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![Page 1: Teleconnections of Atlantic Multidecadal Oscillation Sergey Kravtsov University of Wisconsin-Milwaukee Department of Mathematical Sciences Atmospheric.](https://reader036.fdocuments.in/reader036/viewer/2022062309/5697c01b1a28abf838ccf7ad/html5/thumbnails/1.jpg)
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/
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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)
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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)
![Page 4: Teleconnections of Atlantic Multidecadal Oscillation Sergey Kravtsov University of Wisconsin-Milwaukee Department of Mathematical Sciences Atmospheric.](https://reader036.fdocuments.in/reader036/viewer/2022062309/5697c01b1a28abf838ccf7ad/html5/thumbnails/4.jpg)
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
![Page 5: Teleconnections of Atlantic Multidecadal Oscillation Sergey Kravtsov University of Wisconsin-Milwaukee Department of Mathematical Sciences Atmospheric.](https://reader036.fdocuments.in/reader036/viewer/2022062309/5697c01b1a28abf838ccf7ad/html5/thumbnails/5.jpg)
ATLANTIC MULTIDECADAL OSCILLATION AND NORTHERN
HEMISPHERE’S CLIMATE VARIABILITY
M. G. Wyatt, S. Kravtsov, and A. A. Tsonis
(Published in Climate Dynamics, April 2011)
![Page 6: Teleconnections of Atlantic Multidecadal Oscillation Sergey Kravtsov University of Wisconsin-Milwaukee Department of Mathematical Sciences Atmospheric.](https://reader036.fdocuments.in/reader036/viewer/2022062309/5697c01b1a28abf838ccf7ad/html5/thumbnails/6.jpg)
–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
![Page 7: Teleconnections of Atlantic Multidecadal Oscillation Sergey Kravtsov University of Wisconsin-Milwaukee Department of Mathematical Sciences Atmospheric.](https://reader036.fdocuments.in/reader036/viewer/2022062309/5697c01b1a28abf838ccf7ad/html5/thumbnails/7.jpg)
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
![Page 8: Teleconnections of Atlantic Multidecadal Oscillation Sergey Kravtsov University of Wisconsin-Milwaukee Department of Mathematical Sciences Atmospheric.](https://reader036.fdocuments.in/reader036/viewer/2022062309/5697c01b1a28abf838ccf7ad/html5/thumbnails/8.jpg)
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
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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
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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
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Multidecadal Signal: Stadium Wave
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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
![Page 13: Teleconnections of Atlantic Multidecadal Oscillation Sergey Kravtsov University of Wisconsin-Milwaukee Department of Mathematical Sciences Atmospheric.](https://reader036.fdocuments.in/reader036/viewer/2022062309/5697c01b1a28abf838ccf7ad/html5/thumbnails/13.jpg)
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
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Identification of synchronizing index subsets in 6-index subnets
191719231940
1958
1976
Yellow/orange cells indicate abnormal synchronizations within 6-index subsets
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Identification of synchronizing index subsets in 6-index subnets
1917
1940
1976
“Successful” synchronizations were followed by a climate shift (Tsonis, Swanson, Kravtsov 2007)
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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
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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
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AN EMPIRICAL MODEL OF DECADAL ENSO VARIABILITY
S. Kravtsov
(Submitted to Climate Dynamics)
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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!
![Page 20: Teleconnections of Atlantic Multidecadal Oscillation Sergey Kravtsov University of Wisconsin-Milwaukee Department of Mathematical Sciences Atmospheric.](https://reader036.fdocuments.in/reader036/viewer/2022062309/5697c01b1a28abf838ccf7ad/html5/thumbnails/20.jpg)
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
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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)
![Page 22: Teleconnections of Atlantic Multidecadal Oscillation Sergey Kravtsov University of Wisconsin-Milwaukee Department of Mathematical Sciences Atmospheric.](https://reader036.fdocuments.in/reader036/viewer/2022062309/5697c01b1a28abf838ccf7ad/html5/thumbnails/22.jpg)
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
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Conclusion: Correlation btw large-scale predictors and ENSO is unlikely to be due to random factors
RESULTS
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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
![Page 25: Teleconnections of Atlantic Multidecadal Oscillation Sergey Kravtsov University of Wisconsin-Milwaukee Department of Mathematical Sciences Atmospheric.](https://reader036.fdocuments.in/reader036/viewer/2022062309/5697c01b1a28abf838ccf7ad/html5/thumbnails/25.jpg)
Properties of the empirical ENSO model-I
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Properties of the empirical ENSO model-II
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Algebraic structure of ENSO model
€
dx=f(x,y,t)dt+dw; f≡-∂F/∂x
•F – potential function
![Page 28: Teleconnections of Atlantic Multidecadal Oscillation Sergey Kravtsov University of Wisconsin-Milwaukee Department of Mathematical Sciences Atmospheric.](https://reader036.fdocuments.in/reader036/viewer/2022062309/5697c01b1a28abf838ccf7ad/html5/thumbnails/28.jpg)
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!)
![Page 29: Teleconnections of Atlantic Multidecadal Oscillation Sergey Kravtsov University of Wisconsin-Milwaukee Department of Mathematical Sciences Atmospheric.](https://reader036.fdocuments.in/reader036/viewer/2022062309/5697c01b1a28abf838ccf7ad/html5/thumbnails/29.jpg)
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
![Page 30: Teleconnections of Atlantic Multidecadal Oscillation Sergey Kravtsov University of Wisconsin-Milwaukee Department of Mathematical Sciences Atmospheric.](https://reader036.fdocuments.in/reader036/viewer/2022062309/5697c01b1a28abf838ccf7ad/html5/thumbnails/30.jpg)
THANKS FOR YOUR ATTENTION