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Stratocumulus
Adrian Lock
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Overview
Stratocumulus climatology and recent changes in its representation in the Met Office climate model
Summary of ‘important’ aspects of the boundary layer parametrization
Other issues Cloud scheme complexity Resolution GCM dynamics and grid staggering
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o0
Equator
N30
Northoregion
windTrade
Inversion
Cloud base
Tropopause
Sub-tropics tropics
Inversion
Subsidence
Fq,TFq,T
Entrainment
Increasing SST
Borrowed from Pier Siebesma
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Stratocumulus climatology
Extensive cloud cover in the subtropics Significant SW radiative impact (cooling the planet) Errors lead to
SST errors in coupled climate models surface temperature errors over land
Latest IPCC round includes an almost completely new Met Office model, HadGEM1
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Climate model JJA total cloud amount
HadGAM1(Released 2004) HadGAM1 - HadAM3(Released 1996)
HadGAM1 - ISCCPISCCP
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Climate model improvement in low level cloud amount
ISSCP HADAM3 (~1996) HadGAM1(~2004)
Thick(scale 0-20%)
Intermediate(scale 0-40%)
HadAM3 had excessive thick low cloud and too little thinHadGAM1 very much better
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0
20
40
60
80
100
120
140
160
180
low
clo
ud
s liq
uid
wate
r p
ath
(g/m
2 )
0
20
40
60
80
100
tota
l c
lou
d c
ov
er
(%)
ISCCP
GCM cross-section intercomparison (Siebesma et al 2004 and now GCSS)
GCMs, data from JJA 1998 Cross-section from California to
central Pacific General underestimate of
stratocumulus? HadGAM1 not too bad HadAM3 wouldn’t look too bad either
HadGAM1
x
x
xx
xx
xx
CaliforniaCentral PacificHadGAM1 total cloud cover
California
SSMI (all clouds)HadGAM1 (low clouds)
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Met Office GCM cloud fractionsEUROCS cross-section – 1998 JJA mean
Layer cloud fraction
Convective cloud fraction
CaliforniaITCZ
HadGAM1 (Released 2004)HadAM3 (Released 1996)
ITCZ California
HadGAM1 has more realistic boundary layer structure
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GPCI: SW radiation
More spread between models in radiation fluxes than in the clouds themselves
HadGAM1 has too much cloud in the Sc-Cu transition region
-1 2 5 8 11 14 17 20 23 26 29 32 35240
260
280
300
320
340
360
380
400
net
sh
ort
wave r
ad
iati
on
at
TO
A (W
/m2 )
latitude (degrees)
CERES
-1 2 5 8 11 14 17 20 23 26 29 32 35-380-360-340-320-300-280-260-240-220-200-180-160
su
rfa
ce
do
wn
wa
rd
sh
ort
wa
ve
ra
dia
tio
n
(W/m
2)
latitude (degrees)
CERESHadGAM1 HadGAM1
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LWP Diurnal cycle validation against satellite
Wood et al (2002) analysed mean LWP diurnal cycle from satellite microwave measurements ( )
Lock (2004) compared with HadGAM1 for the data from the EUROCS intercomparison (JJA in NE Pacific) HadGAM1: JJA mean LWP (kgm-2)
Wood et al area
xx
x
xx
X EUROCS points
LWP kgm-2
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Validation against EPIC profiles
Comparison of HadGAM1 October mean profiles with mean profiles from EPIC for 16-21 October 2001 at (85W, 20S)
But HadGAM1 monthly mean LWP ~ 70gm-2 compared to the observed 150gm-2
HadGAM
EPIC
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Why the improvement from HadCM3 to HadGEM1?
