Clouds and theirClouds and theirturbulent environmentturbulent environment

Robin Hogan, Andrew Barrett, Natalie Robin Hogan, Andrew Barrett, Natalie HarveyHarvey

Helen Dacre, Richard Forbes (ECMWF)Helen Dacre, Richard Forbes (ECMWF)

Department of Meteorology, University of Reading Department of Meteorology, University of Reading

OverviewOverview

• Part 1: Why can’t models simulate mixed-phase altocumulus clouds?– These clouds are potentially a key negative feedback for climate– Getting these clouds right requires the correct specification of

turbulent mixing, radiation, microphysics and sub-grid distribution– We use a 1D model and long-term cloud radar and lidar

observations

• Part 2: Can models simulate boundary-layer type, and hence the associated mixing and clouds?– Important for pollution transport and evolution of weather systems– We use long-term Doppler lidar observations to evaluate the

scheme in the Met Office model

Mixed-phase altocumulus cloudsMixed-phase altocumulus cloudsSmall supercooled liquid cloud droplets– Low fall speed– Highly reflective to

sunlight– Often in layers only

100-200 m thick

Large ice particles– High fall speed– Much less reflective for

a given water content

Mixed-phase cloud radiative Mixed-phase cloud radiative feedbackfeedback

• Decrease in subtropical stratocumulus – Lower albedo ->

positive feedback on climate

• Change to cloud mixing ratio on doubling of CO2– Tsushima et

al. (2006)

• Increase in polar boundary-layer and mid-latitude mid-level clouds– Clouds more likely to be liquid

phase: lower fall speed and more persistent

– Higher albedo -> negative feedback– But this result depends on

questionable model physics!

Important processes in altocumulusImportant processes in altocumulus• Longwave cloud-top

cooling• Supercooled droplets form• Cooling induces upside-

down convective mixing• Some droplets freeze • Ice particles grow at

expense of liquid by Bergeron-Findeisen mechanism

• Ice particles fall out of layer

• Many models have prognostic cloud water content, and temperature-dependent ice/liquid split, with less liquid at colder temperatures– Impossible to represent altocumulus clouds properly!

• Newer models have separate prognostic ice and liquid mixing ratios– Are they better at mixed-phase clouds?

How well do models get mixed-phase How well do models get mixed-phase clouds?clouds?

• Ground-based radar and lidar (Illingworth, Hogan et al. 2007)

• CloudSat and Calipso (Hogan, Stein, Garcon and Delanoe, in preparation)

Models typically miss a third of mid-level clouds

• This is cloud fraction – what about cloud water content?

Observations of long-lived liquid Observations of long-lived liquid layerlayer

• Radar reflectivity (large particles)

• Lidar backscatter (small particles)

• Radar Doppler velocity

Liquid at Liquid at –20–20CC

Cloudnet processingCloudnet processing

• Use radar, lidar and microwave radiometer to estimate ice and liquid water content on model grid

• Illingworth, Hogan et al. (BAMS 2007)

21 altocumulus days at Chilbolton21 altocumulus days at Chilbolton

• Met Office models (mesoscale and global) have most sophisticated cloud scheme

• Separate prognostic liquid and ice• But these models have the worst supercooled

liquid water content and liquid cloud fraction• What are we doing wrong in these schemes?

1D “EMPIRE” model1D “EMPIRE” model• Single column model• High vertical resolution

– Default: z = 50m

• Five prognostic variables– u, v, θl, qt and qi

• Default: follows Met Office model– Wilson & Ballard microphysics– Local and non-local mixing– Explicit cloud-top entrainment

• Frequent radiation updates (Edwards & Slingo scheme)• Advective forcing using ERA-Interim• Flexible: very easy to try different parameterization schemes

– Coded in matlab

• Each configuration compared to set of 21 Chilbolton altocumulus days

• Variables conserved under moist adiabatic processes:

• Total water (vapour plus liquid):

• Liquid water potential temperature

lp

l qC

L

T

lt qqq

EMPIRE model simulationsEMPIRE model simulations

Evaluation of EMPIRE control modelEvaluation of EMPIRE control model

• More supercooled liquid than Met Office but still seriously underestimated

Effect of turbulent mixing schemeEffect of turbulent mixing scheme• Quite a small effect!

Effect of vertical resolutionEffect of vertical resolution

• Take EMPIRE and change physical processes within bounds of parameterized uncertainty – Assess change in simulated mixed-phase clouds

• Significantly less liquid at 500-m resolution• Explains poorer performance of Met Office

model• Thin liquid layers cannot be resolved

Effect of ice growth rateEffect of ice growth rate

• Liquid water distribution improves in response to any change that reduces the ice growth rate in the cloud

• Change could be: reduced ice number concentration, increased ice fall speed, reduced ice capacitance

• But which change is physically justifiable?

Summary of sensitivity testsSummary of sensitivity tests

Main model sensitivities appear to be:• Ice cloud fraction

– In most models this is a function of ice mixing ratio and temperature

– We have found from Cloudnet observations that the temperature dependence is unnecessary, and that this significantly improves the ice cloud fraction in clouds warmer than –30C (not shown)

• Vertical resolution– Can we parameterize the sub-grid vertical distribution to get

the same result in the high and low resolution models?

