Recent Evidence for Reduced Climate Sensitivity

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

3. 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.

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 (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

5. 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

6. 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?)

7. 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

8. 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

9. 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

10. 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.

11. 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

12. 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

13. How Do the Observational Estimates of Feedback Compare to Climate Models?

14. 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.