Understanding the impacts of human activities on atmospheric particles, clouds…nenes.eas.gatech.edu/Files/_Aerosol-Cloud.pdf · 2020. 6. 7. · Understanding the impacts of human

Understanding the impacts of human activities on atmospheric

particles, clouds, storms and climate

Athanasios Nenes1Earth and Atmospheric Sciences, Georgia Institute of Technology

2Chemical and Biomolecular Engineering, Georgia Institute of Technology3ICE-HT, Foundation for Research and Technology-Hellas, Greece

Acknowledgments: NASA MAP/ACMAP, Phillips 66, NSF, EPA, DOE ONR, Georgia Tech team (Nenes, Kostantinidis groups)

Earth’s Energy BalanceSunlight

“visible radiation”

Heat

“infrared radiation”

When energy IN = energy OUT, climate is “in balance” (i.e., steady state)

235 Watts per square meter (Wm-2)

235 Watts per square meter (Wm-2)

Clouds: a significant contributor to Earth’s reflectivity of solar radiation

J.T. Houghton: “The science of climate change”

Facts:• Clouds account for ~50% of planetary reflectivity (albedo). • Small changes in clouds yield large changes in global energy balance. •A few % increase in global cloud cover can counteract warming from greenhouse gases.

Consequence:

Good representation of clouds in models is crucial for understanding climate change.

How do (liquid water) clouds form?Clouds form in regions of the atmosphere where there is too much water vapor (it is “supersaturated”).

This happens when air is cooled (primarily through expansion in updraft regions and radiative cooling).

Cloud droplets nucleate on pre-existing particles found in the atmosphere (aerosols) with ~ 0.1µm diameter.

Aerosols that can become droplets are called cloud condensation nuclei (CCN).

CCN that activatesinto a cloud drop

Aerosol particlethat does not activate

Cloud

Can humans affect clouds and the hydrological cycle?Yes! By changing global CCN concentrations (air pollution). Result: Clouds tend to be “whiter”, change in precipitation efficiency. This yields a net cooling on climate and is called the “indirect climatic effect of aerosols”.

Clean Environment CCN

Lower Albedo

(few CCN) Polluted Environment(more CCN)

CCN

Higher Albedo

Increasing particles tends to cool climate (potentially alot).Quantitative assessments done with climate models.

Rosenfeld et al., Science

Red: Clouds with low reflectivity.White: Clouds that reflect alot. Blue: Clear sky.

Observational evidence of indirect effect

Satellite observation of clouds near Australia.

Power plantLead smelter

Port

Oil refineries

Rosenfeld et al., Science

Wind direction

Satellite observation of clouds near Australia.

Red: Clouds with low reflectivity.White: Clouds that reflect alot. Blue: Clear sky.

Air pollution affects cloud reflectivityObservational evidence of indirect effect

Climate Models: the tools of understanding

Divide the Earth into small parts (“grid cells”). For each write equations describing:

• Conservation of Energy, Water, chemical constituents

• Aerosol population balances and their evolution

• Interactions of land/ocean with atmosphere … etc.

3°× 3° grid

climateprediction.net

Prescribe initial conditions (e.g., climatology).

Integrate the equations (numerically) over time.

Models Need Fast Physics: Simple expressions capturing important cloud physics

DynamicsUpdraft VelocityLarge Scale Thermodynamics

Particle characteristicsSize & ConcentrationChemical Composition

Cloud ProcessesCloud droplet formationIce crystal formationEffects of entrainment/mixingCollision/coalescence“Scaleup” of processes

Links/feedbacks need to be incorporated (at appropriate scales).VERY challenging problem (Stevens and Feingold, 2009)

aerosol

Activationnucleation

growth

Goal: Predict drop/ice number concentration for “characteristic” cloud types.

