Atmospheric tropical modes are important drivers of Sahelian springtime heatwaves

Heatwaves pose a serious threat to human health worldwide but remain poorly documented over Africa. This study uses mainly the ERA5 dataset to investigate their large-scale drivers over the Sahel region during boreal spring, with a focus on the role of tropical modes of variability including the Madden–Julian Oscillation (MJO) and the equatorial Rossby and Kelvin waves. Heatwaves were defined from daily minimum and maximum temperatures using a methodology that retains only intraseasonal scale events of large spatial extent. The results show that tropical modes have a large influence on the occurrence of Sahelian heatwaves, and, to a lesser extent, on their intensity. Depending on their convective phase, they can either increase or inhibit heatwave occurrence, with the MJO being the most important of the investigated drivers. A certain sensitivity to the geographic location and the diurnal cycle is observed, with nighttime heatwaves more impacted by the modes over the eastern Sahel and daytime heatwaves more affected over the western Sahel. The examination of the physical mechanisms shows that the modulation is made possible through the perturbation of regional circulation. Tropical modes thus exert a control on moisture and the subsequent longwave radiation, as well as on the advection of hot air. A detailed case study of a major event, which took place in April 2003, further supports these findings. Given the potential predictability offered by tropical modes at the intraseasonal scale, this study has key implications for heatwave risk management in the Sahel

Large‐Scale Drivers of Tropical Extreme Precipitation Events: The Example of French Overseas Territories

International audienceDue to their severity and lack of predictability, understanding and forecasting extreme precipitation events (EPEs) is critical for disaster risk reduction. The present work documents the large‐scale environment of tropical EPEs based on a 42‐year data set combining dense rain‐gauge networks that cover several tropical small islands and coastal regions. Approximately 10%–30% of EPEs are associated with a tropical storm or cyclone (TC), except for Reunion, for which its high topography makes it reach 55%. TCs multiply the EPE probability by a factor of 4–15, especially during TCs of category 1 or higher. A composite analysis demonstrates that the remaining large part of EPEs occurs within large‐scale and strong moist, convective, and cyclonic wind anomalies resulting from the superimposition of intraseasonal, seasonal‐to‐annual, and interannual timescales. These intense anomalies come essentially from intraseasonal variability, and lower frequencies improve the effect of intraseasonal events in creating a favorable environment for EPEs.En raison de leur gravité et de leur manque de prévisibilité, la compréhension et la prévision des événements de précipitations extrêmes (EPE) sont essentielles pour réduire les risques de dommages humains et matériels. Ce travail documente sur la période 1979-2021 l'environnement à grande échelle des EPE tropicaux sur la base d'un réseau de pluviomètres couvrant plusieurs petites îles tropicales et régions côtières des territoires outre-mer français dans les tropiques. Environ 10 à 30 % des EPE sont associés à une tempête tropicale ou à un cyclone, sauf à la Réunion, où la topographie élevée permet d'atteindre 55 %. Ces systèmes multiplient la probabilité d'EPE par un facteur de 4 à 15, en particulier lors des cyclones de catégorie 1 ou plus. Une analyse composite montre que la partie restante des EPE se produit au sein de fortes anomalies humides et convectives -- liée à du cyclonisme et un flux équatorial -- à grande échelle, résultant de la superposition d'échelles de temps intrasaisonnières, saisonnières à annuelles et interannuelles. Ces anomalies intenses proviennent essentiellement de la variabilité intrasaisonnière, les fréquences plus basses apportant plutôt un environnement de fond où les anomalies intrasaisonnières peuvent se développer

Seasonal forecasts of the Saharan Heat Low characteristics: a multi-model assessment

This work is supported by the French National Research Agency in the framework of the “Investissement d’avenir”program (ANR-15-IDEX-02) with the project PREDISAHLIM(2019–2021) and under grant with the projectSTEWARd (2020–2024).International audienceThe Saharan heat low (SHL) is a key component of the West African Monsoon system at the synoptic scale and a driver of summertime precipitation over the Sahel region. Therefore, accurate seasonal precipitation forecasts rely in part on a proper representation of the SHL characteristics in seasonal forecast models. This is investigated using the latest versions of two seasonal forecast systems namely the SEAS5 and MF7 systems from the European Center of Medium-Range Weather Forecasts (ECMWF) and Météo-France respectively. The SHL characteristics in the seasonal forecast models are assessed based on a comparison with the fifth ECMWF Reanalysis (ERA5) for the period 1993–2016. The analysis of the modes of variability shows that the seasonal forecast models have issues with the timing and the intensity of the SHL pulsations when compared to ERA5. SEAS5 and MF7 show a cool bias centered on the Sahara and a warm bias located in the eastern part of the Sahara respectively. Both models tend to underestimate the interannual variability in the SHL. Large discrepancies are found in the representation of extreme SHL events in the seasonal forecast models. These results are not linked to our choice of ERA5 as a reference, for we show robust coherence and high correlation between ERA5 and the Modern-Era Retrospective analysis for Research and Applications (MERRA). The use of statistical bias correction methods significantly reduces the bias in the seasonal forecast models and improves the yearly distribution of the SHL and the forecast scores. The results highlight the capacity of the models to represent the intraseasonal pulsations (the so-called east–west phases) of the SHL. We notice an overestimation of the occurrence of the SHL east phases in the models (SEAS5, MF7), while the SHL west phases are much better represented in MF7. In spite of an improvement in prediction score, the SHL-related forecast skills of the seasonal forecast models remain weak for specific variations for lead times beyond 1 month, requiring some adaptations. Moreover, the models show predictive skills at an intraseasonal timescale for shorter lead times