Seasonal Forecasting Climate Services for the Energy Industry

Dear Colleagues,

The energy industry is amongst the sectors increasingly impacted by climatic events such as heat waves, drought and storms. This is why the industry is seeking to mitigate its losses by making use of the latest advances in seasonal climate forecasting. In this context, the aim of climate services is to offer accurate seasonal climate forecast to help to reduce risk as well as cost. In turn, the optimal use of these forecasts should lead to a better supply–demand balance in the energy sector, therefore positively contributing to both climate change adaptation (forecasts represent soft adaptation measures) and mitigation.

Dynamical and statistical seasonal climate forecasts can bring value compared to the current use of simple climatological information. At its simplest, the latter entails using information from the past few years or considering analogous climatic situations. However, in general, the past is not a good indicator of the future, particularly when strong signals are present, such as in the case of heat waves.

A critical aspect in the uptake of climate services is the proper understanding of the requirements of the industry and how climate information can effectively and practically be used. This understanding ranges from the terminology used by the different actors to an appreciation of the decision-making process, to the co-design and co-development approaches, to the operationalisation of the service.

Several challenges still remain in order to make seasonal forecast climate services mainstream in the industry. This Special Issue therefore invites work that contributes toward the following targets:

1. Demonstrating that dynamical and/or statistical models have sufficient additional information to perform better than current benchmarks (e.g., climatology);
2. Understanding the limitations of using forecast over one or even a few years, even if they have a strong signal (e.g., a heat wave) as typically done for case studies;
3. Identifying the benefits of using multi-model forecast combinations;
4. Understanding the stages of decision making with reference to the specific role and use of climate information;
5. Determining the improvements of co-design and co-development approaches;
6. Operationalising and possibly commercialising a seasonal forecast climate service.

I/We look forward to receiving your contributions.

Prof. Dr. Alberto Troccoli
Dr. Chaofan Li
Guest Editors

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• Seasonal climate forecasts
• Energy operations and management
• Decision making under uncertainty
• Tailored forecasts
• Skill of forecasts
• Multi-model combinations
• Value of seasonal forecasts
• Climate service co-design and co-development
• Trial operational climate services