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Pandora Hope
,
Mei Zhao
,
S. Abhik
,
Gen Tolhurst
,
Roseanna C. McKay
,
Surendra P. Rauniyar
,
Lynette Bettio
,
Avijeet Ramchurn
,
Eun-Pa Lim
,
Acacia S. Pepler
,
Tim Cowan
, and
Andrew B. Watkins
Open access
Sally L. Lavender
,
Tim Cowan
,
Matthew Hawcroft
,
Matthew C. Wheeler
,
Chelsea Jarvis
,
David Cobon
,
Hanh Nguyen
,
Debra Hudson
,
S. Sharmila
,
Andrew G. Marshall
,
Catherine de Burgh-Day
,
Sean Milton
,
Alison Stirling
,
Oscar Alves
, and
Harry H. Hendon

Abstract

Since 2017, the Northern Australia Climate Program (NACP) has assisted the pastoral grazing industry to better manage drought risk and climate variability. The NACP funding is sourced from the beef cattle industry, government, and academia, representing the program’s broad range of aims and target beneficiaries. The program funds scientists in the United Kingdom and Australia, in addition to extension advisers called “Climate Mates” across a region that supports 15 million head of cattle. Many Climate Mates are employed in the cattle sector and have existing relationships in their communities and capacity to meaningfully engage with the program’s intended beneficiaries—red meat producers. The NACP is a prime example of a successful end-to-end program, integrating climate model improvements (research) with tailored forecast products (development), through to direct stakeholder engagement (extension), on-ground application of technologies (adoption), and improvement in industry and community resilience (impact). The climate information needs of stakeholders also feed back to the research and development components, ensuring the scientific research directly addresses end-user requirements. For any scientific research program, ensuring that research output has measurable real-world impact represents a key challenge. This is more difficult in cases where the scientific research is several steps away from the customer’s needs. This paper gives an overview of the NACP and research highlights, discussing how the end-to-end framework could be adapted and applied in other regions and industries. It seeks to provide a roadmap for other groups to follow to produce more targeted research with identifiable real-world benefits.

Free access
Daniela I. V. Domeisen
,
Christopher J. White
,
Hilla Afargan-Gerstman
,
Ángel G. Muñoz
,
Matthew A. Janiga
,
Frédéric Vitart
,
C. Ole Wulff
,
Salomé Antoine
,
Constantin Ardilouze
,
Lauriane Batté
,
Hannah C. Bloomfield
,
David J. Brayshaw
,
Suzana J. Camargo
,
Andrew Charlton-Pérez
,
Dan Collins
,
Tim Cowan
,
Maria del Mar Chaves
,
Laura Ferranti
,
Rosario Gómez
,
Paula L. M. González
,
Carmen González Romero
,
Johnna M. Infanti
,
Stelios Karozis
,
Hera Kim
,
Erik W. Kolstad
,
Emerson LaJoie
,
Llorenç Lledó
,
Linus Magnusson
,
Piero Malguzzi
,
Andrea Manrique-Suñén
,
Daniele Mastrangelo
,
Stefano Materia
,
Hanoi Medina
,
Lluís Palma
,
Luis E. Pineda
,
Athanasios Sfetsos
,
Seok-Woo Son
,
Albert Soret
,
Sarah Strazzo
, and
Di Tian

Abstract

Extreme weather events have devastating impacts on human health, economic activities, ecosystems, and infrastructure. It is therefore crucial to anticipate extremes and their impacts to allow for preparedness and emergency measures. There is indeed potential for probabilistic subseasonal prediction on time scales of several weeks for many extreme events. Here we provide an overview of subseasonal predictability for case studies of some of the most prominent extreme events across the globe using the ECMWF S2S prediction system: heatwaves, cold spells, heavy precipitation events, and tropical and extratropical cyclones. The considered heatwaves exhibit predictability on time scales of 3–4 weeks, while this time scale is 2–3 weeks for cold spells. Precipitation extremes are the least predictable among the considered case studies. ­Tropical cyclones, on the other hand, can exhibit probabilistic predictability on time scales of up to 3 weeks, which in the presented cases was aided by remote precursors such as the Madden–Julian oscillation. For extratropical cyclones, lead times are found to be shorter. These case studies clearly illustrate the potential for event-dependent advance warnings for a wide range of extreme events. The subseasonal predictability of extreme events demonstrated here allows for an extension of warning horizons, provides advance information to impact modelers, and informs communities and stakeholders affected by the impacts of extreme weather events.

Full access