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.
.