Modulation of Atmospheric River Occurrence and Associated Precipitation Extremes in the North Atlantic Region by European Weather Regimes

Abstract

The variability of large‐scale moisture transport by atmospheric rivers (AR) and its linkage to precipitation extremes in the North Atlantic‐European region is studied. A weather regime approach is adopted to describe the variability of the large‐scale circulation. Weather regimes modulate the climatologically mean westerly flow into Europe, in which ARs are commonly embedded. In cyclonic regimes, AR landfall is enhanced in wide parts of Iberia, Western Europe, the British Isles, and southern Scandinavia. In blocked regimes, ARs are deviated around the blocking anticyclone enhancing AR landfall at high latitudes or in the western Mediterranean and North Africa. The likelihood of precipitation extremes increases locally more than twofold in regions of enhanced AR occurrence during the different weather regimes. These results suggest that specific weather regimes establish favorable conditions for AR landfall in Europe. Therefore, accurate forecasts of weather regimes can give guidance for predicting also large‐scale precipitation extremes.

Relationship between atmospheric blocking and warm‐season thunderstorms over western and central Europe

“…; Pasquier et al . ). It is important, however – especially in connection with local‐scale phenomena such as deep moist convection – that the blocking position is correctly predicted, which is currently still a challenge in state‐of‐the‐art global numerical weather prediction models (Quinting and Vitart, ).…”

“…Nevertheless, blocking is not suitable as a single predictor for convection; other approaches should be pursued for this purpose (e.g., Doswell et al, 1996;Sánchez et al, 2009;Mohr et al, 2015b;Púčik et al, 2015;Rädler et al, 2018). Due to its persistence, blocking might contribute to improved thunderstorm potential predictability on sub-seasonal time scales beyond the classical weather forecast time scale of a few days, and complement current activities that investigate the connection of water vapour transport on the sub-seasonal predictability of extremes (e.g., Lavers et al, 2016aLavers et al, , 2016bPasquier et al, 2018). It is important, however -especially in connection with local-scale phenomena such as deep moist convection -that the blocking position is correctly predicted, which is currently still a challenge in state-of-the-art global numerical weather prediction models (Quinting and Vitart, 2019).…”

A weather system perspective on winter–spring rainfall variability in southeastern Australia during El Niño

“…Monthly mean data are available and accessible via the web portal (http://eraiclim.ethz.ch; accessed 25 April 2020). The dataset for this study includes cyclones (Pasquier et al, 2019), cut-off lows (at 315 and 320 K; Wernli and Schwierz, 2006;Wernli and Sprenger, 2007), atmospheric blocking (with the use of the 0.7 PVU anomaly that persists for at least 5 days; Schwierz et al, 2004), regions of anomalous moisture transport excluding the tropical belt (20 • S-20 • N; Pasquier et al, 2019) and warm conveyor belts (WCBs; Madonna et al, 2014). WCBs are the main precipitating and ascending airstreams associated with extratropical cyclones (Browning, 1990).…”

The influence of aggregation and statistical post‐processing on the subseasonal predictability of European temperatures

“…It has been demonstrated that more aggregation indeed leads to a general predictability in upper air fields at longer lead times (Roads, 1986;Jung and Leutbecher, 2008;Buizza and Leutbecher, 2015) and in surface variables such as precipitation (Wheeler et al, 2017). Studies have also tailored the aggregation to a single conditional source of predictability: rainfall events in Europe that are clustered in time due to large-scale dynamics (Economou et al, 2015;Pasquier et al, 2019;Yang and Villarini, 2019), or extreme temperatures occurring simultaneously within a spatial region due to large-scale flow or sea-surface temperatures (Stefanon et al, 2012;McKinnon et al, 2016;Vijverberg et al, 2020). The forecast skill of such derived predictands can be high, but it is conditional on the occurrence of the source mechanism, and might also lose validity for less or more extreme events (Wulff and Domeisen, 2019).…”