Why the aerosol are important for cloudsand

Why clouds are important for the aerosol

Jeff SniderUniversity of Wyoming, Laramie

jsnider@uwyo.edu

Outline -

Basic physics of cloud/aerosol and aerosol/cloud interactions

Properties of aerosol particles that make them good nucleiCloud condensation nuclei (CCN)Ice nuclei

Aerosol-to-CCN closure studies

Populations of aerosol and hydrometeors (i.e., droplets, drops and crystals)

Importance of clouds to the aerosol

In the atmosphere, H2O vapor is often subsaturated

However, saturated conditions exist within clouds

Some cloud environments can be supersaturated

Vapor state (subsaturated, saturated or supersaturated) is quantified with vapor pressure or absolute humidity (or vapor mixing ratio)

Below 0 oC, interesting thermodynamic effects take placeIce is the stable phase, but liquid water can coexistLike water, ice has a temperature-dependent saturation vapor pressureThe saturation vapor pressure over liquid water exceeds that over ice

Below 0 oC, the saturation vapor pressure over liquid water is larger than that over iceFrom the perspective of the ice hydrometeors, mixed phase clouds are supersaturated. The ice can grow by depositionThe water saturation ratio increases by ~10% for every 10 degree of supercooling

Bergeron, 1935 -

Points to the importance mixed-phase cloud, the favored growth of ice in such environments, and consequence for precipitation

Description of conditions in warm clouds-Thermodynamic - water vapor is in equilibrium with liquid waterDynamic - water vapor amount exceeds equilibrium

Description of conditions in cold clouds-Thermodynamic - water vapor is in equilibrium with iceDynamic - water vapor amount exceeds equilibrium(Note: often liquid water and ice coexist....so-called mixed phase clouds)

11001)(

100

S

Te

eSS

w"The supersaturation"

11001)(

100

i

ii S

Te

eSS "The ice supersaturation"

1)(1001)(

)(100

TS

Te

TeSS w

i

ww "The water supersaturation"

How to apportion the latent heat?

))((2 Dwv TDDdtdm

)(2 DTTkDQ

Rate of vapor mass transfer to a droplet of diameter “D”

Rate of sensible heat mass transfer to a droplet of diameter “D”

Combining, and linearizing the Claussius-Clapeyron equation->Maxwell-Mason droplet growth equation

2

2

)(11

)1(4

TRk

lTDv

S

dt

dDD

v

v

wvw

),,,( PTStD

3

31)(

DM

Diconst

D

constDS

s

d

Köhler theory provides two things: 1) A connection between wet diameter and saturation ratio at the interface 2) A connection between critical saturation ratio and dry diameter

2/1

3

31

d

sc

Di

MconstS

We have two equations that account for single particle growth via condensation-

1) Droplet size is related to time and ambient conditionsThe Maxwell-Mason Equation

2) Bdry condition at the droplet/air interface is related to properties of the nucleusThe Koehler Equation

2

2

)(11

))((4

TRk

lTDv

DSS

dt

dDD

v

v

wvw

),,,,,,,,,( iMDPTStD sd

Characteristic of an “active” cloud condensation nucleus:-Large particle size-Contain materials (solute) that dissolve in water-Contain solutes that dissociate in solution-Contain many solute molecules-Contain solutes that reduce the energetic cost of forming an interface

2/1

3

31

d

sc

Di

MconstS

Soluble mass fraction

i van’t Hoff factor

sM Molecular weight of solute, density of dry particle

dD Sphere equivalent dry diameter of particle

Surface energy of solution/air interface

Substancea axis

dimension, nmc axis

dimension, nm

Temperatureto nucleate

ice, oC

Ice 0.45 0.74 0

Substancea axis

dimension, nmc axis

dimension, nm

Temperatureto nucleate

ice, oC

AgI 0.46 0.75 -4

CuO 0.47 0.51 -7

Kaolinite 0.52 0.72 -9

Substancea axis

dimension, nmc axis

dimension, nm

Temperatureto nucleate

ice, oC

Bacteria -- -- -3

Some active ice nuclei have lattice dimensions similar to ice-

Crystal concentrations observed in some clouds do depend on temperature in a manner consistent with generation via nucleation

But, information is needed for describing the connection between ice nuclei sources, nuclei activity spectra and ice crystal concentrations. This is lacking

With ice there are additional complications:Ice can form via secondary processes

Collision between graupel and snowShattering of freezing drops

Ice from one cloud can also "seed" a neighboring cloud

Aerosol-to-CCN Closure Studies

How well dothe predictedand observed CCN concentrationscompare?