Virtually everything is changed! Doubled resolution (vertical and horizontal) New dynamics New Physics:
Microphysics Cloud scheme (Smith + parametrized RHcrit) Convection scheme (revised closure, triggering, CMT) Radiation ~ the same?! Plus new boundary layer scheme…
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The new boundary layer parametrization in HadGEM1
Old scheme (HadAM3) Local Ri-dependent scheme Kept for stable boundary layers in HadGEM1
Non-local parametrization for unstable boundary layers Specified (robust) profiles of turbulent diffusivities, for
turbulence driven from the surface and/or cloud-top Explicit BL top entrainment parametrization Mass-flux convection scheme trigger based on boundary layer
structure rather than local instability BL mixes in subcloud layer, mass-flux in cloud layer
PBL scheme ~ a mixed layer model on a finite-difference grid
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Mixed layer framework - motivation
Marine stratocumulus is often an equilibrium state arising from complicated interactions between many processes that are parametrized in GCMs
Need to replicate this balance within GCM parametrizations, often using schemes that currently operate independently
Simple conceptual framework: mixed layer model turbulent mixing generally ensures that variables
conserved under moist adiabatic ascent are close to uniform in the vertical. For example:
lp
l qc
L
lvt qqq
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Observed profiles from stratocumulus
Price (QJ, 1999)
Stevens et al (QJ,2003)
tq
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Mixed layer model
Integrating over the boundary layer and assuming a discontinuous inversion gives the mixed layer model equations:
where is the jump across the inversion and is the entrainment rate.
Mixed layer model is simple but effective highlights the importance of entrainment
' 'v surf
MLt
i sut fe r
qz
tww Pqq
ie LS
zw wt
' ' esur ML
MLl
p sui f nf erl twzt
L c FPw
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Entrainment efficiency (Stevens, 2002)
Cloud-top radiative cooling both cools the ML but also drives entrainment and so warms (and dries) it
Net effect of radiative cooling on ML evolution is therefore a strong function of the entrainment efficiency
Eg. for ‘minimal’ model:
Uncertain – parametrizations in the literature give widely different entrainment efficiencies (Stevens, 2002):
Fe
Bw A
b
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Entrainment efficiencies (Stevens, 2002)
Differences in variability of entrainment efficiency as well as its magnitude
AL is noticeably weaker
0.4
0.7
0.9
0.7
0.8
0.5
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LESCloud free: = surface heatedSmoke clouds: = radiatively cooled = surf heat + rad coolWater clouds: X = rad cool (no b.r.) + = rad cool + buoy rev = buoyancy reversal only
Observations = Nicholls & Leighton,1986; Price, 1999; Stevens et al 2003
Verification of entrainment parametrization against LES and observations
Parametrized entrainment rate (m/s)
Act
ual entr
ain
men
t ra
te (
m/s
)
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Entrainment parametrization in GCMs
Hard to parametrize on (coarse) GCM grids using traditional down-gradient diffusion because gradients at inversions are large and
K(Ri): local stability dependence is not relevant K(z/zi): very sensitive to the definition of zi
K(TKE): accurate TKE evolution hard to resolve
Explicit parametrization requires a formulation for we Uncertain But you know what you are (or should be) getting
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Numerical handling of inversions
All model processes (turbulence, radiation, LS advection) should be coupled to preserve mixed layer budgets
i.e., no spurious numerical transport across inversion (Stevens et al 1999, Lenderink and Holtslag 2000,
Lock 2001, Grenier and Bretherton 2001, Chlond et al 2004)
Explicit BL top entrainment parametrization with flux-coupling A subgrid inversion diagnosis takes tendencies from subsidence,
radiation and turbulence and realistically distributes them between the mixed layer and the inversion grid-level – ‘inversion flux-coupling’
x
x
x
x
x
Entrainment
SubsidenceMixed layer
GCM inversion grid-level (cools/warms)
Real inversion (rises/falls)
l profile (idealised mixed layer)Free atmosphere
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Diagnosis of subgrid mixed-layer model profile
X
X
X
X
X
Inversion grid-level
