Parameterization in models Introduction to cloud issues [email protected].

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Parameterization in models Parameterization in models Introduction to cloud issues Introduction to cloud issues [email protected]
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Transcript of Parameterization in models Introduction to cloud issues [email protected].

Page 1: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Parameterization in modelsParameterization in modelsIntroduction to cloud issuesIntroduction to cloud issues

[email protected]

Page 2: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Clouds in GCMs - What are the Clouds in GCMs - What are the problems ?problems ?Many of the observed clouds and especially the processes within them are of subgrid-scale size (both horizontally and vertically)

GCM Grid cell 40-400km

Page 3: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

~50

0m

~100km

Macroscale Issues of Macroscale Issues of ParameterizationParameterization

VERTICAL COVERAGEMost models assume that this is 1

This can be a poor assumption with coarse vertical grids.Many climate models still use fewer than 30 vertical levels

currently, some recent examples still use only 9 levels

Page 4: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

~50

0m

~100km

Macroscale Issues of Macroscale Issues of ParameterizationParameterization

HORIZONTAL COVERAGE, a

Page 5: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

~50

0m

~100km

Macroscale Issues of Macroscale Issues of ParameterizationParameterization

Vertical Overlap of cloudImportant for Radiation and Microphysics Interaction

Page 6: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

~50

0m

~100km

Macroscale Issues of Macroscale Issues of ParameterizationParameterization

In cloud inhomogeneity in terms of cloud particle size and number

Page 7: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

~50

0m

~100km

Macroscale Issues of Macroscale Issues of ParameterizationParameterization

Just these issues can become very complex!!!

Page 8: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Clouds in GCMs - What are the Clouds in GCMs - What are the problems ?problems ?

convection

Clouds are the result of complex interactions between a large number of processes

radiation

turbulence

dynamics

microphysics

Page 9: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Clouds in GCMs - What are the Clouds in GCMs - What are the problems ?problems ?

Many of these processes are only poorly understood - For example, the interaction with radiation

Cloud-radiation interaction

Cloud macrophysics Cloud microphysics “External” influence

Cloud fraction and overlap

Cloud top and base height Amount of

condensate

In-cloud conden-sate distribution

Phase of condensate Cloud particle

size

Cloud particle shape

Cloud environment

Page 10: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

What do we want to What do we want to represent?represent?

Ice mass

Ice number

small ice

Medium ice

Large ice

Most GCMs only have simple single-moment schemes

Ice Mass

Liquid Mass

CloudMass

Complexity

“Single Moment”Schemes

“Double Moment”Schemes

“Spectral/Bin”Microphysics

Page 11: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Clouds in GCMs - How ?Clouds in GCMs - How ?

Main variables:

Cloud fraction, a - refers to horizontal cover since cloud fills vertical

Cloud condensate mass (cloud water and/or ice), ql.

Diagnostic approach

,, 111 tt

fa nn

,, 112 tt

fq nnl

Prognostic approach

)()()( aDaSaAt

a

)()()( llll qDqSqA

t

q

NOT DISTINCT - CAN HAVE MIXTURE OF APPROACHES

Page 12: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Cloud microphysical Cloud microphysical processesprocesses• We would like to include into our models:

– Formation of clouds– Release of precipitation– Evaporation of both clouds and precipitation

• Therefore we need to describe– the change of phase from water vapour to water droplets and

ice crystals– the transformation of small cloud droplets/ice crystals to

larger rain drops/ice particles– The advection and sedimentation/falling of these species– the evaporation/sublimation of cloud and precipitation size

particles

Page 13: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Microphysics: Microphysics: Complex Complex System!System!

Overview of(1) Warm Phase Microphysics T>273K(2) Mixed Phase Microphysics 250K<T<273K(3) Pure ice Microphysics T<250K

Page 14: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Droplet ClassificationDroplet Classification

Page 15: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Important effects for particle Important effects for particle activationactivation

TrRe

re

lvs

s

2

exp)(

)(

Planar surface: Equilibrium when e=es and number of molecules impinging on surface equals rate of evaporation

Curved surface: saturation vapour pressure increases with smaller drop size since surface molecules have fewer binding neighbours.

