Teleconnections of Atlantic Multidecadal Oscillation

Sergey Kravtsov

University of Wisconsin-MilwaukeeDepartment 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/

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)

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)

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

ATLANTIC MULTIDECADAL OSCILLATION AND NORTHERN

HEMISPHERE’S CLIMATE VARIABILITY

M. G. Wyatt, S. Kravtsov, and A. A. Tsonis

(Published in Climate Dynamics, April 2011)

–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

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

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 3 yr 2 yr

5 yr 5 yr = 3 yr + 2 yr: Compatible indices

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

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

Multidecadal Signal: Stadium Wave

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

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

Identification of synchronizing index subsets in 6-index subnets

191719231940

1958

1976

Yellow/orange cells indicate abnormal synchronizations within 6-index subsets

Identification of synchronizing index subsets in 6-index subnets

1917

1940

1976

“Successful” synchronizations were followed by a climate shift (Tsonis, Swanson, Kravtsov 2007)

Climate shifts are characterized by change of dominant climate pattern over the NH (e.g. the

1976 shift) and by different NAO & ENSO regime

1940 1976

Strong ENSO/

NAO

Weak ENSO/

NAO

Strong ENSO/

NAO

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

AN EMPIRICAL MODEL OF DECADAL ENSO VARIABILITY

S. Kravtsov

(Submitted to Climate Dynamics)

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 20th 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!

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

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)

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 andcompare with observed

CVs,reconstructed Niño-3 STD,correlation

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

RESULTS

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

€

dx=f(x,y,t)dt+dw

Properties of the empirical ENSO model-I

Properties of the empirical ENSO model-II

Algebraic structure of ENSO model

€

dx=f(x,y,t)dt+dw; f≡-∂F/∂x

•F – potential function

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!)

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

THANKS FOR YOUR ATTENTION