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European climate variability is shaped by atmospheric dynamics over the North Atlantic and local processes. Better understanding their future seasonality is essential to anticipate changes in weather conditions for human and natural systems. We explore atmospheric seasonality over 1979–2017 and 1979–2100 with seasonal circulation regimes (SCRs), by clustering year‐round daily fields of Z500 from the ERA‐Interim reanalysis and 12 Coupled Model Intercomparison Project phase 5 (CMIP5) climate models (historical and RCP8.5 runs). The spatial and temporal variability of SCR structures and associated patterns of surface air temperature are investigated. Climate models have biases but reproduce structures and evolutions of SCRs similar to the reanalysis over 1979–2017: decreasing frequency of winter conditions (starting later and ending earlier in the year) and the opposite for summer conditions. These changes are stronger over 1979–2100 than over 1979–2017, associated with a large increase of North Atlantic seasonal mean Z500 and temperature. When using more SCRs (more freedom in definition of seasonality), the changes over 1979–2100 correspond to a long‐term swap between SCRs, resulting in similar structures (annual cycle and spatial patterns) relative to the evolution of seasonal mean Z500 and temperature. To understand whether the evolution of SCRs is linked to uniform warming, or to changes in circulation patterns, we remove the calendar trend in the Z500 regional average to define SCRs based on detrended data (d‐SCRs). The temporal properties of d‐SCRs appear almost constant whereas their spatial patterns change, indicating that the calendar Z500 regional trend drives the evolutions of SCRs, and that changing spatial patterns in d‐SCRs account for the heterogeneity of this trend. Our study suggests that historical winter conditions will continue to decrease in the future while historical summer conditions continue to increase. However, it also suggests that the spatial and temporal patterns of SCRs would remain similar, relatively to the year‐round Z500 increase. [ABSTRACT FROM AUTHOR]

This study explores the teleconnection between the boreal summer precipitation for the central highlands of Eritrea, with respect to global sea‐surface temperature (SST) and atmospheric parameters (winds, pressure, humidity, and vertical velocity) as indirect indicators of atmospheric circulation. The central‐highland summer (Jul–Aug) precipitation (1914–2015) is significantly negatively correlated with the Atlantic Niño (ATL3) index. Similarly, with a 2‐month (May) or 1‐month lead (Jun), SSTs over the equatorial Atlantic (Gulf of Guinea) are inversely correlated with the central‐highland summer precipitation. Moreover, the regional atmospheric circulation reveals that the boreal summer precipitation series has a significant positive correlation with the westerly wind over the Gulf of Guinea at 700 hPa and a negative correlation with the Tropical Easterly Jet (TEJ) at 200 hPa. Overall, these findings depict the African Walker circulation cell, whereby a low‐level westerly wind flows from the Gulf of Guinea across central Africa, coupled with a rising branch over north‐eastern Africa, and reverses as an easterly wind at a higher level. These significant correlations between Atlantic Ocean SST as the precipitation‐driving factor enabled the development and validation of an area‐specific seasonal precipitation prediction model, as precipitation in this region is temporally and spatially highly variable. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]