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A Stochastic Model of the Madden-Julian Oscillation Charles Jones University of California Santa Barbara. Collaboration : Leila Carvalho (USP), A. Matthews ( UK), B. Pohl (FR). Outline Brief overview of the Madden-Julian Oscillation - PowerPoint PPT Presentation
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A Stochastic Model of the
Madden-Julian Oscillation
Charles Jones
University of California
Santa Barbara
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Collaboration: Leila Carvalho (USP), A. Matthews (UK),
B. Pohl (FR)
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Outline
o Brief overview of the Madden-Julian Oscillation
o The behavior of the MJO on long time scales
o A stochastic model of the MJO
o Current research
30-60 Day OLR anomalies1958-2006
oClear Spectral
Signal
oTime Irregularity3
The Madden-Julian Oscillation
4
4
* Significant case-to-case variability
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Modulate the variability of the monsoons in Asia-Australia, Africa and Americas
Teleconnections with extratropics in both hemispheres
Modulate thermocline variability in the tropical Pacific Ocean via westerly wind bursts
Influence on forecast skills in the tropics and extratropics
Lau, W. K. M., and D. E. Waliser, 2005: Intraseasonal Variability in the Atmosphere-Ocean Climate System.Zhang, C. D. 2005: Madden-Julian oscillation. Reviews of Geophysics, 43, 1-36.
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The MJO and Extreme Precipitation
Barlow et al. (2005) Jones et al. (2004)
Mo and Higgins (1998) Higgins et al (2000) Jones (2000) Bond and Vecchi (2003) Jones et al. (2004)
Carvalho et al. (2004) Liebmann et al. (2004) Jones et al. (2004)
Wheeler and Hendon (2004) Jones et al. (2004)
Jones et al. (2004)
Jones, C., D. E. Waliser, K. M. Lau, and W. Stern, 2004: Global occurrences of extreme precipitation events and the Madden-Julian Oscillation: observations and predictability. J. Climate, 17, 4575-4589.
NH winter
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Summary
oPotential Predictability Limit of the MJO: 20-30 days upper level circulation; 10-15 days precipitation (Waliser et al. 2003, BAMS)
oEl Nino (La Nina) enhances (diminishes) predictability
ObservationsoHigher frequency of extremes during active MJO phases
oOn a global scale, extreme events during active MJO are about 40% higher than in quiescent phases in locations of statistically significant signals (Jones et al. 2004)
Model Experiments
oPredictability experiments indicate higher success in the prediction of extremes during active MJO than in quiescent situations (Jones et al. 2004)
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Climate models have improved in recent years but still produce many unrealistic MJO characteristics
Lin et al. (2006) : 14 GCMs participating in IPCC-AR4
oTotal intraseasonal (2–128 day) precipitation variance is too weak.oHalf of the models have signals of convectively coupled equatorial waves. However, the variances are generally too weak for all wave modes , and the phase speeds are generally too fast.oMJO variance approaches observed value in 2/14 models; less than half of the observed value in the other 12 models.oThe ratio between eastward/westward MJO variance is too small in most models; consistent with lack of highly coherent eastward propagation of the MJO.oMJO variance (13/14 models) does not come from pronounced spectral peak, but usually comes from over-reddened spectrum; associated with too strong persistence of equatorial precipitation.
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Time scales?? ???? ?????? ????????
The behavior of the MJO
Observational knowledge about the MJO: limited to reanalysis data ~ 58 years
Does the MJO have a low-frequency mode of variability?
Will the MJO change as climate continues to warm?
