2000-2010: 2611 journal papers with ENSO or
El Nino in the title
 Influence of El Nino Southern Oscillation (ENSO) on tomato spotted wilt
incidence and peanut yield
 Advantages and limits of integrating forecasts of El Nino Southern
Oscillation (ENSO) on nutrient management for tomato
 ENSO-associated response of field urine osmolality in the insectivorous
arsupial Thylamys elegans
 Agricultural productivity, the electoral cycle and ENSO effects in Papua
New Guinea
 Why are South Asians susceptible to central obesity? The El Nino
hypothesis
 Potential effects of El Nino-South Oscillation (ENSO) on growth of the
American crocodile, Crocodylus acutus (Crocodylia: Crocodilydae) in
captivity
 ENSO lengthens the days of our lives
ENSO Theory: What is new since 2000 and
what is achievable by 2020?
David Battisti and Mark Cane
1. Progress in the past 10 years
•
13 items!
2. What problems remain? (5 items)
3. Questions for the next 10 years (8 items)
4. Priorities (1 item)
w/ input from Alexey Fedorov, Eric Guilyardi, Ben Kirtman
Dan Vimont and Andrew Wittenberg
1. Progress in the past 10 years
 More observational data to force and evaluate high-end
component models, and to confirm the essential dynamics
and thermodynamics of the ENSO mode, as described in
the ZC intermediate model (w/ complete ocean SST
equation)
QuikSCAT winds, satellite-derived OSCAR
currents, more TAO data, atmospheric
reanalyses (NCEP/DOE-2 and ERA40), gridded
ocean reanalyses (GODAS, SODA, EN3, ECMWF,
GFDL), etc

Theory of the anatomy of an ENSO cycle: ENSO is an
eignenmode of the coupled atmosphere ocean system
–
The mode features all of Wyrtki’s ideas, plus important ocean
adjustment dynamics to swing form warm to cold phase
–
There is enough dissipation or nonlinearity to render a warm
event/cold event anatomy, rather than a quasi-periodic cycle
–
Non-Gausian behavior can be explained by finite ocean
thermodynamics and/or nonlinear wind stress response to ocean SST
anomalies
Refs: Zebiak and Cane1985, Jin and Neelin 1993, Thompson and Battisti 2000
The ENSO eigenmode
the leading eigen (Floquet) mode of the coupled atmosphere/ocean
system
“Onset phase”
“Mature phase”
SST
Period 3.5 to 5
years
Decaying mode
Thermocline
Spatial structure
similar to that
observed
Features Wyrtki’s
observation of
equatorial heat
content and
Bjerknes’ feedback
About 1/4 period later
Ref: Thompson (1998),
Thompson and Battisti (2000)
The ENSO Eigenmode
Warm events tend to peak
at the end of the calendar
year and is irregular

