1. Teleconnections of Atlantic Multidecadal Oscillation Sergey Kravtsov University of Wisconsin-Milwaukee Department of Mathematical Sciences Atmospheric Science Group Collaborators: M. Wyatt, University of Colorado, USA, A. A. Tsonis, K. Swanson, C. Spannagle, University of Wisconsin-Milwaukee, USA Presentation at A. M. Obukhov Institute of Atmospheric Physics, Moscow, Russia November 17, 2011 http://www.uwm.edu/kravtsov/

2. My background and research interests 1993 — MIPT, MS: Singular barotropic vortex on a beta-plane (G. M. Reznik, MS advisor) 1998 — FSU, PhD: Coupled 2-D THC/sea-ice models (W. K. Dewar, PhD advisor) 1998–2005 — UCLA, PostDoc: Atmospheric regimes, wave–mean-flow interaction, coupled ocean–atmosphere modes (M. Ghil, post-doc advisor; A. Robertson, J. C. McWilliams, P. Berloff, D. Kondrashov)

3. 2005–present — University of Wisconsin- Milwaukee (UWM), Dept. of Math. Sci., Atmospheric Science group: Multi-scale climate variability: atmospheric synoptic eddies/LFV (S. Feldstein, S. Lee, N. Schwartz, J. Peters), oceanic mesoscale turbulence/large-scale response (W. Dewar, A. Hogg, P. Berloff, I. Kamenkovich, J. Peters) Model reduction (D. Kondrashov, M. Ghil, A. Monahan, J. Culina) Weather/climate predictability, decadal prediction Regional climates and global teleconnections (C. Spannagle, A. Tsonis, K. Swanson, M. Wyatt, P. Roebber, J. Hanrahan)

4. Topics to be considered: Atlantic Multidecadal Oscillation and Northern Hemisphere’s climate variability (with M. Wyatt and A. A. Tsonis) Empirical model of decadal ENSO variability

5. ATLANTIC MULTIDECADAL OSCILLATION AND NORTHERN HEMISPHERE’S CLIMATE VARIABILITY M. G. Wyatt, S. Kravtsov, and A. A. Tsonis (Published in Climate Dynamics, April 2011)

6. –0.5ºC+0.5ºC Leading EOF of the difference between CMIP-3 multimodel ensemble mean and observed surface temperature (2008, (Kravtsov and Spannagle) Dominated by anomalies in North Atlantic region Has a multi-decadal timescale Has been identified in GCMs as an intrinsic mode

7. Network of climate indices NHT — surface air temperature in the NH AMO — Atlantic Multi-decadal Oscillation AT (AC) — Atmospheric mass Transfer (or Atmospheric Circulation) Index NAO — North Atlantic Oscillation PDO — Pacific Decadal Oscillation NPO — North Pacific Oscillation ALPI — Aleutian Low Pressure Index

8. Preliminary analysis 13-yr running-mean filtered indices lagged correlations found between pairs of climate indices Statistical significance of lagged correlations and compatible pairs of indices: 3 yr 2 yr 5 yr 5 yr = 3 yr + 2 yr: Compatible indices

9. M-SSA on our annual climate- index network significance estimates based on uncorrelated red-noise fits to members of index network M-SSA — analogous to EOF analysis, but uses, additionally, lagged covariance info

10. Reconstructed Components: Each index is de- composed into multi- decadal signal (blue) and higher-frequency variability (red) Extended 15-index network Relative variations of the two are to scale

11. Multidecadal Signal: Stadium Wave

12. Summary for Stadium Wave The NH climate indices exhibit a multi-decadal signal inconsistent with random alignment of uncorrelated red-noise time series This stadium-wave signal has the following phase relationships (lags in yr, uncertainties estimated using bootstrap re-sampling of index subsets): Modeling studies provide clues to the dynamics behind the stadium-wave links

13. Multidecadal Pacing of Interannual Deviations From the Stadium Wave Consider the anomalies with respect to the stadium-wave signal (red lines on an earlier Fig.) Fit a multi-dimensional red-noise model that mimics the climatological lag-0, and lag-1 auto- and cross-correlations among the indices Compute (almost) the sum of squared cross- correlations for various subsets of indices over sliding window of 5–10 yr: connectivity measure Identify index subsets and years with abnormal connectivity values exceeding those expected from the red-noise model

