Weather Regimes: Recurrence and Quasi Stationarity

Michelangeli, Paul-Antoine; Vautard, Robert; Legras, Bernard

doi:10.1175/1520-0469(1995)052<1237:wrraqs>2.0.co;2

Cited by 566 publications

(697 citation statements)

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“…Four weather regimes are assumed, as is done in previous studies [Corti et al, 1999;Michelangeli et al, 1995]. The centroids of the weather regime clusters are displayed in Figure 2a.…”

Section: Fall/winter 2006/2007 Weather Regimesmentioning

confidence: 99%

“…One of four regimes (Regime #2, Figure 2b), associated with a mild south-westerly flow over Western Europe (the ''zonal'' regime [Michelangeli et al, 1995]), is more frequent than any other regimes (68 out of 181 days) but this higher frequency is not unprecedented. For instance, fall/winters in the eighties or even in the early fifties were dominated by this regime as well, but the mean temperature, although high, was significantly lower than during fall-winter 2006- …”

Section: Fall/winter 2006/2007 Weather Regimesmentioning

confidence: 99%

See 1 more Smart Citation

Inconsistency between atmospheric dynamics and temperatures during the exceptional 2006/2007 fall/winter and recent warming in Europe

Yiou¹,

Vautard²,

Naveau³

et al. 2007

Geophysical Research Letters

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“…Four weather regimes are assumed, as is done in previous studies [Corti et al, 1999;Michelangeli et al, 1995]. The centroids of the weather regime clusters are displayed in Figure 2a.…”

Section: Fall/winter 2006/2007 Weather Regimesmentioning

confidence: 99%

Section: Fall/winter 2006/2007 Weather Regimesmentioning

confidence: 99%

Inconsistency between atmospheric dynamics and temperatures during the exceptional 2006/2007 fall/winter and recent warming in Europe

Yiou¹,

Vautard²,

Naveau³

et al. 2007

Geophysical Research Letters

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“…Like Vautard (1990) and Michelangeli et al (1995), our so-called WRs are objectively defined through an automatic classification of atmospheric circulation patterns over the North Atlantic and western Europe. Circulation patterns are provided by the daily gridded SLP field as extracted from a more extended data set collected at the Climatic Research Unit (CRU, at UEA, Norwich, UK).…”

Section: The Daily Slp Data Setmentioning

confidence: 99%

“…The partitioning algorithm we use here, after Michelangeli et al (1995), is known as the dynamical cluster algorithm (Diday and Simon, 1976). It does not suffer from the known drawbacks of the Ward methodology.…”

Section: The Dynamical Cluster Algorithmmentioning

confidence: 99%

The impact of El Niño–southern oscillation upon weather regimes over Europe and the North Atlantic during boreal winter

Moron

Plaut

2003

Intl Journal of Climatology

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The influence of the warm and cold sea-surface temperatures in the eastern and central equatorial Pacific associated with El Niño-southern oscillation (ENSO) on the probability of occurrence of weather regimes (WRs) over the North Atlantic sector is investigated for the period November-March. Five WRs are identified from daily sea-level pressure anomalies (SLPAs) during 119 winters over this sector by applying cluster analysis: the positive North Atlantic oscillation (NAO; called ZO for zonal) and negative NAO (called WBL for west blocking) patterns; GA (for Greenland anticyclone), with a positive SLPA shifted north of 60°N; EA (for European anticyclone) with a positive SLPA over Europe but enhanced north-south SLPA gradient over the western and central North Atlantic; and AR (for Atlantic Ridge) with a positive (negative) SLPA over the central North Atlantic (northern and central Europe).El Niño winters are associated with a significant increase (decrease) in the prevalence of ZO (WBL) in November-December and a significant increase (decrease) in the prevalence of GA and WBL (EA and ZO)

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“…Weather regimes are examples of such flow regimes that are manifested as particular atmospheric conditions on a regional scale with time scales roughly on the range of 10-100 days (Reinhold and Pierrehumbert 1982;Barnston and Livezey 1987;Vautard and Legras 1988;Ghil and Robertson 2002). The application of the concept of weather regimes in the analysis of mid-and high-latitude 1 3 synoptic systems has provided us with a deeper understanding of intrinsic climate variability (Molteni et al 1990;Michelangeli et al 1995;Cassou et al 2004;Guemas et al 2009), with potential benefits to weather and climate prediction capability (Mo and Ghil 1988;Brankovic and Molteni 1997;Cassou 2008;Riddle et al 2013) and possibly to longterm climate change (Corti et al 1999).…”

Section: Introductionmentioning

confidence: 89%

Dynamical prediction of Arctic sea ice modes of variability

et al. 2018

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This study explores the prediction skill of the northern hemisphere (NH) sea ice thickness (SIT) modes of variability in a state-of-the-art coupled forecast system with respect to two statistical forecast benchmarks. Application of the K-means clustering method on a historical reconstruction of SIT from 1958 to 2013, produced by an ocean-sea-ice general circulation model, identifies three Arctic SIT clusters or modes of climate variability. These SIT modes have consistent patterns in different calendar months and their discrete time series of occurrences show persistence on intraseasonal to interannual time scales. We use the EC-Earth2.3 coupled climate model to produce five-member 12-month-long monthly forecasts of the NH SIT modes initialized on 1 May and 1 November every year from 1979 to 2010. We use a three-state first-order Markov chain and climatological probability forecasts determined from the historical SIT mode reconstruction as two statistical reference forecasts. The analysis of ranked probability skill scores (RPSSs) relating these three forecast systems shows that the dynamical SIT mode forecasts typically have a higher skill than the Markov chain forecasts, which are overall better than climatological forecasts. The evolution of RPSS in forecast time indicates that the transition from the sea-ice melting season to growing season in the EC-Earth2.3 forecasts, with respect to the Markov chain model, typically leads to the improvement of prediction skill. The reliability diagrams overall show better reliability of the dynamical forecasts than that of the Markov chain model, especially for 1 May start dates, while dynamical forecasts with 1 November start dates are overconfident. The relative operating characteristics (ROC) diagrams confirm this hierarchy of forecast skill among these three forecast systems. Furthermore, ROC diagrams stratified in groups of 3 sequential forecast months show that Arctic SIT mode forecasts initialized on 1 November typically lose resolution with forecast time more slowly than forecasts initialized on 1 May.

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Weather Regimes: Recurrence and Quasi Stationarity

Cited by 566 publications

References 0 publications

Inconsistency between atmospheric dynamics and temperatures during the exceptional 2006/2007 fall/winter and recent warming in Europe

Inconsistency between atmospheric dynamics and temperatures during the exceptional 2006/2007 fall/winter and recent warming in Europe

The impact of El Niño–southern oscillation upon weather regimes over Europe and the North Atlantic during boreal winter

Dynamical prediction of Arctic sea ice modes of variability

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