Statistical-observational analysis of skillful oceanic predictors of heavy daily precipitation events in the Sahel
© 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/hadob...
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Online Access: | https://hdl.handle.net/20.500.14352/6537 https://doi.org/10.3390/atmos11060584 |
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ftunivcmadrid:oai:docta.ucm.es:20.500.14352/6537 2024-09-15T18:35:38+00:00 Statistical-observational analysis of skillful oceanic predictors of heavy daily precipitation events in the Sahel Diakhaté, Moussa Suárez Moreno, Roberto Gómara Cardalliaguet, Íñigo Mohino Harris, Elsa 2020-06 application/pdf https://hdl.handle.net/20.500.14352/6537 https://doi.org/10.3390/atmos11060584 eng eng MDPI AG TRIATLAS (817578) PRE4CAST (CGL2017-86415-R) AMMA-2050 (NE/M020428/1) AGS-1734 760 https://hdl.handle.net/20.500.14352/6537 2073-4433 doi:10.3390/atmos11060584 Atribución 3.0 España https://creativecommons.org/licenses/by/3.0/es/ open access 52 West-African monsoon Sea-surface temperature Interannual variability Extended reconstruction Equatorial Atlantic Tropical Pacific Rainfall SST Impact Biases Física atmosférica 2501 Ciencias de la Atmósfera journal article 2020 ftunivcmadrid https://doi.org/20.500.14352/653710.3390/atmos11060584 2024-08-02T03:34:54Z © 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. 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 ... Article in Journal/Newspaper Sea ice Docta Complutense (Universidad Complutense de Madrid - UCM) Atmosphere 11 6 584 |
institution |
Open Polar |
collection |
Docta Complutense (Universidad Complutense de Madrid - UCM) |
op_collection_id |
ftunivcmadrid |
language |
English |
topic |
52 West-African monsoon Sea-surface temperature Interannual variability Extended reconstruction Equatorial Atlantic Tropical Pacific Rainfall SST Impact Biases Física atmosférica 2501 Ciencias de la Atmósfera |
spellingShingle |
52 West-African monsoon Sea-surface temperature Interannual variability Extended reconstruction Equatorial Atlantic Tropical Pacific Rainfall SST Impact Biases Física atmosférica 2501 Ciencias de la Atmósfera Diakhaté, Moussa Suárez Moreno, Roberto Gómara Cardalliaguet, Íñigo Mohino Harris, Elsa Statistical-observational analysis of skillful oceanic predictors of heavy daily precipitation events in the Sahel |
topic_facet |
52 West-African monsoon Sea-surface temperature Interannual variability Extended reconstruction Equatorial Atlantic Tropical Pacific Rainfall SST Impact Biases Física atmosférica 2501 Ciencias de la Atmósfera |
description |
© 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. 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 ... |
format |
Article in Journal/Newspaper |
author |
Diakhaté, Moussa Suárez Moreno, Roberto Gómara Cardalliaguet, Íñigo Mohino Harris, Elsa |
author_facet |
Diakhaté, Moussa Suárez Moreno, Roberto Gómara Cardalliaguet, Íñigo Mohino Harris, Elsa |
author_sort |
Diakhaté, Moussa |
title |
Statistical-observational analysis of skillful oceanic predictors of heavy daily precipitation events in the Sahel |
title_short |
Statistical-observational analysis of skillful oceanic predictors of heavy daily precipitation events in the Sahel |
title_full |
Statistical-observational analysis of skillful oceanic predictors of heavy daily precipitation events in the Sahel |
title_fullStr |
Statistical-observational analysis of skillful oceanic predictors of heavy daily precipitation events in the Sahel |
title_full_unstemmed |
Statistical-observational analysis of skillful oceanic predictors of heavy daily precipitation events in the Sahel |
title_sort |
statistical-observational analysis of skillful oceanic predictors of heavy daily precipitation events in the sahel |
publisher |
MDPI AG |
publishDate |
2020 |
url |
https://hdl.handle.net/20.500.14352/6537 https://doi.org/10.3390/atmos11060584 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
TRIATLAS (817578) PRE4CAST (CGL2017-86415-R) AMMA-2050 (NE/M020428/1) AGS-1734 760 https://hdl.handle.net/20.500.14352/6537 2073-4433 doi:10.3390/atmos11060584 |
op_rights |
Atribución 3.0 España https://creativecommons.org/licenses/by/3.0/es/ open access |
op_doi |
https://doi.org/20.500.14352/653710.3390/atmos11060584 |
container_title |
Atmosphere |
container_volume |
11 |
container_issue |
6 |
container_start_page |
584 |
_version_ |
1810478821707612160 |