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Climate forecast skill and teleconnections on seasonal time scales over Central Africa based on the North American Multi-Model Ensemble (NMME)

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Abstract

This study examines the skill of the North American Multi-Model Ensemble (NMME) seasonal precipitation forecast and the influence of tropical sea surface temperature (SST) anomalies and their teleconnections on precipitation prediction skill over Central Africa (CA). The skill is assessed for December–February (DJF), March–May (MAM), June–August (JJA), and September–November (SON) seasons, at 0-, 3-, and 6- month lead time. Results show that for all seasons and at all lead times, models used in this study have tendency to overestimate the observed SSTs over the tropical areas. The multi-model ensemble mean (MME) generally succeeds in capturing the spatial differences in the seasonal mean climatology of precipitation and clearly determines the bi-modal and uni-modal natures of observed precipitation over CA. The El Ninõ-Southern Oscillation 3.4 index (Ninõ3.4), Indian Ocean Dipole (IOD) western pole index (IODWP), and IOD eastern pole index (IODEP) teleconnections with tropical SST are well represented by the MME at all seasons and lead times with a pattern correlation coefficient (PCC) >0.6. The quality of these teleconnections decreases when the lead time increases. The Ninõ3.4-induced precipitation’s teleconnection is better represented in MAM at all lead times, and it is found that precipitation is reinforced over northern CA during the El Ninõ years and weakened during the La Niña years. IODWP and IODEP teleconnections with CA precipitation are well represented in MAM and SON, with PCC > 0.8. The IODWP and IODEP could be a very good indicators to predict the increase or decrease of precipitation in CA during MAM and SON seasons.

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Data availability

The data sets generated during and/or analysed during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

The NMME hindcasts data were downloaded from the International Research Institute for Climate and Society (IRI) data library (http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/). The authors thank the CPC, IRI, and NCAR staff for creating, updating, and providing free access to the NMME archives. Support for Thierry C. Fotso-Nguemo was provided in part by DAAD within the framework of the climapAfrica programme with funds of the Federal Ministry of Education and Research (grant number 57516494) and the Abdus Salam International Centre for Theoretical Physics (ICTP) through the Associates Programme (2020–2025). Thanks are also expressed to the four anonymous reviewers whose comments and suggestions have substantially improved the first version of the manuscript.

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Authors and Affiliations

  1. Department of Meteorology and Climatology, Higher Institute of Agriculture, Forestry, Water and Environment, University of Ebolowa, P.O. Box 118, Ebolowa, Cameroon

    Roméo S. Tanessong

  2. Climate Change Research Laboratory (CCRL), National Institute of Cartography, P.O. Box 157, Yaounde, Cameroon

    Thierry C. Fotso-Nguemo & Zéphirin D. Yepdo

  3. Laboratory of Electric Mechatronic and Signal Processing, Department of Electric and Telecommunication Engineering, National Advanced School of Engineering, University of Yaounde 1, P.O. Box 8390, Yaounde, Cameroon

    Samuel Kaissassou

  4. Laboratory of Mechanics and Modeling of Physical Systems, Department of Physics, Faculty of Science, University of Dschang, PO Box 67, Dschang, Cameroon

    G. M. Guenang & A. J. Komkoua Mbienda

  5. Laboratory for Environmental Modelling and Atmospheric Physics (LEMAP), Department of Physics, Faculty of Science, University of Yaounde 1, P.O. Box 812, Yaounde, Cameroon

    Lucie A. Djiotang Tchotchou, Armand F. Tchinda & Derbetini A. Vondou

  6. Department of Physics, Higher Teacher Training College, University of Yaounde 1, P.O. Box 47, Yaounde, Cameroon

    Wilfried M. Pokam

  7. Climate Prediction and Application Centre for Central Africa (CAPC-AC), Department of Weather and Climate Research and Application (DWCRA), Douala, Cameroon

    Pascal M. Igri

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  1. Roméo S. Tanessong
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  2. Thierry C. Fotso-Nguemo
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Tanessong, R.S., Fotso-Nguemo, T.C., Kaissassou, S. et al. Climate forecast skill and teleconnections on seasonal time scales over Central Africa based on the North American Multi-Model Ensemble (NMME). Meteorol Atmos Phys 136, 19 (2024). https://doi.org/10.1007/s00703-024-01018-y

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