scott power, greg kociuba, jeff callaghan centre for australian weather and climate research

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Decadal and longer- term variability in ENSO, ENSO teleconnections, and the Walker circulation Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research Bureau of Meteorology

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Decadal and longer-term variability in ENSO, ENSO teleconnections, and the Walker circulation. Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research Bureau of Meteorology. Contents. - PowerPoint PPT Presentation

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Page 1: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Decadal and longer-term variability in ENSO, ENSO teleconnections, and the Walker circulation

Scott Power, Greg Kociuba, Jeff Callaghan

Centre for Australian Weather and Climate ResearchBureau of Meteorology

Page 2: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Contents

1.1. Decadal – interdecadal changes in ENSO Decadal – interdecadal changes in ENSO and ENSO teleconnections and ENSO teleconnections

2.2. A simple model for decadal variability in A simple model for decadal variability in ENSO and “ENSO-related” patterns of ENSO and “ENSO-related” patterns of variabilityvariability

3.3. An inadequacy of this modelAn inadequacy of this model

=>ENSO-driven multi-year variability =>ENSO-driven multi-year variability

=>Multi-year predictability =>Multi-year predictability

4.4. Anthropogenic changes in the Walker Anthropogenic changes in the Walker circulation and the SOI circulation and the SOI

Page 3: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

The SOI – a product of French Australian cooperation!

One of the world’s most important climatic One of the world’s most important climatic indicesindices

Used extensively to estimate and predict Used extensively to estimate and predict changes linked to ENSO and changes in the changes linked to ENSO and changes in the Walker circulation (e.g. in rainfall, agricultural Walker circulation (e.g. in rainfall, agricultural production, disease, streamflow, …)production, disease, streamflow, …)

If SOI < 0 = > weaker Walker circulationIf SOI < 0 = > weaker Walker circulation

Page 4: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

ENSO event frequencies and the SOI

-8

-6

-4

-2

0

2

4

6

8

10

12

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Year

No

. o

f even

ts &

SO

I

No.(LN) No.(EN) No.(EN)-No.(LN) JJASOND SOI

Power and Smith, Geophys. Res. Lett., 2007; updated

Page 5: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Innisfail 1918

3500 residents, only 12 houses remained intact

Approximately 75-100 deaths

Page 6: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Mackay 1918

Page 7: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

A new homogeneous tropical cyclone data base for north-eastern Australia

Taken over a decade to developTaken over a decade to develop Extensive primary sources of information:Extensive primary sources of information: Longest (non-palaeo) record in SH Longest (non-palaeo) record in SH

[1[1872/73-2009/10]872/73-2009/10] homogeneoushomogeneous

Callaghan and Power, 2010: Variability and Callaghan and Power, 2010: Variability and decline in tropical cyclones making land-fall decline in tropical cyclones making land-fall over eastern Australia since the late 19over eastern Australia since the late 19 thth century. century. Climate DynamicsClimate Dynamics..

Page 8: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research
Page 9: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Delayed-Action Oscillator (DAO)

dT(t) /dt = aT (t) - bT ( t - d)dT(t) /dt = aT (t) - bT ( t - d) T = SOI or east Pacific SST; a, b >0; d=time delayT = SOI or east Pacific SST; a, b >0; d=time delay

Analytic solutions to the DAO Equation (Power 2010) with T(t) = T0 eat, t ≤ 0 :

],)1/(!

)([)(

1

00

kkN

k

at ktk

eTtT

t > 0

Cf. numerical solutions of Battisti and Hirst (1989)

Page 10: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Simple model for DV(ENSO)

dT/dt(t) - bT ( t - d) + Noise ordT/dt(t) - bT ( t - d) + Noise or

dd22T/dtT/dt22 = - = -σσ0022T + 2T + 2σσRR dT/dt dT/dt + Noise+ Noise

Fitting DAO to SOI:

dT/dt = aT-bT(t-d) + Noise

d=7mo, a=0.13/yr, b=1.4/yr

Noise-driven damped oscillation with period =3.8 yr, decay time (e-folding scale) =

0.9 yr

cf. Thompson and Battisti (2000), Jin (1997), Meinen and McPhaden (2000)

Noise has decadal/interdecadal tail so stochastic forcing drives some of the decadal variability in ENSO

(even if ENSO is partially self-sustained)

Page 11: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

“The relationship between ENSO and Australian climate in both the model and the observations is strong in some

decades, but weak in others. A series of decadal-long perturbation experiments are used to show that if these

interdecadal changes are predictable, then the level of predictability is low”.

