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slide1

Impacts of Aerosols on Clouds and Climate

Athanasios NenesSchool of Earth & Atmospheric SciencesSchool of Chemical & Biomolecular EngineeringGeorgia Institute of TechnologyJPL Seminar, 6 May 2010Acknowledgments: NASA, NSF, NOAA, ONR, CIRPASSeinfeld/Flagan group (Caltech), Adams group (CMU), ARCTAS/ARCPAC Science teams (NASA/NOAA)

slide2

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). This process is known as activation.

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

Cloud

CCN that activates

into a cloud drop

Aerosol particle

that does not activate

slide3

Lower A

l

bedo

Higher A

l

bedo

CCN

Clean Environment

CCN

(few CCN)

Polluted Environment

(more CCN)

Humans can affect clouds and the hydrological cycle

By changing global CCN concentrations (air pollution).

Polluted clouds are “whiter”, change in precipitation efficiency.

This yields a net cooling on climate and is called the “indirect climatic effect of aerosols”.

Increasing particles tends to cool climate (potentially alot).

Quantitative assessments done with climate models.

slide4

Observational evidence of indirect effect

Satellite observation of clouds in the Black Sea.

Red: Clouds with low reflectivity.

White: Clouds that reflect alot.

Blue: Clear sky.

Rosenfeld et al., Science

slide5

Observational evidence of indirect effect

Air pollution can affect cloud properties

Satellite observation of clouds in the Black Sea.

Power plant

Lead smelter

Wind direction

Port

Oil refineries

Red: Clouds with low reflectivity.

White: Clouds that reflect alot.

Blue: Clear sky.

Rosenfeld et al., Science

slide6

Quantification of the Indirect Effect in Climate Models

Aerosol

Size Distribution and Chemical Composition

Cloud Radiative Properties, changes in precipitation

Cloud Droplet Number and Size

This problem largely depends on the relationship between aerosol number concentration and cloud droplet number concentration.

Empirical relationships are often used.

slide7

Aerosol Indirect Radiative Forcing (W m-2)

Global

Annual

Average

North America

pollution plumes

Long-range transport

European and Asian

pollution plumes

Biomass burning

Sotiropoulou et al., 2007

Spatial pattern of IF follows that of aerosol variations

slide8

3 3 grid

climateprediction.net

Problems with GCM assessments of aerosol indirect effect

  • Cloud formation happens at smaller spatial scales than global climate models can resolve.
  • Aerosol-cloud interactions are complex.
  • Climate models provide limited information about clouds and aerosols.
  • Describing cloud formation explicitly in global models is VERY expensive. These calculations need to be simplified (“parameterized”).
slide9

(Boucher & Lohmann, 1995)

Droplet Concentration

Increasingly polluted

Aerosol sulfate concentration

(proxy for pollution)

Approach for aerosol-Nd : empirical

  • Large variability.
  • Why?
  • Unaccounted:
  • Meteorology
  • Cloud microphysics
  • Composition
  • etc…

Many studies still utilize this type of approach.

Large predictive uncertainty, without “chances” of improving.

slide10

collision/coalescence

drop growth

activation

aerosol

Current Direction: Use simplified but physically

based approaches for cloud processes

Dynamics

Updraft Velocity

Large Scale Thermodynamics

Particle characteristics

Size

Concentration

Chemical Composition

Cloud Processes

Cloud droplet formation

Drizzle formation

Rainwater formation

Chemistry inside cloud droplets

Links/feedbacks need to be incorporated (in some way).

VERY challenging problem (Feingold and Stevens, 2009)

slide11

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

Start from a pure H2O drop

102

As the droplet size decreases, its equilibrium vapor pressure increases

(Kelvin effect).

Less molecules around in small drops to “pull” H2O in the droplet phase

Kelvin

101

Relative Humidity (%)

100

99

98

0.1

1

10

Wet diameter (mm)

slide12

Solute Concentration (M)

10

1

0.1

102

Kelvin

101

Relative Humidity (%)

100

Raoult’s Law applied to (NH4)2SO4

particle with 20 nm dry diameter

99

98

0.1

1

10

Wet aerosol diameter (mm)

When does an aerosol particle act as a CCN ?

