Transcript Water Vapor and Cloud Feedbacks Dennis L. Hartmann in collaboration with Mark Zelinka Department of Atmospheric Sciences University of Washington PCC Summer Institute 2010

Water Vapor and Cloud
Feedbacks
Dennis L. Hartmann
in collaboration with Mark Zelinka
Department of Atmospheric Sciences
University of Washington
PCC Summer Institute 2010
Basic Greenhouse Effect
• The atmosphere is translucent to solar radiation.
• Because water vapor, other greenhouse gases
and clouds are opaque to Earth’s thermal
emission,
• And because the temperature decreases with
altitude,
• The emission from Earth comes from the
atmosphere about 5km up, where it is about 30˚C
colder than the surface of Earth.
Greenhouse Effect
BB Curve minus OLR
50 mm 20 mm
10 mm
5 mm
Harries, QJ, 1996
Surface
30km
Greenhouse
Effect
Greenhouse Effect = Surface Emission - Outgoing Energy
155 Wm-2
=
390 Wm-2
- 235 Wm-2
W m-2
Water Vapor Feedback
• Saturation water vapor pressure increases
about 7% for every 1˚K increase in
temperature
• So if relative humidity is relatively constant
• The greenhouse effect of water vapor
increases with temperature
• Giving strong water vapor FEEDBACK.
Emission Temperature Lapse Rate and water Vapor
Runaway Greenhouse
Fixed Relative Humidity
Emission
Temperature
Fixed Absolute Humidity
Water Vapor Feedback
• Since Manabe and Wetherald (1967) it has
been estimated that fixed relative humidity is
a good approximation and vapor feedback
roughly doubles the sensitivity of climate.
• Because water vapor is so strongly positive
and interacts with other feedbacks, small
deviations from the fixed relative humidity
behavior would be significant and are worth
studying.
Manabe & Wetherald 1967
300-600 ppm
Fixed Clouds
Clear
Fixed Absolute Humidity DT = 1.33˚K DT = 1.36˚K
Fixed Relative Humidity
DT = 2.36˚K DT = 2.92˚K
Lapse Rate Feedback
• The Greenhouse effect depends on the lapse rate
of temperature.
• If the lapse rate decreases with global warming,
that is a negative feedback, since the difference in
surface temperature and emission temperature
will decrease, all else being equal.
• But all else is not equal, Water vapor feedback
tends to lessen the importance of lapse rate
feedback. See Cess, Tellus, 1975, page 193.
Emission
Temperature
Since water vapor is
the primary
greenhouse gas, and
depends only on
temperature,
emissivity is an
increasing function of
temperature.
When relative humidity is fixed,
so that absolute humidity is a function of temperature,
lapse rate and relative humidity feedbacks on OLR tend to cancel.
Water Vapor Feedback vs Lapse Rate
Feedback in AR4 models
Slope = -1
Validation for Fixed RH Assumption
• Seasonal Variation – Manabe and Wetherald
1967
• Many observational studies.
• Volcanic Eruption – Soden et al 2002 Science
• All the models do it fairly closely – Sherwood
et al 2010 JGR.
• El Niño – La Niña Difference – Zelinka 2011
ENSO Response – Mark Zelinka
30S-30N
ENSO Response 1˚K Tropical Warming - Zelinka
Temperature and Humidity Response to ENSO
Models(top) vs AIRS(bottom)
AR4 Model Control
AIRS Data 2003-2010
Cloud Feedback
• Clouds have a strong impact on the radiation
balance of Earth
• Reduce OLR by about 30 Wm-2
• Reduce Absorbed Solar Radiation ~ 50Wm-2
• Net effect about -20 Wm-2
• If their radiative effects change with global
warming, the effect could be a very significant
cloud feedback.
Feedback Analysis using radiative Kernels
Soden et al. 2008
R  QAbsorbed
Solar
 FOutgoing
Longwave
R(wA ,TA ,C A , aA )
x
RB  RA 
x  K x
x
RB  RA 

i
R
 xi 
xi

x
Ki
 xi
i
Where i represents a vertical level.
And x represents water vapor w, Temperature T, and surface albedo a.
Cloudiness C, is too nonlinear and is done as a residual.
Longwave Kernels: Temperature and Humidity
Average
Cloudiness
KTi
OLR

