1. Observational and model evidence for positive low-level cloud feedback Robert J. Burgman and Amy C. Clement Rosenstiel School of Marine and Atmospheric Sciences, University of Miami Joel R. Norris Scripps Institution of Oceanography University of California, San Diego

2. “Cloud feedbacks remain the largest source of uncertainty [of equilibrium climate sensitivity].” -Summary for Policy Makers, IPCC AR4 WG1 “…simulation of the sensitivity of marine boundary layer clouds to changing environmental conditions constitutes, currently, the main source of uncertainty in tropical cloud feedbacks simulated by GCMs.” -Bony and Dufresne, GRL (2005) Here we address this issue by (1) Examining low-frequency fluctuations in observations of low-level cloud and regional meteorology, and (2) Using these observations to evaluate climate models.

3. Atmospheric response to Pacific Decadal Variability Burgman et al (2008) use multiple EOF (9.6% variance) of 5 NCEP/NCAR reanalysis variables (t, u, v, omega, and sh) for the lowest 8 pressure levels (1000 hPa–300 hPa) in the 60S-60N region over the time period 1970 to 2003. ENSO signal is ‘‘removed’’ by extreme cross correlation at least lag (+/- 12 months). Recent shift to cooler tropical Pacific in late 1990’s allows incorporation of satellite observations to study atmospheric response to decadal changes in SST. Passive Microwave era

4. Atmospheric response to Pacific Decadal Variability 1990’s shift strengthening in the overturning circulation –increased subsidence –drying in the upper and middle troposphere in NE Subtropical Pacific –shoaling of the marine boundary layer –increased lower tropospheric stability. Co-location of increased cloud with persistent cool SSTs indicate a possible feedback between Sc and the underlying SST on decadal timescales. mb/stdv Wm^2/stdv mm/stdv okta/stdv C/stdv

5. Black- total cloud Bars- low cloud Trend noted by Stevens et al. (2007) ISCCP cloud data corrected for satellite view angle, and inter-calibration errors (Norris, personal communication) 19551960196519701975198019851990199520002005 NESP (145W-115W, 15N-25N)

6. Black- total cloud Bars- low cloud Trend noted by Stevens et al. (2007) COADS surface based total cloud (1952-2007) and marine stratiform cloud (1952-1997) from Hahn and Warren (1999) 19551960196519701975198019851990199520002005 NESP (145W-115W, 15N-25N) HW99 msc comprises ordinary stratocumulus, cumulus under stratocumulus, fair- weather stratus, and bad-weather stratus

7. Black- total cloud Bars- low cloud Cloud fluctuations coincide with known climate shifts (1976, and late 1990’s) When SST is high, cloud cover is reduced. 19551960196519701975198019851990199520002005 NESP (145W-115W, 15N-25N)

8. Spatial structure of decadal cloud changes SURFACE OBS Hahn and Warren data available from (1952-1997) Dominated by 1976 shift SATELLITE RETRIEVAL + ALGORTIHM Available from 1983-2006 Dominated by 1990’s shift Regression of low clouds on NE Pacific SST

9. Regional changes are related to large-scale: Regression of meteorology on NE Pacific SST SSTSLP (colors); Surface winds 500 mb subsidenceLower tropospheric stability (t700-tsurf) Both thermal structure and circulation changes are consistent with weaker stratus deck (Klein and Hartmann 1993; Norris and Leovy 1994; Klein et al. 1995; Norris and Klein 2000; Stevens et al. 2007) Familiar PDV pattern

10. Regression of ISCCP ‘cloud radiative effect’ on NE Pacific SST 8 W/m2 cloud shortwave ~balances the upward IR due to 1K increase in SST Cloud change maintains SST anomaly

11. When SST is high, LTS is weak, SLP is low, surface winds and subsidence are weak  Reduced low level cloud Reduced low level cloud maintains SST Observations show that:

12. Models that simulate the wrong sign r(cloud,SST) Wrong sign or no r(cloud,LTS) Wrong sign or no r(cloud,SLP) Wrong sign r(cloud,w500) Is this feedback present in IPCC AR4 models? Physically-based parameterization & correct correlations Observed correlation between NE Pacific cloud and meteorology 20c3m

13. Is this feedback present in IPCC AR4 models? The INM-CM3.0 adopts a more empirical approach that parameterizes low-level cloud cover as a linear function of relative humidity with coefficients that depend on temperature, altitude, land/ocean, and stratification HadGEM1 has higher spatial resolution, more explicit cloud microphysics, interactive parameterization of cloudiness as a function of local variability in humidity, and a sophisticated planetary boundary layer mixing scheme

14. HADGEM1 simulates reduced NE Pacific cloud cover under doubled CO2 change

15. WHY? Model simulates regional changes in SST (increase) and LTS (increase) that are consistent with all other models Circulation changes are also consistent with the multi- model average Weaker overturning circulation (Held and Soden 2006, Vecchi and Soden 2007) Already observed (Zhang and Song 2006)

16. Conclusions It is possible to identify decadal fluctuations in low-level cloud in the NE Pacific in multiple, independent cloud datasets. Cloud changes are physically consistent with local meteorology changes: Cloud cover decreases when SST is high, LTS is weak, SLP is low, and meridional advection and subsidence are weak. When put to the test of simulating the relationship between cloud and thermal structure as well as circulation, only one of the current state of the art model with a physically-based parameterization of clouds passes Under doubled CO2, that model simulates a decrease in cloud cover in the NE Pacific along with robust changes in thermal structure and circulation  Observed decadal variability in low-level clouds as an analogue to how these clouds will change with global warming

17. ISCCP Correction Satellite view angle changes removed by linearly regressing out that portion of cloud variability associated with local changes in satellite view angle. Satellite intercalibration error removed by regressing out from each individual grid box time series the time series of standardized cloud cover anomalies averaged over the entire view area of successive satellites. This procedure will remove any real cloud cover variability occurring on near- hemispheric spatial scales but should have little impact on our regression patterns that focus on differences between regions.

18. Global context

19. Decadal Cloud Changes

20. Changes in CRE

21. SLP Changes