1. interannual variability
Teleconnections and
interannual variability
of the atmosphere
Franco Molteni
European Centre for Medium-Range Weather Forecasts

2. Outline Teleconnection patterns:
Definition from observational datasets
Dynamical origin and interpretation
Explaining interannual variability through teleconnections:
Partition of variance: effect of time and ensemble averaging
Are teleconnection patterns generated by internal variability and boundary forcing/coupling the same?
Predictability of teleconnection/low fr. variability indices
Can we predict tropical rainfall?
Linear indices: examples from the ECMWF System-4 re-forecasts

3. Teleconnections: definitions and examples
Relationship between anomalies of opposite sign (and/or different variables) in different regions of the world
Teleconnection pattern:
Regional or planetary scale pattern of correlated anomalies
Teleconnections may be induced either by internal atmospheric dynamics or by coupling with other components of the climate system (mainly the tropical oceans)

4. Teleconnections: definitions and examples
The Southern Oscillation (and its links to El Niño)
Walker and Bliss (1932); Bjerknes (1969)
SOI: Tahiti – Darwin SLP
Nino3.4 SST

5. ENSO teleconnections: rainfall and temperature

6. Teleconnections: definitions and examples
The North Atlantic Oscillation
Walker and Bliss (1932)
Van Loon and Rogers (1978)
Positive NAO phase Negative NAO phase

7. Teleconnections: definitions and examples
The Indian Ocean Zonal Mode (or I.O. Dipole)
Saji et al. (1999), Saji and Yamagata (2003)
Webster et al. (1999)

8. Seminal papers: Wallace and Gutzler 1981
The Pacific /
North American
(PNA) pattern:
correlations

9. Seminal papers: Wallace and Gutzler 1981
The Pacific /
North American
(PNA) pattern:
composites

10. Seminal papers: Wallace and Gutzler 1981
Eastern Atlantic
(EA) pattern:
composites

11. Seminal papers: Horel and Wallace 1981
Correlation of 700hPa height with
a) PC1 of Eq. Pacific SST
c) SOI index
Schematic diagram of tropical-extratropical teleconnections during El Niño

12. Seminal papers: Gill 1980
Atmospheric response to near-equatorial heating
a) Near-surface
wind and
vertical motion
b) Near-surface wind and
sea-level pressure
c) Motion in x-z plane along the Equator

13. Seminal papers: Hoskins and Karoly 1981
Response to a tropical heat source (15N):
height (lon, z) at 18N
300hPa vorticity
300hPa height

14. Seminal papers: Simmons Wallace Branstator 1983
First barotropic normal mode at four times during a half-cycle:
East. Atlantic phase
PNA phase

15. Seminal papers: Sardeshmukh and Hoskins 1988
Definition of Rossby wave source
generated by near-equatorial heating

16. Seminal papers: Sardeshmukh and Hoskins 1988
Response to heating in trop. West Pacific
Rossby wave source
and abs. vorticity

17. Non-linear models: Charney and DeVore 1979
Multiple steady states of very-low-order barotropic model with wave-shaped bottom topography

18. The new ECMWF Seasonal fc. system (Sys-4)
IFS 36R4
0.7 deg (T255)
91 levels
NEMO
1/1-0.3 d. lon/lat
42 levels
OASIS-3
H-TESSEL
Initial Con.
Ens. Forecasts
4-D variational d.a.
Gen. of
Perturb.
System-4
CGCM
3-D v.d.a. (NEMOVAR)

19. ECMWF System 4: main features
Operational forecasts
51-member ensemble from 1st day of the month
released on the 8th
7-month integration
Experimental ENSO outlook
13-month extension from 1st Feb/May/Aug/Nov
15-member ensemble
Re-forecast set
30 years, start dates from 1 Jan 1981 to 1 Dec 2010
15-member ensembles, 7-month integrations

20. Variability in an ensemble of time-evolving fields
F (t, m) = F (t’, j, m)
t’ = time within year
j = 1, N (no. of years) m = 1, M (no. of ens. members)
Climatology: Fcl (t’) = {F (t’, j, m) }j, m
Anomaly: A (t’, j, m) = F (t’, j, m) - Fcl (t’)
Seasonal mean anomaly: As (j, m) = {A (t’, j, m) }t’
Ens./seas. mean anomaly: Aes (j) = {A (t’, j, m) }t’, m
for an observation/analysis dataset, M =1 !
in a “perfect” model environment , the average correlation between Aes and As is equal to the ratio of their standard deviations.

21. Variability of Z 200hPa in DJF from seasonal ensembles
Standard deviation from 11-member ensembles, DJF 1981/2005
5-day
means
Seasonal-
ensemble
Seasonal
means
St.dev.
ratio

22. ‘Forced’ vs. internal variability in seasonal ensembles
11-member ensembles, DJF 1981/2005
Z 200 hPa
El Niño
composite
La Niña
5-day-mean
EOF 1
EOF 2

23. Can we predict tropical rainfall teleconnections?
Tropical convection tends to occur over warm SST and warm-and-wet land surface, and it induces a number of feedbacks:
Convective clouds decrease solar radiation at the surface
On tropical oceans, anomalous divergent winds tend to increase evaporation to the west/east of the convective region when the climatological near-surface zonal winds are westerly/easterly
The strength of the feedback depends on the heat capacity of the “interactive” surface layer and the phase of the day

24. Variability of tropical rainfall: EOF comparison
GPCP S S4

25. Variability of tropical rainfall: EOF comparison
GPCP S S4

26. Predictability of teleconnections in Sys4: Nino3.4, IOD SST
DJF
IOD
SON

27. Predictability of telecon. in Sys4: South Africa rain (DJF)

28. Predictability of telecon. in Sys4: East Africa rain (SON)

29. Predictability of teleconnections in Sys4: Sahel rain (JJA)

30. Predictability of teleconnections in Sys4: PNA, NAO (DJF)

31. Predictability of telecon. in Sys4: Europe T_2m (MAM)

32. Conclusions
Teleconnections may be induced either by internal atmospheric dynamics or by coupling with other components of the climate system (mainly the tropical oceans).
Although a linear interpretation of teleconnection is adequate in most cases, non linear effects should not be neglected.
Prediction of trop. rainfall teleconnections requires accurate modelling of heat-flux feedbacks from the ocean mixed layer and land surface.
In the ECMWF seasonal fc. System-4, teleconnections related to ENSO are well modelled in the tropical and extra-trop. Pacific region, fairly well over Africa and South America, less so over South Asia.
Predictability over Europe/Atlantic: mostly limited during winter, higher in other seasons when internal variability is reduced.