Take same drop and add some solute; e.g., (NH4)2SO4

Dissolved material decreases vapor pressure (Raoult effect).

As particle grows, this effect is less important

slide13

Solute Concentration (M)

10

1

0.1

102

The combined Kelvin and Raoult effects is known as the Köhler equation.

You can be in equilibrium even if you are above saturation.

Kelvin

Kelvin+Raoult (Köhler)

101

Relative Humidity (%)

100

Raoult for (NH4)2SO4

20 nm dry diameter

99

98

0.1

1

10

Wet aerosol diameter (mm)

When does an aerosol particle act as a CCN ?

Put both effects together: You get the equilibrium vapor pressure of a wet aerosol particle.

slide14

Critical Sc

Critical

Wet

Diameter, Dc

Wet Aerosol

(Haze)

Cloud Droplet

When does an aerosol particle act as a CCN ?

Dynamical behavior of an aerosol particle in a variable RH environment.

102

101

Relative Humidity (%)

100

99

98

0.1

1

10

Wet aerosol diameter (mm)

When ambient saturation ratio exceeds Sc, particles act as CCN.

CCN = f (Size, Chemical Composition)

slide15

1:1

dN/dlogdp

[cm-3]

[CCN]predicted

dp ,nm

[CCN]measured

Testing CCN activation theory:

CCN “Closure” studies

Compare measurements of CCN to predictions using Köhleractivation theory.

CCN Closure

Aerosol Size Distribution

integrate

[CCN]predicted

Use theory to predict the particlesthat can act as CCN based on measured chemical composition and CCN instrument supersaturation.

slide16

Finokalia Aerosol Measurement Campaign (FAME-07) – Summer 2007

Finokalia

DMT CCN counter

Supersaturation range: 0.2-1.0%

TSI 3080 SMPS

Size range: 20-460 nm

Low-vol impactor

Ionic composition measured via IC

WSOC/EC/OC also

measured

(Bougiatioti et al., ACP, 2009)

slide17

Finokalia Aerosol Measurement Campaign (FAME-07) – CCN closure

2% overprediction (on average).

Introducing compreshensive composition into CCN calculation gives excellent CCN closure.

Köhler (CCN activation) theory really works.

(Bougiatioti et al., ACP, 2009)

slide18

t

Smax

drop growth

activation

S

aerosol

CCN activation requires knowledge of cloud RH…

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.

  • 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

slide19

“Mechanistic” Cloud Parameterizations

efficiently solve drop/ice formation problem

Input: P,T, vertical wind, particle size distribution,composition.

Output: Cloud properties (droplet number, size distribution).

How: Solve one algebraic equation (instead of ODE’s).

Examples for liquid clouds:

Abdul-Razzak et al., (2000); Nenes and Seinfeld (2003), Fountoukis and Nenes (2005), Ming et al., (2007); Barahona and Nenes (2007)

Examples for ice clouds:

Liu and Penner (2005); Barahona and Nenes (2008, 2009ab)

Kärcher and Lohmann (2001)

  • Characteristics:
  • 103-106 times faster than numerical cloud models.
  • Impressive agreement with numerical models.
slide20

CIRPAS Twin Otter

NOAA P3

Are these parameterizations “good enough”?

Apply them to real clouds. In-situ data obtained from airborne platforms is ideal for this purpose!

parameterization evaluation cdnc closure
Parameterization EvaluationCDNC “closure”

Observed Aerosol size distribution

Observed Cloud updraft Velocity

Observed Aerosol composition

Parameterization

Observed Cloud Droplet Number

Predicted Cloud droplet number

Compare

slide22

CRYSTAL-FACE (2002)

Cumulus clouds

CIRPAS Twin Otter

Parameterization agrees with observed CDNC

Agreement to within a few % (on average) !

Meskhidze et al.,JGR (2005)

slide23

CRYSTAL-FACE (2002)

Cumulus clouds

CIRPAS Twin Otter

Parameterization agrees with observed CDNC

Agreement to within a few % (on average) !

Great agreement…

…when you know the input.

