Cloud Feedbacks on Climate: A Challenging Scientific Problem

Joel NorrisScripps Institution of Oceanography

Fermilab ColloquiumMay 12, 2010

4th IPCC: Key Uncertainties

• “Cloud feedbacks (particularly from low clouds) remain the largest source of uncertainty [to climate sensitivity].”

• “Surface and satellite observations disagree on total and low-level cloud changes over the ocean.”

• “Large uncertainties remain about how clouds might respond to global climate change.”

• “Cloud feedbacks are the primary source of intermodel differences in equilibrium climate sensitivity…”

Why a challenging problem?

• We have no fundamental theory for how global cloudiness should respond to greenhouse warming

• We have no numerical models that produce sufficiently realistic simulations of global cloudiness

• We have no stable system to monitor changes in global cloudiness and radiation on multidecadal time scales

Outline

• Theory

• Numerical Modeling

• Observations

• Marine Boundary Layer Clouds

• Recent Results

• Recommendations

Theory

A Simple Atmosphere

emittedsurface flux

absorbedsurface fluxfraction

solar flux

absorbedsolar fluxfraction 1p

emittedatmospheric fluxemissivity

absorbedatmospheric flux

surface

top ofatmosphere

transmittedsurface fluxfraction 1

reflectedsolar fluxfraction p

A Simple Atmosphere

Top of Atmosphere

(1 – p) S0 / 4 = Ta4 + (1 – ) Ts

4

Atmosphere

Ts4 = 2 Ta

4

Surface

(1 – p) S0 / 4 = Ts4 – Ta

4

How are Ts and related?

If emissivity increases (more CO2)

surface temperature Ts increases

4/1

0

24

1

ST p

s

The Simplest Climate Theory

sTT

FE

E

FF

F upward radiation flux at top of atmosphere

E external parameter (e.g., CO2, solar output)

Ts global surface temperature

no internal feedbacks

The Simplest Climate Theory

EE

FT BB

0

If equilibrium (F = 0) and zero internal feedbacks, then

where

341

ssBB

TT

F

Planckradiativeresponse

Allow Internal Feedbacks

Ik internal parameter

e.g., cloud, snow/ice, water vapor, vertical temperature profile (lapse rate)

k

ss

k

ks T

dT

dI

I

FT

T

FE

E

FF

Allow Internal Feedbacks

If equilibrium (F = 0), then

where

k kBB

s

TT

1

1

0

s

k

kk dTdI

IF

1

Net Feedback on Climate

This can be rewritten as

where

sum ofindividualfeedbacks

k k

BB

kkff

fTTs

1

10

f > 0 positive feedback: internal response of climate system exacerbates externally forced warming

f < 0 negative feedback: internal response of climate system mitigates externally forced warming

Net Feedback on Climate

This can be rewritten as

fTTs

1

10

high sensitivity: strong warming for a given forcing

low sensitivity: weak warming for a given forcing

Climate Sensitivity

fBB

1

EE

FTs

Climate sensitivity is the ratio of temperature response to external forcing

Individual Major Feedbacks

• Snow/ice albedo feedback – obviously positive

• Lapse rate feedback – small negative

• Water vapor feedback – almost certainly positive

• Cloud feedback – sign unknown, maybe positive

Water Vapor Feedback

water vapor is a greenhouse gas (the strongest), so

where q is water vapor mixing ratio (kg water vapor per kg dry air)

sBB

wv

BBwv dT

dq

q

Ff

0q

F

Water Vapor Feedback

where r is relative humidity and qsat is saturation water vapor mixing ratio

qsat rapidly increases with temperature

r controlled by turbulent dynamics of the atmosphere

s

sats

satsat

ss dT

drq

dT

dqrrq

dT

d

dT

dq

Saturation Mixing Ratio

From Hartmann’s Global Physical Climatology

Water Vapor Feedback

use values for location of maximum emission to space: r 0.4, T 250 K, qsat 1 g/kg

q 0.1 g/kg (10% change) for either:

T 2.5 K (1% change)

r 0.1 (25% change)

rqTdT

dqrq sat

sat

Water Vapor Feedback

• To first order, water vapor feedback is controlled by saturation vapor dependence on temperature

• Changes in relative humidity have second order influence

Good understanding of dynamical control of humidity not required for basic knowledge of water vapor feedback

Cloud Feedback

reflection of solar radiation

where C can represent multiple cloud characteristics

sBBcld dT

dC

C

Ff

0C

F0

C

F cloud greenhouse effect

sign of net radiation flux depends on type of cloud

Cloud Radiative Effects

low-level cloud

reflection >> 0

greenhouse ~ 0

cools the earth

high-level cloud

reflection ~ 0

greenhouse << 0

warms the earth

thick cloud

reflection >> 0

greenhouse << 0

(reflection + greenhouse) ~ 0

Comparison with CO2

• Reflection of solar radiation by all clouds:+48 W m-2

• Reduction in outgoing thermal radiation by all clouds: –31 W m-2

• Net cloud radiative effect of all clouds:+17 W m-2 more radiation to space

• Reduction in outgoing thermal radiation by CO2 increase since 1750 (280 380 ppm):

–1.6 W m-2

Comparison with CO2

1.6 W m-2 (35% increase in CO2) equal to either:

• 3% change in the reflection of solar radiation by clouds

• 5% change in the reduction of outgoing thermal radiation by clouds

• 9% change in net effect of clouds on radiation

Cloud Response to Temperature

clouds exist where r ≥ 1, absent where r < 1

r controlled by turbulent dynamics of the atmosphere

ss dT

dr

dr

dC

dT

dC

Cloud Feedback

• Changes in clouds on the order of 1% can have major impacts on Earth’s radiation budget

• Radiative impacts of different cloud types can have opposite sign

• Changes in relative humidity have first order influence

Good understanding of dynamical control of humidity is required for basic knowledge of cloud feedback

Numerical Modeling

Numerical Models

Global or smaller-domain numerical models explicitly solve equations at scales above the grid resolution

T,q

T,qwindssolar radiationthermal radiationtemperaturemoisture

Numerical Models

Processes at scales below the grid resolution must be parameterized (approximated in terms of grid-scale values)

cloudssmall-scalecirculations

100 km

1 km

Numerical Models

• Ideally, sub-grid turbulence should be homogeneous, isotropic, and cascade downscale to viscous dissipation

• Turbulence with these characteristics typically occurs only at scales less than 10-100 meters

• Global climate models must parameterize turbulence that is inhomogeneous, non-isotropic, and non-linear

• Cloud parameterizations do not represent the underlying processes with sufficient accuracy

Cloud Feedbacks in Models

Change in cloud radiation effects due to 2 x CO2

warming is completely inconsistent between models!

figure from Ringer et al. (2006)

Models predict different signs of cloud change

Simulated Cloud Change for 2CO2

Courtesy of Brian Soden

Numerical Models

• Global climate models do not correctly and consistently simulate cloudiness and its radiative effects

• Model climate sensitivity (warming per CO2 increase) depends most on what is understood least (cloud parameterizations)

Observations

Cloud Observations

• Surface visual observations of clouds have had a consistent (?) identification procedure since 1950

• Semi-standardized observations of clouds from weather satellites are available since the early 1980s

• Observing systems are designed for monitoring weather, not climate – no built-in long-term stability!

Surface and SatelliteCloud

Low-level cloudiness is the largest contributor to the

apparent artifact in total amount (not shown).

Satellite Cloud Record

Low-level cloudiness is the largest contributor to the

apparent artifact in total amount (not shown).