• Ice growth rate– Is there something wrong with the size distribution assumed in

models that causes too high an ice growth rate when the ice water content is small?

Resolution dependence: idealised Resolution dependence: idealised simulationsimulation

•Liquid Ice

Resolution dependenceResolution dependence

Typical NWP resolution

Best NWP resolution

Effect 1: thin clouds can be missedEffect 1: thin clouds can be missed

• Consider a 500-m model level at the top of an altocumulus cloud

• Consider prognostic variables l and qt that lead to ql = 0

θl qt qlTP1

P2

– But layer is well mixed which means that even though prognostic variables are constant with height, T decreases significantly in layer

– Therefore a liquid cloud may still be present at the top of the layer

Gridbox-mean liquid can be parameterized

• Diffusional growth:

qi = ice mixing ratio, ice diameter

RHi = relative humidity with respect to ice

RHi qi

dqi

Effect 2: Ice growth too high at Effect 2: Ice growth too high at cloud topcloud top

dt

)1RH()1RH( 3/2 iiii qADdt

dq

3/2iqD

100% 0 0

– qi zero at cloud top: growth too high

Assume linear qi profile to enable gridbox-mean growth rate to be estimated: significantly lower than before

P1

P2

•Liquid Ice

Parameterization at workParameterization at work•Liquid Ice

Parameterization at workParameterization at work

• New parameterization works well over full range of model resolutions

• Typically applied only at cloud top, which can be identified objectively

Standard ice particle size Standard ice particle size distributiondistribution

log(N)

D

N0 = 2x106

• Inverse exponential fit used in all situations

• Simply adjust slope to match ice water content– Wilson and Ballard

scheme used by Met Office

– Similar schemes in many other models

Increasing ice water content

• But how does calculated growth rate versus ice water content compare to calculations from aircraft spectra?

Parameterized growth ratesParameterized growth rateslog(N)

DN0 = constant

Rati

o o

f para

mete

riza

tion t

o a

ircr

aft

spect

ra

Ice water content

Fall speed

Ice growth rate

• Ice clouds with low water content:– Ice growth rate

too high– Fall speed too low

• Liquid clouds depleted too quickly!

Adjusted growth ratesAdjusted growth rates

N0 ~ IWC3/4

log(N)

Rati

o o

f para

mete

riza

tion t

o a

ircr

aft

spect

ra

Ice water content

Fall speed

Ice growth rate

D

• Delanoe and Hogan (2008) result suggests N0 smaller for low water content– Much better

agreement for growth rate and fall speed

New ice size distribution leads to better agreement in liquid water content

New ice size distribution leads to better agreement in liquid water content

Mixed-phase clouds: summaryMixed-phase clouds: summary

• Mixed-phase clouds drastically underestimated in climate models, particularly those that have the most sophisticated physics!– Very difficult to simulate persistent supercooled layers

• Experiments with a 1D model evaluated against observations show:– Strong resolution dependence near cloud top; can be

parameterized to allow liquid layers that only partially fill the layer vertically

– More realistic ice size distribution has fewer, larger crystals at cloud top: lower ice growth and faster fall speeds so liquid depleted more slowly

– Many other experiments have examined importance of radiation, turbulence, fall speed etc.

• Next step: apply new parameterizations in a climate model– What is the new estimate of the cloud radiative feedback?

Part 2Part 2Boundary layer type from Doppler Boundary layer type from Doppler

lidarlidar• Turbulent mixing in the boundary layer transports:

– Pollutants away from surface: important for health– Water: important for cloud formation, and hence climate and weather

forecasting– Heat and momentum: important for evolution of weather systems

• Mixing represented in four ways in models:– Local mixing (shear-driven mixing)– Non-local mixing (buoyancy-driven with strong capping inversion)– Convection (buoyancy-driven without strong capping inversion)– Entrainment (exchange across tops of stratocumulus clouds)

• Models must diagnose boundary-layer type to decide scheme to use– Getting the clouds right is a key part of this diagnosis

• Doppler lidar can measure many important boundary layer properties– Can we objectively diagnose boundary-layer type?

How is the boundary layer How is the boundary layer modelled?modelled?

