Global Warming: One Scientist

1
Recent Evidence forReduced Climate Sensitivity
Roy W. Spencer, Ph.D Principal Research
Scientist The University of Alabama In
Huntsville March 4, 2008
2
Natural Climate Variability Gives the
Opportunity to Investigate Climate Sensitivity
(1/feedbacks)
NASA Terra satellite
NASA Aqua
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Climate Sensitivity 1/feedbacksso, Positive or
Negative Feedbacks?

• With zero feedbacks, 2XCO2 gt 1 deg. C warming
  (yawn)

• Climate Modelers say Feedbacks Positive, possibly
  strongly positive (tipping points,etc.)
• Positive water vapor feedback (natural greenhouse
  effect)
• Positive LW cloud feedback (natural greenhouse
  effect)
• Positive SW cloud feedback (albedo effect)
• Negative lapse rate feedback (warming incr. with
  height)

4
Recent Research Supporting Reduced Climate
Sensitivity (negative feedback, or reduced
positive feedback)

• Spencer, Braswell, Christy, Hnilo, 2007 Cloud
  and Radiation Budget Changes Associated with
  Tropical Intraseasonal Oscillations, Geophysical
  Research Letters, August 9.
• A composite of the 15 strongest tropical
  intraseasonal oscillations during 2000-2005 show
  strong negative cloud feedback (Lindzens
  Infrared Iris)

• Spencer Braswell, 2008 Potential Biases in
  Feedback Diagnosis from Observational Data A
  Simple Model Demonstration, J. Climate
  (conditionally accepted).
• Daily random cloud cover variations can cause SST
  variability that looks like positive cloud
  feedback

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Spencer et al., 2007 Composite Analysis of 15
Tropical Intraseasonal Oscillations
With 4 instruments from 3 satellites, we studied
a composite of 15 tropical intraseasonal
oscillations (ISO) in tropospheric temperature.
Compositing done around day of Max. tropospheric
temperature (AMSU ch. 5)
2 Separate Satellites (NOAA-15 NOAA-16)
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Composite of 15 Major ISOs, March 2000 through
2005
Tair (AMSU) SST, Vapor, Sfc. Wind speed
(TRMM TMI) (increasing wind speed and
vapor during tropospheric warmingexpected)
Rain Rates (TRMM TMI) (rain rates above normal
during tropospheric warmingexpected)
SW and LW fluxes (Terra CERES) (reflected SW
increase during rainy periodexpected..
BUTincreasing LW during rainy period UNEXPECTED)
SW and LW fluxes normalized by rain rate (rain
systems producing less cirroform cloudiness
during warming?)
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MODIS Verifies Decreasing Ice Cloud Coverage
During Peak Tropospheric Temperatures
Tair (tropospheric temperature)
Cirroform clouds decrease during tropospheric
warmth
MODIS Ice and liquid cloud coverages
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CERES-Measured Changes In emitted LWreflected
SW During the Composite Intraseasonal
Oscillation (ISO) Suggest Negative Cloud Feedback
(6.5 W m-2 SWLW loss per deg. C warming is MORE
than the temperature effect alone (3.3 W m-2), so
negative feedback)
CERES
6.5 W m-2K-1
AMSU-A Ch. 5
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Cooling (loss of IR radiation) by dry air to space
Infrared Iris
NATURES AIR CONDITIONER? Most of our atmosphere
is being continuously recycled by precipitation
systems, which then determines the strength of
the Greenhouse Effect
Heat released through condensation causes air to
rise, rain falls to surface
Sunlight absorbed at surface
Boundary layer
warm, humid air
cool, dry air
evaporation removes heat
Ocean or Land
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Spencer Braswell, 2008 A Simple Model
Demonstration of How Natural Variability Causes
Errors in Feedback Estimates
Introducing the Worlds Smallest Climate
Model (Guinness record)
Cp(dT/dt) Mankind aT Nature
Anthropogenic forcing (0 for demonstration)
Natural variability in radiative flux (e.g.
daily noise in low cloud cover)
Feedback parameter ( 3.3 feedbacks)
Finite difference version run at daily time
resolution, use Cp equivalent to a 50 m deep
swamp ocean.
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Example Model Run (a 3.5 W m-2 K-1 noise
sufficient to match satellite SW variability)
First 30 years of daily SST variations gt
80 years of monthly averages to estimate feedback
parameter gt
2.94 diagnosed -3.50 specified -0.56 W m-2 K-1
bias in diagnosed feedback
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Model Runs with daily cloud Noise (N) and other
SST noise (S)..that ALSO produce monthly SST
variability and reflected SW variability like
that observed by satellitesresult in
feedback errors of -0.3 to -0.8 W m-2
K-1 (positive feedback bias)
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How Do the Observational Estimates of
Feedback Compare to Climate Models?
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Conclusions

• Recent research supports reduced climate
  sensitivity
• - Tropical Intraseasonal Oscillations show
  strong negative feedback
• - Observational estimates of feedbacks are
  likely biased
• positive due to neglect of natural
  variability
• 2. Accommodation of these results by the climate
  modeling community in their cloud
  parameterizations could greatly reduce climate
  model projections of future warming.