The Pacific Meridional Mode: The Pacific Meridional Mode: Diagnostics and ImpactsDiagnostics and Impacts
Dan Vimont Department of Atmospheric and
Oceanic SciencesCenter for Climatic Research
University of Wisconsin, Madison
John Chiang Department of Geography &
Berkeley Atmospheric Sciences Center
University of California, Berkeley
Climate Diagnostics and Prediction WorkshopClimate Diagnostics and Prediction WorkshopOctober 21, 2004October 21, 2004
Madison, WIMadison, WI
October 21, 2004 Climate Diagnostics and Prediction Workshop
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The Pacific Meridional Mode Analogies between the Atlantic and
Pacific Modeling the tropical response to mid-
latitude forcing The spatial structure of decadal ENSO-
like variability Conclusions
October 21, 2004 Climate Diagnostics and Prediction Workshop
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The Pacific Meridional Mode Analogies between the Atlantic and
Pacific Modeling the tropical response to mid-
latitude forcing The spatial structure of decadal ENSO-
like variability Conclusions
October 21, 2004 Climate Diagnostics and Prediction Workshop
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The Atlantic Meridional Mode Dominant statistical
mode of tropical Atlantic interannual to decadal variability: Meridional SST
gradient Cross-gradient
boundary-layer flow towards warmer water
ITCZ shift towards warmer hemisphere
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Tropical Mean States: SST
Cold tongue weighted toward east
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Tropical Mean States: Precip.
Mean ITCZ along cold tongue’s northern edge
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Tropical Seasonal Cycle
Atlantic:
Pacific:
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Pacific Meridional Mode: Motivation Each basin possesses similar mean states and
seasonal cycle: cold tongue to the east, similar seasonal cycles of ITCZ and cold tongue (Mitchell and Wallace, 1992)
Model studies without a thermocline-SST feedback produce meridional variability as the dominant mode (e.g. Xie and Saito, 2001)
Similar evidence for mid-latitude forcing of tropical variability in each basin (Curtis and Hastenrath, 1995; Nobre and Shukla; 1996, Xie and Tanimoto; Czaja et al., 2002; Vimont et al., 2001, 2003a, b)
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Observational Analysis Data: NCEP reanalysis
10m winds and SST; CPC merged precipitation
Defined over regions (in the Pacific and Atlantic) with similar mean states
Best fit linear regression to CTI (ENSO) removed from data
Method: Maximum Covariance Analysis (SVD analysis): defines patterns between two fields that are strongly coupled
MCA applied to 10m winds and SST
Data regressed onto SST expansion coefficient
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Analogous Meridional Modes
Leading statistically coupled mode for SST and 10m winds
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Analogous Meridional Modes
Precipitation regressed on SST time series from leading MCA mode
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Temporal evolution
Variations occur on many time scales, including interannual and decadal
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Temporal Evolution
Wind time index has maximum variance during boreal winter (NDJF)
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Temporal Evolution
SST time series has maximum variance during boreal spring (MAM)
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Temporal Evolution
Lag correlation peaks when wind time series leads SST time series
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Meridional Modes A “meridional mode” of tropical ocean-
atmosphere variability is identified in the Pacific. The Pacific meridional mode resembles the Atlantic
meridional mode in both spatial and temporal structure. The strong similarity between the two basins suggests that
the meridional modes arise from analogous processes
October 21, 2004 Climate Diagnostics and Prediction Workshop
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The Pacific Meridional Mode Analogies between the Atlantic and
Pacific Modeling the tropical response to
mid-latitude forcing The spatial structure of decadal ENSO-
like variability Conclusions
October 21, 2004 Climate Diagnostics and Prediction Workshop
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Analogous Meridional Modes
(i) Trade wind relaxation(ii) Up-gradient flow
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CCM3 Response to Meridional Mode SST
CCM forced by meridional mode SST: Up-gradient flow reproduced
Relaxed subtropical trades not reproduced
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Source of subtropical winds
Trades relax in response to NAO in the Atlantic, and NPO in the Pacific
North Pacific
Oscillation
North Atlantic
Oscillation
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Meridional Mode Evolution Wintertime fluctuations in the strength of the
subtropical trade winds affect the subtropical SST through surface heat fluxes
In the deep tropics, the atmosphere responds by producing surface winds that blow towards the warmer SST
Model simulation: we will force a coupled model with heat flux anomalies associated with the NPO during winter, then allow the model to freely evolve.