The mode has continuous
spectrum with most energy
in interannual band
Additional features of the ENSO eigenmode
–
–
–
The mode evolves in a manner consistent with observed ENSO cycle
(e.g., in evolution and structure of SST and upper ocean heat
content, Bjerknes feedback, coordinated to calendar year, …)
It is irregular (flavors of ENSO)
When stochastically forced, it features a spring barrier and SST and
thermocline EOFs similar to that observed
Ref: Thompson (1998), Thompson and Battisti (2000,01); Roberts and Battisti (2010)
 Editorial Comments on toy models of ENSO
–
Toy models of ENSO, including the Delayed Oscillator (DO; Suarez and
Shopf 1988, Battisti and Hirst 1989) and Recharge Oscillator (RO; Jin
et al 1997), are simplifications of the physics of the ENSO eigenmode,
in nature and in intermediate models.
–
The DO and RO toy models highlight the same physics in different
shorthand (aside: not surprisingly the DO and RO can be derived from
each other).
–
These toy models have served their purpose/done their damage
–
The toy models developed thus far are not useful for evaluating GCMS
because this neglect things we KNOW are important, such as the
seasonality in forcing, in the richness of the upper ocean heat budget,
the seasonality in the mean state, and in the coupling dynamics.
 Observational evidence for the dynamical
adjustment of the upper ocean consistent with
the ENSO eigenmode
Refs: Meinen and McPhaden 2000, Wittenberg 2002, van Oldenborgh et al.
2005, Philip & van Oldenborgh 2006, Bosc and Delcroix 2008, Brown and
Fedorov 2008, Fedorov 2010
 Observational verification of richness of ocean
surface heat budget during ENSO that is in
qualitative agreement with budget in the ENSO
eigenmode from simpler coupled models (e.g.,
ZC model) and high-end climate models
–
The dominant term in the upper ocean heat budget depends on
where you are in the ENSO mode (in space and time)
Ref: Wang and McPhaden 2000
 The major source of the energy/forcing for
ENSO variability is now known
–
The stochastically forced Meridional Mode accounts for most of
the seasonal westerly activity in the central Pacific that is not
(simply) related to ENSO
–
The stochastically off-equatorial forcing of the Meridional
Mode explains about half to 2/3 of variance in ENSO
–
Variability in the boreal wintertime jet in the Pacific is
particularly good at forcing the MM, and accounts for about
half of the variance in ENSO
Refs: Vimont et al 2003, Chiang and Vimont 2004, Chang et al 2007, Zhang et al
2009
The Meridional Mode and ENSO
(observed)
Meridional Mode
Amp. of
MM wind
Wind
leads SST
Chang et al 2007
Indices of
MM wind (FMAM)
and
ENSO (CTI) NDJ
Refs: Vimont et al 2003, Chiang and Vimont 2004, Chang et al 2007, Zhang et al 2009
The Seasonal Footprinting Mechanism
(observed)
Composite around |CTI| > 1sigma
Winter Heat Flux &
Summer SST
(contoured)
Integrated winter wind anomaly drives spring
SST anomalies that persist into summer,
when it drives the atmosphere (w/ positive
a/o feedbacks) to generate zonal wind
anomalies in the central equatorial Pacific
that give rise to ENSO.
Ref: Vimont et al 2003
 Why ENSO is coordinated with the seasonal
cycle
–
Coordination of the ENSO eigenmode by the annual cycle
(Thompson and Battisti 2001, Kallummal and Kirtman 2008)
–
Seasonality in the stochastic energy source of ENSO: the winter
storm track variability in the midlatitude Pacific and
atmospheric feedbacks (Vimont et al 2003)
–
A minor contribution by nonlinear interaction of the ENSO
mode and the annual cycle
 Importance of the atmospheric boundary
layer (convergence) response to setting
precipitation anomalies, and consequently
the zonal wind stress that the ocean cares
about
Refs: Chiang et al 2001; see also Kim et al. 2008, Neale et al 2008
 Appreciation of importance of non-normal
growth
Stemming from the pioneering work of
Blumenthal (1991), there is an increasing
appreciation for non-normal growth and the
fundamental impact of the seasonal cycle for
modulating non-normal growth and in
controlling predictability of ENSO
Refs: Thompson and Battisti 2001, Tang at al 2006, Kallummal and Kirtman 2008
 More high-end climate models have realistic
ENSOs (but most still do not)
Posit that the limited progress is due to faster
machines and more experimentation (tuning),
and less so to the systematic evaluation of model
biases and the identification of offending physics
and the subsequent attention to the offending
paramerizations.
Refs: AchutaRao and Sperber 2006, Guilyardi 2006, Wittenberg et al. 2006,
Guilyardi et al. 2009 and others
 Further evidence that errors in the mean state of
the high end models affects the ENSO mode
characteristics in a way expected from ENSO
theory
Refs: Fedorov and Philander 2001, Guilyardi et al 2004, van Oldenborgh et al
2005, Guilyardi 2006, Belmadani et al 2010
 Structural errors in the ENSO simulated by
high end climate models are consistent with
the anatomy of the ENSO eigenmode
For example, relationship between too
strong/weak zonal currents and period
of the ENSO mode.
Refs: Capotondi et al 2006, Capotondi 2009, Belmadani et al 2010 and many
more
 More evidence that the linear stochastic physics can
explain multidecadal shifts in ENSO statistics
without exotic physics or any apparent change in
