Page 1© Crown copyright 2005 Stratocumulus Adrian Lock.

© Crown copyright 2005

Stratocumulus

Adrian Lock


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


o0

Equator

N30

Northoregion

windTrade

Inversion

Cloud base

Tropopause

Sub-tropics tropics

Inversion

Subsidence

Fq,TFq,T

Entrainment

Increasing SST

Borrowed from Pier Siebesma


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


Climate model JJA total cloud amount

HadGAM1(Released 2004) HadGAM1 - HadAM3(Released 1996)

HadGAM1 - ISCCPISCCP


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


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)


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


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


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


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


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…


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


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


Observed profiles from stratocumulus

Price (QJ, 1999)

Stevens et al (QJ,2003)

tq


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


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


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


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

)


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


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


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


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


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


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


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


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


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


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


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!


Other issues

Cloud schemePrognostic vs diagnostic – does it matter?

Vertical resolutionDecoupling – structure within ‘mixed’ layersNew Dynamics


Other issues – cloud scheme complexity

Cloud-top height dependence on cloud scheme:

HadGAM1 (Smith)

PC2 (prognostic ql

and CF)


Other issues – partial cloud cover

Near HawaiiCF~0.1

Convection scheme

Somewhere in between!CF~0.5

?

Near LACF~1

BL scheme


Other issues – cloud scheme

-1258

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 (%)

-1258

111417202326293235

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


Other issues

Cloud schemeVertical resolution

How much is sufficient?Currently 38 levels ~280m at 1km

~ cloud thicknessDecoupling – structure within ‘mixed’ layersNew Dynamics


Namibian Stratocumulus region31st March 2004

Mod

el

Leve

l

Lock (2001)

Striping in cloud fields not observedin albedo from GERB instrument (not shown)


Other issues

Cloud schemeVertical resolutionDecoupling – structure within ‘mixed’ layersNew Dynamics


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


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


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)


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


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?


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?


Questions?


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


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

)


Spin-up of cloud in forecast models

Significant underestimate of cloud cover in the analysis followed by spin-up, at all horizontal resolutions


Lidar versus IR retrieval – Wylie, from Internet!

Page 1© Crown copyright 2005 Stratocumulus Adrian Lock.

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Transcript of Page 1© Crown copyright 2005 Stratocumulus Adrian Lock.