Advances in the Lead Time of Sahel Rainfall Prediction With the North American Multimodel Ensemble. Issue 9 (25th April 2020)

• Record Type: Journal Article
• Authors: Giannini, A.; Ali, A.; Kelley, C. P.; Lamptey, B. L.; Minoungou, B.; Ndiaye, O.
• Is Part Of: Geophysical research letters. Volume 47:Issue 9(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-04-25
• Subjects: seasonal climate prediction -- Sahel -- PRESA‐SS -- precipitation -- El Niño–Southern Oscillation -- climate services; Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1029/2020GL087341
• Languages: English
• ISSNs: 0094-8276

Abstract

Abstract: We assess the deterministic skill in seasonal climate predictions of Sahel rainfall made with the North American Multimodel Ensemble (NMME). We find that skill for a regionally averaged rainfall index is essentially the same for forecasts for the July–September target season made as early as February/March and as late as June. The two dominant influences on the climate of the Sahel, the North Atlantic and the global tropical oceans, shape this predictability. Multimodel ensemble skill hinges on the combination of skillful predictions of the El Niño–Southern Oscillation made with one model (CMC2‐CanCM4) with those of North Atlantic sea surface temperatures made with another (NASA‐GEOSS2S). Plain Language Summary: The seasonal climate outlook forum for the Sudano‐Sahelian region of West Africa convenes in middle/late April at the earliest, because the statistical models currently in use to make predictions for the July–September rainy season have little skill before then. Here we show that the North American Multimodel Ensemble (NMME), a seasonal climate prediction system based on dynamical models, predicts Sahel‐wide July–September rainfall anomalies in February/March with essentially the same skill as in June. An earlier, by 2–3 months, outlook is consequential to decisions that can exploit it for better preparedness, such as purchasing, stocking, and distributing adapted seed varieties or triggering humanitarian intervention to prevent regional food insecurity. The NMME prediction system owes its skill to the correct characterization of oceanic influence on Sahel rainfall, which is achieved by combining output from two models particularly skillful at predicting North Atlantic and tropical Pacific sea surface temperature anomalies respectively. Recognition that the oceanic source of predictability is the same for the entire region means that whether the forecast for the regional average holds in a given year, at a specific location, largely depends on the strength of oceanic influence in that year, rather than on any local condition or consideration. Key Points: The North American Multimodel Ensemble predicts July–September Sahel‐wide precipitation as skillfully in February/March as in June Skill comes from the ability to predict tropical Pacific and North Atlantic surface temperatures, attributable to two models in particular Skill in predicting the spatial average is significantly higher than the spatial average of local/grid point skill