)
![Page 15: Stephan de Roode (KNMI) Entrainment in stratocumulus clouds.](https://reader036.fdocuments.us/reader036/viewer/2022081506/56649efe5503460f94c1342e/html5/thumbnails/15.jpg)
Key questions
• How well is stratocumulus represented in models?
• Entrainment
- what is it?
- why important?
- how parameterized?
• Boundary-layer mixing and cloud liquid water path
- perfect boundary-conditions, perfect cloud structure?
• FIRE I observations revisited
- a different view on entrainment
![Page 16: Stephan de Roode (KNMI) Entrainment in stratocumulus clouds.](https://reader036.fdocuments.us/reader036/viewer/2022081506/56649efe5503460f94c1342e/html5/thumbnails/16.jpg)
The clear convective boundary layer (CBL) -Entrainment scaling from observations
Entrainment rate we scales as
• A ≈ 0.2
• H boundary-layer height
• (g/θ0) Δθv buoyancy jump across the inversion
• w* convective velocity scale: vertically integrated buoyancy flux
we=A w*
3
gθ0
H Δθv
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Buoyancy flux in stratocumulus
convective velocity scale w* depends on entrainment rate we
w'θv'T =−weΔθv,sat
-0.04 -0.03 -0.02 -0.01 0 0.01 0.020
200
400
600
800
virtual potential temperature flux <w' θv> [ / ] ' Km s
entrainment
longwave radiative cooling
condensation
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Solve entrainment rate
we=A w*
3
gθ0
H Δθv we =
2.5AWNE
Δθv +2.5A T2Δθv,dry+T4Δθv,sat( )
solve for entrainment rate
we __________forcing WNE
"jumps"
![Page 19: Stephan de Roode (KNMI) Entrainment in stratocumulus clouds.](https://reader036.fdocuments.us/reader036/viewer/2022081506/56649efe5503460f94c1342e/html5/thumbnails/19.jpg)
Solve entrainment rate
we=A w*
3
gθ0
H Δθv we =
2.5AWNE
Δθv +2.5A T2Δθv,dry+T4Δθv,sat( )
we __________forcing WNE
"jumps"
-0.04 -0.02 0 0.02 0.040
0.2
0.4
0.6
0.8
1
<w'θv>'
WE
WNE
<w'θv>'
solve for entrainment rate
![Page 20: Stephan de Roode (KNMI) Entrainment in stratocumulus clouds.](https://reader036.fdocuments.us/reader036/viewer/2022081506/56649efe5503460f94c1342e/html5/thumbnails/20.jpg)
Solve entrainment rate
we=A w*
3
gθ0
H Δθv we =
2.5AWNE
Δθv +2.5A T2Δθv,dry+T4Δθv,sat( )
we __________forcing WNE
"jumps"
-0.04 -0.02 0 0.02 0.040
0.2
0.4
0.6
0.8
1
<w'θv>'
WE
WNE
<w'θv>'
solve for entrainment rate
![Page 21: Stephan de Roode (KNMI) Entrainment in stratocumulus clouds.](https://reader036.fdocuments.us/reader036/viewer/2022081506/56649efe5503460f94c1342e/html5/thumbnails/21.jpg)
Solve entrainment rate
we=A w*
3
gθ0
H Δθv we =
2.5AWNE
Δθv +2.5A T2Δθv,dry+T4Δθv,sat( )
we __________forcing WNE
"jumps"
-0.04 -0.02 0 0.02 0.040
0.2
0.4
0.6
0.8
1
<w'θv>'
WE
WNE
<w'θv>'
solve for entrainment rate
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Stability jumps
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Stability jumps
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Stability jumps
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Entrainment parameterizations for stratocumulus -Results based on LES results
• Nicholls and Turton (1986)
• Stage and Businger (1981) Lewellen and Lewellen (1998) VanZanten et al. (1999)
• Lock (1998)
• Lilly (2002)
we = 2.5AWNE
Δθv +2.5A T2Δθv,dry +T4Δθv,sat( )
we = 2.5AWNE
Δθv,NT +2.5A T2Δθv,dry+T4Δθv,sat( )
we = AWNE
T2Δθv,dry+T4Δθv,sat
we = 2AAL WNE +αtAwΔFL / ρcp
Δθv
we = ADLWNE,DL
Δθv,DL +ADL L 2Δθv,dry+L4Δθv,sat( )
• Based on observations of clear CBL
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Sensitivity of entrainment parameterizations to inversion jumps
observations from ASTEX Flight A209__________________________________cloud base height = 240 mcloud top height = 755 msensible heat flux = 10 W/m2
latent heat flux = 30 W/m2
longwave flux jump = 70 W/m2
max liquid. water content = 0.5 g/kgLWP = 100 g/m2
Compute entrainment rate from parameterizations as a function of inversion jumps
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Entrainment rate [cm/s] sensitivity to inversion jumps
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Entrainment rate [cm/s] parameterizationsof observed cases
Parameterization Case Observed
Moeng Lock Lilly Nicholls-Turton
Lewellen
North Sea NT620 0.55 0.50 0.13 0.30 0.30 0.33
North Sea NT624 0.56 0.76 0.28 0.55 0.66 0.61
ASTEX A209 0.9 ± 0.3 1.23 0.42 0.86 1.06 0.97
ASTEX RF06 1.0 ± 0.6 1.24 0.48 1.04 1.31 1.33
DYCOMSII RF01 0.38 ± 0.10 0.72 0.69 0.62 0.60 0.64
FIRE I 0.58 ± 0.08
(mean LES)
0.57 0.16 0.37 0.35 0.50
high low
Entrainment results mirror the LES results where they are based on
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• Turbulent flux at the top of the boundary layer due to entrainment:
("flux-jump" relation)
• Top-flux with K-diffusion:
Entrainment parameterizations -
Implementation in K-diffusion schemes
w'ψ'T =−weΔψ
w'ψ'T =−KψΔψΔz
⇒ Kψ =weΔz
![Page 30: Stephan de Roode (KNMI) Entrainment in stratocumulus clouds.](https://reader036.fdocuments.us/reader036/viewer/2022081506/56649efe5503460f94c1342e/html5/thumbnails/30.jpg)
Key questions
• How well is stratocumulus represented in models?
