Subseasonal Temporal Clustering of Extreme Precipitation in the Northern Hemisphere: Regionalization and Physical Drivers

Tuel, Alexandre; Martius, Olivia (2022). Subseasonal Temporal Clustering of Extreme Precipitation in the Northern Hemisphere: Regionalization and Physical Drivers. Journal of Climate, 35(11), pp. 3537-3555. American Meteorological Society 10.1175/JCLI-D-21-0562.1

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Temporal clustering of extreme precipitation (TCEP) at subseasonal time scales often results in major impactson humans and ecosystems. Assessment and mitigation of the risk of such events requires characterization of their weather/climate drivers and their spatial dependence. Here, we introduce a regionalization method that identifies coherent regions in which the likelihood of subseasonal TCEP exhibits similar dependence to large-scale dynamics. We apply this method to each season in the Northern Hemisphere using ERA5 reanalysis data. The analysis yields spatially coherent regions, primarily at high latitudes and along the eastern margins of ocean basins. We analyze the large-scale and synoptic conditions associated with TCEP in several of the identified regions, in light of three key ingredients: lifting, moisture availability, and persistence in synoptic conditions. We find that TCEP is often directly related to distinct cyclone and blocking frequency anomalies and upper-level wave patterns. Blocking and associated Rossby wave breaking are particularly relevant at high latitudes and midlatitudes. At upper levels, meridional wave patterns dominate; however, in western Europe and parts of North America, TCEP is sometimes associated with zonally extended wave patterns. The flow features associated with TCEP in the eastern Pacific and eastern Atlantic Oceans exhibit similarities. For some regions, moisture flux anomalies are present during clustering episodes whereas in others forced lifting alone is sufficient to trigger heavy precipitation. Our results provide new information on the dynamics and spatial dependence of TCEP that may be relevant for the subseasonal prediction of clustering episodes.

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