Meskhidze et al.,JGR (2005)

slide24

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

Aerosol Problem: Complexity

An integrated “soup” of

Inorganics, organics (1000’s)

Particles can have uniform composition with size…

… or not

Can vary vastly with space and time (esp. near sources)

In-situ data to study the aerosol-CCN link:

Usage of CCN activity measurements to “constrain” the above “chemical effects” on cloud droplet formation.

slide25

Understanding & parameterizing CCN activity…

Organics and inorganics add solute and can be expressed in terms of a “hygroscopicity parameter”, k (Petters and Kreidenweis, 2007)

k = org korg + inorg kinorg

kinorg ~ 0.6 for(NH4)2SO4 to 0.9 for NaCl

korg ~ 0.0-0.3 and depends on oxidation state and precursor.

If organics are composed of a “soluble” and an “insoluble”

fraction, then

korg = sol ksol + insolkinsol ~ sol ksol

0

See how the k of water-soluble organics vary in samples

slide26

Determining k: size-resolved CCN measurements

Size Selection

Aerosol Generation

Polydisperse Aerosol

Shaker, Atomizer, or

ambient sample

Scanning Mobility Particle Sizer

Monodisperse Aerosol

Condensation Particle Counter, 3010

Particle Detection

Count CCN

Continuous Flow Streamwise Thermal Gradient Counter

Count CN

slide27

Aerosol Generation

Polydisperse Aerosol

Activated CCN

Fraction CN

Burrell Wrist Action Shaker

=

Determining k: size-resolved CCN measurements

Size Selection

Results: “activation curves”

CCN/CN as a function of d

Scanning Mobility Particle Sizer

Monodisperse Aerosol

Activated Fraction of particles (CCN/CN)‏

Condensation Particle Counter, 3010

Mobility Diameter (nm)

Particle Detection

Count CCN

Continuous Flow Streamwise Thermal Gradient Counter

Count CN

slide28

Aerosol Generation

d50 : aerosol becomes more hygroscopic

Polydisperse Aerosol

Burrell Wrist Action Shaker

Determining k: size-resolved CCN measurements

Size Selection

d50 : diameter for which 50 % of the particles activateinto cloud droplets.

Scanning Mobility Particle Sizer

Monodisperse Aerosol

Activated Fraction of particles (CCN/CN)‏

Condensation Particle Counter, 3010

Mobility Diameter (nm)

Particle Detection

Count CCN

Continuous Flow Streamwise Thermal Gradient Counter

Count CN

slide29

Parameterizing the CCN activity data

using methods based on Köhler-theory

  • Determine d50 dependence on supersaturation.
  • Fit the measurements to a power law expression.
  • Relate fitted coefficients to aerosol properties (e.g. hygroscopicity parameter k) by applying theory:

(NH4)2SO4

(NH4)2SO4

… but k is a normalized average molecular weight of the solute in the aerosol and can be used to infer its nature.

Petters and Kreidenweis, ACP (2007); Padró et al., ACP (2007)

slide30

from experimental data

Inferring molar volume (molecular weight)

For a particle containing only water-soluble organics:

so

Average molecular

weight over density

(molar volume)

Constants

This is Köhler Theory Analysis (Padró et al., ACP, 2007)

slide31

Major findings on water-soluble organics

Many “aged” soluble organics (SOA) from a wide variety of

sources have a remarkably similar hygroscopicity.

Aged organics in Mexico City aerosol from MILAGRO (Padró et al, in press).

ksol = 0.28 ± 0.06, regardless of location and time !

Molar volume/surfactant (KTA) analysis suggests that:

Daytime samples contain organics ~200 amu on average

and depress surface tension a few %

Nighttime samples contain organics ~400 amu on average

and depress surface tension 10-15 %

Changes in surface tension partially compensates for shifts in average molar volume to give the constant ksol

slide32

Major findings on water-soluble organics

Data from SOA chamber experiments

Organic Aerosol from biogenic VOC emissions

a-pinene, monoterpene oxidation (Engelhart et al., ACP, 2009).

ksol ~ 0.28

b-caryophyllene (Asa-Awuku et al., ACP, 2009).

ksol ~ 0.26

KTA suggests that: both systems contain organics ~200 amu on average and depress surface tension a few %

Anthropogenic VOCs (Asa-Awuku et al.,ACP, 2010)

terpinolene, cycloheptene, 1-methylcycloheptene ozonolysis

ksol ~ 0.26-0.33

slide33

Major findings on water-soluble organics

Even biomass-burning aerosol exhibits similar properties:

Aerosol samples from prescribed fire burning (Sullivan et al., 2006)

ksol ~ 0.33 (Asa-Awuku et al., 2008)

Speciation across samples varies considerably, but their cumulative effects on CCN activity are about the same.

