weather regimes - ecmwf events (indico) · • atmospheric weather regimes may be defined on a...
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© ECMWF March 24, 2020
Weather regimes
Definition, predictability and operational applications
Franco Molteni, Laura Ferranti
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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
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X X Y
Y XZ rX Y f
Z XY bZ
= − +
= − + − +
= −
What is the
impact of f on
the attractor?
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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
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El Niño and the Southern Oscillation
Walker and Bliss (1932); Bjerknes (1969)
SOI: Tahiti – Darwin SLP
Nino3.4 SST
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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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October 29, 2014
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)
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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
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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
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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?
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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
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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.
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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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