The influence of modes of climate variability on the sub-seasonal temporal clustering of extreme precipitation.
Temporally clustered precipitation extremes can have catastrophic impacts. Therefore, understanding their drivers is paramount for risk assessment in current and future climates. Here, we model for each season 3-week extreme precipitation event counts with Poisson Generalized Linear Models and nine...
| Main Authors: | , |
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| Format: | Article in Journal/Newspaper |
| Language: | unknown |
| Published: |
Elsevier
2022
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.48350/166033 https://boris.unibe.ch/166033/ |
| Summary: | Temporally clustered precipitation extremes can have catastrophic impacts. Therefore, understanding their drivers is paramount for risk assessment in current and future climates. Here, we model for each season 3-week extreme precipitation event counts with Poisson Generalized Linear Models and nine major modes of climate variability as covariates. Model goodness-of-fit is highest in the tropics, particularly over the equatorial Pacific, the Maritime Continent, and East Africa, where ENSO, the Indian Ocean Dipole (IOD) and the MJO are the major drivers of sub-seasonal temporal clustering of extreme precipitation. The IOD and MJO also matter over Southwest Asia during boreal fall and winter. In the Northern Hemisphere, the North Atlantic Oscillation impacts clustering west of the Iberian Peninsula and over Scandinavia and Greenland, and the Pacific North American pattern matters over the central/northern Pacific Ocean. Finally, our models show very little skill in the Southern Hemisphere, where temporal clustering is also less frequent. |
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