Changes in surface tension partially compensates for shifts in average molar volume to give the constant ksol

What matters is the fraction of soluble organic…

Complexity sometimes simplifies things for us.

korg = (0.25±0.05) esol

slide34

Test organic parameterization on Arctic CCN

NOAA ARCPAC: Aerosol Radiation and Cloud Processes affecting Arctic Climate

NASA ARCTAS: Arctic Research of the Composition of the Troposphere from Aircraft and Satellites

Date: April, June, July 2008.

Platforms: NASA WB-P3, DC-8

Arctic haze / pollution; Boreal Forest Fires

Date: April, 2008.

Platform: NOAA WB-P3

Arctic haze / pollution; Flights over ice / leads

Map of all Alaskan flights during the

ARCPAC campaign.

Flight tracks for the ARCTAS-B summer deployment.

Jacob et al, ACP, 2009

slide35

CCN In Fresh Biomass Burning Plumes:

neglecting organics

  • Closure Inputs:
  • AMS bulk chemistry
  • SS: 0.3 - 0.6%
  • Surface Tension of Water

Closure Error Histogram

Large under-prediction for biomass burning!

• Tendency towards under-prediction.

• WSOC and AMS data suggest 30-60% of organics in fires are water-soluble.

Lathem et al., manuscript in preparation

slide36

Before (# 1)

After (# 2)

CCN In Fresh Biomass Burning Plumes:

including organics

Large under-prediction is now gone!

  • • Explicitly include organic impacts on CCN activity as korg = WSOC * 0.25
  • •Most of the CCN prediction bias can be accounted for with a simple rule.
  • The remaining scatter can be attributed (mostly) to size-dependent composition.
slide37

Some “take-home” messages

CCN theory is adequate for describing cloud droplet formation.

Physically based formulations for description of cloud droplet formation in GCMs are comprehensive… but are still computationally feasible.

Size-resolved measurements of CCN activity are very useful for constraining the extent and sources of aerosol hygroscopicity on cloud droplet formation.

The water-soluble fraction of oxidized organics is very hygroscopic, and is surprisingly constant.

The cumulative effect of organics on CCN activity can likely be described by a simple relationship of the form:

korg = (0.25±0.05) esol

cirrus ice cloud parameterization
Cirrus (Ice) Cloud Parameterization

Ice Crystal Population

Multiplemechanisms for ice formation can be active.

http://www.alanbauer.com

Heterogeneous Freezing (Immersion, deposition, contact, …)

Also depends on the material and surface area

Homogeneous Freezing

Mainly depends on RHiand T

Wet aerosol

particles

+ Insoluble Material (“Ice Nuclei”)

conceptual model of ice formation in cirrus
Conceptual Model of Ice Formation in Cirrus

Homogeneous freezing of droplets

Crystal growth, fresh IN continue to freeze and deplete vapor

Heterogeneous IN freezing

begin forming ice

Expansion cooling and

ice supersaturation development

Soluble and insoluble

aerosol initial distribution

Height (km)

Ice Crystals

10

9.5

9

8.5

8

Homogeneous

V

Heterogeneous

Liquid droplets + Insoluble material

100 110 120 130 140 150 160

RHi (%)

the effect of in on ice crystal number concentration
The Effect of IN on Ice Crystal Number Concentration

Homogeneous and Heterogeneous

Heterogeneous

Homogeneous

Ice Crystal Concentration (cm-3)

Nc=Nlim

Ice Nuclei Concentration (cm-3)

Barahona and Nenes, ACP, 2009a.

