Prediction skill of Sahelian heatwaves out to subseasonal lead times and importance of atmospheric tropical modes of variability

Guigma, Kiswendsida H, MacLeod, David, Todd, Martin and Wang, Yi (2021) Prediction skill of Sahelian heatwaves out to subseasonal lead times and importance of atmospheric tropical modes of variability. Climate Dynamics. ISSN 0930-7575

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Global warming has increased the frequency of extreme weather events, including heatwaves, over recent decades. Heat early warning systems are being set up in many regions as a tool to mitigate their effects. Such systems are not yet implemented in the West African Sahel, partly because of insufficient knowledge on the skill of models to predict them. The present study addresses this gap by examining the skill of the ECMWF ENS extended-range forecasting system (ENS-ext) to predict Sahelian heatwaves out to subseasonal lead-times. It also assesses the importance of tropical modes of variability, which were previously identified as important large-scale drivers of heatwave occurrence in the Sahel. The results show that ENS-ext is able to predict Sahelian heatwaves with significant skill out to lead-week 2–3. With increasing lead-time, heatwaves are more predictable at nighttime than at daytime. Likewise, the pre-monsoon season heatwaves have a longer predictability than those occurring in late winter. The model is also able to relatively well simulate the observed relationship between heatwave occurrence and tropical mode activity. Furthermore, the prediction skill is better during the active phases of the modes, suggesting that they are good sources of heatwave predictability. Therefore, improving the representation of tropical modes in models will positively impact heatwave prediction at the subseasonal scale in the Sahel, and gain more time and precision for anticipatory actions.

Item Type: Article
Schools and Departments: School of Global Studies > Geography
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 19 Mar 2021 09:18
Last Modified: 28 Feb 2022 14:13

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Project NameSussex Project NumberFunderFunder Ref
Towards Forecast-based Preparedness Action (ForPAc): Probabilistic forecasts information for defensible preparedness decision-making and actionG2043NERC-NATURAL ENVIRONMENT RESEARCH COUNCILNE/P000673/1