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

Abstract

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.

Heat Waves: Physical Understanding and Scientific Challenges

“…Strategies include improvements in the signal‐to‐noise ratio through temporal and/or spatial data aggregation, or refined techniques to optimize the error losses or the limited sample size associated with extreme events (e.g., by using learning from less extreme events; Jacques‐Dumas et al., 2022; López‐Gómez et al., 2022; Vijverberg et al., 2020). ML methods have also been employed to discover subseasonal drivers of European high temperatures at different time leads (van Straaten et al., 2022), and windows of opportunity for enhanced subseasonal forecasts of HWs (Guigma et al., 2021). Overall, the uncovered linkages confirm the key role of well‐known drivers (tropical forcing and soil moisture) already revealed by dynamical forecasts, but also specific regional features in extratropical SSTs, sea ice and snow cover anomalies, which may vary with the region, timing and lag considered.…”

Impact of Uncertainties in Land Surface Processes on Subseasonal Predictability of Heat Waves Onset Over the Yangtze River Valley

“…Notably, the uncertainties of subseasonal HW predictions can not only be reduced by targeting observations to eliminate errors in key parameters but also by ensemble forecast systems. Most S2S models have been shown to notably underestimate the intensity of HWs (Guigma et al., 2021; Lin et al., 2022; Vitart et al., 2017; Wulff & Domeisen, 2019). Reportedly, the main physical processes are analogous in these S2S systems, although they have different land modules.…”

“…(2018) discovered that the ECMWF model captured temperature anomalies 3 weeks in advance. Most authors agree that S2S models are capable of predicting extreme HWs 2–3 weeks in advance, but the intensity of HWs is always greatly underestimated (Ardilouze et al., 2017; Guigma et al., 2021; Lin et al., 2022; Marshall et al., 2014; Osman & Alvarez, 2018). In general, the subseasonal prediction of HWs still contains large uncertainties.…”

“…To improve the prediction skill of HWs and to understand the subseasonal predictability of HWs, numerous studies on the predictability sources of HWs on subseasonal timescales have been conducted (Déqué et al., 2018; Gao et al., 2018; Guigma et al., 2021; Jia et al., 2022). One of the primary predictability sources is atmospheric intraseasonal oscillations.…”

Systematic Assessment of the Effects of Space Averaging and Time Averaging on Weather Forecast Skill

“…As one approach, spectral decomposition is one common way to identify different spatial scales, where each spatial scale can be identified with a wavelength of a different spectral basis function, such as a sinusoid or a spherical harmonic. Spectral decompositions have long been used in analyzing predictability on different scales, e.g., [5,[12][13][14] and also have been used for various applications such as to identify predictability of different types of convectively coupled equatorial waves (CCEWs) [15][16][17][18][19][20][21]. As a second approach, one can use a regional spatial average to identify a particular spatial scale, using an approach as in Figure 1 but for space instead of time.…”