The Impact of ENSO on NAO VariabilityThe Impact of ENSO on NAO Variability

Zhaohua Wu, B. P. Kirtman, E. K. SchneiderZhaohua Wu, B. P. Kirtman, E. K. SchneiderCenter for Ocean-Land-Atmosphere StudiesCenter for Ocean-Land-Atmosphere Studies

andand

N. E. HuangN. E. HuangGoddard Space Flight Center, NASAGoddard Space Flight Center, NASA

OUTLINEOUTLINE

A questionA question A natural filter based on EMDA natural filter based on EMD ENSO and NAO in observationENSO and NAO in observation Mechanism (incomplete)Mechanism (incomplete) SummarySummary

AN OLD STORYAN OLD STORY““Editorial NoteEditorial Note: : In the following suite of formal comments In the following suite of formal comments and responses we are following and responses we are following unusualunusual procedureprocedure; we ; we are providing a venue for a valuable but are providing a venue for a valuable but unusual unusual scientific exchangescientific exchange that involves multiple articles. that involves multiple articles.””

BAMS, 1999, p2721BAMS, 1999, p2721

1.1. Wunsch, BAMS, 1999, p245-255.Wunsch, BAMS, 1999, p245-255.2.2. Comments on “1” by Trenberth and HurrellComments on “1” by Trenberth and Hurrell3.3. Reply to “2” by WunschReply to “2” by Wunsch4.4. Comments on “2” by Rajagoplan, Lall, and CaneComments on “2” by Rajagoplan, Lall, and Cane5.5. Reply to “4” by Trenberth and HurrellReply to “4” by Trenberth and Hurrell

A QUESTIONA QUESTION

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000-5

0

5

NAO Index

Does white noise like NAO index contains the signals related to the interannual ENSO variability?

(The lag one autocorrelation for the observed NAO index is 0.08.)(The lag one autocorrelation for the observed NAO index is 0.08.)

X(t) = S(t) + N(t)

BAND PASS FILTERINGBAND PASS FILTERING

Traditional FiltersTraditional Filters

• Subjective parametersSubjective parameters

• ““local” -> “global”local” -> “global”

ResultsResults

• TunedTuned

• fuzzyfuzzy

EMPIRICAL MODE DECOMPOSITIONEMPIRICAL MODE DECOMPOSITION

signal source 1

receiver

signal source 2

signal source 3

signal source 4

signal source 5

SIFTING PROCESSSIFTING PROCESS

-2

0

2

SO

I

-2

0

2

SO

I

-2

0

2

Sift

1

1935 1940 1945 1950 1955 1960-1

0

1

C4

IMFs OF NAO INDEXIMFs OF NAO INDEX

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000-0.4-0.200.2R

-0.50

0.5

C9

-0.50

0.5

C8

-0.50

0.5

C7

-101

C6

-101

C5

-202

C4

-202

C3

-505

C2

-505

C1

-100

10

Raw

Dat

a

STATISTICAL SIGNIFICANCESTATISTICAL SIGNIFICANCE

0 1 2 3 4 5 6 7 8 9 10-10

-9

-8

-7

-6

-5

-4

-3

-2

-1

0Energy of IMFs as a Function of Period

ln T

ln E

1 mon 1 yr 10 yr 100 yr

BAND PASS FILTERBAND PASS FILTER

EMD FilterEMD Filter• Adaptive without Adaptive without

prescribed parameterprescribed parameter• ““local” ->”local” ->”locallocal””• PhysicalPhysical

ResultsResults• ClearClear• DyadicDyadic

0 1 2 3 4 5 6 7 8 90

0.5

1

1.5

spec

trum (1

0**-3)

Fourier Spectra of IMFs

1 1.5 2 2.5 3 3.50

0.2

0.4

0.6

0.8

1

ln T

spec

trum (1

0**-3)

Shifted Fourier Spectra of IMFs

““LOCAL” -> “LOCAL” -> “LOCALLOCAL””

0 100 200 300 400 500 600 700 800 900 10000

5

10

Sig

nal

0 100 200 300 400 500 600 700 800 900 10000

10

20

F. S

pect

ra

0 100 200 300 400 500 600 700 800 900 1000-4-2024

IMF

1

0 100 200 300 400 500 600 700 800 900 1000

-202

IMF

2

0 100 200 300 400 500 600 700 800 900 1000

-202

IMF

3

0 100 200 300 400 500 600 700 800 900 1000-1

0

1

IMF

4

AN IDEALIZED MODELAN IDEALIZED MODEL NAO = Internal Variability + ENSO forced signalNAO = Internal Variability + ENSO forced signal

• For simplicity, assumingFor simplicity, assuming

• When the standard deviation of the noise is one, When the standard deviation of the noise is one, AA is 0.4, is 0.4, TT is about 4.2 yr. is about 4.2 yr.

