Simulating Stratocumulus clouds sensitivity to representation of: Drizzle Cloud top entrainment
Modeling Stratocumulus Clouds: From Cloud Droplet to the Meso-scales Stephan de Roode Clouds,...
-
Upload
juliet-morrison -
Category
Documents
-
view
217 -
download
3
Transcript of 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