Modes of variability and teleconnections: Part II Hai Lin Meteorological 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 dateline in a linear model with observed DJF basic flow

Feedback from transient eddies

Feedback from transient eddies

Sheng et al. Jclim, PNA −PNA Low frequency anomaly, e.g., PNA: Shifts jet stream and storm tracks or transient eddy 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: 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,

Lagged probability of the NAO index Positive: upper tercile; Negative: low tercile Phase Lag −5 −35%−40%+49% Lag −4 +52%+46% Lag −3 −40%+46% Lag −2 +50% Lag −1 Lag 0 +45%−42% Lag %+45%−46% Lag %+50%+42%−41% −42% Lag %−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 forcingExp2 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 E Similar pattern for heating W 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 composite After 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) = a 1 (t)RMM1(0) + a 2 (t)RMM1(-1) +b 1 (t)RMM2(0)+b 2 (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 2 o of horizontal resolution times a month (1 st, 11 th and 21 st ) 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 skill extended winter – Nov – March tropical influence A simple measure of skill: temporal correlation btw forecast and observations

(Lin et al. GRL, 2010a)

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