| 25 Aug 2025
Assessing the ability of the ECMWF seasonal prediction model to forecast extreme September-to-November rainfall events over Equatorial Africa
Abstract. This study investigates the predictability of rainfall over Equatorial Africa (EA) and evaluates the forecasting performance of the European Centre for Medium-Range Weather Forecasts fifth-generation seasonal forecast version 5.1 (ECMWF-SEAS5.1) for the September–November (SON) periode during 1981–2023 (43 years). The analysis considers two lead-times, focusing on initial conditions (ICs) from September and August. Regression, spatiotemporal and composite analyses are applied to highlight the relationship between extreme precipitation events over EA and the various associated atmospheric circulation drivers. The analysis reveals that ECMWF-SEAS5.1 successfully reproduces the observed annual precipitation cycle and seasonal spatial pattern of rainfall over the region for both ICs, with notably better skills for September. In addition, the model effectively captures the teleconnections between EA rainfall and tropical sea surface temperature, including the Indian Ocean dipole and El Niño-Southern Oscillation, for both ICs. Regions with highest potential predictability skills coincide with regions where the model accurately represents strong (weak) composite rainfall anomalies, associated with strong (weak) moisture flux convergence (divergence) values, although the magnitude tends to be underestimated. However, other important observed features, such as the components of the African easterly jet, are well represented by the model for the September IC, but not for August. While many atmospheric mechanisms driving precipitation in the region are well simulated, their underestimation likely explains the model’s general tendency to underestimate the magnitude of extreme rainfall events. The results of this study support efforts to improve forecast outputs in the national national weather services across the region by integrating ECMWF model outputs into operational weather bulletins.
Review of paper titled ‘Assessing the ability of the ECMWF seasonal prediction model to forecast extreme September-to-November rainfall events over Equatorial Africa’ by Nana et al.
Review by Indrani Roy
This paper focuses on rainfall predictability over Eastern Africa for September to November by exploring ECMWF-SEAS5.1 data during 1981-2023, Using regression, spatiotemporal and composite analyses, the authors studied extreme precipitation events and atmospheric circulations. Two lead times are used for initial conditions (IC) eg., September and August, while better skill is noted for September IC in terms of annual precipitation cycle and seasonal spatial pattern. Teleconnection between rainfall and ENSO, IOD are captured well for both ICs. Certain areas of underestimation are also identified. Results have implications for improved operational forecast and I recommend a revision.
Main points:
1. In Table 1, there are only two years for WY in L1. Mention that significant results are obtained using only two years. Similarly for SY, there are only four years for L1. Discuss briefly whether a lesser number of years has any influence on the figure that you showed in Fig. 8 (e-h).
Also in Fig. 7, some years could be identified as SY in models (2015 for both L0 and L1, 2002 for L1) or WY (1984 for L1,1996 for both L0 and L1, 2021 for L0 and 2022 for L1) but were not captured in the observation. Were those years included in Fig. 8 (e-h)? Discuss those. How does the inclusion and exclusion of those years affect the results and regions with significant signals?
In Table 1, did you check if ERA5 is also showing the same SY and WY as CHIRPS? If ERA5 is included in Fig. 7, some borderline years (eg. 1994) or other years could be different. Hence, caution should be taken in sampling the years of SY and WY parts. ERA5 data are used in all analyses of mechanisms.
2. As Fig.9 shows there are differences between CHIRPS and ERA5, it is better to include ERA5 in Fig.7 as well as in Table 1. You included composites of SY and WY in Fig.10 for ERA5 too, but those years are chosen using CHIRPS. However, SY and WY of CHIRPS and ERA5 may differ based on your selection criteria of the threshold. As the sampling years are very few for observation, addition or subtraction of one or two years can make a difference.
To overcome such issues, you might consider years where both CHIRPS and ERA5 identify the same SY and WYs. Thresholds of 1 SD can also be adjusted. All the results of compositing that you presented could still be similar; however, The results and discussion will be much robust.
3. Caution should be taken linking any mechanisms involving the Atlantic part. Those are not very clear in the current analyses.
Line 532- 533: No significant influence from the Atlantic Ocean is seen for SY years in observation/reanalyses or models. For WY, some influence is present, but models overestimate observation/reanalyses. Also, for ERA5 it is nominal and for CHIRPS it is not from the ‘eastern equatorial Atlantic ocean’. Mention those. In Fig.10, for WYs, the SST signals in box regions are practically missing in observation/reanalyses and L0; discuss that part. It indicates the asymmetric influence in WY compared to that from SY.
Line 569: Signal in the equatorial Atlantic for SST is not significant. Also, there is no signal there in Fig.11 (a, c, e, f).
Minor points: