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Weather regimes

Definition, predictability and operational applications

Franco Molteni, Laura Ferranti

October 29, 2014

Outline

• Examples of recurrent circulation anomalies

• Dynamical definition of regimes and seminal papers

• Impact of anomalous forcing in non-linear systems with flow regimes

• Detection of flow regimes through PDF estimation and cluster analysis

• Predictability of regime occurrence as a result of tropical forcing at seasonal and sub-seasonal scales

• Operational products and diagnostics based on regimes definition at ECMWF:

• Medium-range ensemble products

• Sub-seasonal and seasonal diagnostics

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Recurrent flow patterns: examples

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A sequence of 5-day mean

fields of 500 hPa

geopotential height

during boreal winter …

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Recurrent flow patterns: examples

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…but each of them

occurred in a different

winter

5-9 Jan 1985 4-8 Feb 1986

10-14 Jan 1987

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Regional regimes: Atlantic and Pacific blocking

5-day means of 500 hPa height

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Weather regimes and related dynamical concepts

Weather regime:

A persistent and/or recurrent large-scale atmospheric circulation pattern which is associated with specific weather conditions on a regional scale

Flow regime:

A persistent and/or recurrent large-scale flow pattern in a (geophysical) fluid-dynamical system

Multiple equilibria:

Multiple stationary solutions of a non-linear dynamical system

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Regimes as quasi-stationary states

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Multiple equilibria in a low-order barotropic model with topography:Charney and DeVore, J. Atmos. Sci. 1979

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Flow regimes in a barotropic model:Legras and Ghil, J. Atmos. Sci. 1985

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Hemispheric weather regimes:Reinhold and Pierrehumbert, Mon. Wea. Rev. 1982

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Regional weather regimes:Vautard and Legras, J. Atmos. Sci. 1988

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X X Y

Y XZ rX Y

Z XY bZ

= − +

= − + −

= −

Lorenz E., 1963: Deterministic non-periodic flow

A prototype non-linear model

with flow regimes

X X Y

Y XZ rX Y f

Z XY bZ

= − +

= − + − +

= −

What is the

impact of f on

the attractor?

The influence of f on the state vector probability function is itself predictable.

f=0 f=2

f=3 f=4

Add external steady forcing f to the Lorenz (1963) equations

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Detecting regimes: multi-modality in one-dim. PDFHansen and Sutera, J. Atmos. Sci. 1986

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Bimodality in the

probability density function

(PDF)

of an index of N. Hem.

planetary wave amplitude

(zonal wave-numbers 2-4)

Map of regime difference (500 hPa height)

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Multi-modality in two-dim. PDFfrom principal components:Corti et al., Nature 1999

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Regimes as clusters in a multi-dimensional PC phase space

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Mo and Ghil 1988 (N. Hem.)

Cheng and Wallace 1993 (N. Hem.)

Michelangeli et al. 1995 (Atl. - Europe)

Straus et al. 2007 (N. Pac. - N. America)

NAO +

32%

Atl. Ridge

22%

Blocking

25%

NAO -

21%

Four Euro-Atlantic regimes

from K-means cluster analysis

of ERA-Interim 5-day means of

500 hPa height, DJF 1980-2013

El Niño and the Southern Oscillation

Walker and Bliss (1932); Bjerknes (1969)

SOI: Tahiti – Darwin SLP

Nino3.4 SST

Teleconnections with ENSO

Correlation of 700hPa height with a) PC1 of Eq. Pacific SSTc) SOI index

Schematic diagram of tropical-extratropical teleconnections during El Niño

Horel and Wallace 1981

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Seasonal predictability of Pacific-N. American regimes: Straus et al. 2007

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Regime frequencies in NCEP re-analysis (blue) and in seasonal

ensembles with observed SST run with COLA model (red/green)

Sub-seasonal variability: the Madden-Julian Oscillation(Wheeler and Hendon 2004)

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Regime frequencies are affected by MJO phase (Cassou, Nature 2008)

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ECMWF regime diagnostics and operational products in the medium range

1. Identification of cluster scenarios to reduce the dimension of the ensemble forecast

distribution (51 members → max of 6 scenarios)

2. Association of each cluster scenario to climatological weather regime.

3. Ensemble distribution in a 2-dim. space spanning NAO +/- and blocking regimes

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From scenarios to climatological regimes

3 4 5 7 8 10 11 15

Lead Time [days]

R2

Blocking

R1

NAO +

R3

NAO -

R4

Atl-Ridge

S1

S2

S3

S1

S2

S1

S2

S3

S1

S2

S3

S4

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Regime diagnostics and operational products in the medium range (1)

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Regime diagnostics and operational products in the medium range (2)

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Regime diagnostics and operational products in the medium range (3)

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2 leading EOFs

Ferranti, L. et al. 2018 QJRMS, 144

doi:10.1002/qj.3341

The observed frequencies are indicated by a circle, while the frequencies from theECMWF operational high resolution and the unperturbed forecasts are indicatedby a pointing-down and a pointing-up triangle respectively.

Climatological frequency distribution for the 4 Euro-Atlantic regimes

as simulated by the ECMWF ensemble at different forecast ranges

Anomaly correlation of the ensemble means for the four forecast categories as afunction of forecast range. The bars, based on 1000 subsamples generated with thebootstrap method, indicate the 95% confidence intervals.

Which flow pattern leads to more/less accurate forecasts?

Extended-range predictions: Forecast

from 27 January valid for 10-16 Feb 2020 NAO predictions valid for 14-16 Feb

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Regime diagnostics on the seasonal scale

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+NAO: high storminess, but mild

temperatures over Europe

BL: cold temperatures

over Europe

Ferranti, L. et al. 2018 QJRMS, 144

doi:10.1002/qj.3341

Winter 2019/20

NA

O+

Blocking+

NA

O+

Blocking -

Z500 DJF 2019/20

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Z500 predictions from C3S multi-modelensemble - DJF 2019/20

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Did the polar vortex enhance predictability ?

https://www.cpc.ncep.noaa.gov/

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Did Indian Ocean rainfall anomalies play a role? (see Molteni et al., Clim. Dyn. 2015)

ECMWF

NCEP

Summary

• Atmospheric weather regimes may be defined on a hemispheric or regional domain. Regime behaviour can be reproduced in a variety of dynamical models of different complexity.

• Detection of regimes in atmospheric and model datasets is usually performed by PDF estimation or cluster analysis; results are dependent on adequate space/time-filtering and proper use/interpretation of statistical significance tests.

• The impact of forcing anomalies on regime properties is often manifested in changes of regime frequencies (although bifurcation effects may occur for very strong forcing anomalies).

• Predictability of regime frequencies as a function of the ENSO and MJO phases has been detected in ensembles of GCM simulations, and offers an alternative approach to extended and long range prediction.

• ECMWF has developed diagnostics and operational products based on regime definitions applicable to medium-range, subseasonal and seasonal forecasts.

October 29, 2014

References and further reading• Cassou, C., 2008: Intraseasonal interaction between the Madden-Julian Oscillation and the North Atlantic Oscillation. Nature,

255, 523-527.

• Charney, J.G. and J.G. DeVore. 1979: Multiple flow equilibria in the atmosphere and blocking. J. Atmos. Sci., 36, 1205-1216

• Charney J. G. and D. M. Straus, 1980: Form-drag instability, multiple equilibria, and propagating planetary waves in

baroclinic, orographically forced, planetary wave systems. J. Atmos. Sci., 37, 1157-1176.

• Cheng, X. and J.M. Wallace, 1993: Cluster analysis of the Northern Hemisphere wintertime 500-hPa height field: Spatial

patterns. J. Atmos. Sci., 50, 2674-2696.

• Corti, S., F. Molteni and T.N. Palmer, 1999: Signature of recent climate change in frequencies of natural atmospheric

circulation regimes. Nature, 398, 799-802.

• Ferranti L., L. Magnusson, F. Vitart and D.S. Richardson, 2018: How far in advance can we predict changes in large-scale

flow leading to severe cold conditions over Europe? Q. J. Roy. Met. Soc., 144, 1788–1802.

• Haines, K. and J. Marshall, 1987: Eddy-forced coherent structures as a prototype of atmospheric blocking. Q. J. R. Meteorol.

Soc 113, 681-704.

• Hansen, A.R., and A. Sutera, 1986: On the probability density distribution of large-scale atmospheric wave amplitude. J.

Atmos. Sci., 43, 3250-3265.

• Horel, J.D. and J.M. Wallace, 1981: Planetary-scale atmospheric phenomena associated with the Southern Oscillation. Mon.

Wea. Rev. 109, 813-829.

• Kimoto M. and M. Ghil, 1993: Multiple flow regimes in the northern hemisphere winter. Part I: methodology and hemispheric

regimes. J. Atmos. Sci., 50, 2625-2643.

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References and further reading (2)

• Legras, B., and M. Ghil, 1985: Persistent anomalies, blocking and variations in atmospheric predictability, J. Atmos. Sci., 42, 433-471

• Lorenz, E.N, 1963: Deterministic nonperiodic flow. J. Atmos. Sci. 20, 130-141.

• Michelangeli, P.-A., R. Vautard, and B. Legras, 1995: Weather regimes: Recurrence and quasi-stationarity. J. Atmos. Sci., 52, 1237-

1256.

• Mo, K., and M. Ghil, 1988: Cluster analysis of multiple planetary flow regimes, J. Geophys. Res., 93D, 10927-10952.

• Molteni, F., L. Ferranti, T.N. Palmer and P. Viterbo, 1993: A dynamic interpretation of the global response to equatorial Pacific SST

anomalies. J. Climate, 6, 777-795.

• Molteni F., T. Stockdale and F. Vitart, 2015:Understanding and modelling extra-tropical teleconnections with the Indo-Pacific region

during the northern winter. Clim. Dynamics, 45, 3119-3140

• Palmer, T.N., 1993: Extended-range atmospheric predictions and the Lorenz model. Bull. Amer. Met. Soc., 74, 49-65.

• Reinhold, B., and R. T. Pierrehumbert, 1982: Dynamics of weather regimes: Quasi-stationary waves and blocking. Mon. Wea. Rev.,

121, 2355-1272.

• Straus, D. M., S. Corti and F. Molteni, 2007: Circulation regimes: Chaotic variability versus SST-forced predictability. J. Climate, 20,

2251–2272.

• Vautard, R., and B. Legras, 1988: On the source of midlatitude low-frequency variability. Part II: nonlinear equilibration of weather

regimes. J. Atmos. Sci., 45, 2845-2867.

• Wheeler, M.C. and H. H. Hendon, 2004: An all-season real-time multivariate MJO index: Development of an index for monitoring and

prediction. Mon. Wea. Rev. 132, 1917-1932

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