Surface molecule has fewer neighbours

radius drop

droplet of tension Surface

r

Page 16: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Nucleation of Water:Nucleation of Water:Homogeneous NucleationHomogeneous Nucleation

• Drop of pure water forms from vapour

• Small drops require much higher super saturations

• Kelvin’s formula for critical radius for initial droplet to be “survive”

• strongly dependent on supersaturation

• Would require several hundred percent supersaturation (not observed in the atmosphere).

sLv

vlc

eeTR

Rln

2

droplet of tension Surface

Radius Critical

cR

Page 17: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Nucleation of Water:Nucleation of Water:Heterogeneous NucleationHeterogeneous Nucleation

• Collection of water molecules on a foreign substance, RH > ~80% (Haze particles) (Note, not same when drying)

• These (hydrophilic) solluble particles are called Cloud Condensation Nuclei (CCN)

• CCN always present in sufficient numbers in lower and middle troposphere

Page 18: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Important effects for particle Important effects for particle activationactivation

Planar surface: Equilibrium when e=es and number of molecules impinging on surface equals rate of evaporation

Curved surface: saturation vapour pressure increases with smaller drop size since surface molecules have fewer binding neighbours.Effect proportional to r-1

Presence of dissolved substance: saturation vapour pressure reduces with smaller drop size due to sollute molecules replacing solvent on drop surface (assuming esollute<ev)Effect proportional to r-3

Surface molecule has fewer neighbours

Dissolved substance reduces vapour pressure

Page 19: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Heterogeneous NucleationHeterogeneous Nucleation

“Curvature term”Small drop – high radius of curvature

easier for molecule to escape

“Solution term”Reduction in

vapour pressure due to dissolved

substance

activated"" 12.0

,01.1/

mr

ese

e/e s

equi

libriu

m

Haze particle in equilibrium

Page 20: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Parameterizing Nucleation Parameterizing Nucleation and droplet growthand droplet growth• Nucleation: Since “Activation” occurs at

supersaturations less than 1% most schemes assumes all supersaturation is immediately removed as liquid water

• Note that this assumption means that models can just use one “prognostic” equation for the total water mass, the sum of vapour and liquid

• Usually, the growth equation is not explicitly solved, and in single-moment schemes simple (diagnostic) assumptions are made concerning the droplet number concentration when needed (e.g. radiation). These often assume more CCN in polluted air over land.

Page 21: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Diffusion growth (water)Diffusion growth (water)

• Nucleation small droplets• once droplet is activated, water

vapour diffuses towards it = condensation

• reverse process = evaporation• droplets that are formed by

diffusion growth attain a typical size of 0.1 to 10 m

• rain drops are much larger than that

– drizzle: 50 to 100 m– rain: >100 m

• other processes must also act in precipitating clouds

)1(1

STR

De

rdt

dr

vL

s

For r > 1 m and neglecting diffusion of heat

D=Diffusion coefficient, S=SupersaturationNote inverse radius dependency

Page 22: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Collision-CoalescenceCollision-Coalescence

• Drops of different size move with different fall speeds - collision and fusion

• large drops grow at the expense of small droplets

• Collection efficiency low for small drops

• process depends on width of droplet spectrum and is more efficient for broader spectra - Paradox

• large drops can only be produced in clouds of large vertical extent – Aided by turbulence and entrainment

• important process for low latitudes where deep clouds of high water content are present

Page 23: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Parameterizating Parameterizating “Autoconversion” of cloud drops “Autoconversion” of cloud drops to raindropsto raindrops

Autoconversion (Kessler, AMS monogram 1969)

otherwise0

if0critcrit

lllll qqqqc

t

q

qlqlcrit

Gp

Sundqvist, QJRMS, 1978

2

0 1 crit

l

l

q

q

ll eqc

t

q

qlqlcrit

Gp“Non-local” collectionPcF 11 1

1F1F

P=Precipitation Flux

what are the issues for data assim

ilation?

Page 24: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Heterogeneous NucleationHeterogeneous NucleationRH>78% (Haze)RH>78% (Haze)

Schematic of Warm Rain Schematic of Warm Rain ProcessesProcesses

CCN

~10 microns~10 microns

RH>100.6%RH>100.6%““Activation”Activation”DiffusionalDiffusional

GrowthGrowth

Different fall speedsDifferent fall speeds

Coalescence

Page 25: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Ice NucleationIce Nucleation

• Ice processes complex and poorly understood• Droplets do not freeze at 0oC! • Can also be split into Homogeneous and

Heterogeneous processes• Processes depend on temperature and history of cloud• Homogeneous freezing of water droplet occurs between

–35 and –40oC (often used assumption in microphysical schemes).

• Frequent observation of ice at warmer temperatures indicates role for heterogeneous processes

Page 26: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Ice NucleationIce Nucleation

• Spontaneous freezing of liquid droplets smaller than 5 m requires temperature less than -40oC.

• Observations of liquid in cloud are common at -20oC.

• Ice crystals start to appear in appreciable numbers below around -15oC.

