Publication: Statistical-observational analysis of skillful oceanic predictors of heavy daily precipitation events in the Sahel
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2020-06
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MDPI AG
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Abstract
In this paper, the sea surface temperature (SST) based statistical seasonal forecast model (S4CAST) is utilized to examine the spatial and temporal prediction skill of Sahel heavy and extreme daily precipitation events. As in previous studies, S4CAST points out the Mediterranean Sea and El Niño Southern Oscillation (ENSO) as the main drivers of Sahel heavy/extreme daily rainfall variability at interannual timescales (period 1982–2015). Overall, the Mediterranean Sea emerges as a seasonal short-term predictor of heavy daily rainfall (1 month in advance), while ENSO returns a longer forecast window (up to 3 months in advance). Regarding the spatial skill, the response of heavy daily rainfall to the Mediterranean SST forcing is significant over a widespread area of the Sahel. Contrastingly, with the ENSO forcing, the response is only significant over the southernmost Sahel area. These differences can be attributed to the distinct physical mechanisms mediating the analyzed SST-rainfall teleconnections. This paper provides fundamental elements to develop an operational statistical-seasonal forecasting system of Sahel heavy and extreme daily precipitation events.
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© 2020 by the authors. We are indebted to the Climate Hazards Group for providing the InfraRed Precipitation with Station data (CHIRPS; http://chg.geog.ucsb.edu/data/chirps/) the Met-Office Hadley Centre for the Sea Ice and Sea Surface Temperature dataset (HadISST; https://www.metoffice.gov.uk/hadobs/hadisst/) and the European Centre for Medium-Range Weather Forecasts for the ERA-Interim reanalysis (https://www.ecmwf.int/en/forecasts/ datasets/reanalysis-datasets/era-interim). We also thank the two anonymous reviewers, whose pertinent comments and suggestions have contributed to improve this manuscript. This research work was funded by the NERC/DFID Future Climate for Africa programme under the AMMA-2050 project, Grant NE/M020428/1, the EU H2020 project TRIATLAS (no. 817578) and the Spanish Ministry of Economy and Competitiveness (MINECO) project PRE4CAST (CGL2017-86415-R). Roberto Suárez-Moreno was supported by NSF award AGS-1734 760.