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Abstract Increases in climate hazards and their impacts mark one of the major challenges of climate change. Situations in which hazards occur close enough to one another to result in amplified impacts, because systems are insufficiently resilient or because hazards themselves are made more severe, are of special concern. We consider projected changes in such compounding hazards using the Max Planck Institute Grand Ensemble under a moderate (RCP4.5) emissions scenario, which produces warming of about 2.25 °C between pre-industrial (1851–1880) and 2100. We find that extreme heat events occurring on three or more consecutive days increase in frequency by 100%–300%, and consecutive extreme precipitation events increase in most regions, nearly doubling for some. The chance of concurrent heat and drought leading to simultaneous maize failures in three or more breadbasket regions approximately doubles, while interannual wet-dry oscillations become at least 20% more likely across much of the subtropics. Our results highlight the importance of taking compounding climate extremes into account when looking at possible tipping points of socio-environmental systems. 

Abstract Statistical relationships between atmospheric rivers (ARs) and extratropical cyclones and anticyclones are investigated on a global scale using objectively identified ARs, cyclones, and anticyclones during 1979–2014. Composites of circulation and moisture fields around the ARs show that a strong cyclone is located poleward and westward of the AR centroid, which confirms the close link between the AR and extratropical cyclone. In addition, a pronounced anticyclone is found to be located equatorward and eastward of the AR, whose presence together with the cyclone leads to strong horizontal pressure gradient that forces moisture to be transported along a narrow corridor within the warm sector of the cyclone. This anticyclone located toward the downstream equatorward side of the cyclone is found to be missing for cyclones not associated with ARs. These key features are robust in composites performed in different hemispheres, over different ocean basins, and with respect to different AR intensities. Furthermore, correlation analysis shows that the AR intensity is much better correlated with the pressure gradient between the cyclone and anticyclone than with the cyclone/anticyclone intensity alone, although stronger cyclones favor the occurrence of AR. The importance of the horizontal pressure gradient in the formation of the AR is also consistent with the fact that climatologically ARs are frequently found over the region between the polar lows and subtropical highs in all seasons. 

We investigate how the Madden‐Julian Oscillation (MJO), the dominant mode of tropical subseasonal variability, modulates the lifecycle of cool‐season North Pacific atmospheric rivers (ARs). When the enhanced (suppressed) convection center is located over the Indian Ocean (western Pacific), more AR events originate over eastern Asia and with fewer over the subtropical northern Pacific. When the enhanced (suppressed) convection is over the western Pacific (Indian Ocean), the opposite changes occur, with more AR events originate over the subtropical northern Pacific and fewer over eastern Asia. Dynamical processes involving anomalous MJO wind and seasonal mean moisture are found to be the dominant factors impacting these variations in AR origins. The MJO‐related anomalous geopotential height patterns are also shown to modulate the propagation of the AR events. These MJO–AR lifecycle relationships are further supported by model simulations.

 

We characterize the sensitivity of atmospheric river (AR)‐derived seasonal snowfall estimates to their atmospheric reanalysis‐based detection over Sierra Nevada, USA. We use an independent snow data set and the ARs identified with a single detection method applied to multiple atmospheric reanalyses of varying horizontal resolutions, to evaluate orographic relationships and contributions of individual ARs to the seasonal cumulative snowfall (CS). Spatial resolution differences have relatively minor effects on the number of ARs diagnosed, with higher‐resolution data sets identifying four more AR days per year, on average, during the 1985–2015 winters. However, this can lead to ~10% difference in AR attribution to the mean domain‐wide seasonal CS and differences up to 47% snowfall attribution at the seasonal scale. We show that identifying snow‐bearing ARs provides more information about the seasonal CS than simply knowing how many ARs occurred. Overall, we find that higher‐resolution atmospheric reanalyses imply greater attribution of seasonal CS to ARs.