parameterization development part 1 pure homogeneous freezing
Parameterization DevelopmentPart 1: Pure Homogeneous Freezing

Parcel Ice Saturation

  • Define freezing probability as a function of T and P
  • Link the freezing probability to crystal size and concentration
  • Solve for crystal concentration at the maximum RHi

Ice Crystal

So’

growth

freezing

time

Barahona and Nenes, JGR, 2008

parameterization development part 1 pure homogeneous freezing1
Parameterization Development Part 1: Pure Homogeneous Freezing

(Crystal population balance)

Find a Solution of:

parameterization development part 1 pure homogeneous freezing2
Parameterization Development Part 1: Pure Homogeneous Freezing

(Crystal population balance)

Find a Solution of:

parameterization development part 1 pure homogeneous freezing3
Parameterization Development Part 1: Pure Homogeneous Freezing

(Crystal population balance)

Find a Solution of:

Calculate freezing probability:

parameterization development part 1 pure homogeneous freezing4
Parameterization Development Part 1: Pure Homogeneous Freezing

(Crystal population balance)

Find a Solution of:

Calculate freezing probability:

Calculate size distribution:

parameterization development part 1 pure homogeneous freezing5
Parameterization Development Part 1: Pure Homogeneous Freezing

(Crystal population balance)

Find a Solution of:

Calculate freezing probability:

Calculate size distribution:

Solve supersaturation balance:

parameterization development part 1 pure homogeneous freezing6
Parameterization Development Part 1: Pure Homogeneous Freezing

(Crystal population balance)

Find a Solution of:

LOTS AND LOTS OF MATH (and scaling) …

Calculate freezing probability:

Calculate size distribution:

Solve supersaturation balance:

parameterization development part 1 pure homogeneous freezing7
Parameterization Development Part 1: Pure Homogeneous Freezing

The solution of the PDE system reduces to:

≡ f (aerosol size and

concentration, P and T )

Homogeneously Frozen Fraction

Aerosol Number Concentration

Ice Crystal Number Concentration

A few lines of FORTRAN code. It is completely theoretical

(i.e. rigorous and robust) and captures the (complex) physics of

ice nucleation. We can also calculate the size distribution… (not shown)

Barahona and Nenes, JGR, 2008

slide49

Parameterization DevelopmentPart 2: Including Effects of IN

Height

Ice Crystals

  • IN tend to freeze early, grow and deplete water vapor
  • Can inhibit homogeneous nucleation
  • IN also contribute to the total ice crystal concentration

RHi

Homogeneous

V

Crystal

Growth

Heterogeneous

Liquid droplets + insoluble material

Homogeneous

(%)

100 110 120 130 140 150

Heterogeneous

slide50

Parameterization DevelopmentPart 2: Including Effects of IN

Energy balance

Global water vapor balance

Ice water vapor condensation

Ice crystal size distribution evolution = nucleation + growth

… even more math and scaling…

Ice crystal growth

Barahona and Nenes, JGR, 2008; ACP, 2009a

slide51

3

/

2

é

ù

3

/

2

æ

ö

N

ç

÷

ê

ú

=

-

IN

f

f

1

ç

÷

c

,

IN

c

N

ê

ú

è

ø

ë

û

lim

Parameterization DevelopmentPart 2: Including Effects of IN

Homogenously frozen droplet fraction with IN effects

Homogenously frozen fraction without IN (Part 1)

Ice nuclei concentration that prevents homogeneous nucleation

Analytical solution; explicitly depends on cloud formation conditions and ice nuclei characteristics

Barahona and Nenes, ACP, 2009a.

slide52

Parameterization DevelopmentPart 2: Including Effects of IN

Heterogeneously Frozen fraction

  • Applies whether homogeneous freezing contributes crystals (i.e., if NIN < Nlim) or not (i.e., NIN > Nlim).
  • Keeps the heterogeneous nucleation spectrum in its most general form:
    • Can account for several freezing modes and aerosol species
    • Can use theoretical and/or empirical IN data

Final Answer: Crystal concentration

Barahona and Nenes, ACP, 2009b.

slide53

Cirrus Parameterizationevaluation with numerical model

Evaluation over a broad range of updraft, temperatures, and aerosol (soluble and insoluble) concentrations

Average error below 5 ± 10 %, for combined homogeneous and heterogeneous freezing

Barahona and Nenes, ACP, 2009b.

problem with implementation of all this in climate models
Problem with Implementation of all this in Climate Models

Cloud formation: < 1 km

GCM grid cell size: 100 km

Mesoscale grid cell size: 16 km

problem with implementation of all this in climate models1
Problem with Implementation of all this in Climate Models

Cloud formation: < 1 km

Clouds form at much smaller scales than models can resolve.