ENSOinternalNAO FVI

noisewhiteN,Vinternal

Tt2πsinAFENSO

DATADATA

0 10 20 30 40 50 60 70 80 90 100-4

-2

0

2

4Monthly Random DATA

0 10 20 30 40 50 60 70 80 90 100-4

-2

0

2

4Synthetic NAO Index

0 10 20 30 40 50 60 70 80 90 100-4

-2

0

2

4Difference

year

SIGNIFICANCE TESTSIGNIFICANCE TEST

0 1 2 3 4 5 6 7 8-8

-7

-6

-5

-4

-3

-2

-1

0Significance Test (diamond: RND; circle: NAO)

ln T

ln E

PHYSICAL DECOMPOSITIONPHYSICAL DECOMPOSITION

0 10 20 30 40 50 60 70 80 90 100-1

-0.5

0

0.5

1C6 of Monthly Random DATA

0 10 20 30 40 50 60 70 80 90 100-1

-0.5

0

0.5

1C6 of Synthetic NAO Index

0 10 20 30 40 50 60 70 80 90 100-1

-0.5

0

0.5

1Differences (red: raw data; green:C6s

year

ENSO/NAO IN OBSERVATIONENSO/NAO IN OBSERVATION

POSSIBLE ROUTESPOSSIBLE ROUTES

APPROACHESAPPROACHES GCM Studies: Globally coupled GCM Studies: Globally coupled vs.vs.

regionally uncoupledregionally uncoupled • In the regional uncoupled cases, the In the regional uncoupled cases, the

atmospheric model is forced by climatological atmospheric model is forced by climatological SST while the ocean model is driven by the SST while the ocean model is driven by the interactive fluxes.interactive fluxes.

WAVE CHAIN ROUTEWAVE CHAIN ROUTE• NAO and SST variability in the North Atlantic will NAO and SST variability in the North Atlantic will

still have a significant simultaneous (or slightly still have a significant simultaneous (or slightly delayed) correlation with ENSO cycle if the delayed) correlation with ENSO cycle if the causal effect is through PNA type of wave chains causal effect is through PNA type of wave chains in the North Atlantic uncoupled case.in the North Atlantic uncoupled case.

LOW LATITUDE ROUTELOW LATITUDE ROUTE

• If the ENSO effect is through the low If the ENSO effect is through the low latitude route by affecting the wind stress latitude route by affecting the wind stress that drives the subtropical gyre, the that drives the subtropical gyre, the correlation between ENSO and NAO will correlation between ENSO and NAO will be missing.be missing.

GCMS & EXPERIMENTSGCMS & EXPERIMENTS COLA Anomaly Coupled GCM:

• ATMOSPHERE: COLA AGCM version 2, T42, 18 layers• OCEAN: GFDL MOM 2, 1.5˚*0.5˚ in the tropics,

1.5˚*1.5˚ in the high latitude, 25 layers (15 layers in upper 250 m)

• ICE: observed climatology EXPERIMENTS:

• CONTROL: coupled globally• PAC_UNCOUPLED: Tropical Pacific (between 30˚N and

30˚ S) uncoupled• ATL_UNCOUPLED: North Atlantic (north of 15˚N)

uncoupled• Additional

ENSO/NAO IN MODEL (I)ENSO/NAO IN MODEL (I)

ENSO/NAO IN MODEL (II)ENSO/NAO IN MODEL (II)

ENSO/NAO IN MODEL (III)ENSO/NAO IN MODEL (III)

STORY TO BE CONTINUESTORY TO BE CONTINUE

…… …… …… ……

CONCLUSIONSCONCLUSIONS The new natural filtering method provides a The new natural filtering method provides a

unique opportunity to examine the impact of unique opportunity to examine the impact of ENSO on NAO variability on interannual timescaleENSO on NAO variability on interannual timescale

The observational evidence showed that NAO The observational evidence showed that NAO interannual variability is well correlated to ENSO interannual variability is well correlated to ENSO variability in the tropical Pacificvariability in the tropical Pacific

The modeling studies demonstrate that the The modeling studies demonstrate that the relation of ENSO and NAO is hidden in the relation of ENSO and NAO is hidden in the seemingly white noise like NAO variabilityseemingly white noise like NAO variability

More analysis need to be carried out to figure out More analysis need to be carried out to figure out causal relationships between ENSO and NAOcausal relationships between ENSO and NAO