Atmospheric GCM Response to
Extratropical SST Anomalies:
Synthesis and Evaluation
Kushnir et al. (2002) (J. Climate)
Motivation
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Interactions between ET ocean and atmos.
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We saw how ENSO may help climate prediction
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We saw how ENSO and midlats may be linked
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How does atmos. respond to SST
anomalies?
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Feedbacks
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Deep mixed layers linked with extratropical
SST anomalies. Changes persist.
Predictability?
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Do ET ocean anomalies affect large scale?
Observed SST Anomalies
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Large, basin scale size. Similar in scale to lowfrequency atmospheric variability.
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SSTA e-folding timescale 3-5 months. Reflect
well-mixed upper-ocean layer heat content
changes.
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Monthly & seasonal ET SSTAs well correlated
with surface air temp anomalies.
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Dominant patterns of monthly & seasonal SSTAs
well correlated with primary patterns of atm circ
anomalies. Strongest during winter and when
atm leads ocean by about a month.
Observed SST Anomalies
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Neg. ET SSTAs assoc. with stronger than
normal westerlies above. Cyclone poleward
and anticyclone equatorward.
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Equivalent barotropic vertical structure for
atmospheric anomalies during winter.
Vertical cross section along 52.5oN of Dec-Mar air temperature and geopotential height
anomalies (linear regression) with the leading PC of NA SST.
Surface Flux Focing of SST
Anomalies
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Correlation between upward surface flux and
SST is negative (Figure 1).
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Changes in wind speed and advection (heat
& moisture) anomalies in MBL both
important (Seager et al. 2000).
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Ekman and local sfc flux forcing often same
order of magnitude and in phase (NA & NP).
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500 hPa hgt data and weekly SST data show
atm leads ocean 2-3 weeks (Deser and
Timlin 1997).
Fall Season Reemergence Of
Winter-Forced SSTAs
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Covered in Deser et al. (2002)
Atmospheric Response
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Response to Reemergence & Persistence of
Anomalies
Statistically signifiant covariance between 500-hPa hgts
during winter and SST up to six months earlier.
Atmospheric response shows NAO pattern (Czaja and
Frankignoul).
•
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Thermal inertial & reemergence (perhaps other factors)
SS95 = 0.5
SS95 = 0.25
(a)
(b)
Autocorrelation fcn of first PC of monthly, year-round NA SSTAs, fcn of calendar month.
Cross correlation between first PC of monthly, year-round SLP, and first PC of monthly, yearround North Atlantic SSTAs
Decadal Variability
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Bjerknes (1964) - multiyear SSTAs in Gulf Stream
extension region, distinct from interannual SST
variability.
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Decadal SST anomalies caused by an alteration in magnitude
and location of wind-driven, sub-tropical gyre, a response to
multiyear changes in basin-scale atm. circ.
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More recent evidence - both Pacific (Zhang et al. 1997;
Nakamura et al. 1997) and Atl (Deser and Blackmon
1993; Kushnir 1994).
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El Nino related oceanic heat advection? Predictable
form of atm-ocean interaction possible if atmosphere
responds to SSTAs.
Tropical Vs. Midlat Forcing
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SST anomalies in tropical Pacific produce
global atmospheric teleconnection patterns.
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Leads to more uncertainty in the interpretation of
observational and modeling studies dealing with
atmospheric-oceanic interactions.
Linear Response To Heating
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Magnitude of dynamical atm response in midlats
often measured as 500-hPa hgt response to a
surface thermal anomaly.
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•
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Forced variability often smaller than unforced variability.
Obs stand. dev. of 500-hPa hgts on monthly to interannual
timescales on order of 50 to 100-m.
Impermiability & Heating Responses.
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•
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Heating balanced by either zonal or meridional temperature
advection, depending on depth of heating.
If deep heating, meridional adv dominates => downstream
shift of low.
For shallow heating, both zonal and merdional adv important
=> baroclinic warm core system downstream
Linear Response Cont.
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Vort equation determines the balance at upper levels. PV
sink balanced by either zonal advection (=> low west of
heating and high downwind) or meridional advection across
the mean PV gradient (=> downstream low). For horizontal
spatial scale of typical SST anomaly, zonal advection
dominates, giving a downstream high.