Why Closure Studies?

Chemical Transport Model use a mass balance, constrained by aerosol source and sink processes, to derive aerosol size spectra. Parameterization, based on observation, is often used in the models.

Questions:How reliable are the observational data sets used in the parameterizations?Does systematic error in the measurements alter the sign or sensitivity of themodel prediction to alterations in aerosol properties?Under what circumstances are the simplifying assumptions OK?

A common assumption is that the particles are spherical, often they are not

Picture from Alexei Kiselev

The Wyoming static diffusion CCN Instrument

DMT (Scrips/Caltech/GT) Continuous-flow CCN Instrument

Two CCN instruments:

University of WyomingStaticNon-linearity of ew(T)Snider et al. (2006)Scattering from ensemble of dropletsTemp. difference between outer and inner wallWall material may alter ew(T)Activation spectrum broadening

DeveloperChamber typeOperating principleCalibrationCCN detectionNon-ideality #1Non-ideality #2Non-ideality #3

DeveloperChamber typeOperating principleCalibrationCCN detectionNon-ideality #1Non-ideality #2

Droplet Measurement Technologies (DMT)Continuous flowDissimilarity of vapor and heat diffusivitiesLance et al. (2006)Single particle scatteringTemp. difference between outer and inner wallConcentration bias

WyomingStatic Thermal DiffusionCCN Instrument

DMT (Scrips/Caltech/GT)Continuous-flow CCN Instrument

Aerosol flow stream is surrounded by sheath flowH2O vapor diffuses (inward) faster than sensible heatMaximum supersaturation is near exit to OPCActivated (growing) CCN are counted in OPCResistance to heat flow across wall

Efficiency = (Th'-Tc')/(Th-Tc) ~ 0.7 Efficiency is evaluated in laboratory studies

DMT Calibration: Th-Tc = 5.35 oC, Qtot=0.5 L/min, P = 0.8 atm, SAR = 10

Particles of known size and composition are produced in a DMAAmmonium sulfate is preferred, but there are issues

Koehler theory used to infer particle SSc from the DMA-selected DdSmall test particles (i.e. particle SSc > maximum chamber SS) -> no activationLarge test particles (i.e. particle SSc < maximum chamber SS) -> complete activationActivated fraction = 0.5 defines the maximum chamber SSOuter wall temperatures, efficiency, chamber model -> from max chamber SS

Snider et al., in press, Journal of Atmospheric and Oceanic Technology

Snider et al., in press, Journal of Atmospheric and Oceanic Technology

Wyoming calibration: ΔT = 2.2 oC, Tt=20 oC, P = 0.8 atm

ΔT SSnom

K/M, 1975Chamber

Model

KöhlerModel

D50 Seff

Snider et al., in press, Journal of Atmospheric and Oceanic Technology

From measurement of activated fraction versussphere equivalent diameter, the size at 50% activation is determined - > D50

Snider et al., in press, Journal of Atmospheric and Oceanic Technology

We concluded that the supersaturation determined from temperature measurement,and a model of the chamber, that the nominal supersaturation is a factor of 1.6larger than the supersaturation evaluated from particle size and a Koehler model

The cause of this discrepancy...

AerosolAmmonium Sulfate

Soot Coated withAmmonium Sulfate

Soot Coated withLevoglucosan

# of Experiments 8 6 3

Average 1.01 1.01 1.03

Standard Deviation

0.06 1.10 -----

DMT / Wyoming CCN comparison experiments in Leipzig, November 2005Simple (ammonium sulfate) and complicated (soot-coated) test particlesDeterminations of Critical Supersaturation in both instruments Table shows statistics (average and standard dev) for the DMT / Wyoming Ratio

Droplets=1000 and 50 cm-3, No Ice, zbase = 500 m, Tbase = -10 oC

The physics of S(t) in a parcel model -

dt

drw

dt

dS

w Vertical velocity

Positive constant

Positive constant

r Liquid Mixing ratio

LWC lags the adiabatic liquid mixing ratio, and more so when there arefewer droplets, i.e., 50 cm-3

