Multiple Weather Regimes over the North Atlantic: Analysis of Precursors and Successors

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  • 1 Department of Atmospheric Sciences, University Of California, Los Angeles, and Laboratoire de Météorologie Dynamique, Paris, France
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Abstract

The algorithm developed by Vautard and Legras is applied to a series of 37 winter of 700 mb geopotential height observations, in order to identify quasi-stationary patterns occurring over the extratropical North Atlantic area. Weather regimes are obtained as solutions of a act of nine nonlinear statistical equations, giving the balance of the large-scale average tendency. Four regime patterns are found. The first one exhibits the typical European blocking dipole. The second one is an enhanced zonal flow. The third one consists of a positive anomaly over Greenland, and the last one is characterized by a ridge over the eastern Atlantic Ocean. The role played by the transient small scales in the maintenance of thew four regimes is discussed.

Next, the backward and forward memory times of the different weather regimes are established. Evidence is brought that the atmosphere keeps its memory of regime occurrences into the medium range (10–20 days). Backward memory times are shorter (5–10 days), showing that the onsets of the regimes are rather sudden. Preferred precursor and successor patterns are identified as the maxima of the composite probability densities of the anomalies occurring before the onsets and after the breaks. Some transitions between regimes are quite smooth, while others consist of a dramatic change, within a couple of days, of the weather pattern. For instance, blocking is often preceeded by a positive anomaly in the mid-Atlantic together with a trough over Europe.

The preferred transitions between regimes are examined. It is shown, in particular, that zonal flows am likely to become blocked flows, and that the Greenland Anticyclone regime (third regime) is likely to succeed blocking events. All statistics are tested against climatology. The systematic aspect of this study gives rise to multiple applications, especially for case and composite studies on model prediction errors.

Abstract

The algorithm developed by Vautard and Legras is applied to a series of 37 winter of 700 mb geopotential height observations, in order to identify quasi-stationary patterns occurring over the extratropical North Atlantic area. Weather regimes are obtained as solutions of a act of nine nonlinear statistical equations, giving the balance of the large-scale average tendency. Four regime patterns are found. The first one exhibits the typical European blocking dipole. The second one is an enhanced zonal flow. The third one consists of a positive anomaly over Greenland, and the last one is characterized by a ridge over the eastern Atlantic Ocean. The role played by the transient small scales in the maintenance of thew four regimes is discussed.

Next, the backward and forward memory times of the different weather regimes are established. Evidence is brought that the atmosphere keeps its memory of regime occurrences into the medium range (10–20 days). Backward memory times are shorter (5–10 days), showing that the onsets of the regimes are rather sudden. Preferred precursor and successor patterns are identified as the maxima of the composite probability densities of the anomalies occurring before the onsets and after the breaks. Some transitions between regimes are quite smooth, while others consist of a dramatic change, within a couple of days, of the weather pattern. For instance, blocking is often preceeded by a positive anomaly in the mid-Atlantic together with a trough over Europe.

The preferred transitions between regimes are examined. It is shown, in particular, that zonal flows am likely to become blocked flows, and that the Greenland Anticyclone regime (third regime) is likely to succeed blocking events. All statistics are tested against climatology. The systematic aspect of this study gives rise to multiple applications, especially for case and composite studies on model prediction errors.

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