14. Identification of synchronizing index subsets in 6-index subnets 1917 1923 1940 1958 1976 Yellow/orange cells indicate abnormal synchronizations within 6-index subsets

15. Identification of synchronizing index subsets in 6-index subnets 1917 1940 1976 “Successful” synchronizations were followed by a climate shift (Tsonis, Swanson, Kravtsov 2007)

16. Climate shifts are characterized by change of dominant climate pattern over the NH (e.g. the 1976 shift) and by different NAO & ENSO regime 19401976 Strong ENSO/ NAO Weak ENSO/ NAO Strong ENSO/ NAO

17. Discussion A multi-decadal climate signal is tentatively generated in the North Atlantic Ocean due to intrinsic variability of the MOC (THC) This signal “propagates” across the entire NH as a sequence of delayed teleconnections — stadium wave The stadium wave is associated with climate regime shifts which alter the character of interannual climate variability (ENSO and NAO) The dynamical processes behind regime shifts may themselves feed back onto and pace the stadium wave

18. AN EMPIRICAL MODEL OF DECADAL ENSO VARIABILITY S. Kravtsov (Submitted to Climate Dynamics)

19. Conjecture: Modulation of ENSO activity is due to “stadium wave” teleconnections Consider seasonal sea-surface temperature (SST) time series on a 5x5º grid (30ºS–60ºN) during 20 th century Use spatiotemporal filter to isolate multidecadal signal! Examples: EOFs (Preisendorfer 1988), M-SSA (Ghil et al. 2002), OPPs (DelSole 2001, 2006), DPs (Schneider and Held 2001), APT (DelSole and Tippett 2009a,b). Despite multidecadal and interannual variability have different spatial patterns, which vary according to their respective predominant time scales, they may still be dynamically linked!

20. SST discriminants Patterns that maximize ratio of multidecadal to interannual SST variance (Schneider and Held 2001) ; SST data is based on Kaplan (1998). Time series correlated with global Ts This and next pattern ~AMO+PDO

21. Multidecadal variations in Niño-3 Niño-3 SST is natu- rally dominated by interannual variability (DPs’ contribution is small) Niño-3 variance exhibits multidecadal modulation anti- correlated with the AMO index (cf. Federov and Philander 2000; Dong and Sutton 2005; Dong et al. 2006; Timmermann et al. 2007)

22. Niño-3 modulation an artifact? Due to random sampling (Flügel et al. 2004) CVs themselves are largely the long-term modulation of ENSO Analysis Procedure: Generate surrogate SST time series using multivariate linear inverse modeling (LIM) Decompose surrogate SSTs into CVs and anomalies, regress Niño-3 STD onto three leading compute correlation between actual and compare with observed CVs, reconstructed Niño-3 STD, correlation

23. Conclusion: Correlation btw large-scale predictors and ENSO is unlikely to be due to random factors RESULTS

24. Let’s model this process statistically Model Niño-3 index x as a 1-D stochastic process where f is a polynomial function of x with coefficients that depend on time t (seasonal cycle) and external decadal variables y given by leading Canonical Variates (CV) of SST; dw is a random deviate. Study the numerical and algebraic structure of this model and use it to estimate potential predictability of decadal ENSO modulations

25. Properties of the empirical ENSO model-I

26. Properties of the empirical ENSO model-II

27. Algebraic structure of ENSO model F – potential function

28. Cross-validated hindcasts of ENSO STD: Jack-knifing with 15- yr segments omitted/predicted Linearly extrapolated or fixed external predictors (fixed better!) 2 or 3 external predictors (2 better!)

29. Summary These results argue that decadal ENSO modulations are potentially predictable, subject to our ability to forecast AMO-type climate modes. We used statistical SST decomposition into multidecadal and interannual components to define low-frequency predictors ( CVs ). An empirical Niño-3 model trained on the entire 20th- century SST data and forced by CVs captures a variety of observed ENSO characteristics, including multidecadal modulation of ENSO intensity. The cross-validated hindcasts using linear extrapolation of external predictors are promising

30. THANKS FOR YOUR ATTENTION