Calculating droplet number in a climate model

Approach: use the “simple story of droplet formation”

Basic ideas: Solve conservation laws for energy and the water vapor condensing on aerosol particles in cloudy updrafts in each grid cell.

aerosol

activationdrop growth

S

Smax

t

Steps are:• Air parcel cools• Eventually exceeds dew point• Water vapor is supersaturated• Droplets start forming on

existing CCN.• Condensation of water

on droplets becomes intense.• S reaches a maximum• No more droplets form

A “classical” nucleation/growth problem

Examine the equilibrium vapor pressure of a wet aerosol particle.Consider the effects of solute and droplet curvature

So… when does an aerosol particle act as a CCN ?

0.1 1 10

Wet aerosol diameter (µm)

Solute effect on vapor pressure

Curvature effect on vapor pressureKelvin+Raoult:

Equil. RH of wet particle

98

99

100

101

102

Rel

ativ

e H

umid

ity (%

)

The combined Kelvin and Raoult effects gives rise to the Köhler equation

(1922).

You can be in equilibrium even if

you are above saturation.

Wet Aerosol(Haze)

0.1 1 10


98

99

100

101

102

Clo

ud, D

ropl

et E

quili

briu

mR

elat

ive

Hum

idity

(%)

“Critical” point

Cloud Droplet Particles act as CCN if the ambient relative humidity exceeds its “critical”

relative humidity

When does an aerosol particle act as a CCN ?Dynamical behavior of an aerosol in a variable relative humidity

environment.

Cloud humidity

When does an aerosol particle act as a CCN ?

A: parameter related to surface tension (controls the Kelvin

effect)

B: parameter related to the moles of solute

in the particle (controls the Raoult

effect)

2/3~ −dryc ds 2/1~ −

solubleεcs

Thermodynamic theory links the critical RH to particle size and composition

2/13

274

=

BAsc

0.1 1 10

1.00

1.01

1.02


Rel

ativ

e H

umid

ity (%

)

98

99

100

101

102

aerosol

activationdrop growth

S

Smax

t

1. Calculate smax (approach-dependent) 2. Nd is equal to the CCN with sc < smax

Algorithm for calculating Nd

(Mechanistic parameterization)

“Putting it together” for droplet number in models

Input: P,T, vertical wind, particle size distribution,composition.Output: Cloud properties (droplet number, size distribution).

Mechanistic Parameterizations: Twomey (1959); Abdul-Razzak et al., (1998); Nenes and Seinfeld, (2003); Fountoukis and Nenes, (2005); Kumar et al. (2009), Morales and Nenes(2014), and others.

Comprehensive review & intercomparison:Ghan, et al., JAMES (2011); Morales and Nenes (2014)

:

Aerosol Problem: ComplexityAn integrated “soup” of

Inorganics, organics (1000’s)Particles can have uniform composition with size…… or notCan vary vastly with space and time (esp. near sources)

Organic species are a headache They can facilitate cloud formation by acting as surfactants

and adding solute (hygroscopicity) Oily films can form and delay cloud growth kinetics

In-situ data to study the aerosol-CCN link:Usage of CCN activity measurements to “constrain” the above “chemical effects” on cloud droplet formation.

Continuous-Flow Streamwise Thermal Gradient Chamber

wet wallwet

wall

Outlet: [Droplets] = [CCN]

Inlet: Aerosol

Roberts and Nenes (2005), US Patent 7,656,510Lance et al., (2006), Lathem and Nenes (2011),

Raatikainen et al. (2012)

Metal cylinder with wetted walls

Streamwise Temperature Gradient

Water diffuses faster than heat

Supersaturation, S, generated at the centerline = f (Flowrate, Pressure, and Temp. Gradient)

Operated as a spectrometerusing Scanning Flow CCN Analysis(Moore and Nenes, 2010)

Development phases of cloud chambersc

ale

= 1

m

1st versionApril 2002

2nd versionJanuary 2003

Commercial ver. July 2004

Mini-instrumentAugust 2015

Roberts and Nenes, AS&T (2005); Lance et al., AS&T (2006)

Locations sampled over the past 8 years…

Measured:CCN, Aerosol concentrations and size distributions, and aerosol chemistry

Cloud hydrometeor distributions (liquid/ice) and dynamics.