J. Climate, 2006

Page 12: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Suppose Suppose EELFLF is a low pass filtered ENSO index, e.g.: is a low pass filtered ENSO index, e.g.:

11 mm

EELF LF = ------------ Σ E = ------------ Σ E t-kt-k., and ., and r(E, SST)= r(E, SST)= αα.. m+1m+1 k=0k=0

< E< ELFLF, SST, SSTLFLF>>

Then r(EThen r(ELFLF, SST, SSTLFLF) = ________________________ . (1)) = ________________________ . (1)

√ √ [ < E[ < ELFLF, E, ELFLF > <SST > <SSTLFLF, SST, SSTLFLF> ]> ]  

Now Now < E< ELFLF, E, ELFLF > = < E > = < Ett, E, Ett + E + Et-1 t-1 + E+ Et-2 t-2 + E+ Et-3 t-3 + … + E+ … + Et-mt-m> / (m+1)> / (m+1)22

+ < E+ < Et-1t-1, E, Ett + E + Et-1 t-1 + E+ Et-2 t-2 + E+ Et-3 t-3 + … E+ … Et-mt-m> / (m+1)> / (m+1)22

+ … + < E+ … + < Et-mt-m, E, Ett + E + Et-1 t-1 + E+ Et-2 t-2 + E+ Et-3 t-3 + … E+ … Et-mt-m>/ (m+1)>/ (m+1)22

= (m+1)/(m+1)= (m+1)/(m+1)22=1 / (m+1), (2)=1 / (m+1), (2)  where we have used the fact that E is white noise. Similarly where we have used the fact that E is white noise. Similarly

  < SST< SSTLFLF, SST, SSTLFLF > = 1/(m+1), so (3) > = 1/(m+1), so (3)  

< SST< SSTLFLF, E, ELFLF > = < SST > = < SSTtt, E, Ett + E + Et-1 t-1 + E+ Et-2 t-2 + …+ E+ …+ Et-mt-m>/ (m+1)>/ (m+1)22

+ < SST+ < SSTt-1t-1, E, Ett + E + Et-1 t-1 + E+ Et-2 t-2 + … E+ … Et-mt-m> / (m+1)> / (m+1)22

+ … + < SST+ … + < SSTt-mt-m, E, Ett + E + Et-1 t-1 + E+ Et-2 t-2 + … E+ … Et-mt-m>/ (m+1)>/ (m+1)22

= (m+1) < SST= (m+1) < SSTtt, E, Ett >/ (m+1) >/ (m+1)22

= α/ (m+1). (4)= α/ (m+1). (4)  

Using (2)-(4) in (1) then gives Using (2)-(4) in (1) then gives r(Er(ELFLF, SST, SSTLFLF) = α.) = α.

Power and Colman, 2006: Climate DynamicsPower and Colman, 2006: Climate Dynamics

Page 13: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Decadal pattern much broader

=> Different physics in off-

equatorial “wings”

c.f. Zhang et al. 1998; Mantua et al. 1997; Power

et al. 1999; Power and

Colman 2006

Page 14: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

CGCM: NINO3(4yra) & 310m Wing Index

-0.4

-0.2

0

0.2

0.4

0 20 40 60 80 100

Year

Ind

ex

Discovery: Sub-surface ENSO-driven off-equatorial decadal variability is highly predictable

Discovery: Sub-surface ENSO-driven off-equatorial decadal variability is highly predictable

Decadal-long Perturbation Experiments

Decadal-long Perturbation Experiments

13 years 13 years

Off-equatorial sub-surface variability is a low pass filtered version of ENSO variability

Off-equatorial sub-surface variability is a low pass filtered version of ENSO variability

Power and Colman, Climate Dynamics, 2006; cf. Newman et al. 2002Power and Colman, Climate Dynamics, 2006; cf. Newman et al. 2002

Low pass filtering due to dominance of low frequency oceanic Rossby waves in response to ENSO wind-stresses

Low pass filtering due to dominance of low frequency oceanic Rossby waves in response to ENSO wind-stresses

Page 15: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Conclusions so far1.1. Large and important decadal – interdecadal Large and important decadal – interdecadal

changes in ENSO and ENSO indicators have changes in ENSO and ENSO indicators have been observed and modelledbeen observed and modelled

