The Added Value of Seasonal Climate Forecasting for Integrated Risk Assessment (SECLI-FIRM) EU H2020 Project

The central objective of The Added Value of Seasonal Climate Forecasts for Integrated Risk Management Decisions (SECLI-FIRM, www.secli-firm.eu) European Union H2020 project (Feb 2018 – July 2021) is to demonstrate how the use of improved climate forecasts, out to several months ahead, can add practical and economic value to decision-making processes and outcomes, primarily in the energy sector, but also in the water sector. Specifically, for the energy sector, SECLI-FIRM will assess the impact on operational planning and portfolio management, such as hedging and asset optimization, thus enabling quantification of the value-add provided by seasonal forecasts which have been calibrated, evaluated and tailored for each specific application. Improvements in management decisions will ultimately lead to an improved supply-demand balance and therefore to a more efficient energy system, particularly with respect to renewable energy, with corresponding benefits for climate change mitigation.

The evaluation of the added value of the seasonal climate forecast is divided into two distinct phases: i) an objective validation and ii) an evaluation of the economic impact. The objective validation consists in a statistical comparison of the forecasted industry parameters obtained through the application of seasonal forecasts applied to existing models (e.g. econometric models), and comparing their output to when historical climatological data is instead used. In the second phase, the estimation of how the business cycle could be improved when seasonal forecasts is applied to past real cases is investigated. This will be measured mainly in terms of estimated increase of margin and reduction of the risk. This approach is being applied to nine case studies for Europe and South America. These case studies focus on recent seasons with anomalous/extreme climate conditions which have had problematic and quantifiable impacts for the energy and water industry. Crucially, the case studies are being co-designed by both industrial and research partners.

A key component of the project is the optimisation of the performance of seasonal climate forecast produced by many global dynamical models, in a multi-model approach, for predictands relevant for the specific case studies considered in SECLI-FIRM. The role of major phenomena such as the NAO, Artic Oscillation, ENSO and Indian Ocean dipole on the selected predictands is identified first. Then a model selection will try to properly represent both the prediction systems able to maximize the skill coming from teleconnections and those able to make the most of the skill coming from local processes (soil moisture, snow and vegetation). An important aspect of this work is also the development of a multi-model seasonal prediction dataset from independent sources.

This paper will present results from the first phase of this multi-million project led by the University of East Anglia (UK) in collaboration with eight European partners. The presentation will also include a discussion about stakeholder engagement activities and the potential for establishing external collaborations with the project team.