Recent Evidence forReduced Climate SensitivityRoy W. Spencer, Ph.D

Principal Research ScientistThe University of Alabama In HuntsvilleMarch 4, 2008

Natural Climate Variability Gives the Opportunityto Investigate Climate Sensitivity (1/feedbacks)NASA Terra satelliteNASA 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.

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 feedbackLW Cloud FeedbackSW Cloud Feedback

Spencer et al., 2007: Composite Analysis of 15 Tropical Intraseasonal OscillationsWith 4 instruments from 3 satellites, we studied a compositeof 15 tropical intraseasonal oscillations (ISO) in tropospheric temperature.2 Separate Satellites(NOAA-15 & NOAA-16)Compositing done around day ofMax. tropospheric temperature (AMSU ch. 5)1 year of Tropical IntraseasonalOscillations in tropospheric temperature

Tair (AMSU); SST, Vapor, Sfc. Wind speed (TRMM TMI)(increasing wind speed and vaporduring tropospheric warmingexpected)Composite of 15 Major ISOs, March 2000 through 2005Rain 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 cirroformcloudiness during warming?)

Tair(tropospheric temperature)MODISIce and liquid cloudcoveragesCirroform clouds decrease duringtropospheric warmthMODIS Verifies Decreasing Ice Cloud Coverage DuringPeak Tropospheric Temperatures

6.5 W m-2K-1CERES-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 warmingis MORE than the temperature effect alone (3.3 W m-2),so negative feedback)CERESAMSU-A Ch. 5

Boundary layerCooling (loss of IR radiation)by dry air to spacewarm, humid aircool, dry airevaporationremoves heatOcean or LandHeat released throughcondensationcauses air to rise,rain falls to surfaceNATURES AIR CONDITIONER?Most of our atmosphere is being continuouslyrecycled by precipitation systems, which thendetermines the strength of the Greenhouse Effect Sunlight absorbed at surfaceInfrared Iris

Spencer & Braswell, 2008: A Simple Model Demonstration of How Natural Variability Causes Errors in Feedback EstimatesCp(dT/dt) = Mankind aT + NatureIntroducing theWorlds 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 Cp equivalent to a 50 m deep swamp ocean.

First 30 years ofdaily SST variations =>Example Model Run(a = 3.5 W m-2 K-1; + noise sufficient to match satellite SW variability)80 years of monthlyaverages to estimatefeedback parameter => 2.94 diagnosed-3.50 specified-0.56 W m-2 K-1 biasin diagnosed feedbackDecadal SST variability causedby daily noise (only)!

Model Runs with daily cloudNoise (N) and other SST noise(S)..that ALSO produce monthly SST variability and reflected SWvariability like that observed bysatellitesresult in feedbackerrors of -0.3 to -0.8 W m-2 K-1(positive feedback bias)Many Models RunsTo Estimate RangeOf Biases in FeedbackEstimationDots match satellite-measuredmonthly variability in SST & SW

How Do the Observational Estimates of FeedbackCompare to Climate Models?

ConclusionsRecent 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.