Jagadish Shukla Department of Atmospheric, Oceanic and Earth Sciences (AOES)

1. RSMAS, U. of Miami, Dec 16, 2011 On the Role of Unforced Multidecadal Variability in Twentieth Century Global Warming Jagadish Shukla Department of Atmospheric, Oceanic and Earth Sciences (AOES) George Mason University (GMU) Center for Ocean-Land-Atmosphere Studies (COLA) Institute of Global Environment and Society (IGES) 16 December 2011

2. Combined Land-surface, Air and Sea Surface Temperature anomaly Center of Ocean-Land-Atmosphere studies

3. An Earth System Prediction Initiative: Putting It All Together October 2010

4. Brunet, G., et al, 2010: Collaboration of the Weather and Climate Communities to Advance Sub-Seasonal to Seasonal Prediction. BAMS, Vol. 91, 1397-1406 Shapiro, M., J. Shukla, et al, 2010: An Earth-System Prediction Initiative for the 21st Century. BAMS, Vol.91, 1377-1388 Shukla, J., T.N. Palmer, R. Hagedorn, B. Hoskins, J. Kinter, J. Marotzke, M. Miller, and J. Slingo, 2010: Towards a New Generation of World Climate Research and Computing Facilities. BAMS, Vol.91, 1407-1412 Shukla, J., R. Hagedorn, B. Hoskins, J. Kinter, J. Marotzke, M. Miller, T.N. Palmer, and J. Slingo, 2009: Revolution in climate Prediction is Both Necessary and Possible: A Declaration at the World Modelling Summit for Climate Prediction.BAMS, Vol.90, 16-19 An Earth system Prediction Initiative

5. Nature Poses a Challenge to IPCC oC 7-year running mean Annual mean HadCRU3 Global Mean Surface Temperature Anomalies, departure from 1901-2000 climatology (13.9oC)

6. Outline Part I: (Ocean; DelSole, Tippet & Shukla, 2010) • Decadal Variability in unforced coupled models • Role of unforced decadal variability in global warming • Prospects for prediction of unforced decadal variability Part II: (Land; Jia & DelSole, 2011, Jia, Ph.D. Thesis) • Predictable components of Land Surface Air Temp. (SAT) • Role of oceans in predictability over land • Forced and unforced predictable components of land SAT

7. Recent Papers on Decadal Modulations of Global Warming “A significant Component of Unforced Multidecadal Variability in Twentieth Century Global Warming” Timothy DelSole, Michael K. Tippett, Jagadish Shukla (J. of Climate, 2011, pp. 909-926) “The Impact of North Atlantic-Arctic Multidecadal Variability on Northern Hemisphere Surface Air Temperature” Vladimir A. Semenov, Mojib Latif, Dietmar Dommenget, Noel S. Keenlyside, Alexander Strehz, Thomas Martin, Wonsun Park (J. of Climate, 2010, pp. 5668-5677) “On the Trend of the Global Mean Surface Temperature” Wu, Huang, Wallace, Smoliak, Chen (Climate Dynamics, 2011, DOI 10.1007/s00382-011-1128-8)

8. Global-mean Surface Temperature On the Time-Varying Trend in Global-Mean Surface Temperature by Wu, Huang, Wallace, Smoliak, Chen EEMD: Ensemble Empirical Mode Decomposition; MDV: Multi Decadal Variability

9. Question Is the observed multi-decadal variability externally forced (GHGs, aerosols, solar, volcanic, etc.) ? Or Is this variability internally forced (atmosphere-ocean- land-cryosphere interactions) ?

10. Separating Forced and Un-Forced Patterns

11. Challenges in Separating Forced and Un-Forced Patterns • Forcing may project strongly on un-forced patterns. • Time series of IMP in different ensemble members are uncorrelated in most (but not all) models. • Model estimates of forced pattern may be wrong. • Results are the same if no model is used to estimate forced pattern, and the observed trend pattern is used for the “forced pattern.” • Forced response may not be captured by one pattern. • Including second signal-to-noise EOF does not change the results. • Second signal-to-noise EOF is statistically insignificant.

12. How to Define: • Forced Response Pattern • Trend Pattern: A linear trend between 1850 – 2005 • Signal to noise EOF for 20th century IPCC runs • Internal (Unforced) Pattern • New Approach: IPCC pre-industrial controls

13. Trend Patterns: To be interpreted as Response Pattern to Forcings Fit linear trend between 1850-2005, plot the slope expressed as degrees per decade.