GCM cell-averages
Subgrid profile
Parcel ascent
vl = l ( 1+rmqt )
Flux grid-levels
Identify ‘inversion grid-level’ Extrapolate GCM profiles
into the inversion grid-level Assume a discontinous
subgrid inversion Calculate subgrid inversion
height and strength
iz
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Example calculation of grid-level entrainment fluxes (ignoring subsidence)
Radiative flux
X
X
= GCM fluxes
Heat Fluxes
iz
X
X
X
X
X
e tw q
' 'tw q
izH
surfH
X
Inversion grid-level
Flux grid-levels
' 'lw
= mixed layer model fluxes
e lw
' 'tsurf
w q
Subsidence fluxes across the inversion must be coupled similarly
X
X X
X
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Impact of inversion treatment
SCM: Time-height contour plots of liquid water from simulations with subsidence > entrainment but ML budget (so cloud depth) stationary
With inversion flux-coupling
Without inversion flux-coupling
GCM: JJA mean liquid water path
With inversion flux-
coupling:
Impact of removing inversion flux-
coupling:
Inversion height
Errors from not coupling fluxes amount to spurious additional entrainment
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Equilibrium mixed layers – Stevens 2002
Minimal model with entrainment efficiency of 0.5
For this fixed entrainment efficiency, heading towards California (increasing divergence, reducing SST):
Strong response: zi decreases, wTsurf increases (contours)
Weaker response: LWP decreases, wqsurf decreases (grey-scales)
Minimal model not too far from reality
Towards California
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Mixed layer model sensitivity to entrainment efficiency
Minimal model with entrainment efficiency of 0.5
Efficiency of 1: lower LWP, much reduced wTsurf
Towards California
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GCM cloud sensitivity to entrainment
Std Met Office
2 x we
No flux coupling
Test GCM sensitivity to cloud-top entrainment More active entrainment (either explicit or numerical) gives an
equilibrium state with smaller surface heat fluxes and less stratocumulus (as in Stevens, 2002)
Removing inversion flux-coupling is more severe than doubling we
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Surface heat flux sensitivity to entrainment
Standard Met Office
2 x we
No flux coupling
Expect spurious numerical entrainment to be so increases as you approach the coast
Implies wT surf would reduce towards the coast
As it does if flux-coupling removed in Met Office GCM As it does in other GCMs from EUROCS Spurious or deliberate increase in parametrized entrainment efficiency?
subsw
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Entrainment efficiencies (Stevens, 2002)
None of these proposed parametrizations show entrainment efficiency increasing ‘towards California’
0.4
0.7
0.9
0.7
0.8
0.5
0.8 1.0
1.0
0.8
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What about precipitation?
But AL is still a ‘weak’ entrainment parametrization Is this being compensated for in HadGAM1 by a
spuriously high drizzle rate?
Requires a drizzle climatology – satellite borne radar?
-1 2 5 8 11 14 17 20 23 26 29 32 350.00.20.40.60.8
2
3
4
5
s
tra
tifo
rm p
rec
ipit
ati
on
(m
m/d
)
latitude (degrees)
HadGAM1
No obs!
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Other issues
Cloud schemePrognostic vs diagnostic – does it matter?
Vertical resolutionDecoupling – structure within ‘mixed’ layersNew Dynamics
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Other issues – cloud scheme complexity
Cloud-top height dependence on cloud scheme:
HadGAM1 (Smith)
PC2 (prognostic ql
and CF)
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Other issues – partial cloud cover
Near HawaiiCF~0.1
Convection scheme
Somewhere in between!CF~0.5
?
Near LACF~1
BL scheme
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Other issues – cloud scheme
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111417202326293235
lati
tud
e (d
egre
es)
-1258
111417202326293235
lati
tud
e (d
egre
es)
-1258
111417202326293235
lati
tud
e (d
egre
es)
0 10 20 30 40 50 60 70 80 90 100
total cloud cover (%)
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lati
tud
e (
de
gre
es)
0 10 20 30 40 50 60 70 80 90 100
low cloud cover (%)
CA
M 3
.0G
SM
041
2H
adG
AM
RA
CM
O2
0 10 20 30 40 50 60 70 80 90 100
total cloud cover (%)
Cloud cover PDFs from GPCI
Medium cloud cover Relatively low occurrence in
HadGAM1 Mixed layer model would break down Possible cause of excessive cloud
problems in Sc-Cu transition?