• Met Office model has explicit boundary-layer types (Lock et al. 2000)

Stable profile: Local mixing schemeShear-driven mixing only: diffusivity K is a function of local Richardson number Ri

Unstable profile: Non-local schemeBuoyancy-driven mixing: diffusivity profile determined by parcel ascents/descents

Entrainment schemeIf stratocumulus is present, entrainment velocity is parameterized explicitly

Shallow cumulus schemeIf cumulus present, mixing determined by mass-flux scheme

• Hogan et al. (QJRMS 2009)

Input of sensible heat “grows” a new cumulus-capped boundary layer

during the day (small amount of stratocumulus in early

morning)

Convection is “switched off” when sensible heat flux goes

negative at 1800

Surface heating leads to convectively generated

turbulence

Turbulence from Turbulence from Doppler lidarDoppler lidar

Heig

ht

Potential temperature Potential temperature

Longwave cooling

Shortwave heating

Cloud

Heig

ht

Negatively buoyant plumes generated at cloud top: upside-down convection and negative skewness

Positively buoyant plumes generated at surface: normal convection and positive skewness

Stratocumulus cloud

• Can diagnose the source of turbulence

SkewnessSkewness

Boundary-layer types from Boundary-layer types from observationsobservations

Lock type I

Lock type III

v

Probabilistic decision treeProbabilistic decision tree

Stable cloudles

s

Stable stratus

Forced Cu under

Sc

Decoupled Sc over

stable

Clear well

mixed

Cloudy well

mixed

Decoupled Sc

Decoupled Sc over

Cu

Cumulus

Test surface sensible heat flux

Test skewness

Test skewness &

velocity variance

Use lidar backscatter

Example day: 18 October 2009Example day: 18 October 2009

• Usually the most probable type has a probability greater than 0.9• Now apply to two years of data and evaluate the type in the Met

Office model

Most probable boundary-layer type

II: Stratocu over stable surface layer

IIIb: Stratocumulus-topped mixed layer Ib: Stratus

Harvey, Hogan and Dacre (2012)

Winter

Summer

Autumn

SpringComparison Comparison

to Met to Met Office Office modelmodel

Model has:• Too little stable• Too little well-

mixed• Too much cumulusNote:• Model cumulus

needs to be >400 m thick

• Use radar to apply this criterion to obs

Harvey, Hogan and Dacre (2012)

Winter Spring Summer Autumn

Obs

Model

Comparison with Met OfficeComparison with Met Officeversus season and time of dayversus season and time of day

Forecast skillForecast skill

• 6x6 contingency table is difficult to analyse– Most skill scores

operate on a 2x2 table: a (hits), b (false alarms), c (misses), d (correct negatives)

– Instead consider each decision separately

• Use symmetric extremal dependence index (SEDI) of Ferro & Stephenson (2011): many desirable properties (equitable, robust for rare events etc)

• Where hit rate H = a/(a+c) and false alarm rate F = b/(b+d)

)1()1(ln1

1ln

SEDIFFHH

FF

HH

Forecast skill: Forecast skill: stabilitystability

• Surface layer stable?– Model very skilful (but

basically predicting day versus night)

– Better than persistence (predicting yesterday’s observations)

b a

d c

random

Forecast skill: Forecast skill: cumuluscumulus

• Cumulus present (given the surface layer is unstable)?– Much less skilful than in

predicting stability– Significantly better than

persistence

b a

d c

random

Forecast skill: Forecast skill: decoupleddecoupled

• Decoupled (as opposed to well-mixed)?– Not significantly more skilful

than a persistence forecast

b ad c

random

Forecast skill: Forecast skill: multiple cloud multiple cloud

layers?layers?• Cumulus under statocumulus

as opposed to cumulus alone?– Not significantly more skilful

than a random forecast– Much poorer than cloud

occurrence skill (SEDI 0.5-0.7)

b ad c

random

Forecast skill: Forecast skill: Nocturnal Nocturnal stratocustratocu

• Stratocumulus present (given a stable surface layer)?– Marginally more skilful than a

persistence forecast– Much poorer than cloud

occurrence skill (SEDI 0.5-0.7)

b ad c

random

Summary and future workSummary and future work

• Doppler lidar opens a new possibility to evaluate boundary layer schemes– Model rather poor at predicting boundary layer type– In addition to boundary-layer type, can we evaluate the

diagnosed diffusivity profile – this is what matters for evolution of weather?

– How do models perform over oceans or urban areas?

• How can boundary layer schemes be improved?– Combination of radar-lidar retrievals and 1D modelling

demonstrated that shortcomings of altocumulus models could be identified and fixed

– The same strategy could be applied to the boundary layer

Model Model evaluation evaluation

using using CloudSat and CloudSat and

CalipsoCalipso• Use DARDAR cloud

occurrence

• Hogan, Stein,

Garcon and Delanoe (in preparation)

Radiative propertiesRadiative properties

• Using Edwards and Slingo (1996) radiation code• Water content in different phase can have different

radiative impact

Modelling mixed-phase clouds - Modelling mixed-phase clouds - GCMsGCMs

• Until recently: most models diagnostic split• More recently: improved computer power and desire for

‘physicality’ prognostic ice (Met Office, ECMWF, DWD)

•All liquid

•All ice

•Mixed phase

Ice cloud fraction parameterisationIce cloud fraction parameterisation

Ice particle size distributionIce particle size distribution

• Large ice crystals are more massive and grow faster than smaller crystals

• Small crystals have largest impact on growth rate

SkewnessSkewness

• Skewness defined as – Positive in convective daytime boundary layers– Agrees with aircraft observations of LeMone

(1990) when plotted versus the fraction of distance into the boundary layer

• Useful for diagnosing source of turbulence