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

+-

-

-

-

+

+

+

+

+- -

+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