Interactions between the Madden-Julian Oscillation and the North Atlantic Oscillation
Hai Lin, Gilbert BrunetMeteorological Research Division, Environment Canada
Jacques DeromeMcGill University
TTISS, Monterey, September 14, 2009
Outlines
• Observed MJO – NAO connection
Lin et al. 2009 (J. Climate)
• Intraseasonal variability in a dry GCM
Lin et al. 2007 (J. Atmos. Sci.)
NAO and MJO connection
• NAO: dominant large scale pattern in the extratropics with significant influence on weather from eastern North America to Europe
• MJO: dominant tropical intraseasonal mode, coupled with convections and variability in diabatic heating
• One-way impact, or two-way interaction?
• A possible mechanism for both the NAO and MJO
Data and methodology
Definition of the NAO: 2nd REOF of monthly Z500
NAO index: projection of pentad Z500 anomaly onto this pattern
Period: 1979-2003
Extended winter, November to April (36 pentads each winter)
Data and methodology
Definition of the MJO: combined EOF of OLR, u200 and u850 in the band of 15°S – 15°N (Wheeler and Hendon, 2004)
NAO index: RMM1 and RMM2
Period: 1979-2003
Extended winter, November to April (36 pentads)
Composites of tropical
Precipitation rate.
Winter half year
November-April
Xie and Arkin pentad data, 1979-2003
Pentads in MJO phases
Extended winter from 1979 to 2004
Phase 1 2 3 4 5 6 7 8
Number of pentads
55 79 78 78 63 71 87 66
Mean amplitude
1.67 1.66 1.81 1.78 1.66 1.70 1.62 1.75
Lagged composites of the NAO index
Phase 1 2 3 4 5 6 7 8
Lag −5 −0.39 0.28
Lag −4 −0.26 0.28
Lag −3 −0.29
Lag −2 0.26
Lag −1
Lag 0 −0.41
Lag +1 0.26 0.27 0.26 −0.25 −0.35
Lag +2 0.34 0.36 −0.31 −0.33 −0.29
Lag +3 0.35 −0.35 −0.41
Lag +4 −0.35 −0.31
Lag +5 −0.27
Lagged composites of the NAO index
Phase 1 2 3 4 5 6 7 8
Lag −5 −0.39 0.28
Lag −4 −0.26 0.28
Lag −3 −0.29
Lag −2 0.26
Lag −1
Lag 0 −0.41
Lag +1 0.26 0.27 0.26 −0.25 −0.35
Lag +2 0.34 0.36 −0.31 −0.33 −0.29
Lag +3 0.35 −0.35 −0.41
Lag +4 −0.35 −0.31
Lag +5 −0.27
Lagged probability of the NAO indexPositive: upper tercile; Negative: low tercile
Phase 1 2 3 4 5 6 7 8
Lag −5 −35% −40% +49% +49%
Lag −4 +52% +46%
Lag −3 −40% +46%
Lag −2 +50%
Lag −1
Lag 0 +45% −42%
Lag +1 +47% +45% −46%
Lag +2 +47% +50% +42% −41% −41% −42%
Lag +3 +48% −41% −48%
Lag +4 −39% −48%
Lag +5 −41%
Tropical influence
Wave activity flux and 200mb streamfunction anomaly
Lagged regression of 200mb U to NAO index
Extratropical influence
Lagged regression of 200mb U to NAO index
Extratropical influence U200 composites
Tropical intraseasonal variability (TIV) in a dry GCM
Model and experiment
• Primitive equation AGCM (Hall 2000).
• T31, 10 levels
• Time-independent forcing to maintain the winter climate (1969/70-98/99) all variabilities come from internal dynamics
• No moisture equation, no interactive convection
• 3660 days of integration
Unfiltered data
20-100 day band-pass
Zonal propagation
10S-10N
Model Result
Stronger in eastern Hemisphere
TIV in the dry model
• Kelvin wave structure
• Phase speed: ~15 m/s (slower than free Kelvin wave, similar to convective coupled Kelvin wave, but there is no convection)
What causes the TIV in the dry model?
• 3-D mean flow instability (Frederiksen and Frederiksen 1997)
• Tropical-extratropical interactions (all wave energy generated in the extratropics)
Moisture and convection related mechanisms are excluded
Possible mechanisms
ISO in a dry model
Linked to tropical eastward propagation in the eastern Hemisphere Global propagation of low-frequency wave activity
250 hPa PV’ and wave activity flux
Summary
• Two-way interaction between the MJO and the NAO
• Increase of NAO amplitude 5~15 days after the MJO-related convection anomaly reaches western Pacific
• Certain MJO phases are preceded by strong NAOs
• TIV generated in a dry GCM
• Tropical-extratropical interactions are likely responsible for the model TIV
Implication to the MJO
• A possible mechanism for the MJO: triggering, initialization
• Contribution of moisture and tropical convection: spatial structure, phase speed
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