• Heterogenous Nucleation responsible: Process less clear

• Ice nuclei: Become active at various temperatures less than 0oC, many fewer

• Observations: – < -20oC Ice free clouds are rare– > -5oC ice is unlikely– ice supersaturation ( > 10% ) observations are

common

Fletcher 1962

Page 27: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Heterogeneous NucleationHeterogeneous Nucleation

Supercooled drop

aerosol

I will not discuss heterogeneous ice nucleation in great detail in this course due to lack of time and the fact that

these processes are only just starting to be tackled in Large-scale models. See

recent work of Ulrike Lohmann for more

details

Page 28: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Ice HabitsIce HabitsIce habits can be complex, depends on temperature: influences fall speeds and radiative properties

http://www.its.caltech.edu/~atomic/snowcrystals/

Page 29: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Mixed Phase clouds: Bergeron Mixed Phase clouds: Bergeron Process (I)Process (I)The saturation water vapour pressure with respect to ice is smaller than with respect to water

A cloud, which is saturated with respect to water is supersaturated with respect to ice !

Page 30: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Bergeron process (II)Bergeron process (II)

Ice particle enters water cloud

Cloud is supersaturated with respect to ice

Diffusion of water vapour onto ice particle

Cloud will become sub-saturated with respect to water

Water droplets evaporate to increase water vapour

Ice particles grow at

the expense of water droplets

Page 31: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Modification of Sundqvist to take Modification of Sundqvist to take Bergeron Process into accountBergeron Process into account

Sundqvist, QJRMS, 1978

2

0 1 crit

l

l

q

q

ll eqc

t

q

qlqlcrit

Gp

Collection

Bergeron Process

PcF 11 1

TcF 2681 22

21FF21FF

Otherwise, most schemes have neglected ice processes, removing ice super-saturation “al la Warm rain” – See Lohman and Karcher JGR 2002(a,b) for first

attempts to include ice microphysics in GCM

Page 32: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

AggregationAggregation

• Ice crystals can aggregate together to form snow• Temperature dependent, process increases in efficiency

as temperature exceeds –5C, when ice surface becomes sticky

• Also a secondary peak between –10 and –16C when dendrite arms get entangled

Page 33: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

RimingRiming

• If vapour exceeds the water saturation mixing ratio, water can condense on ice crystal, and then subsequently freeze to form “graupel”: Round ice crystals with higher densities and fall speeds than snow dendrites

• Graupel and Hail are also formed by aggregating liquid water drops in mixed phased clouds (“riming”)

– If the Latent heat of condensation and fusion keeps temperature close to 273K, then high density hail particle forms, since the liquid water “spreads out” before freezing. Generally referred to as “Hail” – The higher fall speed (up to 40 m/s) imply hail only forms in convection with strong updraughts able to support the particle long enough for growth

http://www.its.caltech.edu/~atomic/snowcrystals/

Page 34: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Aggregation and Riming: Aggregation and Riming: Simple stratified picture Simple stratified picture

Page 35: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Ice HabitsIce Habits Ice habits can be complex: influences fall speeds and radiative properties

From Fleishauer et al 2002, JAS

Note shape/diameter distribution not monotonic with height, Turbulence!

Page 36: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Falling PrecipitationFalling Precipitation

• Need to know size distribution• For ice also affected by ice habit• Poses problem for numerics

From R Hogan www.met.rdg.ac.uk/radar

Cou

rtes

y: R

Hog

an,

U.

Re

adin

g

Page 37: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Pure ice Phase: Homogeneous Pure ice Phase: Homogeneous Ice NucleationIce Nucleation

• At cold temperature (e.g. upper troposphere) difference between liquid and ice saturation vapour pressures is large.

• If air mass is lifted, and does not contain significant liquid particles or ice nuclei, high supersaturations with respect to ice can occur, reaching 160 to 170%.

• Long lasting contrails are a signature of supersaturation

Institute of Geography, University of Copenhagen

Page 38: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Heteorogeneous NucleationHeteorogeneous Nucleation

• On the other hand, in air polluted by organic and mineral dust, supersaturation achieve perhaps 130%.

• Research is ongoing to determine the nature of ice nuclei at these colder temperatures

• This process probably more prevalent in NH where air is less ‘clean’

22nd April 2003: Modis Image from http://modis.gsfc.nasa.gov

Page 39: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Schematic of parcel evolution, T<-Schematic of parcel evolution, T<-3838ooCC

No IN Few IN Many IN

160%

130%

100%

RHice

Lifting/cooling

aerosol

RH

<1

60%

Page 40: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Simple Parcel model calculationsSimple Parcel model calculationsMaximum humidity important for determining number of Maximum humidity important for determining number of ice crystals nucleatedice crystals nucleated

From Ren and Mackenzie QJRMS 2005

e.g. Jensen 94, Spice et al. 97, Gierens 2003, Ren and Mackenzie 2005

Parcelcontainingaerosols

Lift (cool)parcel

Single aerosol type

4 major issues!