2.2. A simple model for decadal variability in A simple model for decadal variability in ENSO and ENSO-like patterns of variability ENSO and ENSO-like patterns of variability was presentedwas presented

3.3. While useful in capturing some of the While useful in capturing some of the variability, the simple model is inadequate variability, the simple model is inadequate because ocean acts as LPF on ENSO forcing because ocean acts as LPF on ENSO forcing

4.4. This yields e.g. ENSO-driven multi-year This yields e.g. ENSO-driven multi-year predictability in off-equatorial wingspredictability in off-equatorial wings

Page 16: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Other evidence for more sophisticated physics

Kirtman and Scopf (1998)Kirtman and Scopf (1998) Kleeman et al. (1999)Kleeman et al. (1999) Wang et al. (20xx)Wang et al. (20xx) McGregor et al. (2008)McGregor et al. (2008) ……

Page 17: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Anthropogenic changes in the Walker circulation and the SOI

4. 4.

Page 18: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

The SOI [and N(EN) – N(LN)]

Page 19: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Power and Kociuba, Climate Dynamics, 2010 (submitted); see also Vecchi et al. (2006); Meehl et al. (2007)

Page 20: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

The Walker circulation weakens in response to global warming(Vecchi et al. 2006; Meehl et al. 2007 /IPCC AR5;Power and Kociuba 2010)

“This obviously means that the SOI also declines in response to global warming”

“So part of observed decline in SOI due to global warming”

Page 21: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Or is it?

Page 22: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research
Page 23: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

The Walker circulation weakens in response to global warming

The SOI does not decline in response to global warming.

The large observed decline in the SOI is therefore natural.

Power & Kociuba, Climate Dynamics (submitted), 2010

Page 24: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

We can therefore infer (taking models at face value) that: Observed weakening of Walker circulation Observed weakening of Walker circulation

over 20over 20thth century due to century due to bothboth natural natural variability and external forcingvariability and external forcing

Supports conclusions of Meehl et al. 2009

Page 25: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Future work (for dessert*)

Further clarify causes and relative importance of Further clarify causes and relative importance of decadal variability in ENSO activitydecadal variability in ENSO activity

Important that simulation of ENSO in CGCMs Important that simulation of ENSO in CGCMs becomes more realisticbecomes more realistic

* * wine earned by providing numerous references wine earned by providing numerous references during talkduring talk

Page 26: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

The End – thank you for listening!

Scott Power

Centre for Australian Weather and Climate ResearchBureau of Meteorology

Page 27: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

SOI used extensively to estimate and predict changes linked SOI used extensively to estimate and predict changes linked to ENSO and changes in the Walker circulation (rainfall, to ENSO and changes in the Walker circulation (rainfall, streamflow, disease, tropical cyclones, …)streamflow, disease, tropical cyclones, …)

Correlation coefficient between SOI and equatorial MSLP Correlation coefficient between SOI and equatorial MSLP pressure gradient (pressure gradient (ΔΔP) P) = 0.83= 0.83

ΔP = BoxE(5˚S-5˚N, 200˚E-280˚E) - BoxW(5˚S–5˚N, 80˚E–160˚E)

El Niño, weaker Walker circulation, SOI < 0El Niño, weaker Walker circulation, SOI < 0

Impact of global warming on the SOI

Page 28: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Summary There has been pronounced interdecadal variability There has been pronounced interdecadal variability

in the Walker circulation during the 20in the Walker circulation during the 20 thth century and century and an overall weakening of the Walker circulation in an overall weakening of the Walker circulation in recent decades.recent decades.

Weakening due to both global warming and natural Weakening due to both global warming and natural variability.variability.

Global warming weakens Walker circulation but Global warming weakens Walker circulation but (surprisingly) increases SOI. The SOI is not a good (surprisingly) increases SOI. The SOI is not a good guide to changes in Walker circulation forced by guide to changes in Walker circulation forced by global warming. global warming.

Observed decline in SOI natural and largely due to a Observed decline in SOI natural and largely due to a natural increase in dominance of El Ninatural increase in dominance of El Niñño over La o over La NiNiñña activity.a activity.