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CCM3.10 Experiments
NPO heat flux imposed during winter months: NDJFM
Ensemble simulations allow investigation of coupled variability (forced by NPO) without prohibitively long model integrations
Fluxes generated
by ATM and SOM
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CCM3.10 Coupled ResponseWinter
NPO heat flux forces SST anomalies
SummerCoupled response alters and prolongs
tropical SST anomaly
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Coupled response: WES feedback
SST anomaly amplifies slightly during winter (imposed forcing)
Latent heat flux continues to amplify SST anomaly during summer (after imposed forcing is shut off)
Coupled WES feedback enhances persistence and amplitude of SST anomalies
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Model Results The meridional mode can be excited by
mid-latitude atmospheric variability Wintertime variations in the trade wind strength
associated with the NAO or NPO alter subtropical SSTs through changes in surface heat fluxes
The tropics respond to these SST anomalies during spring and summer
The coupled WES feedback increases the amplitude and persistence of the tropical response
October 21, 2004 Climate Diagnostics and Prediction Workshop
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The Pacific Meridional Mode Analogies between the Atlantic and
Pacific Modeling the tropical response to mid-
latitude forcing The spatial structure of decadal
ENSO-like variability Conclusions
October 21, 2004 Climate Diagnostics and Prediction Workshop
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Interannual & Decadal EOFs
Highpass-filtered and lowpass-filtered EOFs reproduce ENSO and ENSO-like variability
(Zhang et al., 1997)
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Reconstructed data What happens if we reconstruct the lowpass-filtered data using
ONLY the highpass-filtered spatial information? Start by projecting the highpass-filtered EOFs (interannual spatial
information) onto the unfiltered data:
Next, apply a lowpass filter to these reconstructed pseudo-PC’s, and reconstruct the data using a subset of these pseudo-PC’s combined with the highpass-filtered EOFs:
€
z iR = Xe i
HP
€
XLPR = z iLPRe i
HP
i=1,2,3,4∑
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Reconstructed data Result: XLPR contains decadal temporal information only, and
interannual spatial information only. Next, perform EOF/PC anlaysis on XLPR.
If decadal processes are responsible for generating the meridionally broadened structure of ENSO-like decadal variability, then the EOFs of XLPR should not have an ENSO-like structure
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EOFs of reconstructed SST
EOF1 of the reconstructed SST reproduces the ENSO-like structure
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HP EOF3: ENSO
“leftovers”
Components of EOFLPR
Contributions from the interannual EOFs
HP EOF1: ENSO
Three HP EOF’s contribute to EOFLPR.Each HP EOF has a known relationship to ENSO.
HP EOF4: ENSO
precursor
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Components of EOFLPR
Temporal Relationships with ENSO
Lagged correlation between peak of
ENSO (PPC1) and PPC3 or PPC4:
PPC4 leads ENSO by 1-12
months
ENSO leads PPC3 by 1-12
months
October 21, 2004 Climate Diagnostics and Prediction Workshop
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The Pacific Meridional Mode Analogies between the Atlantic and
Pacific Modeling the tropical response to mid-
latitude forcing The spatial structure of decadal ENSO-
like variability Conclusions
October 21, 2004 Climate Diagnostics and Prediction Workshop
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Pacific Meridional Mode: Conclusions
A meridional mode of variability is identified in the Pacific The Pacific meridional mode has very similar spatial and
temporal characteristics as its Atlantic counterpart The Pacific and Atlantic meridional modes evolve via
coupled processes in the ITCZ - Cold Tongue region Both the Pacific and Atlantic meridional modes can be
excited by mid-latitude forcing in their respective Northern Basins (perhaps Southern?)
Model results indicate that positive coupled feedbacks enhance the meridional mode persistance and amplitude
The strong similarities between the Pacific and Atlantic meridional modes suggest that the modes are “real”
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Pacific Meridional Mode: Conclusions
The Pacific meridional mode may be an important contributor to interannual ENSO and decadal ENSO-like variability Meridional mode variability tends to precede ENSO by 2-4
seasons The spatial structure of decadal ENSO-like variability is well
reproduced as an average over ENSO precursors, the peak of an ENSO event, and ENSO “leftovers”. This suggests that decadal ENSO-like variability is realized through processes associated with the interannual ENSO cycle
Seasonality is very important Our understanding of climate variability is enhanced by an
understanding of the seasonal cycle