Page 41: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Cooling rates-Vertical velocity Cooling rates-Vertical velocity

• Even in “polluted” NH air, homogeneous nucleation can dominate in strong updraughts

• Knowledge of local velocities is crucial• However important wind fluctuations

occur on length-scales comparable to/smaller than grid length

– Upscale cascade from turbulence– Gravity waves– Subgrid instabilities (cloud top instabilities)……

• It is clear that models are deficient in representing these

• E.g: Lohmann shows inadequacy of ECMWF model, and how enhancement of turbulence activity can produce improved spectra

From

haag and Kaerchner

ECMWF resolved motions only

aircraft

Page 42: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Limb Sounder and Mozaic DataLimb Sounder and Mozaic Data(Pictures courtesy of Klaus Gierens and Peter Spichtinger, DLR)(Pictures courtesy of Klaus Gierens and Peter Spichtinger, DLR)

• Observations (e.g. Mozaic aircraft and Microwave sounders) have revealed supersaturated states are common, also seen in radiosonde datasets

3000 km supersaturated segment observed ahead of front

Page 43: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Summary: Warm CloudSummary: Warm Cloud

E.g: Stratocumulus

Condensation

(Rain formation - Fall Speeds - Evaporation of rain)

Evaporation

Page 44: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Summary: Deep Convective Summary: Deep Convective CloudCloud

• Precipitation Falls Speeds• Evaporation in Sub-Cloud Layer

• Heteorogeneous Nucleation of ice• Splintering/Bergeron Process• Melting of Snow and Graupel

Page 45: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Summary: Cirrus cloudSummary: Cirrus cloud

Homogeneous Nucleation(representation of supersaturation)

Heterogeneous Nucleation(representation of nuclei type and concentration)

Sedimentation of Ice crystals?Size distribution and formation of snow?

Page 46: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Cloud Schemes - A Brief Cloud Schemes - A Brief HistoryHistory

60s

Condensation(non-convective)

qv > qs

Radiationeffects

Prescribedzonal meanalbedo andemissivity

Convection No cloudinteraction

Microphysics none

60s 70s

Condensation(non-convective)

qv > qs qv > qs

Radiationeffects

Prescribedzonal meanalbedo andemissivity

a diagnostic[usually f(RH)]ql prescribed

Convection No cloudinteraction

acu = f(CP)ql cu prescribed

Microphysics none none

60s 70s 80s

Condensation(non-convective)

qv > qs qv > qs ql prognostica diagnostic

Radiationeffects

Prescribedzonal meanalbedo andemissivity

a diagnostic[usually f(RH)]ql prescribed

a = as cloudscheme

Convection No cloudinteraction

acu = f(CP)ql cu prescribed

acu = f(CP)ql cu prescribed

Microphysics none none Simple bulkmicrophysics

60s 70s 80s 90s-

Condensation (non-convective)

qv > qs qv > qs ql prognostic a diagnostic

ql prognostic a prognostic (directly or indirectly)

Radiation effects

Prescribed zonal mean albedo and emissivity

a diagnostic [usually f(RH)] ql prescribed

a = as cloud scheme

a = as cloud scheme

Convection No cloud interaction

acu = f(CP) ql cu prescribed

acu = f(CP) ql cu prescribed

Direct link to a and ql

Microphysics none none Simple bulk microphysics

Complex bulk microphysics (ice)

C

C

CC

C

Page 47: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Simple Bulk MicrophysicsSimple Bulk Microphysics

VAPOUR (prognostic)

CLOUD (prognostic)

RAIN (diagnostic)

WHY?

Evaporation

Autoconversion

Evaporation

Condensation

Page 48: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Microphysics - a “complex” Microphysics - a “complex” GCM schemeGCM scheme

Fowler et al., JCL, 1996

Similar complexity to many schemes in use in CRMs

Mostly treated as instant“No supersaturation

assumption”

“Threshold” linear or exponential terms

with efficiency adjustments

)( vsat qqKR

Page 49: Parameterization in models Introduction to cloud issues tompkins@ictp.it.

Cloud Cover: Why Important?Cloud Cover: Why Important?

In addition to the influence on radiation, the cloud cover is important for the representation of microphysics

Imagine a cloud with a liquid condensate mass ql

The incloud mass mixing ratio is ql/a

a largea small

GC

M g

rid b

ox

precipitation not equal in each case sinceautoconversion is nonlinear

Reminder: Autoconversion (Kessler, 1969)

otherwise0

if0critcrit

llllP

qqqqcG

Complex microphysics perhaps a wasted effort if assessment of a is poor