We have sampled:The arctic, urban pollution, biomass burning, marine aerosol, hurricanes, Oil spills, the tropics….

Test the ability to predict CCN concentrations against ambient measurements.

Testing CCN activation theory: CCN “Closure” studies

[CCN]measured

[CC

N] pr

edic

ted

1:1

Compare measurements of CCN to predictions using theory and a simple description of molar volume for organics

Aerosol Size Distribution

dN/dlogdp

[cm-3]

dp , nm

Use theory to predict the particles that can act as CCN based on measured chemical composition and CCN

instrument supersaturation.

[CCN]predicted

integrate

CCN Closure

2% overprediction(on average).

CCN prediction theory reallyworks.

The simple treatment of organic hygroscopicitytreatment reallyworks too.

(Bougiatioti et al., ACP, 2009; 2012)

Example: Finokalia Aerosol Measurement Campaigns (Crete, Greece)

Film-forming compounds (e.g., Feingold & Chuang,2002)Slow solute dissolution kinetics (e.g., Asa-Awuku & Nenes, 2007)Glassy states (e.g., Virtanen, 2010)

They can slow down the condensation of water onto growing droplets, because they provide an additional kinetic barrier in addition to gas-phase mass transfer.

These effects are parameterized in terms of changes in the water uptake coefficient, α

Water uptake kinetics: a “big” (important) unknown

water molecule

Slow growthα << 1

water molecule

Rapid growthα ∼ 1

Papers published over 40 years… suggest a from 10-5 to 1.

Water uptake kinetics: a “big” (important) unknown

Varying α changes the condensation rate of water during nucleation of droplets hence smax (and Nd)

NCAR CAM5 Global annual average Nd

Current day Nd (cm-3)

α Uncertainty in the value of α can have a huge impact on predicted droplet droplet number …

Water uptake kinetics: the “big” (important) unknown

Variability of Nd from changes in α can overwhelm the Anthropogenic (Preind-Current day) aerosol indirect effect.

… unless a is between 0.1-1.0 OR remains constant over time.This has never been looked at to date (in a global sense)

wet wallwet

wall

Outlet: [Droplets] = [CCN]

Inlet: Aerosol

Roberts and Nenes (2005), US Patent 7,656,510Lance et al., (2006), Lathem and Nenes (2011),

Raatikainen et al. (2012, 2013)

Standard CCN measurement (>100 instruments in operation).

Metal cylinder with wetted walls

Streamwise Temp. Gradient Water diffuses faster than heat

Supersaturation, S, generated at the centerline = f (Flowrate, Pressure, and Temp. Gradient)

They offer THE opportunity to infer kinetics of water uptake for global aerosol

Water uptake kinetics from CCN observations

Num

ber

Droplet Size (µm)

Initial AerosolDistribution

(polydisperse)

Kinetic modeling: tool to interpret CCN measurements

Num

ber

Droplet Size (µm)

Initial AerosolDistribution

(polydisperse)Aerosol composition,

CCN Instrument OperatingConditions

Iterate to find supersaturation,water vapor uptake coefficient, α

that produces observeddroplet distribution

Lathem and Nenes (2011)Raatikainen et al., (2012)

Kinetic modeling: tool to interpret CCN measurements

Activation Kinetics of SOA sampled during the Deep Water Horizon Gulf Oil Spill

Opportunity to study fresh,

hydrocarbon-rich (i.e.,“oily”) aerosol above

a clean background

Moore et al. (2012)

• Model captures with remarkable fidelity the observed size variations.• Predictive understanding of droplet size ensures that we fully understand the

CCN instrument AND the kinetic analysis• Oily SOA does not exhibit kinetic limitations (α range: 0.1-1.0)• Kinetic analysis with instrument model crucial for this conclusion.

Moore et al. (2012)

June 8, 2010

Activation Kinetics of SOA sampled during the Deep Water Horizon Gulf Oil Spill

Predictions (fully coupled model)Observations

Extend kinetic analysis to global data

• Major global airmass types sampled over 10 field campaigns.• No activation kinetics delays observed in any data to date• Any value of α between 0.1 and 1.0 fits the kinetic activation data• α uncertainty is not a big problem (so far) for climate modeling.

