Extreme weather events have devastating impacts on human health, economic activities, ecosys tems, and infrastructure. It is therefore crucial to anticipate extremes and their impacts to allow for preparedness and emergency measures. There is indeed potential for probabilistic subseasonal prediction on timescales of several weeks for many extreme events. Here we provide an overview of subseasonal predictability for case studies of some of the most prominent extreme events across the globe using the ECMWF S2S prediction system: heatwaves, cold spells, heavy precipitation events, and tropical and extratropical cyclones. The considered heatwaves exhibit predictability on timescales of 3-4 weeks, while this timescale is 2-3 weeks for cold spells. Precipitation extremes are the least predictable among the considered case studies. Tropical cyclones, on the other hand, can exhibit probabilistic predictability on timescales of up to 3 weeks, which in the presented cases was aided by remote precursors such as the Madden-Julian Oscillation. For extratropical cyclones, lead times are found to be shorter. These case studies clearly illustrate the potential for event - dependent advance warnings for a wide range of extreme events. The subseasonal predictability of extreme events demonstrated here allows for an extension of warning horizons, provides advance information to impact modelers, and informs communities and stakeholders affected by the impacts of extreme weather events.

Sudden stratospheric warming (SSW) events have been suggested to be followed by a surface impact, though this response varies between events. Using reanalysis data, we identify two types of tropospheric responses to SSWs: Two thirds of the SSW events are dominated by a zonally symmetric tropospheric response with an equatorward shift of the jet in the Atlantic, consistent with the canonical SSW response in the form of a negative signature of the North Atlantic Oscillation. For the remaining third of SSW events, a zonally asymmetric response is found, associated with a poleward shift of the jet in the Atlantic. The Pacific is found to contribute to the sign of the North Atlantic response, as synoptic wave propagation from the Eastern Pacific links the Pacific and Atlantic storm tracks for both equatorward and poleward jet responses.

Abstract. Marine cold air outbreaks (MCAOs) in the northeastern North Atlantic occur due to the advection of extremely cold air over an ice-free ocean. MCAOs are associated with a range of severe weather phenomena, such as polar lows, strong surface winds and intense cooling of the ocean surface. Given these extreme impacts, the identification of precursors of MCAOs is crucial for improved long-range prediction of associated impacts on Arctic infrastructure and human lives. MCAO frequency has been linked to the strength of the stratospheric polar vortex, but the study of connections to the occurrence of extreme stratospheric events, known as sudden stratospheric warmings (SSWs), has been limited to cold extremes over land. Here, the influence of SSW events on MCAOs over the North Atlantic ocean is studied using reanalysis datasets. Overall, SSW events are found to be associated with more frequent MCAOs in the Barents Sea and the Norwegian Sea compared to climatology and less frequent MCAOs in the Labrador Sea. In particular, SSW events project onto an anomalous dipole pattern of geopotential height 500 hPa, which consists of a ridge anomaly over Greenland and a trough anomaly over Scandinavia. By affecting the variability of the large-scale circulation patterns in the North Atlantic, SSW events contribute to the strong northerly flow over the Barents and Norwegian seas and thereby increase the likelihood of MCAOs in these regions. In contrast, the positive geopotential height anomaly over Greenland reduces the probability of MCAOs in the Labrador Sea after SSW events. As SSW events tend to have a long-term influence on surface weather, these results are expected to benefit the predictability of MCAOs in the Nordic Seas for winters with SSW events.

Marine cold-air outbreaks (MCAOs) create conditions for hazardous maritime mesocyclones (polar lows) posing risks to marine infrastructure. For marine management, skilful predictions of MCAOs would be highly beneficial. For this reason, we investigate (a) the ability of a seasonal prediction system to predict MCAOs and (b) the possibilities to improve predictions through large-scale causal drivers. Our results show that the seasonal ensemble predictions have high prediction skill for MCAOs over the Nordic Seas for about 20 days starting from November initial conditions. To study causal drivers of MCAOs, we utilize a causal effect network approach applied to the atmospheric reanalysis ERA-Interim and identify local sea surface temperature and atmospheric circulation patterns over Scandinavia as valuable predictors. Prediction skill for MCAOs is further improved up to 40 days by including MCAO predictors in the analysis.

Abstract. Marine cold air outbreaks (MCAOs) in the Arctic are associated with a range of severe weather phenomena, such as polar lows, strong surface winds and intense cooling of the ocean surface. While MCAO frequency has been linked to the strength of the stratospheric polar vortex, a connection to the occurrence of extreme stratospheric events, known as sudden stratospheric warmings (SSWs), has dominantly been investigated with respect to cold extremes over land. Here, the influence of SSW events on MCAOs in the Barents Sea is studied using observational and reanalysis datasets. Overall, more than a half of SSW events lead to more frequent MCAOs in the Barents Sea. SSW events with an enhanced MCAO response in the Barents Sea are associated with a ridge over Greenland and a trough over Scandinavia, leading to an anomalous dipole pattern of 500-hPa geopotential height and strong northerly flow over the Norwegian Sea. As SSW events tend to have a long-term influence on surface weather, these results can shed light on the predictability of MCAOs in the Arctic for winters with SSW events.