Publication Abstracts
Coelho et al. 2025
Coelho, C.A.S., , F.M. de Andrade, B.S. Guimarães, P.Y. Kubota, D.C. Souza, S.N. Figueroa, A.L.O. Neves, A. Fernandes, E.S.P.R. Martins, F.C. Vasconcelos Junior, D.H. Cardoso, D.C. Sales, J.L.M. Freire, I.M. Scricco, D. Ferreira, M. Salvador, and M. Seabra, 2025: Numerical seasonal predictions in South America. In Oxford Research Encyclopedia of Climate Science. H. von Storch, Ed., Oxford University Press, doi:10.1093/acrefore/9780190228620.013.972.
Numerical seasonal predictions provide information about the expected climate conditions for the forthcoming seasons. It is common practice every month in operational centers to produce and issue numerical seasonal predictions for the following 3-month season. These predictions are relevant for various socioeconomic activities such as water resources management, agricultural planning, and hydro-electricity production, to name a few. Precipitation and near-surface air temperature are the most important variables of interest impacting these sectoral activities.
The main climate driver of seasonal precipitation anomalies over South America is El Niño-Southern Oscillation, a phenomenon that manifests through ocean-atmosphere interactions in the tropical Pacific, with remote effects observed through atmospheric teleconnections affecting tropical and southeastern South America. Another important driver of climate anomalies is the tropical Atlantic inter-hemispheric meridional sea surface temperature anomaly gradient (also referred to as the tropical Atlantic dipole). This feature modulates the position of the Intertropical Convergence Zone over the tropical Atlantic and therefore has impacts on precipitation over northern and northeastern South America. Developing and operating climate models able to reproduce these phenomena along with the associated teleconnections is key for successfully predicting the seasonal climate over South America.
Efforts have been undertaken by the Center for Weather Forecast and Climate Studies (CPTEC) of the National Institute for Space Research, in collaboration with the National Institute of Meteorology (INMET) and Ceará Institute for Meteorology and Water Resources (FUNCEME) in Brazil, and by the International Research Institute for Climate and Society (IRI) of Columbia Climate School, Columbia University in the United States, for developing the capabilities and producing skillful numerical seasonal predictions for South America.Over the years, the scientific community has recognized the importance of (a) combining ensemble predictions from different models to better sample uncertainties in initial conditions and model formulation, and (b) applying procedures for calibrating predictions using historical (past) observations and the corresponding retrospective model predictions. Activities on multi-model ensemble seasonal predictions have been led by CPTEC and IRI for producing well-calibrated predictions for South America, including seasonal precipitation forecasts and retrospective prediction performance. The research developed and predictions produced by CPTEC (in collaboration with INMET and FUNCEME) and by IRI contribute to international activities such as the Global Framework for Climate Services and Regional Climate Outlook Forums (RCOFs) organized in South America under the auspices of the World Meteorological Organization (WMO). As a WMO-designated Global Producing Centre for Seasonal Prediction, CPTEC generates seasonal prediction products with global coverage each month and makes them available, with the corresponding retrospective prediction performance products, through the WMO Lead Centre for Seasonal Prediction Multi-Model Ensemble for use by National Meteorological and Hydrological Services, Regional Climate Centres, and RCOFs. The scientific-based climate predictions produced by CPTEC, INMET, FUNCEME, and IRI support climate-sensitive decision making and response actions in agriculture, food security, disaster risk reduction, energy, health, and water management.
Export citation: [ BibTeX ] [ RIS ]
BibTeX Citation
@inbook{co03510x,
author={Coelho, C. A. S. and Robertson, A. W. and de Andrade, F. M. and Guimarães, B. S. and Kubota, P. Y. and Souza, D. C. and Figueroa, S. N. and Neves, A. L. O. and Fernandes, A. and Martins, E. S. P. R. and Vasconcelos Junior, F. C. and Cardoso, D. H. and Sales, D. C. and Freire, J. L. M. and Scricco, I. M. and Ferreira, D. and Salvador, M. and Seabra, M.},
editor={von Storch, H.},
title={Numerical seasonal predictions in South America},
booktitle={Oxford Research Encyclopedia of Climate Science},
year={2025},
publisher={Oxford University Press},
doi={10.1093/acrefore/9780190228620.013.972},
}
[ Close ]
RIS Citation
TY - CHAP ID - co03510x AU - Coelho, C. A. S. AU - Robertson, A. W. AU - de Andrade, F. M. AU - Guimarães, B. S. AU - Kubota, P. Y. AU - Souza, D. C. AU - Figueroa, S. N. AU - Neves, A. L. O. AU - Fernandes, A. AU - Martins, E. S. P. R. AU - Vasconcelos Junior, F. C. AU - Cardoso, D. H. AU - Sales, D. C. AU - Freire, J. L. M. AU - Scricco, I. M. AU - Ferreira, D. AU - Salvador, M. AU - Seabra, M. ED - von Storch, H. PY - 2025 TI - Numerical seasonal predictions in South America BT - Oxford Research Encyclopedia of Climate Science DO - 10.1093/acrefore/9780190228620.013.972 PB - Oxford University Press ER -
[ Close ]
