An Empirical Model of Decadal ENSO Variability

Sergey Kravtsov

University of Wisconsin-MilwaukeeDepartment of Mathematical Sciences

Atmospheric Science Group

Collaborators:

M. Ghil, ENS & UCLA; D. Kondrashov, UCLA; A. W. Robertson, IRI

EGU General Assembly, Vienna, Austria May 2–7, 2010

http://www.uwm.edu/kravtsov/

Multidecadal-vs.-interannual climate variability: Are they separable?

• The simplest way to isolate lowest-frequency variability

from the rest is to use temporal filters.

Problem: The filtered signal is contaminated by noise.

• Various spatiotemporal filters may work better!

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

may 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

Niño-3 decomposition• 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)

Methodology• 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

Properties of the empirical ENSO model-III

Algebraic structure of ENSO model

€

dx=f(x,y,t)dt+dw; f≡-∂F/∂x

•F – potential function

ENSO Forecasts: Procedure• Compute and extrapolate decadal predictors (CVs)

• Do stochastic-model runs forced by extrapolated CVs• Compute probabilistic measures of ENSO events

• Compare with

actual obs.

ENSO “decadal” forecast skill

Spaghetti-Plot of All Retroactive Forecasts

• The retroactively forecasts are much less impressive

than hindcasts. Why? — CV extrapolation is not skillful!

Forecast skill of CV extrapolation• One-discriminant based extrapolation is most skillful, and captures an anthropogenically forced warming trend.

• The inclusion of AMO/PDO related predictors lowers the extrapolation forecast skill.

• The latter lack of skill limits the

predictive capacity of our

empirical ENSO model

(cf. Wittenberg 2009)

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 retroactive forecast skill of this model is limited

chiefly by the lack of skill in CV extrapolation.

Selected referencesDelSole, T., 2006: Low-frequency variations of surface temperature in observations and simulations. J.

Climate, 19, 4487–4507.

DelSole, T., and M. K. Tippett, 2009b: Average predictability time. Part II: Seamless diagnoses of predictability on multiple time scales. J. Atmos. Sci., 66, 1188–1204.

Dong, B. W., R. T. Sutton, and A. A. Scaife, 2006: Multidecadal modulation of El Niño Southern Oscillation (ENSO) variance by Atlantic Ocean sea surface temperatures. Geophys. Res. Lett., 3, L08705, doi:10.1029/2006GL025766.

Federov, A., and S. G. Philander, 2000: Is El Niño changing? Science, 288, 1997–2002. doi: 10.1126/science.288.5473.1997.

Ghil M., R. M. Allen, M. D. Dettinger, K. Ide, D. Kondrashov, M. E. Mann, A. Robertson, A. Saunders, Y. Tian, F. Varadi, and P. Yiou, 2002: Advanced spectral methods for climatic time series. Rev. Geophys., 40(1), 1003, doi:10.1029/2000RG000092

Schneider, T., and I. M. Held, 2001: Discriminants of twentieth-century changes in earth surface temperatures. J. Climate, 14, 249–254.

Timmermann, A., Y. Okumura, S. I. An, A. Clement, B. Dong, E. Guilyardi, A. Hu, J. H. Jungclaus, M. Renold, T. F. Stocker, R. J. Stouffer, R. Sutton, S. P. Xie , J. Yin, 2007: The influence of a weakening of Atlantic meridional overturning circulation on ENSO. J Climate, 20, 4899–4919, doi:10.1175/JCLI4283.1.

Wittenberg, A. T., 2009: Are historical records sufficient to constrain ENSO simulations? Geophys. Res. Lett., 36, L12702, doi:10.1029/2009GL038710.

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