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Beyond El Niño: unsung climate modes drive African floods

Ficchi, A., Cloke, H. ORCID: https://orcid.org/0000-0002-1472-868X, Neves, C. ORCID: https://orcid.org/0000-0003-1201-5720, Woolnough, S. ORCID: https://orcid.org/0000-0003-0500-8514, Coughlan de Perez, E., Zsoter, E., Pinto, I., Meque, A. and Stephens, E. ORCID: https://orcid.org/0000-0002-5439-7563 (2021) Beyond El Niño: unsung climate modes drive African floods. Weather and Climate Extremes, 33. 100345. ISSN 2212-0947

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To link to this item DOI: 10.1016/j.wace.2021.100345

Abstract/Summary

The El Niño Southern Oscillation (ENSO) dominates the conversation about predictability of climate extremes and early warning and preparedness for floods and droughts, but in Africa other modes of climate variability are also known to influence rainfall anomalies. In this study, we compare the role of ENSO in driving flood hazard over sub-Saharan Africa with modes of climate variability in the Indian and Atlantic Oceans. This is achieved by applying flood frequency approaches to a hydrological reanalysis dataset and streamflow observations for different phases of the ENSO, Indian Ocean Dipole and Tropical South Atlantic climate modes. Our results highlight that Indian and Atlantic Ocean modes of climate variability are equally as important as ENSO for driving changes in the frequency of impactful floods across Africa. We propose that in many parts of Africa a larger consideration of these unsung climate modes could provide improved seasonal predictions of associated flood hazard and better inform adaptation to the changing climate.

Item Type:Article
Refereed:Yes
Divisions:Science > School of Archaeology, Geography and Environmental Science > Department of Geography and Environmental Science
Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
Science > School of Mathematical, Physical and Computational Sciences > NCAS
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics > Applied Statistics
ID Code:99016
Publisher:Elsevier

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