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Primitive Equations on Sphere (with varying basic
states and complex heat sources) - solutions complex
but consistent with QG framework (Hoskins & Karoly
1981; Hendon & Hartman 1982; Valdes and Hoskins
1989; Ting 1991; Ting and Peng 1995; Peng and
Whitaker 1999).
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GCM responses to ET SSTAs show greater sensitivity
to underlying climatology than linear calculations and
show equiv. barotropic response like obs (Hall et al
2001).
Response to (top) deep
and (bottom) shallow
heating of the linear
QG model in a wide
Beta channel with a
westerly, baroclinic jet
in its center.
Nonlinear Response
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Nonlinearities in dynamics
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e.g., transient eddies (often coming from quadratic nonlinearities
in the equations of motion; i.e., advection; important in formation
and maintenance of equilibrium response to an SSTA.
Can use transients from a GCM to force a linear model.
Response to transient eddy vorticity flux can reverse linear,
near-surface response to shallow heating! Depends on
climatological flow.
Nonlinearities in physical parameterizations
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e.g., bulk aerodynamic formula
GCM response to stationary and
simplified SST anomalies
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SST anomalies are simplified in detail or just the ET
portion is kept. Prescribed SST anomalies are often
amplified to get a detectable response pattern.
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Climatological SST background restricted to single
calendar month to reduce variability (keep solar zenith
angle constant, land surface constant, etc)
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Integrate for longer than a month to reach atm.
equilibrium with ocean anomaly or shorter-term
ensembles with same SSTA in calendar month.
Compare to unforced runs or runs with opposite sign
anomaly.
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Diverse results (linear/nonlinear, baroclinic/equiv.
barotropic)
1) Role of Background
Climatology
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Response depends on underlying model climate (given
by SZA and unperturbed SST) (Peng et al 1995).
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A realistic warm SSTA in western NA, different responses
under Nov. and Jan. conditions. Cold SSTA no effect.
Nov: downstream equiv. barot. high
Jan: weaker equiv. barot. low
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Peng et al. (1997) NP warm anomaly. Weaker and
much more zonal flow in Feb. than Jan. Jan has
baroclinic response with shallow low and Feb has an
equiv. barot. high.
2) Role Of Baroclinic Eddy
Feedback
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Linear response to midlat heating is baroclinic (largely
insensitive to model details).
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Small differences between responses in different basic
states may amplify through interactions with transient
eddy storm tracks.
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Ting and Peng (1995) took transient eddy forcing terms from
GCM experiments of Peng et al. (1995) and forced a linear
model. Jet in November is more zonal than Jan => heating with
same SSTA induces upper-level response that weakens jet in
Nov. but strengthens it in Jan.
Peng and Whitaker (1999) considered Peng et al (1997) results
for Pacific SSTAs. Take heating and transient forcing from GCM
results and force linear, time-dependent, primitive equations.
Role of BaroclinicEddy Feedback
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Take sum of GCM climatological flows for each month and a
linear response to heating to create a new basic state. Put into
a linear, QG storm-track model. Perturb with stochatic forcing to
get baroclinic eddy statistics, with eddy vorticity fluxes that go
with the given basic state. Then one can calculate geopotential
height tendency. Use this tendency to force the linear, primitive
equation model giving an anomalous flow driven by eddy
momentum forcing.
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See also Figure 5b.
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Overall, eddy feedback depends on configurations of
climatological storm track, SSTA, and relative positions.
Relationship to Intrinsic Model
Variability
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Peng & Robinson (2001) examine relationship between
SST-forced response from Peng et al. (1997) and
model's unperturbed internal variability. => SST-forced
response has local and direct linear response to lowlevel heating and an eddy-driven component
resembling model's internal variability (barotropic).
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Internal variability differs month-to-month. Eddy-driven
component is equiv. barotropic. For warm SSTAs over
NP to induce equiv-barotropic high in center of basin,
model's internal variability must have well-defined
center of action there. PDFs of leading EOFs of
monthly 500-hPa anomalies changes.
