Transcript The Influence of Tropical-Extratropical and Atmosphere

The Influence of
Tropical-Extratropical Interactions
on ENSO Variability
Michael Alexander
NOAA/Earth System Research Lab
Pacific Ocean-Atmosphere Variability Nov-Mar
EOF 1 SST
Z500
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CI = 5 m
Deser and Blackmon, 1995, J. Climate
Response to tropical SSTAs
Z 200 (m)
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SSTA
°C
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Blackmon, Geisler and Pitcher 1983
Response to North Pacific SSTAs
Z 200 (m)
SSTA
°C
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Pitcher, Blackmon, Bates, and Muñoz, 1988
Response to Tropical and
North Pacific SSTs
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Pacific SST Structure and
Atmospheric Forcing/Feedback
Pitcher et al.:
• Noted that past work indicated significant correlation
between tropical and midlatitude SSTs and that the
two might be related
• “For example, a tropical Pacific SST could produce
an atmospheric anomaly which could produce a
midlatitude SST anomaly”
• “Another possibility is that an enhanced atmospheric
anomaly might occur if both a tropical SST anomaly
and a favored midlatitude SST anomaly were to
occur simultaneously”
• “Other permutations of possible cause and effects
can be imagined.”
Oceanic Response to ENSO:
the “Atmospheric Bridge”
Mixed Layer
Ocean Model
L
Prescribed Climatological SSTs
Alexander 1990 Climate Dyn.; Alexander 1992 J Climate
ENSO’s impact on the North Pacific
Qnet
W m-2
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SST
x 10
°C
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Response to Pacific SST Anomalies
z200 (m) DJF
Tropical
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North
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Shading: > 95% significance by t-test
Response to ENSO:
Role of Ekman transport
El Niño – La Niña SST (˚C) JFM
Alexander et al. 2002 J. Climate; Alexander and Scott 2007
Z500 (m) Niño - Niña Composite
Obs
EKM
MLM
∆
Niño - Niña SST, SLP,  JFM
Extratropical => Tropical
Air Sea Interactions
Seasonal Foot Printing Mechanism (SFM)
• NPO in NDJ (-1)

• Winds & Heat Flux

• SST in MAM (0)

• Tropical Winds

• Bjerknes Feedback

• El Nino in NDJ(0)
(Vimont et al. 2001, 2003a&b J. Climate)
Experiment Design
• Model (Chang et al. 2007, GRL):
•AGCM: CCM3
•Reduced Gravity Ocean (Cane-Zebiak) Model 30S-30N in Pacific.
•Slab model over remainder of the ocean
•Models are anomaly coupled
•100-year Control run
•SFM Experiment
•Add additional heat flux forcing associated with the NPO
•20°S-60°N; similar results when forcing > 10°N
•Initiate 60 heat flux anomaly runs from Nov in control run.
•Apply Heat flux anomaly during first Nov-Mar
•Then let model evolve with unperturbed fluxes for 12 more months.
• Compare ENSO evolution in perturbation and control runs.
• Note: model already includes SFM
Additional SFM Forcing
• NPO from AGCM
2nd EOF SLP & Qnet Nov-Mar
– With Climatological SST
• Isolates intrinsic variability
– 2nd EOF of SLP EOF in
North Pacific in Winter
– Regress Sfc Heat flux on PC
– double flux values
• Max values of ~30 Wm-2
SLP
• Add identical/constant forcing in
each of the experiments
Qnet
Exp n
Exp n+2
Exp n+1
Control
Nov
Mar
Nov
Mar
Nov
Mar
Experiment - Control
SST (°C)
MJJ(0)
Winds
&
SST
Experiment - Control
Thermocline depth (m)
SST (°C)
NDJ(1)
Experiment - Control Nino 3.4 SST (°C)
NDJ(1)
Run number
In 43 of the 60 cases (~72%) SSTs warmed in the Nino 3.4 region in the
subsequent winter after the forcing was applied. The Mean difference
between Exp and Control is 0.47°C (significant at the 99% level).
Forcing added 11 more warm events - Nino 3.4 NDJ > 1  control.
Summary
• Connection Between tropical Pacific and extratropics:
• Atmospheric Bridge
• Global, including N. Pacific & N Atlantic
• Ekman transport important in generating SSTs
• Feedback of bridge-related SST anomalies on the atmosphere:
•
•
•
•
~1/3 of response to ENSO SSTs (signal/noise issues)
May involve multiple bridges
Nature of feedback depends on region/seasonal cycle
Model dependence?
• Extratropics => Tropics
• Atmosphere
• Seasonal Footprinting Mechanisms
• Ocean Pathways
• Rossby waves
• Subtropical Cells
Additional Slides
Model Configration
Net Surface Heat FLux
Ekman
U200 (m/sec)
Composite Niño - Niña JFM
Precipitation, 200 mb streamlines
Air-Sea Feedback N. Atlantic
N. Atl: Climatological SST
N. Atl: 50 m slab ocean
Atmospheric Response
500 mb January
Africa
N. Atl: 75 m slab + Ekman Trans
South
America
SSTA
Peng, Robinson, Li, Hoerling, Alexander
Optimal
Structure
SST pattern that undergoes
maximum growth - defined
here as the domain
integrated SSTA variance
When is Optimal Structure most
likely to occur?
Evolution of Air-Sea
System based on OS:
Difference maps between
composite averages when
the correlation between
the OS and the observed
SSTA field > 0.4 and
<
-0.4 during MAM.
Starting in the winter
before through the
following winter
Composite Based on Optimal Structure in MAM
Composite Based on Optimal Structure II