Climate (Oct 2022)

The Value-Add of Tailored Seasonal Forecast Information for Industry Decision Making

  • Clare Mary Goodess,
  • Alberto Troccoli,
  • Nicholas Vasilakos,
  • Stephen Dorling,
  • Edward Steele,
  • Jessica D. Amies,
  • Hannah Brown,
  • Katie Chowienczyk,
  • Emma Dyer,
  • Marco Formenton,
  • Antonio M. Nicolosi,
  • Elena Calcagni,
  • Valentina Cavedon,
  • Victor Estella Perez,
  • Gertie Geertsema,
  • Folmer Krikken,
  • Kristian Lautrup Nielsen,
  • Marcello Petitta,
  • José Vidal,
  • Martijn De Ruiter,
  • Ian Savage,
  • Jon Upton

DOI
https://doi.org/10.3390/cli10100152
Journal volume & issue
Vol. 10, no. 10
p. 152

Abstract

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There is a growing need for more systematic, robust, and comprehensive information on the value-add of climate services from both the demand and supply sides. There is a shortage of published value-add assessments that focus on the decision-making context, involve participatory or co-evaluation approaches, avoid over-simplification, and address both the quantitative (e.g., economic) and qualitative (e.g., social) values of climate services. The 12 case studies that formed the basis of the European Union-funded SECLI-FIRM project were co-designed by industrial and research partners in order to address these gaps while focusing on the use of tailored sub-seasonal and seasonal forecasts in the energy and water industries. For eight of these case studies, it was possible to apply quantitative economic valuation methods: econometric modelling was used in five case studies while three case studies used a cost/loss (relative economic value) analysis and avoided costs. The case studies illustrated the challenges in attempting to produce quantitative estimates of the economic value-add of these forecasts. At the same time, many of them highlighted how practical value for users—transcending the actual economic value—can be enhanced; for example, through the provision of climate services as an extension to their current use of weather forecasts and with the visualisation tailored towards the user.

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