In an adiabatic parcel their is no supersaturation, vapor and liquidare at equilibrium. In other words the supersaturating effect of the cooling is exactly balanced by the formation of liquid (dS/dt = 0, and S=1)

It follows that the initial rate of increase of S is larger for the parcel with fewerdroplets, compared to the parcel with more droplets

The physics of S(t) in a parcel model (continued)

Also, the characteristic time for adjustment to a steady state is longerin the case of the of the simulation with fewer droplets, i.e., 50 cm-3

Hence, the peak saturation ratio is larger, and it occurs higher in the cloud whenthere are fewer droplets, i.e., 50 cm-3

dt

drw

dt

dS

w Vertical velocity

Positive constant

Positive constant

r Liquid Mixing ratio

Have I contradicted myself?

Large cloud droplet concentration -> small maximum saturation ratioSmall cloud droplet concentration -> large maximum saturation ratio

Does this imply that an increase in nuclei (i.e., pollution) will decrease themaximum saturation ratio enough to decrease the droplet concentration?I.e., causing a reverse of the first indirect effect of aerosol on climate?

polluted

unpolluted

polluted

unpolluted

from Snider et al., JGR, 2003

Mcfiggans et al., Atmospheric Chemistry and Physics, 2006

Parcel Model

w

Parcel Model

w

Parcel Model Calculation - Sensitivity of droplet concentration to aerosol and updraft

Droplets=50 cm-3, Ice=100 L-1, zbase = 500 m Tbase = -10 oC

Cloud, and especially precipitation associated with clouds, has a profound impact on the

aerosol!

Aerosol are removed from the atmosphere by precipitationCoalescence scavengingAerosol scavenging by precipitation falling below cloudAerosol attachment to cloud and precip via brownian motion

Aerosol number concentration can be decreased even if precipitation evaporates

On average there is a steady state between aerosol source and aerosol removal

In some cloud regimes there is an imbalance between source and sink Marine stratocumulus

Marine summertime clouds - DYCOMS-II (2001)Marine stratus, July, cloud top temperatures > 0 oC, 300 km west of California

Aerosol Source Processes - Wind speeds in the marine boundary layer (MBL) < 10 m/s

Characteristic time for sea salt aerosol source ~ 10 dayEntrainment of free troposphere (FT) into MBL characterized using tracers

Characteristic time for entrainment of FT aerosol into MBL ~ 10 day

Aerosol Sink Processes -Drizzle rates were surprisingly large (~10 mm/day, 100 mm/day locally!)Coalescence scavenging thought to dominate

Aerosol source rates < Aerosol sink rates

Aerosol concentrations decrease

Aerosol surface area decreases, a threshold is reached, new particle formation occurs

Evidence for new particle formation in the MBL on July 11, 2001 (RF02)Heavy drizzle, open-cell cloud structure also documented in new particle region

Leon et al., Journal of Atmospheric and Oceanic Technology, 2006

Leon et al., Journal of Atmospheric and Oceanic Technology, 2006

Drizzle is most intense in regions of rising air motion (w ~ 1 m/s)Ascent is driven by horizontal convergence at the base of the MBLCoupling of ascent and drizzle implies longer drizzle growth times and enhanced removal of cloud droplets (drizzle scavenging) compared to drizzle formation elsewhere in the MBL

Petters et al., Journal of Geophysical Research, 2006

We concluded:

1) New aerosolparticles wereformed in response to a depletion of the preexistingaerosol surfacearea by heavydrizzle.

2) Organizationof the cloud intoopen cell structuresmay be either necessaryfor new particleformation or a consequence of it.

Concluding Remarks -

1. Aerosol size spectra and number concentration are influenced by precipitation and this in turn influences the properties of clouds

2. No measurement is perfect, but through intercomparison instrumentbias uncovered and accounted for.

3. Models need data sets for parameterization and also for initialization. Thesedata sets should be as free of measurement bias as is possible.

4. Through collaboration we reach our objectives sooner and with greater understanding of the consequence of our efforts.

Acknowledgements -

The group at Warsaw (Hanna, Tymon, etc.)Markus Petters (Colorado State University)David Leon (University of Wyoming)