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Recent Evidence for Reduced Climate Sensitivity. Roy W. Spencer, Ph.D Principal Research Scientist The University of Alabama In Huntsville March 4, 2008. Natural Climate Variability Gives the Opportunity to Investigate Climate Sensitivity (1/feedbacks). NASA Terra satellite. NASA Aqua.

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Recent Evidence for Reduced Climate Sensitivity

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Recent Evidence forReduced Climate Sensitivity

Roy W. Spencer, Ph.D

Principal Research Scientist

The University of Alabama In Huntsville

March 4, 2008


Natural Climate Variability Gives the Opportunity

to Investigate Climate Sensitivity (1/feedbacks)

NASA Terra satellite

NASA Aqua


Climate Sensitivity ~ 1/feedbacksso, Positive or Negative Feedbacks?

  • 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)

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

I will address these.


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 (Lindzen’s “Infrared Iris”)

LW Cloud Feedback

  • 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

SW Cloud Feedback


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)

1 year of Tropical Intraseasonal

Oscillations in tropospheric temperature


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 warming…expected)

Rain Rates (TRMM TMI)

(rain rates above normal

during tropospheric warming…expected)

SW and LW fluxes (Terra CERES)

(reflected SW increase during rainy period…expected.. BUT…increasing LW during rainy period UNEXPECTED)

SW and LW fluxes normalized

by rain rate

(rain systems producing less cirroform

cloudiness during warming?)


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


CERES-Measured Changes In [emitted LW+reflected SW]

During the Composite Intraseasonal Oscillation (ISO)

Suggest Negative Cloud Feedback

(6.5 W m-2 SW+LW 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


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


Spencer & Braswell, 2008: A Simple Model Demonstration of How Natural Variability Causes Errors in Feedback Estimates

Introducing the

“World’s 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 W m-2 K-1+ feedbacks)

Finite difference version run at daily time resolution,

use Cp equivalent to a 50 m deep “swamp” ocean.


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 =>

Decadal SST variability caused

by daily noise (only)!

80 years of monthly

averages to estimate

feedback parameter =>

2.94 diagnosed

-3.50 specified

-0.56 W m-2 K-1 bias

in diagnosed feedback


Many Models Runs

To Estimate Range

Of Biases in Feedback

Estimation

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

satellites…result in feedback

errors of -0.3 to -0.8 W m-2 K-1

(positive feedback bias)

Dots match satellite-measured

monthly variability in SST & SW


How Do the Observational Estimates of Feedback

Compare to Climate Models?


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.


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