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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
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?
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
Middle latitude planetary Rossby waves
, ,,
,
Middle latitude planetary Rossby waves
500 hPa height anomaly response to an equatorial diabatic heating at datelinein a linear model with observed DJF basic flow
Feedback from transient eddies
Feedback from transient eddies
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
Interaction with mean flow
___
Central North Pacific and central North Atlantic ∂U/ ∂x < 0
500mb geopotential height
DJF JJA
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
Connection between the MJO and NAO
Data
NAO index: pentad average
MJO RMMs: pentad average
Period: 1979-2003
Extended winter, November to April (36 pentads each winter)
Composites of tropical
Precipitation rate for 8 MJO phases, according to Wheeler and Hendon index.
Xie and Arkin pentad data, 1979-2003
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)
Tropical influence
(Lin et al. JCLIM, 2009)
Correlation when PC2 leads PC1 by 2 pentads: 0.66
Lin et al. (2010)
Normalized Z500 regression to PC2
Lin et al. (2010)
Thermal forcing
Exp1 forcing Exp2 forcing
Lin et al. (2010)
Z500 response
Exp1
Exp2
Lin et al. (2010)
• 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 ?
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
Impact on Canadian surface air temperature
Lagged winter SAT anomaly in Canada
(Lin et al. MWR, 2009)
Impact on North American surface air temperature
Lagged regression of SAT with −RMM2
T2m anomaly compositeAfter MJO phase 3
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)
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.
Wave activity flux and 200mb streamfunction anomaly
(Lin et al. JCLIM, 2009)
The MJO
The NAO
Two-way MJO – NAO interaction
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
NAO forecast skillextended winter – Nov – Marchtropical influence
A simple measure of skill:
temporal correlation btw forecast and observations
(Lin et al. GRL, 2010a)
(Lin et al. GRL, 2010a)
Correlation skill: averaged for pentads 3 and 4
Correlation skill: averaged for pentads 3 and 4
MJO forecast skill--- impact of the NAO
(Lin et al. GRL, 2010b)
Skill averaged for days 15-25
(Lin et al. GRL, 2010b)
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