the mean state
Intermediate models with prescribed
mean states, and in very long runs of
high-end climate models show no
relationship between mean state
changes and changes in ENSO variance.
Refs: Knutson et al. 1997, Thompson and Battisti 2001, AchutaRao and
Sperber 2002, Yukimoto and Kitamura 2003, Yeh et al. 2004, Yeh and Kirtman
2004, An et al. 2005, Vimont 2005, Meehl et al. 2006, Lin 2007b, Wittenberg
2009, and others
 Intercomparison of ENSOs simulated by high-
end models; development of ENSO metrics
Mainly to tell when and where the
models go wrong and to identify
systematic biases, and the relationship
between mean state biases and ENSO
biases across models.
Refs: AchutaRao and Sperber 2002, 2006; Cai et al 2003; Capatondi et al 2006;Collins 2000, 2004,
2007, 2009; Wang et al. 2005, Collins et al. 2010,Guilyardi 2006, Guilyardi et al 2004, 2009,
Jungclaus et al 2006; Marryfield 2006, van Oldenborgh et al 2005, Toniazzo 2006, Wittenberg etal
2006, DiNezio et al. 2009; Yeh and Kirtman 2007
2. What problems remain?
 Most high-end climate models do not have
realistic ENSOs (and they still suffer from gross
biases in their mean states)
Refs: Guilyardi et al 2004, Van Oldenborgh et al. 2005, Capotondi et al 2006,
Wittenberg 2006, Guilyardi et al 2009, Belmadani et al 2010; Brown et al 2010
Problem areas: Ocean mixing, treatment of
shallow clouds, deep atmospheric convection,
atmospheric and oceanic pbl processes,
horizontal (and vertical) resolution of the
atmospheric models, …
 Many studies suggest a major problem is in
the response of the atmosphere GCM to the
observed SST anomalies
Refs: Sun et al 2003, Guilyardi et al 2004, Sun et al 2006, Sun et al 2008, Neale
et al 2008, Lloyd et al 2009, Wu et al 2009, Guilyardi et al 2009, Zhang et al
2009, and many others
 Seasonal prediction skill using dynamical
models is arguably no better than that from
the empirical models.
Ref: Kirtman and Min 2009
Posit that this is due to problems with model
parameterizations/approximations, and not due to
initialization problems (e.g., lack of data, better DA).
How will we know if ENSO has changed?
Stochastic variability
w/ no mean state
change?
Impact of lower
frequency (mean
state) variability on
ENSO?
Nonlinear interaction
of ENSO with X?
What would it take to recognize a forced climate change?
Wittenberg 2009
3. Questions for the next 10 years
 Is the ENSO mode unstable or stable? Four
views:
– ENSO as a chaotic non-linear dynamical system (stochastic forcing is
relatively unimportant)
– ENSO as a damped stochastically forced system. Details of the noise
matter in the sense of of projecting onto the stochastic optimals.
– ENSO as damped stochastically forced system where the noise is state
dependent.
– ENSO as a non-linear self-sustained oscillation where noise produces
irregularity
 Does it matter?
 What is the relationship between ENSO and the
Indian Monsoon?
 The sub-interannual “Modoki” pattern
–
–
–
–
Is it a true mode?
Is it a consequence of weak positive feedback between
the atmosphere and ocean in the Meridional Mode
physics?
Is it a pattern that is robust over time (e.g., do we see it
in the PI climate)?
Or is it linked to changes in the mean state in the past 30
years associated w/ increasing GH gases?
 What would it take to recognize a multi-
decadal mean state modulation of ENSO?
 What is the relationship, if any, between
multidecadal to centennial variability and
ENSO in the unforced climate system?
 Where is the remaining energy for ENSO coming
from?
About 2/3 of the energy for ENSO is associated with the
stochastic forcing by the midlatitude wintertime storm track
(through the SFM/MM physics). This also accounts for most of
the seasonal zonal wind activity in the central/western Pacific
that is not related to ENSO.
–
What fraction of the low-frequency variability in westerly wind
activity in the central-western Pacific is not already accounted
for by the midlatitude jets and (Kug et al 2009) the quasi-steady
adjustment of the atmosphere to the changing ENSO mode
itself?
–
What else could add energy to the ENSO mode besides
uncoupled zonal wind variance?

Why was the variance in ENSO reduced in the early-mid
Holocene?

How will ENSO change in a world with increased
greenhouse gases? Clues: we think we know how parts
to the mean state will change:



Consensus on mean state with weakened Trades, decreased the
east-west SST gradient, increased upper ocean temperature
gradient and shallowing and decreased thermocline tilt along the
equator (e.g., Vecchi et al 2006; 2008, DiNezio et al 2009, Collins et
al 2010, …)
Do you trust these changes from models w/ gross biases in the
mean state (e.g., double ITCZ, shallow cloud errors, gross
paramerizations of ocean mixing and atmospheric pbl processes)?
Strength of the stochastic forcing?
4. Priorities (1)
 A large engineering effort to fix high end
climate models
– so they have realistic climatological mean states
– and so they have ENSOs that don’t violate robust
observational constraints
One indicator that high-end climate models
still can’t do ENSO well
Standard Deviation of Nino3
Preindustrial
2x CO2
Guilyardi et al 2009
How will ENSO change due to
increasing GH gases?
Six of 17 models have
that have “the best”
(good?) ENSOs don’t
agree
Collins et al 2010