1. AMSR-E Evidence for Changes in Precipitation Microphysics During Tropospheric Warming Roy W. Spencer AMSR Team Meeting Telluride, CO July 14, 2008

2. Background: There is increasing evidence of net negative radiative feedback in the real climate system. Spencer, Braswell, Christy, & Hnilo, 2007: Cloud and Radiation Budget Changes Associated with Tropical Intraseasonal Oscillations, Geophysical Research Letters, August 9. –A composite of 15 tropical intraseasonal oscillations show a stong negative radiative feedback on tropospheric temperature Spencer, R.W., and W.D. Braswell, 2008: Potential Biases in Cloud Feedback Diagnosis: An Energy Balance Model Demonstration. J. Climate, in press. –Internally-generated radiative forcing due to stochastic cloud fluctuations has caused a positive bias in satellite diagnoses of feedback. Spencer, R.W., 2008: Chaotic radiative forcing, feedback stripes, and the overestimation of climate sensitivity. Bull. Amer. Met. Soc., submitted. –A simple model is used to explain 6-years of satellite data, and show why previous satellite estimates have overestimated climate sensitivity.

3. Cloud and water vapor feedbacks must ultimately be connected to precipitation system behavior 1. Free-tropospheric vapor (Earth’s main greenhouse gas) is largely governed by precipitation efficiency (even in the Arctic). 2. Many clouds are governed by - detrainment from precipitation systems (middle & upper trop.), or - temperature inversions (lower trop., due to subsidence forced by latent heat release in precipitation systems)

4. 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 Precipitation Processes: One Key to Climate Sensitivity? The atmosphere is being continuously recycled by precipitation systems, which then determines the strength of the global Greenhouse Effect. Sunlight absorbed at surface

5. ….a quick review…. Satellite observations support Lindzen’s “Infrared Iris” Hypothesis of climate stabilization (Spencer et al., Aug. 9, 2007 GRL) 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)

6. T air (AMSU); SST, Vapor, Sfc. Wind speed (TRMM TMI) (increasing wind speed and vapor during tropospheric warming…expected) Composite of 15 Major ISOs, March 2000 through 2005 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. 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

8. Cirrus Changes with Tropospheric Warming: The Necessary Connection to Precipitation Processes Tropics: Cirroform clouds represent detrained water that did not precipitate Extratropics: Same as tropics OR synoptic- scale lift of water vapor which was previously detrained by precipitation systems Thus AMSR-E suggests that thinning cirroform cloudiness with tropical warming is connected to microphysical changes in precipitation cores. AMSR-E [V18-H18] < 33 deg. C (“Rainiest” 1% of Tropical Oceans) Less Large Ice During Tropospheric Warmth

9. IF SATELLITE MEASUREMENTS SUGGEST NEGATIVE FEEDBACK, WHY DO CLIMATE MODELS PRODUCE POSITIVE FEEDBACK? With a Very Simple Model of Temperature Variability around an Equilibrium State dT/dt = (F - T)/Cp Heat Capacity (we’ll assume 50 m “swamp” ocean) Forcing (radiative imbalance from CO2, aerosols, volcanoes…internally- generated radiative forcings, OR non-radiative imbalances generated internally) Feedback (we’ll assume = 4 W m -2 K -1 )  feedback parameter  = 3.3 Wm -2 K -1 + vap + cld + ….. If < 3.3, then positive feedback, If > 3.3, then negative feedback, If < 0, then climate is potentially unstable to perturbations (“Hansen Effect”…only 3.3 Watts of positive feedback from disaster)

10. If this simple model is run with NON-Radiative Forcing… (daily random heat flux vars e.g. from evaporation) …then diagnosed Feedback is Accurate

11. But if simple model is run with RADIATIVE Forcing only… (e.g. daily random “cloud” variations, 1-year smoothing)..then, diagnosed Feedback is near-Zero

12. Now, let’s put 4-year smoothing on the Radiative forcing (+ Non- Radiative Forcing)…..now the model is starting to look like the satellite data… (high freq. non-radiative forcing + low-freq. radiative forcing) …and do 90-day smoothing of model output to allow time-evolution to be seen

13. So, in the general case, “Feedback stripes” (from non-radiative forcing) are superimposed upon “Radiative Forcing Spirals” But, how do we separate the two signals?

14. Answer:“Local Slopes Analysis” to isolate the feedback signal 1. Compute 2-month local slopes every day throughout 90(+) day low-pass filtered time series. 2. Look for a peak in the frequency distribution of those slopes Correct Feedback Diagnosed

15. Even with radiative forcing only (radiative forcing spirals, but no feedback stripes) the feedback signature is revealed by Local Slopes analysis Correct Feedback Again Diagnosed

16. Local Slopes Analysis of Satellite Measurements CERES vs. AMSU 60N-60S Oceans Mar 2000 thru Dec 2005 Local Slopes Method peak in LW+SW freq. distribution ~7 W m -2 K -1 (strong negative feedback) (same peak found for avg. periods from 10 days to 2 years…..feedback independent of timescale?) LW+SW

17. Now, what do the IPCC models show? Local Slopes Analysis of NCAR-CCSM3.0 (least sensitive of IPCC models analyzed by F&T [2006]) Note the models also have chaotic “radiative forcing spirals”, too.

18. Reappraisal of Forster & Gregory (2006) Feedback Parameter Diagnoses from Earth Radiation Budget Satellite (ERBS) vs. T sfc (1985-1996) Since the radiative forcing signal is UN-correlated and the feedback signal is highly correlated, can we extrapolate F&G’s diagnoses from different time periods to a correlation of 1.0? Test w/ Simple model (F&G results) 4.7 3.4 ANOTHER potential technique to isolate the feedback signal 6 (specified) So, even previously published feedback estimates are supportive of negative feedback!

19. PDO SOI Tsfc What if the atmospheric & oceanic circulation changes associated with known modes of climate variability cause cloud changes? (internal radiative forcing…not feedback) Or nonlinear interactions? (e.g. - SOI*PDO) Strongly neg. feedback means incr. CO2 can’t explain warming over last 100 yrs So, what Could Have Caused Past Warming?

20. Let’s force our Simple Climate Model with Natural Cloud variations proportional to PDO and El Nino/La Nina (SOI) 1,000 m deep ocean (top 27%); = 4.0 W m -2 K -1  (0.64*[-SOI] + 0.36*[PDO]) In same model, CO2 forcing to 1.4 W/m 2 / 100 years provides remaining warming (~0.25 deg. C)

21. But is there any evidence that the PDO & SOI have associated cloud variations? YES, ~ 0.8 W m -2 K -1 per PDO unit …& our 20 th Century Warming model? ~ 0.7 W m -2 K -1 per PDO unit

22. Conclusions 1.New ways of interpreting satellite data suggest that the behavior of clouds in the climate system has been misinterpreted - cause and effect have been confused - as a result, climate models have cloud parameterizations that make the models far too sensitive. 2.AMSR-E data, along with other satellite instrument datasets will continue to provide new physical insight into the processes which govern feedbacks (climate sensitivity).