Page 29: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

MMEM of the SOI (A2 and C20), the observed SOI, and associated confidence levels, 30yr averages, JJASOND

-4

-2

0

2

4

6

8

1900 1920 1940 1960 1980 2000 2020 2040 2060 2080 2100

SO

I (O

bser

ved

and

mod

elle

d)

95% confidence interval for 30yra MMEM SOI 95% confidence interval for 30yra SOI

MMEM, 30yra Obs SOI, 30yra

Page 30: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Power (2010), Theoret. Appl. Climatol.; cf. numerical sols of Battisti and Hirst (1987)

-3

-2

-1

0

1

2

3

-1 1 3 5 7 9T(t

*)

t*=time/(delay time)

Analytic Solutions of the Linear DAO Equation, a=0.13/yr, delay=7mo, ICs:

T(t<0)=T(0)exp(at)

b=0.01

b=0.5

b=1.4

b=3

Page 31: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Contents

1.1. Australian tropical cyclones: interannual-Australian tropical cyclones: interannual-decadal variability, and long-term trends decadal variability, and long-term trends and links to ENSO and the SPCZand links to ENSO and the SPCZ

2.2. Interdecadal variability and trends in the Interdecadal variability and trends in the Walker circulation, ENSO activity, and the Walker circulation, ENSO activity, and the Southern Oscillation IndexSouthern Oscillation Index

3.3. Predictability of ENSO teleconnections, Predictability of ENSO teleconnections, origin of decadal ENSO-like patternsorigin of decadal ENSO-like patterns

Page 32: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

CGCM: SST IndicesNINO3 & SST (205-270E, 8-12S)

-1.3

-0.65

0

0.65

1.3

0 20 40 60 80 100

Year

Te

mp

An

om

aly

(K

)

Power and Colman, Climate Dynamics, 2006Power and Colman, Climate Dynamics, 2006

Page 33: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Wind-stress forced shallow water model and simplified coupled models

Equatorial region forcing Off-equatorial region forcing

McGregor et al., 2007McGregor et al., 2007

Off-equatorial region forcing

Page 34: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

First EOFs from Wind-forced Shallow Water Model

McGregor, Holbrook and Power, 2007; see also Wang et al. 2003, Part 1 – “wind-stress in eastern tropical &

subtropical basin most effective in driving this kind of zonal equatorial response (from theory and wind-forced

SWM)

McGregor, Holbrook and Power, 2007; see also Wang et al. 2003, Part 1 – “wind-stress in eastern tropical &

subtropical basin most effective in driving this kind of zonal equatorial response (from theory and wind-forced

SWM)

Forcing applied everywhere

Forcing applied everywhere

Off-equatorial forcing only

Off-equatorial forcing only

Page 35: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Summary DCV in El Niño-Southern Oscillation is an DCV in El Niño-Southern Oscillation is an

important part of DCV in the Pacificimportant part of DCV in the Pacific Randomness seems to explain a lotRandomness seems to explain a lot Nevertheless predictability is evidentNevertheless predictability is evident

as ENSO-modified red noiseas ENSO-modified red noise equatorial variability driven by off-equatorial variability driven by off-

equatorial wind-stressesequatorial wind-stresses ENSO-driven decadal climate variability in ENSO-driven decadal climate variability in

sub-surface ocean sub-surface ocean ……

Page 36: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

CGCM: SST IndicesNINO3 & SST (205-270E, 8-12S)

-1.3

-0.65

0

0.65

1.3

0 20 40 60 80 100

Year

Te

mp

An

om

aly

(K

)

Power and Colman, Climate Dynamics, 2006; see also Newman et al. 2007 for similar behaviour in PDO Index

Power and Colman, Climate Dynamics, 2006; see also Newman et al. 2007 for similar behaviour in PDO Index

dT/dt = -aT + bE + dT/dt = -aT + bE + cNcN

Page 37: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

No. severe land-falling TCs & the SPIstandardized, normalized, 11yr raves, Correl Coeff=0.64

-2

-1

0

1

2

3

1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Nov Apr neg SPIa_11yra/SD nTCsa_11yra/SD

Page 38: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Newspaper archives Brisbane Courier MailBrisbane Courier Mail held at the held at the

Queensland State LibraryQueensland State Library Maryborough ChronicleMaryborough Chronicle researched by researched by

the Hervey Bay City Councilthe Hervey Bay City Council Townsville Historical SocietyTownsville Historical Society Mackay MercuryMackay Mercury Cairns PostCairns Post Rockhampton Morning BulletinRockhampton Morning Bulletin Cairns Historical Society Cairns Historical Society Archives of various newspapers held by Archives of various newspapers held by

the Bureau of Meteorology, Brisbanethe Bureau of Meteorology, Brisbane

Page 39: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Interannual variability in the SOI is an Interannual variability in the SOI is an excellent proxy for interannual variability in excellent proxy for interannual variability in ΔΔPPequatorequator and and the Walker circulation:the Walker circulation: SOI > 0 => La Niña SOI > 0 => La Niña SOI < 0 => El Niño SOI < 0 => El Niño

