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A perspective for advancing climate prediction services in Brazil

Coelho, C. A. S., Baker, J. C. A., Spracklen, D. V., Kubota, P. Y., de Souza, D. C., Guimarães, B. S., Figueroa, S. N., Bonatti, J. P., Sampaio, G., Klingaman, N. P. ORCID: https://orcid.org/0000-0002-2927-9303, Chevuturi, A. ORCID: https://orcid.org/0000-0003-2815-7221, Woolnough, S. J. ORCID: https://orcid.org/0000-0003-0500-8514, Hart, N., Zilli, M. and Jones, C. D. (2022) A perspective for advancing climate prediction services in Brazil. Climate Resilience and Sustainability. ISSN 2692-4587

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To link to this item DOI: 10.1002/cli2.29

Abstract/Summary

The Climate Science for Service Partnership Brazil (CSSP-Brazil) project provides Brazil and UK partners the opportunity to address important challenges faced by the climate modeling community, including the need to develop subseasonal and seasonal prediction and climate projection services. This paper provides an overview of the climate modeling and prediction research conducted through CSSP-Brazil within the context of a framework to advance climate prediction services in Brazil that includes a research-to-services (R2S) and a services-to-research (S2R) feedback pathway. The paper also highlights plans to advance scientific understanding and capability to produce beneficial climate knowledge and new products to improve climate prediction services to support decisions in various industries in Brazil. Policy-relevant outcomes from climate modeling and prediction exercises illustrated in this paper include supporting stakeholders with climate information provided from weeks to months ahead for (a) improving water management strategies for human consumption, navigation, and agricultural and electricity production; (b) defining crop variety and calendars for food production; and (c) diversifying energy production with alternatives to hydropower.

Item Type:Article
Refereed:Yes
Divisions:Science > School of Mathematical, Physical and Computational Sciences > NCAS
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:101694
Publisher:Royal Meteorological Society

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