HadGAM1:
Lat
itud
e
0 Low cloud cover 1
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Other issues
Cloud schemeVertical resolution
How much is sufficient?Currently 38 levels ~280m at 1km
~ cloud thicknessDecoupling – structure within ‘mixed’ layersNew Dynamics
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Namibian Stratocumulus region31st March 2004
Mod
el
Leve
l
Lock (2001)
Striping in cloud fields not observedin albedo from GERB instrument (not shown)
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Other issues
Cloud schemeVertical resolutionDecoupling – structure within ‘mixed’ layersNew Dynamics
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Change in total cloud cover
Increasing the susceptibility to decoupling
Control
Revised scheme
Bias
RMS speed error
EUROCS diurnal cycle of FIRE1 stratocumulus motivated changes to decoupling diagnosis (include subgrid cloudbase)
Operational testing (10 global forecast case studies) showed:
Reduced cloudiness, as expected Significant improvement to
tropical winds at 850hPa
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Improved tropical winds hypothesis
Decoupling implies moister near surface implies reduced surface moisture fluxes in subtropical stratocumulus areas
Implies less moisture transported to tropics implies reduced tropical rainfall implies reduction in current model excessive tropical hydrological cycle implies beneficial reduction in trade wind strength
Implies mixed layer structure is important
Change in total cloud cover Change in surface moisture flux
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Other things
Cloud schemeVertical resolutionDecoupling – structure within ‘mixed’ layersNew dynamical formulation included:
Semi-implicit, semi-Lagrangian dynamics Revised timestep (parallel calculation of ‘slow’ physics
increments and BL diffusion coefficients) Revised vertical grid staggering (better handling of normal
modes and control of computational modes) Showed a general improvement in low cloud But no clean comparison (eg. with the same physics)
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HadSM3 Cloud Response along GCSS Pacific Cross Section transect
Coupled atmosphere/slab-ocean models Sc in HadSM4 (old dynamics) very
different from HadGSM1 (new dynamics) HadSM4 includes Lock et al (2000)
scheme but not flux-coupling
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Stratocumulus issues
Met Office model appears to have overcome the 1st order problem (it has stratocumulus-like clouds in some of the right places) but…
Entrainment Still large spread in parametrizations (Stevens 2002) Still large spread in LES (Duynkerke et al 2004 or Stevens et al 2005) Is a special treatment of fluxes across inversions necessary in GCMs? Need observed cloud-top height to validate NWP (eg. lidar or radar?)
Drizzle Large term in mixed layer budget How well is it represented in GCMs (lack of observed climatology)? Role and parametrization of aerosols (cf land/sea split in NWP)? Stabilising feedback on turbulent dynamics (Ackerman et al 2004)?
Mixed layer structure in subtropics is important for downwind deep convection in tropics
Beneficial impact from changes to decoupling Testing revisions to non-local part of flux parametrization How well-mixed are the wind profiles?
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More parametrization issues
What level of cloud scheme complexity is necessary? Smith scheme couples liquid water and cloud fraction too strongly Is a prognostic scheme necessary for BL clouds?
Inhomogeneity – important for SW and drizzle How to model partial cloud cover?
Massflux versus turbulent diffusion Would unification help?
Communication would be easier (eg. Cu detraining into Sc) Current massflux schemes seem incompatible with the top-down
mixing predominant in stratocumulus Debate is fuelled by a lack of understanding (limited case studies -
need more extensive examination of parameter space) If we knew (physically) how to parametrize BL clouds in general,
wouldn’t the choice be made on numerical stability and accuracy?
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Questions?
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Stratocumulus forecasting
Important for forecasting surface temperatures Case study of widespread low cloud over Europe in
December 2004 (eg. 12Z on the 10th)
Low cloud fraction in global UM T+48
xBudapest
x
Thanks to Martin Kohler
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Stratocumulus forecasting
Boundary layer depth evolution mimics the observed
General underestimate of boundary layer depth by ~100m (< grid size)
General cold moist bias Analysis tends to ‘decouple’
12Z 10th December
Day of December 2004
Surf
ace
mix
ed layer
depth
(m
)
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Spin-up of cloud in forecast models
Significant underestimate of cloud cover in the analysis followed by spin-up, at all horizontal resolutions
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+ LW -
CFMIP cloud feedback classes: Low positive class contributes most to uncertainty in sensitivity
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Lidar versus IR retrieval – Wylie, from Internet!