But under global warming But under global warming ΔΔPPequatorequator

decreases whereas the SOI increasesdecreases whereas the SOI increases

Page 40: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Paths of cyclones in El Nino (top) vs La Nina (bottom) years

Page 41: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research
Page 42: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research
Page 43: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research
Page 44: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research
Page 45: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research
Page 46: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Power et al., 1999: Clim. Dyn.

Page 47: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

CGCM: All-Australia Rainfall v. NINO4

-0.8

-0.4

0

0.4

0.8

0 100

Year

CG

CM

vari

ab

le

CGCM: All-Australia Rainfall v. NINO4

-0.8

-0.4

0

0.4

0.8

0 100

Year

CG

CM

vari

ab

le

Correlation Coefficients in 13 yr running blocks NINO4/-ozT, NINO4/ozR in CGCM

-1

-0.5

0

0.5

1

0 20 40 60 80 100

time

co

rre

l. c

oe

ff

Page 48: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research
Page 49: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Non-linear impact of ENSO on southwest USA/Mexico?

Power et al. 2006

Page 50: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Summary so far ENSO teleconnections to Australia vary substantially from ENSO teleconnections to Australia vary substantially from

generation-to-generationgeneration-to-generation This variability seems to have very little predictability This variability seems to have very little predictability The changes are in phase with IPO/PDO because of non-The changes are in phase with IPO/PDO because of non-

linearity in the ENSO teleconnection to Australialinearity in the ENSO teleconnection to Australia This gives interesting effects that seem to point to decadal This gives interesting effects that seem to point to decadal

predictability but this is not necessarily the casepredictability but this is not necessarily the case Non-linear teleconnections may exist elsewhereNon-linear teleconnections may exist elsewhere The IPO and PDO have strong linksThe IPO and PDO have strong links Discussion of PDO emphasizes North Pacific Discussion of PDO emphasizes North Pacific

Page 51: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Correlation Coefficients

Page 52: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research
Page 53: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

The SOI and beer consumption in Australia

Hot, dry Thirsty

Go to pub to get drink

Cool, wet => Go to pub to get out of rain

Number of visits to pub/month

Page 54: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

CGCM: All-Australia Rainfall v. NINO4

-0.8

-0.4

0

0.4

0.8

0 100

Year

CG

CM

vari

ab

le

CGCM: All-Australia Rainfall v. NINO4

-0.8

-0.4

0

0.4

0.8

0 100

Year

CG

CM

vari

ab

le

Power et al., J. Climate, 2006

Page 55: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Decadal pattern much broader

=> Different physics in off-

equatorial “wings”

Page 56: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Brisbane - 1893 flood – 23 deaths

Page 57: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Next: Decadal changes in ENSO teelconnections

Page 58: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

All-Australia Rainfall v. SOI, 1900-2004, Annual Data

200

400

600

800

-25 -20 -15 -10 -5 0 5 10 15 20 25

SOI

Rain

fall

ObservationsObservations

Power et al. 2006: J. Climate.Power et al. 2006: J. Climate.

CGCM: All-Australia Rainfall v. NINO4 (JJASOND)

-2

-1

0

1

2

3

4

5

-10 -5 0 5 10

NINO4

Rain

fall

Coupled GCM

Coupled GCM

Discovery: Australian response to ENSO is asymmetric in observations and CGCM

Page 59: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Power 2010

Fitting DAO to SOI:

dT/dt = aT-bT(t-d) + Noise

d=7mo, a=0.13/yr, b=1.4/yr

damped oscillation with period =3.8 yr, decay time (e-folding scale) = 0.9 yr

Page 60: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

Decadal changes in ENSO teleconnections

Page 61: Scott Power, Greg Kociuba, Jeff Callaghan Centre for Australian Weather and Climate Research

SPI (Nov-Apr) & SOI (June-Dec), Correl Coeff = -0.79, 11yra

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Nov Apr neg SPIa_11yra/SD June-Dec SOIa_11yra/SD