Recent Evidence for Reduced Climate Sensitivity Roy W. Spencer, Ph.D Principal Research Scientist The University of Alabama In Huntsville March 4, 2008.

• Slide 1

Recent Evidence for Reduced Climate Sensitivity Roy W. Spencer, Ph.D Principal Research Scientist The University of Alabama In Huntsville March 4, 2008 Slide 2 Natural Climate Variability Gives the Opportunity to Investigate Climate Sensitivity (1/feedbacks) NASA Terra satellite NASA Aqua Slide 3 Climate Sensitivity ~ 1/feedbacks so, 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. Slide 4 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) Recent Research Supporting Reduced Climate Sensitivity (negative feedback, or reduced positive 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 LW Cloud Feedback SW Cloud Feedback Slide 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. 2 Separate Satellites (NOAA-15 & NOAA-16) Compositing done around day of Max. tropospheric temperature (AMSU ch. 5) 1 year of Tropical Intraseasonal Oscillations in tropospheric temperature Slide 6 T air (AMSU); SST, Vapor, Sfc. Wind speed (TRMM TMI) (increasing wind speed and vapor during tropospheric warmingexpected) Composite of 15 Major ISOs, March 2000 through 2005 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?) Slide 7 T air (tropospheric temperature) MODIS Ice and liquid cloud coverages Cirroform clouds decrease during tropospheric warmth MODIS Verifies Decreasing Ice Cloud Coverage During Peak Tropospheric Temperatures Slide 8 6.5 W m -2 K -1 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 AMSU-A Ch. 5 Slide 9 Boundary layer Cooling (loss of IR radiation) by dry air to space warm, humid aircool, dry air evaporation removes heat Ocean or Land Heat released through condensation causes air to rise, rain falls to surface NATURES AIR CONDITIONER? Most of our atmosphere is being continuously recycled by precipitation systems, which then determines the strength of the Greenhouse Effect Sunlight absorbed at surface Infrared Iris Slide 10 Spencer & Braswell, 2008: A Simple Model Demonstration of How Natural Variability Causes Errors in Feedback Estimates C p (dT/dt) = Mankind T + Nature Introducing the Worlds Smallest Climate Model (Guinness record) Feedback parameter (= 3.3 W m -2 K -1 + feedbacks) Anthropogenic forcing (=0 for demonstration) Natural variability in radiative flux (e.g. daily noise in low cloud cover) Finite difference version run at daily time resolution, use C p equivalent to a 50 m deep swamp ocean. Slide 11 First 30 years of daily SST variations => Example Model Run ( = 3.5 W m -2 K -1 ; + noise sufficient to match satellite SW variability) 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 Decadal SST variability caused by daily noise (only)! Slide 12 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) Many Models Runs To Estimate Range Of Biases in Feedback Estimation Dots match satellite-measured monthly variability in SST & SW Slide 13 How Do the Observational Estimates of Feedback Compare to Climate Models? Slide 14 Conclusions 1.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.