Raatikainen et al., PNAS (2013)

Karydis et al., ACP (2012)

Understanding now the “big picture”

hygroscopicity of soluble particles

updraft velocity

Water uptake kinetics

water adsorption for insoluble particles

aerosol number

aerosol mass

How important is each parameter? Much difference across parameterizations? … and why?

Ice Nuclei Concentrations

Karydis et al., ACP (2012)


updraft velocity

Water uptake kinetics

water adsorption for insoluble particles

aerosol number

aerosol massIce Nuclei

Concentrations

Ultimate Goal: SENSITIVITIES

for any parameter that affects crystal/droplet number

Over space and time….

∂Nd

∂p

How important is each parameter? Much difference across parameterizations? … and why?

Understanding now the “big picture”

Traditionally: finite differences • Multiple simulations required per input• Truncation or approximation errors

possible• One sensitivity calculation for one grid

cell

We use automatic differentiation.• One simulation needed for all inputs• Analytical precision • Flexibility and portability

𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕

≅ ∆𝜕𝜕𝜕𝜕∆𝜕𝜕𝜕𝜕𝜕𝜕

= 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕𝜕𝜕+∆𝜕𝜕𝜕𝜕𝜕𝜕 −𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕𝜕𝜕−∆𝜕𝜕𝜕𝜕𝜕𝜕2∗∆𝜕𝜕𝜕𝜕𝜕𝜕

For all grid points and every timestep!

Behind droplet/crystal number sensitivity

size distribution


updraft velocity

uptake coefficient

AFHH and BFHH of insoluble particles

aerosol number

Number of DropletsNd = f{Fs(s), smax}

Estimate maximum supersaturation, smax

Soluble ParticlesCalculate supersaturation integral by Fountoukis and

Nenes (2005)

Insoluble ParticlesCalculate supersaturation integral by Kumar et al.

(2009)

πγρw

2aVIK 0,spart( )+ IK spart ,smax( )+ IFHH 0,smax( )

− 1= 0

Smax determined

iterate

Compute CCN spectrum ( Fs(s))

Adjoint Sensitivity Analysis: how it works

Karydis et al., ACP, 2012

Algorithm (calltree) for calculating droplet number

Track calltree & propagate a differential perturbation in Nd

(differential calculus)

updraft velocity

uptake coefficient


aerosol number




Nenes (2005)


(2009)

πγρw


− 1= 0

Smax determined

iterate




size distribution


updraft velocity

uptake coefficient


aerosol number




Nenes (2005)


(2009)

πγρw


− 1= 0

Smax determined

iterate




size distribution




dN∂




Nenes (2005)


(2009)

πγρw


− 1= 0

Smax determined

iterate






dN∂

p∂ for allparameters

size distribution


updraft velocity

uptake coefficient


aerosol number




Nenes (2005)


(2009)

πγρw


− 1= 0

Smax determined

iterate



Karydis et al., ACP, 2012dN∂

p∂ for allparameters

size distribution


updraft velocity

uptake coefficient


aerosol number

∂Nd

∂pNd

One Nd calculation gives:

for all p’s and !

Examples of Nd and its sensitivity to input parmeters in the Community Aerosol Model 5.1

[ cm-3 ]

Morales and Nenes, ACP (2014)

Nd

Present Day Emissions (2000)mean: 117 cm-3

Ultrafine particles Coarse particlesFine particles

Nd variability in the CAM 5.1:

Morales and Nenes, ACP (2014)

• How to determine the contribution from each parameter ( χ j ) to the total droplet number variability for each grid cell?• We may be interested in seasonal, monthly or annual average variability…

The adjoint sensitivities allow us to efficiently attribute the contribution from each parameter over space and

time

Aerosol Number

Kappa

Mode Diameter

Ultrafine particles Coarse particles

Updraft Velocity Contribution (%) to Nd variability

Fine particles

Aerosol Number

Kappa

Mode Diameter

Updraft Velocity

2 variables captures most of the Ndvariability (>90%)

% of variability on Nd

Ultrafine particles Coarse particlesFine particles

Lifetime and albedo effect as originally proposed (and implemented in global climate models).