Satellite Cloud Record

Statistical Correction to Data

before after

Cloud Observations

• Surface and satellite cloud records are dominated by spurious variability

• Observational uncertainty is much larger than the magnitude of significant radiative impacts on climate

• Statistical correction of data can provide more realistic regional variability

• Very precise after-the-fact calibration must be applied to satellite observations to produce a climate-ready dataset

Marine Boundary Layer Clouds

Low-Level Cloud and Net Radiation

Low-level clouds and especially marine stratocumulus cool the planet (solar reflection by clouds greater than greenhouse effect of clouds)

Cloud with tops below 680 mb (less than 3 km)

Hartmann et al. 1992

Subtropical Marine Boundary Layer

sea surface

temperatureinversion

moistboundarylayer

dryfreetroposphere

cloudlayer

subcloudlayer

Td T

500 to2000 m

50+ m

Subtropical Marine Boundary Layer

sea surface

temperatureinversion

moistboundarylayer

dryfreetroposphere

cloudlayer

subcloudlayer

ws < 0subsidence

divergence

entrainment

we

ws = 0

subsidence entrainment

Subtropical Marine Boundary Layer

sea surface

temperatureinversion

moistboundarylayer

dryfreetroposphere

cloudlayer

subcloudlayer

subsidence

divergence

entrainment

drying and heating

moistening and heating

radiative cooling

advection frommidlatitudes

entrainmentdrying + drizzle

entrainment +surface warming

radiative +advective cooling

surface moistening

drizzle loss

Subtropical Marine Boundary Layer

sea surface

temperatureinversion

moistboundarylayer

dryfreetroposphere

cloudlayer

subcloudlayer

subsidence

divergence

entrainment

drying and heating

moistening and heating

radiative cooling

buoyancygeneration

entrainment +dissipation

negativebuoyancy

positivebuoyancy

convection andturbulent mixing

advection frommidlatitudes

drizzle loss

Boundary Layer Structure and Clouds

surface

inversion

cloudlayer

surfacelayer

qt e

well-mixedboundary layer

Stratocumulus

eqt

Cumulus

conditionallyunstable

boundary layer

stablelayer

qt e

cloud layerdecoupled from

surface layer

Cu-under-Sc

Recent Results

collaborators: Amy Clement and Robert Burgman

NE Pacific Decadal Variability

Does a cloud feedback promote decadal variability in sea surface temperature and circulation?

Line- total cloud

Bars- low cloud

NE Pacific Decadal Variability

warm sea surface temperatureweak sea level pressure

weak wind(corrected for artifacts)

less stratocumulus cloudmore ocean heating

less boundary-layer cooling

NE Pacific Decadal VariabilityBasin-wide regression on NE Pacific SST time series

models with wrong sign r(cloud,SST)

Correct signr and robustsimulation

Observed rNE Pacific cloud and meteorology

Is this feedback present in IPCC AR4 models?

models with wrong sign r(cloud,LTS)

wrong sign r(cloud,SLP)

wrong sign r(cloud,500)

HadGEM1 2CO2 ChangeObserved Decadal 2CO2 Simulation

cloud change

2CO2 cloud and circulation changes

resemble observed decadal

cloud and circulation changes

Circulation and Cloud Feedbacks

• On decadal time scales, decreased stratocumulus cloud cover is associated with warmer sea surface temperature and weaker atmospheric circulation

• Likely regional positive cloud feedback on decadal timescales due to solar warming of ocean and reduced cooling of atmospheric boundary layer

• Only one robust IPCC AR4 model reproduces correct sign for all 5 cloud-meteorological correlations

• This model exhibits stratocumulus decrease and weaker circulation for 2CO2 that resembles observed pattern

Conclusion

Cloud feedback on climate is a challenging problem but progress is slowly being made

Recommendations

• We need a stable observational system to monitor global cloudiness and radiation on decadal time scales

• We need greater integration between observations, numerical modeling, and theory (inside and outside of parameterizations)

• We need comprehensive quantitative understanding of cloud and meteorological co-variability in observations and models

• We need new ideas!

Thank You!