Ti
Ts
Ki
OLR

Ts
Kiw
OLR

wi
Longwave Radiative Kernels: Surface Vs Atmospheric Temperature
Zero
Surface Contribution
Atmosphere Contribution
Total
For Uniform 1˚K Temperature increase
Longwave Radiative Kernels: Surface vs Atmospheric Temperature
Surface
Atmosphere
Total
Heavy Lines for modeled AR4 Temperature Change
Vertically Integrated Feedback
• Multiply the Kernels times the changes and
integrate vertically to get the change in top-ofatmosphere energy flux required by feedback
processes.
RB  RA 

x
Ki
 xi
i
Radiation Balance Change = Radiative Kernel x Change in state variable
Temperature and Humidity Changes
SRES A2 Scenario AR4 Ensemble
 wi
 Ti
Normalized for 1˚K Global Mean
Surface Temperature increase
Feedback = Kernel x Response
Normalized to 1˚K global warming
Total
Clear
Vertically Integrated Feedbacks TOA
Net Positive feedback
near Equator
Some Consistent Wiggles
Implies increased poleward heat flux due to feedbacks
Integrated Feedbacks
LW and Shortwave Cloud feedbacks
Consistency of Longwave Cloud Feedback
Zelinka from AR4 SRES A2
Longwave Feedbacks Only
Summary of Feedback Analysis
• Net positive feedback near equator comes
from longwave water vapor and cloud
feedbacks that seem robust.
• Consistent wiggle in Southern Ocean comes
from shortwave cloud feedback and ocean
upwelling, which provide heat sinks and cause
atmosphere to increase its transport.
Feedbacks and Meridional Transport
• If you subtract the global mean and integrate
the feedback over a polar cap, you get the
change in meridional transport associated
with feedback processes for each degree of
global warming.
• If you combine this with the change in surface
heat fluxes you can obtain the changes in
atmospheric and oceanic heat flux. See also
Dargan’s talk on Thursday.
Feedbacks including surface fluxes
Feedbacks including surface fluxes
Warming induced
Surface
Flux changes: More
heat from
atmosphere to
surface in high
latitudes, especially
in SH: Heat Uptake
by Ocean
Warming induced Net
flux into atmosphere
from combined TOA
and surface flux
changes. Note net loss,
and gradient in net loss
are greater than for
surface fluxes.
Transport Changes
Annual Averages for AR4 Model Ensemble
Feedbacks
Flux Feedbacks
In AR4 sRES A2 Model Ensemble:
Oceanic Heat fluxes decrease, but
atmospheric fluxes
overcompensate to increase net
flux ~ 0.1 PW K-1.
Cause: Atmospheric Feedbacks.
Net O & A Flux Feedbacks
Main Points
• Combined Temperature, Water Vapor and
Cloud longwave feedbacks give a net positive
feedback in the equatorial region. In AR4
models and we think also in nature.
• Cloud Shortwave feedback is still uncertain,
but models seem to give a consistently
negative feedback in high latitudes.
• Atmospheric feedbacks and ocean heat uptake
combine to give interesting changes in
meridional heat transport in the atmosphere
and ocean.
The End
Radiative Kernels: Temperature
Average
Cloudiness
KTi
Clear
R

Ti
Radiative Kernels: Water Vapor
Average
Cloudiness
Kiw
Clear
R

wi
Relative humidity in Models
Sherwood et al 2010
Models vs Observed ENSO
Response of RH to Warming
Models: Sherwood et al 2010 JGR
AIRS Data Tropical SST Regression: Zelinka
Longwave Radiative Kernels: Surface Vs Atmospheric Temperature
Greenhouse Effect
Sensitivity of OLR to Water Vapor
Harries, QJ, 1996
50 mm 20 mm
10 mm
5 mm
Manabe & Wetherald 1967
Manabe & Wetherald 1967
Longwave Radiative Kernels: Temperature Clear vs Cloudy
Average
Cloudiness
KTi
OLR

Ti
Clear
Water vapor Longwave Radiative Kernels: Clear vs Cloudy
Average
Cloudiness
Kiw
OLR

wi
Clear
Cloud Feedback
• Cloud feedback has been identified as one of
the primary uncertainties in global warming
projections for at least 20 years.
• Longwave cloud feedback seems to be more
consistently modeled
• Shortwave cloud feedback seems to be very
poorly constrained in models and uncertain in
nature.