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Cloud Feedbacks on Climate: A Challenging Scientific Problem. Joel Norris Scripps Institution of Oceanography Fermilab Colloquium May 12, 2010. 4 th IPCC: Key Uncertainties. “Cloud feedbacks (particularly from low clouds) remain the largest source of uncertainty [to climate sensitivity].”

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cloud feedbacks on climate a challenging scientific problem

Cloud Feedbacks on Climate: A Challenging Scientific Problem

Joel Norris

Scripps Institution of Oceanography

Fermilab Colloquium

May 12, 2010

4 th ipcc key uncertainties
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
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
Outline
  • Theory
  • Numerical Modeling
  • Observations
  • Marine Boundary Layer Clouds
  • Recent Results
  • Recommendations
a simple atmosphere
A Simple Atmosphere

reflected

solar flux

fraction ap

transmitted

surface flux

fraction 1-e

top of

atmosphere

solar flux

absorbed

surface flux

fraction e

emitted

atmospheric flux

emissivity e

absorbed

solar flux

fraction 1-ap

surface

emitted

surface flux

absorbed

atmospheric flux

a simple atmosphere1
A Simple Atmosphere

Top of Atmosphere

(1 – ap) S0 / 4 = e sTa4 + (1 – e) sTs4

Atmosphere

e sTs4 = 2 e sTa4

Surface

(1 – ap) S0 / 4 = sTs4 – e sTa4

how are t s and e related
How are Ts and e related?

If emissivity e increases (more CO2)

surface temperature Ts increases

the simplest climate theory
The Simplest Climate Theory

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 theory1
The Simplest Climate Theory

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

where

Planck

radiative

response

allow internal feedbacks
Allow Internal Feedbacks

Ik internal parameter

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

allow internal feedbacks1
Allow Internal Feedbacks

If equilibrium (DF = 0), then

where

net feedback on climate
Net Feedback on Climate

This can be rewritten as

where

sum of

individual

feedbacks

net feedback on climate1
Net Feedback on Climate

This can be rewritten as

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

climate sensitivity
Climate Sensitivity

Climate sensitivity l is the ratio of temperature response to external forcing

high sensitivity: strong warming for a given forcing

low sensitivity: weak warming for a given forcing

individual major feedbacks
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 Feedback

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

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

water vapor feedback1
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

saturation mixing ratio
Saturation Mixing Ratio

From Hartmann’s Global Physical Climatology

water vapor feedback2
Water Vapor Feedback

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

Dq 0.1 g/kg (10% change) for either:

DT  2.5 K (1% change)

Dr  0.1 (25% change)

water vapor feedback3
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
Cloud Feedback

where C can represent multiple cloud characteristics

reflection of solar radiation

cloud greenhouse effect

sign of net radiation flux depends on type of cloud

cloud radiative effects
Cloud Radiative Effects

high-level cloud

reflection ~ 0

greenhouse << 0

warms the earth

thick cloud

reflection >> 0

greenhouse << 0

(reflection +

greenhouse) ~ 0

low-level cloud

reflection >> 0

greenhouse ~ 0

cools the earth

comparison with co 2
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-2more radiation to space
  • Reduction in outgoing thermal radiation by CO2 increase since 1750 (280  380 ppm): –1.6 W m-2
comparison with co 21
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
Cloud Response to Temperature

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

r controlled by turbulent dynamics of the atmosphere

cloud feedback1
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 isrequired for basic knowledge of cloud feedback

numerical models

T,q

T,q

Numerical Models

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

winds

solar radiation

thermal radiation

temperature

moisture

numerical models1
Numerical Models

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

1 km

clouds

small-scale

circulations

100 km

numerical models2
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
Cloud Feedbacks in Models

figure from Ringer et al. (2006)

Change in cloud radiation effects due to 2 x CO2

warming is completely inconsistent between models!

slide33

Simulated Cloud Change for 2CO2

Courtesy of Brian Soden

Models predict different signs of cloud change

numerical models3
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)
cloud 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!
satellite cloud record
Satellite Cloud Record

Low-level cloudiness is the largest contributor to the

apparent artifact in total amount (not shown).

satellite cloud record1
Satellite Cloud Record

Low-level cloudiness is the largest contributor to the

apparent artifact in total amount (not shown).

cloud observations1
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
slide43

Low-Level Cloud and Net Radiation

Hartmann et al. 1992

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

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

subtropical marine boundary layer
Subtropical Marine Boundary Layer

dry

free

troposphere

Td

T

temperature

inversion

50+ m

cloud

layer

moist

boundary

layer

500 to

2000 m

subcloud

layer

sea surface

subtropical marine boundary layer1
Subtropical Marine Boundary Layer

dry

free

troposphere

ws < 0

subsidence

subsidence  entrainment

temperature

inversion

entrainment

we

cloud

layer

moist

boundary

layer

divergence

subcloud

layer

ws= 0

sea surface

subtropical marine boundary layer2

entrainment

drying + drizzle

surface moistening

entrainment +

surface warming

radiative +

advective cooling

Subtropical Marine Boundary Layer

dry

free

troposphere

subsidence

temperature

inversion

entrainment

radiative cooling

drying and heating

cloud

layer

moist

boundary

layer

divergence

drizzle loss

subcloud

layer

advection from

midlatitudes

moistening and heating

sea surface

subtropical marine boundary layer3

buoyancy

generation

entrainment +

dissipation

Subtropical Marine Boundary Layer

dry

free

troposphere

subsidence

temperature

inversion

entrainment

radiative cooling

negative

buoyancy

drying and heating

cloud

layer

moist

boundary

layer

convection and

turbulent mixing

divergence

drizzle loss

subcloud

layer

advection from

midlatitudes

positive

buoyancy

moistening and heating

sea surface

boundary layer structure and clouds
Boundary Layer Structure and Clouds

Stratocumulus

Cu-under-Sc

Cumulus

qt

qe

qt

qe

qt

qe

inversion

cloud

layer

stable

layer

surface

layer

surface

well-mixed

boundary layer

cloud layer

decoupled from

surface layer

conditionally

unstable

boundary layer

slide50

NE Pacific Decadal Variability

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

slide51

NE Pacific Decadal Variability

Bars- low cloud

warm sea surface temperature

weak sea level pressure

weak wind

Line- total cloud

(corrected

for artifacts)

less stratocumulus cloud

more ocean heating

less boundary-layer cooling

slide52

NE Pacific Decadal Variability

Basin-wide regression on NE Pacific SST time series

slide53

Is this feedback present in IPCC AR4 models?

Observed r

NE Pacific

cloud and

meteorology

Correct sign

r and robust

simulation

wrong sign

r(cloud,w500)

wrong sign

r(cloud,SLP)

models with

wrong sign

r(cloud,LTS)

models with

wrong sign

r(cloud,SST)

slide54

HadGEM1 2CO2 Change

2CO2 Simulation

Observed Decadal

cloud change

2CO2 cloud and circulation changes

resemble observed decadal

cloud and circulation changes

circulation and cloud feedbacks
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 2CO2 that resembles observed pattern
conclusion
Conclusion

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

recommendations
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!
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