14. Estimated Response to Anthropogenic and Natural Forcings Signal to Noise EOF Local Trend Pattern

15. How to Define: • Forced Response Pattern • Trend Pattern: A linear trend between 1850 – 2005 • Signal to noise EOF for 20th century IPCC runs • Internal (Unforced) Pattern • New Approach: IPCC pre-industrial controls

16. How to Define the Response to Climate Forcing?

17. Signal-to-Noise EOFs: Response Pattern to Forcings (Anthropogenic and Natural (Solar, Volcanic) • Find components that maximize the ratio of variances: • Discriminant analysis (Fisher 1938) • Seasonal Predictability (Straus et al. 2003) • Decadal Predictability (Venzke et al. 1999) • Climate Change (Ting et al. 2009) (No IPCC Control Runs) • Response pattern to climate forcing estimated by finding the pattern that maximizes the ratio

18. Forced-to-Unforced Discriminant from Control Runs

19. How to Define: • Forced Response Pattern • Trend Pattern: A linear trend between 1850 – 2005 • Signal to noise EOF for 20th century IPCC runs • Internal (Unforced) Pattern • New Approach: IPCC pre-industrial controls

20. How to Define Patterns of Multidecadal variability/predictability New approach: Average Predictability Time (APT)

21. Average Predictability Time (APT)

22. Important Properties of APT • It is invariant to nonsingular, linear transformations of the variables and hence is independent of the arbitrary basis set used to represent the state • Upper and lower bounds on the APT of linear stochastic models can be derived from the dynamical eigenvalues • It is related to the shape of the power spectra and hence clarifies the relation between predictability and power spectra • The factor of 2 make APT agree with the usual e-folding time in the univariate case

24. Estimating APT With Only One Ensemble Member

25. Identifying Internal Multidecadal Patterns (IMP) Find a pattern that maximizes APT (unlike EOF which maximizes variance). Average Predictability Time (APT) Average predictability can be characterized in a way that is independent of lead time by integrating the predictability metric, which always decreases with time. For example, the rate of decay is much slower and enhance the integral is much higher for decadal variation than seasonal variation. (DelSole & Tippett, 2009, JAS)

26. Decomposing Predictability

27. Optimize APT in Control Runs

28. Leading Predictable Component (APT)Internal Multi-decadal Pattern (IMP) tos.ann.terp.glo apt(5.92yr) Mode-1 (40EOFs; 300yrs; 20yr Lag) (°C)

29. Leading Predictable Component (APT):Internal Multi-decadal Pattern (IMP) (°C)

30. Fingerprinting Method

31. Forced-to-Unforced Discriminant from Control Runs

32. Forced Pattern

33. Fingerprinting Method

34. Leading Predictable Component (APT):Internal Multi-decadal Pattern (IMP) (°C)

35. Internal Multi-decadal Pattern (IMP)

36. Amplitude of Forced and Unforced Patterns

37. (DelSole et. al.) (Huang et. al.) Reconstruction of the raw GST time series using ST only (Red lines) and ST+MDV (Green lines)

39. Global Mean SST

40. Scientific Basis for Decadal Predictability

41. Interim Summary of Part I

42. Summary (1) • An unforced, multidecadal SST pattern is identified in simulations using IPCC pre-industrial control runs and observations by a new statistical method. • Maximizing the ratio of forced to internal variability indicates only one forced pattern in SST. Pattern has cooling in N. Atlantic. • Both the forced and unforced patterns are estimated by optimal spatial filtering techniques. • Forced component contributes uniform 0.1K/decade of warming.

43. Summary (2) • An Internal Multi-decadal Pattern (IMP) is identified that explains about 0.1C fluctuations in low-pass, global average SST. • Amplitude of this pattern helps explain major multi-decadal fluctuations in global mean temperature in the 20th century. • Amplitude of IMP matches AMO and is sufficient amplitude to explain acceleration in warming between 1946-1977 and 1977-2008. • Forced response projects only weakly on IMP, if at all. • Cooling trend over 10-year periods not statistically significant.

44. Part II Prospects for Continental Scale Decadal Prediction

45. Scientific Basis for Decadal Predictability • Slowly varying climate components • Atmosphere-ocean interactions (Pohlmann et al., 2006; Stouffer et al., 2006, 2007; Latif and Barnett, 1996; Held et al., 2005; Knight et al., 2006; Zhang and Delworth, 2006). •Decadal predictability in oceans (Griffes and Bryan, 1997; Collins and Sinha, 2003; Collins et al., 2006, Msadek et al., 2010, DelSole et al., 2010). •Potential predictability of temperature, precipitation, sea level pressure (Collins, 2002; Boer, 2004; Boer and Lambert2008; Pohlmann et al., 2004, 2006, Smith et al., 2007; Keenlyside et al., 2008). • Predictable external forcing(Hegerl et al., 2007).

46. Example of Unforced Predictability Study Percent of potential predictable variance of 5-yr mean Boer &Lambert, 2008, Geophys.Res. Lett. Little to no predictability over land !

47. Limitations of Previous Studies • Univariate (noise dominates on grid scales). • No decomposition in terms of distinct spatial patterns with associated time series. • Mixed predictable patterns, thus is hard to interpret physically. • Time averaging (e.g., 5- or 10-yr means).

48. Recent Results on Multi-year Predictability over Land Diagnosis of Multi-year Predictability on Continental Scales Liwei Jia and Timothy DelSole (J. Climate,2011, in press) Robust Multi-Year Predictability on Continental Scales Liwei Jia (Ph.D. Thesis, George Mason University, 2011)

49. Regression of SAT and Precipitation SAT Precipitation Regression coefficients between the leading component of SST and SAT (K per unit predictable component) and precipitation (mm/day per unit predictable component).

50. Predictability over Land in IPCC Pre-Industrial Control Runs (SST effect)