Response to time-varying SST
variability in "realistic" integ.
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Use history of SSTAs and compare with observations.
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Studies use global SSTAs. As such, ET signal from ET
anomalies mixed with response from tropical SSTAs.
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Most studies neglect focus on mechanisms assoc. with
response. Most work measures the degree of internal
vs. SST-forced variability and characteristics of SSTforced variability.
1) Relative importance of internal
vs. forced vblty
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Analysis of variance (or similar approaches)
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Partition total variance into internal & SST-forced components
Potential predictability = (SST-forced var)/(Total Var) "Level of
predictability that could be achieved given perfect knowledge of
the boundary conditions."
Fig. 7 -tropical belt has high SST-forced variance (60-80%). Mid
and High lats exemplify 20% Pot. Predictability.
2) Response to SSTAs
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Construct composites based on some chosen SST
indices (Refer to Figure 1). Use of SST index
appropriate when we expect a response to a SSTAs
and for high signal-to-noise ratios (extratropics are
obscured) Therefore:
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EOFs, singular value decompositions, and canonical
correlation analysis.
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Attemps at identifying SST-forced variability.
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NP shows PNA-like responses to ENSO
NA Both ENSO and NA SST pattern (winter and spring).
Perhaps the SSTAs force P.P. fluctuations in NAO index?
Increased variance (compared to climatological SSTs)
with varying SSTAs (true for NAO). Probably mostly
due to tropical SSTAs.
Coupled Model Studies of ET
Interaction
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Interaction often captured in coupled model
setting.
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Barsugli and Battisti (1998)
BB98 Review
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Surface heat flux due to atmospheric vblty and
atmospheric response to SST determine time evolution
of SST.
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BB98 linear model - SSTA tendency depends linearly
on anomalous atm column mean temp. and SSTA.
dTa
  aTa  bTo  N (t )
dt
dTo

 cTa  dTo
dt
BB98 Cont.
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Allows for direct comparison of different types of GCM
exp. coupled and uncoupled and assess the effect of
thermal coupling on SST and atm. temperature vblty.
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Coupling enhances variance and persistence of atm.
and oceanic temps.
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Air-sea coupling decreases surface heat flux between
ocean and atm. In experiments with prescribed SSTAs,
the heat flux at low freq. likely too large and of wrong
sign.
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Reduction in thermal damping exerted by ocean on
atmosphere (ocean responds to changing atmosphere!)
BB98 Cont.
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Bretherton and Battisti (2000)
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Ensemble mean skill in reproducing the NAO vblty on
interannual timescales when forced with a time history of
observed SSTAs. BB98 model run in AMIP mode show same
mean skill. But here, all vblty is driven by unpredictable
atmospheric noise. In the mean a large ensemble of GCM runs
forced with SSTAs, high-frequency vblty filtered out leaving low
freq. atm response selecting weak influence of SST vblty
common to all members of ensemble. No implied predictability
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In coupled system, atmosphere continually forces SST,
information is lost in the timescale of SSTA..
Coupled GCM Experiments
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Compare atm. vblty of coupled midlat system with uncoupled
vblty associated with climatological SST conditions.
All levels of sophistication point to coupling with midlat oceans
increases the low-frequency atmospheric thermal variance and
extends persistence of atm anomalies.
Results indicate coupling mostly due to thermodynamic effects
(slab vs. full ocean models similar results)
Reduced thermal damping, coupling can increase persistence of
atm. structures most sensitive to damping.
Conclusions
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ET ocean influences atmosphere outside of
MBL, but small amplitude compared to
internal variability.
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Vblty projects strongly on internal modes of
vblty. Governed by interactions between
transients and large-scale flow. Must
investigate pdfs of internal modes.
Conclusions
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A linear stochastic model explains much of
the behoavior and predictability of the
complex coupled ocean-atm system.
Dominant influence of midlat ocean on the
overlying atm is to reduce thermal damping
of atmospheric low-frequncy vblty.
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Future research - how relatively weak
influence of ocean on atm, with strong atm
influence on ocean, determines vblty of ET,
coupled system. (transition seasons)