• Entrainment
- what is it?
- why important?
- how parameterized?
• Boundary-layer mixing and cloud liquid water path
- perfect boundary-conditions, perfect cloud structure?
• FIRE I observations revisited
- a different view on entrainment
![Page 31: Stephan de Roode (KNMI) Entrainment in stratocumulus clouds.](https://reader036.fdocuments.us/reader036/viewer/2022081506/56649efe5503460f94c1342e/html5/thumbnails/31.jpg)
Compute eddy- diffusivity
coefficients from FIRE I
LES
Kψ =−w'ψ'
∂ψ / ∂z
288 292 296 300 3040
200
400
600
800
1000
Liquid water potential temperature θl [ ]K
0.005 0.008 0.010
200
400
600
800
1000
total water content [g/kg]
-0.04 00
200
400
600
800
1000
<w'θl> [ / ]' mK s
0 100
1.5 10-5
0
200
400
600
800
1000
<w'qt'> [(g/kg) m/s]
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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
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Importance of eddy-diffusivity coefficients on internal boundary-layer structure
• Change magnitude K profiles
• Compute vertical profiles θl and qt from integration
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]
∂ψ∂z
=−w'ψ'Kψ
same
change
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Total water content profiles for different K-profiles but identical vertical flux
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]
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Liquid water content profiles for different K-profiles
K factor LWP [g/m2]
0.2 2
0.5 52
1.0 79
2.0 94
5.0 103
109
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]
![Page 36: Stephan de Roode (KNMI) Entrainment in stratocumulus clouds.](https://reader036.fdocuments.us/reader036/viewer/2022081506/56649efe5503460f94c1342e/html5/thumbnails/36.jpg)
Key questions
• How well is stratocumulus represented in models?
• Entrainment
- what is it?
- why important?
- how parameterized?
• Boundary-layer mixing and cloud liquid water path
- perfect boundary-conditions, perfect cloud structure?
• FIRE I observations revisited
- a different view on entrainment
![Page 37: Stephan de Roode (KNMI) Entrainment in stratocumulus clouds.](https://reader036.fdocuments.us/reader036/viewer/2022081506/56649efe5503460f94c1342e/html5/thumbnails/37.jpg)
FIRE I stratocumulus over the Pacific Ocean -
Aircraft lidar observations of cloud-top height
0
200
400
600
800
1000
0 10 20 30 40 50 60 70
horizontal distance [km]
![Page 38: Stephan de Roode (KNMI) Entrainment in stratocumulus clouds.](https://reader036.fdocuments.us/reader036/viewer/2022081506/56649efe5503460f94c1342e/html5/thumbnails/38.jpg)
Thermodynamic structure of clear air above cloud top depressions
clear air value
mean in-cloud value
![Page 39: Stephan de Roode (KNMI) Entrainment in stratocumulus clouds.](https://reader036.fdocuments.us/reader036/viewer/2022081506/56649efe5503460f94c1342e/html5/thumbnails/39.jpg)
Evaporation of cloud top by turbulent mixing horizontal winds
vertical velocity
liquid water content
liquid water potential temperature
total water content
turbulence turbulence
evaporation
12 km
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Observations of moist and cold layers on top of stratocumulus
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Entrainment mixing scenario
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Conclusions
• Entrainment parameterizations
- extrapolation of Large-Eddy Simulation results
- considerable differences
different heat and moisture budgets
• Cloud liquid water path and K-diffusion turbulence schemes
- different solutions for identical surface and cloud-top fluxes
different albedo
• Entrainment observations
- may induce the formation of moist layers above cloud top
opposes general view on the entrainment process
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stability jumps