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The behavior of the MJO
Jones and Carvalho (2006) J. ClimateoPositive linear trends in U200 and U850 intraseasonal anomalies in summer and winter. oPositive trends in the number of summer MJO events.oMean winter LF MJO activity: ~uniform variability from 1960s to the mid-1990soMean summer LF MJO changes: regime of high activity and low activity during 1958-2004 (~ 18.5 yr)
Current Research Objectiveso Investigate the mechanisms controlling
periods of extended MJO activity
o Develop a stochastic model capable to
reproduce the statistical properties of the
MJO including dynamical forcings of its
variability (e.g. ENSO, extratropics etc)
o This presentation: preliminary analysis of
stochastic model of the MJO
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Data preparation Wheeler and Hendon (2004) :
• Daily OLR, U200 and U850 anomalies; averaged 15S-15N; 1979-2006
• Combined EOF analysis (OLR, U200, U850)
• Use (EOF1, PC1), (EOF2, PC2)
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PC2
PC1
Phase angle between PC1 and PC2
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MJO IdentificationCriteria:oConsistent eastward propagation
at least 1--> 4 oMinimum amplitude:
A = (PC12 + PC22)1/2 > 0.35oEntire duration between 30-70 daysoMean amplitude during event > 0.9o110 MJO events in 1979-2006
Phase 1
Phase 2
Phase 3
Phase 4
Phase 5
Phase 6
Phase 7
Phase 8
OLR Anomalies
We
st. H
em. &
Afr
ica M
aritim
e Co
ntin
ent
Indian Ocean
Western Pacific
01
2 3
4
7 6
8 5
14
Stochastic Model of the MJO
Time variabilityMarkov Model using time series of phases (Xt=000111222333444556677880000…)
Spatial structureDefined by mean composites
Amplitude (work in progress)Stochastic model based on observed composites (mean and standard deviation)
State 0 State 1
P01
P11
P10
P00
Xt
Xt+1
Transition Probabilities
ttttt
XXPXXXP |...,|111
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Time series Xt = 00001100010111001100001111000010011110…
Markovian property
s total # of
sg s followin#P
0
0101 10100 PP
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Xt=000111222333444556677880000111222434445556667770000222333434450…
MJO Phase Propagation
No-
MJO
Single MJOConsecutive MJOs
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We
st. H
em. &
Afr
ica M
aritim
e Co
ntin
ent
Indian Ocean
Western Pacific
01
2 3
4
7 6
8 5
0 Non-MJO
SiXSjXP tt |1
s total # of
sg s followin#P
0
0000
8,0,8,0, iSijSj
81 parameters:
s total # of
sg s followin#P
0
0101 etc
0 1 2 3 4 5 6 7 80 0.98483 0.01012 0.00206 0.00150 0.00150 0 0 0 01 0 0.80148 0.19666 0.00186 0 0 0 0 02 0 0.03609 0.78647 0.17594 0.00150 0 0 0 03 0 0 0.03888 0.78072 0.17885 0.00156 0 0 04 0.00446 0 0 0.02232 0.78571 0.18750 0 0 05 0.00311 0 0 0 0.03106 0.77950 0.18478 0.00155 06 0.00484 0 0 0 0 0.02419 0.77903 0.18710 0.004847 0.01389 0.00174 0 0 0 0 0.03125 0.77604 0.177088 0.12287 0.05293 0 0 0 0 0 0.02268 0.80151
Xt+1
Xt
Transition Probabilities
We
st. H
em. &
Afr
ica M
aritim
e Co
ntin
ent
Indian Ocean
Western Pacific
01
2 3
4
7 6
8 5
Primary MJO
We
st. H
em. &
Afr
ica M
aritim
e Co
ntin
ent
Indian Ocean
Western Pacific
01
2 3
4
7 6
8 5
Secondary MJO
We
st. H
em. &
Afr
ica M
aritim
e Co
ntin
ent
Indian Ocean
Western Pacific
01
2 3
4
7 6
8 5
MJO Ends
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Observed MJO Phase transitions
Simulated MJO Phase transitions
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Phase 0
Snapshot of 140 yrs simulation
MJO
140 yrs: 361 events
OLR Anomalies
Spatial structure and intensity same as observed composites
Simulated MJO Evolution
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Composite of simulated MJO
140 yrs: 361 events
Spatial structure and intensity same as observed composites
22
OBS: 110 eventsSIM: 361 events
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Summary/Conclusions
MJO is the most important mode of tropical intraseasonal variability with a distinct role in climate variabilityKnowledge of long-term variability of the MJO is limited to ~60 yearsStochastic model of the MJO is being developed to investigate the low-frequency behavior of the oscillation and trends in climate change scenarios
Work in ProgressExtend the stochastic model to non-homogeneous Markov ModelStochastic model of intensities
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01111
1101101
1
PPr
PPP
Stationarity
Probability
Persistence Parameter
Order of Markov Model
Can be tested using log-likelihood method (minimization of Akaike information criterion –AIC – or Bayesian information criterion –BIC)
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Simulation of Two State Transitions
Uniform Random Number (r)
r < 1 Xt0 = 1 r 1 Xt0 = 0
t0
Every grid point
If Xt0 = 0 if r P01 Xt1 = 1 if r P01 Xt1 = 0
If Xt0 = 1 if r P11 Xt1 = 1 if r P11 Xt1 = 0
r t1
tn
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