modes of variability and teleconnections: part ii hai lin meteorological research division,...
TRANSCRIPT
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Modes of variability and teleconnections: Part II
Hai LinMeteorological Research Division, Environment Canada
Advanced School and Workshop on S2S
ICTP, 23 Nov- 4 Dec 2015
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Outlines• What are modes of variability?
• Why are they important to S2S predictions?
• Methods for identifying modes of variability
e.g., Pacific North American (PNA) pattern,
North Atlantic Oscillation (NAO)
• Tropical modes of variability: ENSO, IOD, MJO, QBO, etc
• Extratropical response to tropical heating
• MJO-NAO interactions
How do teleconnections provide sources of predictability?
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Atmospheric response to tropical heating
- Tropics:
The Gill model (Gill 1980) has proven quite successful at capturing the essential features of the tropical atmospheric response to diabatic heating
- Extratropics:
1) Horizontal energy propagation of planetary waves
2) Feedback from transient eddies
3) Kinetic energy transfer from the climatological mean state
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Middle latitude planetary Rossby waves
, ,,
,
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Middle latitude planetary Rossby waves
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500 hPa height anomaly response to an equatorial diabatic heating at datelinein a linear model with observed DJF basic flow
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Feedback from transient eddies
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Feedback from transient eddies
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Sheng et al. Jclim, 1998
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+PNA
−PNA
Low frequency anomaly,e.g., PNA:
Shifts jet stream and storm tracks or transienteddy activity
Transient eddies feedback to PNA and reinforce the PNA
Positive feedback
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Interaction with mean flow
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Central North Pacific and central North Atlantic ∂U/ ∂x < 0
500mb geopotential height
DJF JJA
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Atmospheric response to tropical heating
In order for a climate model to have the right response to tropical heating (teleconnection)
1) a realistic structure of the diabatic heating
2) a right mean flow (small model bias) – for Rossby wave propagation and wave-mean flow interaction
3) a realistic simulation of transient eddies
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Connection between the MJO and NAO
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Data
NAO index: pentad average
MJO RMMs: pentad average
Period: 1979-2003
Extended winter, November to April (36 pentads each winter)
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Composites of tropical
Precipitation rate for 8 MJO phases, according to Wheeler and Hendon index.
Xie and Arkin pentad data, 1979-2003
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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%
(Lin et al. JCLIM, 2009)
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Tropical influence
(Lin et al. JCLIM, 2009)
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Correlation when PC2 leads PC1 by 2 pentads: 0.66
Lin et al. (2010)
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Normalized Z500 regression to PC2
Lin et al. (2010)
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Thermal forcing
Exp1 forcing Exp2 forcing
Lin et al. (2010)
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Z500 response
Exp1
Exp2
Lin et al. (2010)
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• Linear integration, winter basic state
• with a single center heating source
• Heating at different longitudes along the equator from 60E to 150W at a 10 degree interval, 16 experiments
• Z500 response at day 10
Why the response to a dipole heating is the strongest ?
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Day 10 Z500 linear response
80E
110E
150E
Similar pattern for heating 60-100E
Similar pattern for heating 120-150W
Lin et al. MWR, 2010
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Impact on Canadian surface air temperature
Lagged winter SAT anomaly in Canada
(Lin et al. MWR, 2009)
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Impact on North American surface air temperature
Lagged regression of SAT with −RMM2
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T2m anomaly compositeAfter MJO phase 3
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It is possible to predict North American temperature using the MJO information
With a statistical model
For strong-MJO initial condition. Window of opportunity
Ridney et al. MWR (2013)
T(t) = a1(t)RMM1(0) + a2(t)RMM1(-1)
+b1(t)RMM2(0)+b2(t)RMM2(-1)+c(t)T(0)
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Rodney et al. MWR, 2013
Fraction of correct temperature forecasts based on categories of above-, near-, and below-normal temperatures for MJO events with an amplitude > 2 in phases 3, 4, 7, and 8 with lead times of (a) 1, (b) 2, (c) 3, and (4) pentads.
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Wave activity flux and 200mb streamfunction anomaly
(Lin et al. JCLIM, 2009)
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The MJO
The NAO
Two-way MJO – NAO interaction
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hindscast with GEM
GEM clim of Canadian Meteorological Centre (CMC)--
GEMCLIM 3.2.2, 50 vertical levels and 2o of horizontal resolution
1985-2008
3 times a month (1st, 11th and 21st)
10-member ensemble (balanced perturbation to NCEP reanalysis)
NCEP SST, SMIP and CMC Sea ice, Snow cover: Dewey-Heim (Steve Lambert) and CMC
45-day integrations
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NAO forecast skillextended winter – Nov – Marchtropical influence
A simple measure of skill:
temporal correlation btw forecast and observations
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(Lin et al. GRL, 2010a)
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(Lin et al. GRL, 2010a)
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Correlation skill: averaged for pentads 3 and 4
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Correlation skill: averaged for pentads 3 and 4
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MJO forecast skill--- impact of the NAO
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(Lin et al. GRL, 2010b)
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Skill averaged for days 15-25
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(Lin et al. GRL, 2010b)
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Summary
• Two-way interactions between the MJO and NAO
• Lagged association of North American SAT with MJO
• NAO intraseasonal forecast skill influenced by the MJO
• MJO forecast skill influenced by the NAO