Implementing all this parameterized physics is not at all “trivial”!

GCM grid cell size: 100 km

Mesoscale grid cell size: 16 km

slide56

Probability that vertical velocity lies

between w and w+dw

Treating subgrid cloud processes

Use the probability density function (PDF) of vertical velocity, p(w), in the grid cell

:

Typical Assumption:

Gaussian PDF

Average updraft

velocity

p(w)

Fitting parameters:

w (m s-1)

slide57

Average updraft velocity:

Diagnosing the PDF from GCM

Assume (boundary layer, stratiform)

Turbulent kinetic energy (gravity wave energy, etc.) resolved in model can be used to diagnose s

e.g., TKE ~ s 2 (in boundary layers)

and

slide58

Fraction of droplets from

vertical velocities between

w and w+dw

Using PDF for grid-average Nd

Add up the droplets emerging from each

updraft in the PDF.

Upon integration, we get for grid-average Nd:

PDF integrations have been used in large

scale models [e.g., Ghan et al., 1997] but is

slow because of numerical integration.

slide59

Characteristic Velocity:

The “fast” and “consistent” approach

Use the characteristic velocity, w*, that gives the PDF-averaged property:

If we knew w*, then we get PDF-averagedvalues

by calling a parameterization once.

Much faster than a numerical integration, but

of comparable accuracy.

Morales and Nenes, in review

slide60

Characteristic Velocity

Each property/process has its own w*

Droplet number

Effective Radius

Autoconversion rate

different between each other and from

Because Nd, reff and A scale differently with Nd (w)

Morales and Nenes, in review

slide61

Deriving Characteristic Velocity

The most simple (elegant) form possible

Twomey [1959] parameterization a simple power law. k is an aerosol-dependent parameter.

Substituting into

gives

gives

or 89% of the average updraft

Morales and Nenes, in review

slide62

Diagnose

from simulation (dynamics)

Applying Characteristic Velocities

in a GCM simulation

Kharoutdinov & Kogan (2000)

Call Nd

parameterization

PDF-Average Nd

PDF-Average reff

PDF-Average A

slide63

Biases associated with NOT using characteristic Velocity

instead of “appropriate” w*

(current practice)

Use

~8% overestimation

in grid-average Nd

(not bad)

~10-15% underestimation

in grid-average reff

1-2 mm bias

(important)

Factor of 5 underestimation

in grid-average A

Kharoutdinov & Kogan (2000)

(very important)

Using characteristic velocity can take care of

an important source of “tuning” done in GCMs

Morales and Nenes, in review

some take home messages
Some take home messages
  • PDF-averaging is a good (first-order) way to address the scale-gap when applying parameterizations in models.
  • Characteristic velocity is a fast and consistent way of calculating PDF (grid-) average cloud parameters and processes in models.
  • Each process (cloud quantity) has a characteristic velocity, but it always scales “nicely” to the PDF characteristics
  • Using characteristic velocity can account for important biases that GCMs currently treat through empirical “tuning”.
slide65

Summary

  • Physically-based representations of droplet and ice formation in GCMs is becoming sophisticated… but still computationally feasible.
  • CCN theory is adequate for describing cloud droplet formation. Simplified treatments of aerosol composition get “most” of the CCN number right.
  • Size-resolved measurements of CCN activity are very useful for constraining aerosol complexities (especially organic effects) on cloud droplet formation.
  • Correct coupling of the parameterizations with dynamics and consideration of its subgrid variability is a key issue.
  • Uncertainty of climate model predictions of the indirect effect will still be significant (and probably grow), but we are getting much better at knowing how much it really is.