Stevens and Feingold (2009)

Challenge: aerosol-cloud feedbacksDirect aerosol-cloud links for describing drop and ice formation is now included in climate models.

Stevens and Feingold (2009)

Cloud feedbacks partially buffer “perturbations”.

Feedbacks span from cloud-to-global scales

Feedbacks not well understood nor represented in models.

Particle feedbacks on storm intensity

Clouds developing under low aerosol loads:• Precipitation develops early on (before freezing level)• Much of the water “falls” near its source• Relatively little water reaches freezing level• Cold pool from downdrafts generates secondary clouds• Convection localized and generally not intense

Rosenfeld (2008)

Clouds developing under high aerosol loads:• Aerosols reduce drizzle - precipitation delayed• More water reaches freezing level – additional latent

heat “energizes” the storm• Cold pools generates stronger secondary clouds• Convection is intensified

Rosenfeld (2008)

Particle feedbacks on storm intensity

“Invigoration of clouds and the intensification of rain rates is a preferred response to an increase in aerosol concentration.”

Koren et al., Nature Geosci. (2012).

Aerosol-Precipitation interactions

Can particles affect Hurricanes?Violent tropical cyclonic storm systems.

Predictions of storm intensity are particularly challenging, not improving over time.

Are we missing something important in models?

Some more slides on the topic:

In-situ observations of aerosol-cloud interactions that we collected from

hurricanes.

Properties & Responses• Measured CCN / IN activity of bacteria.• Relate observations to surface properties – and see if it

can be linked to bacterial membrane structure.

CCN/IN activity

Contact Angle

In-situ sampling of microbesCIRPAS Twin Otter

NASA DC8

Sampling system

Finokalia, Crete

Collect samples from: • Aircraft for high/low altitudes

and many locations.• Ground-based sites for

seasonal variations.

Bacteria-Cloud interactions:Approach and Goals

Characterization of microbes

Amplification Sequencing

DNA extraction

Assembly Gene search

Molecular & bioinformatics tools used for: • Community composition (16S rDNA)• Characterizing the metagenome of samples.• Look for IN (inaZ) or CCN-relevant genes.• Detect stress response genes.

Current Focus: CCN ActivityCurrent state of knowledge: • Range of critical supersaturations: 0.1-1.0%• Few studies published to date (<10).• Low diversity of bacteria characterized.• Metabolic state (vegetative/spore, live/dead) unknown.Importance: • Role of bacteria as Giant CCN (warm rain initiation)• Cloud scavenging very important for long range

transport of microbes and biogeography.• Mechanistic understanding of how water condenses on

bacteria that act as immersion mode IN.

Some more slides on the topic:

In-situ observations of bioaerosolsand their lifecycle in the marine

boundary layer. Methods development can also be

shown.

Take-home messagesPhysically-based representations of droplet and ice formation in climate models is becoming sophisticated… but still computationally feasible.

Measurements of ambient CCN were critical for bypassing complexities (related to composition and kinetic constants) on cloud droplet formation.

Instrumentation that we helped develop really answered a lot of the outstanding questions around droplet formation.

Direct sensitivity (adjoint) methods are very useful for understanding the important parameters for droplet formation, and what causes its variations in models.

Take-home messagesThere is a lot of work to be done to understand the feedbacks of particles on storm formation and the climate system.

Neglecting these feedbacks can explain the inability to predict e.g., hurricane intensity.

There is also the realization that new particle types (e.g. bacteria and other bioparticles) are present – and with surprising impacts.

Understanding their potential impacts and source function is an open area of research… so keep tuned!

For more information and PDF reprints, please go to

http://nenes.eas.gatech.edu

THANK YOU !!

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Understanding the impacts of human activities on atmospheric particles, clouds…nenes.eas.gatech.edu/Files/_Aerosol-Cloud.pdf · 2020. 6. 7. · Understanding the impacts of human