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How to identify weather patterns associated with occurrences of extreme events in models (D1.1)

Ruggieri, Paolo


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    <subfield code="a">How to identify weather patterns associated with occurrences of extreme events in models (D1.1)</subfield>
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    <subfield code="a">&lt;p&gt;Meridional transport of heat by transient atmospheric eddies is a key component of the energy budget of the Arctic and high latitude regions. While transport in the mid-latitudes is known to be modulated by large-scale low frequency flow regimes, little is known about the link between heat flux in the polar cap and mid-latitude circulation regimes. Recent studies suggest that heat and moisture transport into polar regions happens in bursts that are associated with propagation of storms and atmospheric blocking. While the picture is evolving, a systematic assessment is still lacking.&lt;/p&gt;

&lt;p&gt;We investigate the modulation of transient, poleward atmospheric eddy heat flux induced by the variability of the atmospheric circulation in the North Atlantic sector. Heat transport is defined by meridional advection of moist static energy, and the circulation anomalies are diagnosed with a widely used clustering technique, a jet latitude index and a blocking index. The analysis is carried out for the extra tropics in the Northern Hemisphere but special emphasis is given to heat transport crossing the 70 ◦N latitude circle. Results are based on an atmospheric reanalysis for a total of 38 extended cold seasons. Establishing quantitative relationships between circulation regimes and poleward heat transport by transient eddies can help understand linkages between mid-latitudes and the Arctic, and evaluate how they are represented in coupled GCMs. In principle, it can help exploit predictability on a sub- seasonal time scale. The relationship between extreme events of strong heat flux and circulation regimes is also assessed and the analysis indicates a fundamental role of blocking in the North Atlantic sector.&lt;/p&gt;

&lt;p&gt;The presented empirical relationship between heat flux variability and extreme events can serve as a powerful tool for predictability analysis, but also for the evaluation of model variability.&lt;/p&gt;</subfield>
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