Periodic Reporting for period 3 - SECLI-FIRM (The Added Value of Seasonal Climate Forecasts for Integrated Risk Management Decisions )
Reporting period: 2021-02-01 to 2021-10-31
The Added Value of Seasonal Climate Forecasts for Integrated Risk Management (SECLI-FIRM) is an EU H2020 funded 45-month project launched in February 2018 and ended in October 2021. The immediate aim of SECLI-FIRM was to offer accurate seasonal climate forecast to help reduce risk as well as cost. This was done by assessing the impact of climate information on operational planning and portfolio management and by quantifying the value-add provided by seasonal forecasts which were calibrated, evaluated and tailored for each specific application. In turn, the optimal use of these forecasts should lead to a better supply-demand balance in the energy and water sectors, therefore positively contributing to both climate change adaptation (forecasts represent soft adaptation measures) and mitigation.
The practical application of seasonal forecasting and the benefits this brings to industry end-users has been demonstrated through nine industry case studies for Europe and South America (especially Colombia). The case studies have been co-designed by industrial and research partners. Further information, including a 4/5-page brochure for each of the case study, is available on the project website: www.secli-firm.eu
The successful implementation of the case study was made possible by the close engagement of the industrial partners, ENEL and Alperia, as well as a number of external ‘committed stakeholders’ from the energy and water industries, including Celsia (Colombia, Case Study 5), TenneT (Netherlands, Case Study 6), National Grid (UK, Case Study 7), Shell (UK, Case Study 8) and Thames Water (UK, Case Study 9). These stakeholders pro-actively contributed to the Case Studies, which are the building blocks and final objectives of the project. The involvement of all these globally-leading industry experts also ensured that the route to impact for the SECLI-FIRM solutions is optimised.
The SECLI-FIRM project objectives were:
1. Characterization of the end-user requirements and associated decision-making issues for a wide range of stakeholders.
2. Optimization of the climate prediction performance targeted at the case studies considered.
3. Quantification of the value-adding of climate forecast to decision-making.
4. Real-time application of seasonal forecasts for integration within the industries’ decision processes.
The practical application of seasonal forecasting and the benefits this brings to industry end-users has been demonstrated through nine industry case studies for Europe and South America (especially Colombia). The case studies have been co-designed by industrial and research partners. Further information, including a 4/5-page brochure for each of the case study, is available on the project website: www.secli-firm.eu
The successful implementation of the case study was made possible by the close engagement of the industrial partners, ENEL and Alperia, as well as a number of external ‘committed stakeholders’ from the energy and water industries, including Celsia (Colombia, Case Study 5), TenneT (Netherlands, Case Study 6), National Grid (UK, Case Study 7), Shell (UK, Case Study 8) and Thames Water (UK, Case Study 9). These stakeholders pro-actively contributed to the Case Studies, which are the building blocks and final objectives of the project. The involvement of all these globally-leading industry experts also ensured that the route to impact for the SECLI-FIRM solutions is optimised.
The SECLI-FIRM project objectives were:
1. Characterization of the end-user requirements and associated decision-making issues for a wide range of stakeholders.
2. Optimization of the climate prediction performance targeted at the case studies considered.
3. Quantification of the value-adding of climate forecast to decision-making.
4. Real-time application of seasonal forecasts for integration within the industries’ decision processes.
SECLI-FIRM has developed and refined the approach taken by the nine case study, by identifying and addressing users’ requirements. With the ultimate aim to assess the value of including seasonal forecasts in the decision making of the nine case studies, to augment or replace climatological information, appropriate evaluation methods were designed for each case study.
A key component of the refinement of the case studies was the definition and use of decision trees as a visualisation tool and for engagement with the case-study teams. These have been used at different levels:
• To provide context for the systematic and consistent visualisation of key decisions, especially climate-driven ones, by case-study leads.
• To identify the points/nodes in the decision trees where SECLI-FIRM partners can provide and use improved climate information and where the value of adopting this information can be assessed.
• To assess how best to embed the probabilistic format of seasonal forecasts in current decision-making processes.
SECLI-FIRM developed a purpose-built comprehensive dataset with more than ten independent seasonal forecasts systems on a platform which allowed partners to have a common and unique workplace where they could closely interact. This dataset greatly assisted with the scientific developments in SECLI-FIRM.
Scientific research in SECLI-FIRM explored a variety of approaches for extracting the most signal from the forecast for each case study. Approaches range from tailored downscaling, to the use of weather regimes, to the exploitation of large-scale climatic drivers (such as the North Atlantic Oscillation) to the tuning of multi-model combinations, including by means of machine learning models. Aside from having extensively documented these results in public project reports, several of these have been, or are being, published in journals such as the MDPI Climate Special Issue "Seasonal Forecasting Climate Services for the Energy Industry".
Some of the conclusions from this work are:
• The optimal selection of models is different depending on the region/phenomenon. However, by using independence information it is possible to reduce the number of models and data to produce the optimized forecasts.
• Downscaling is one of the most requested features of seasonal forecasts. To get local forecasts is best to downscale the large scale of the dynamical model rather the local features. Clearly, availability of observations is key to optimising local forecasts
• More advanced post-processing methods (e.g. using non-linear statistical models) generally require more data / statistics than the historical records typically available
• "Classic" daily weather types aren't very useful for seasonal forecasts (due to a lack of skill - they often work in "perfect forecast" conditions). However, a more tailored approach was shown to be useful
• The average skill of weather regimes forecast is not good enough to improve the downscaling in general. But in case of good predictability (window of opportunity), it improves its quality for wind and precipitation in mountainous areas
The co-development of the individual Case Studies into trial climate services is another key learning of SECLI-FIRM towards the development of the services, including early conversations through to the presentation of the forecasts in forms useful to the users, to the write up of deliverables. The use of diverse approaches to communicating the seasonal forecasts, from the sharing of plots with a commentary to online visualisation tools (e.g. Teal, https://tealtool.earth) is a strength of the SECLI-FIRM trial climate services.
SECLI-FIRM has shown how tailoring seasonal forecasts to specific case studies can enhance its predictive power. Crucially, the interaction, or co-production, at the core of SECLI-FIRM activities, have given users sufficient confidence in the benefit of using seasonal forecasts, particularly as a long-term strategy, as in the case of ENEL.
A key component of the refinement of the case studies was the definition and use of decision trees as a visualisation tool and for engagement with the case-study teams. These have been used at different levels:
• To provide context for the systematic and consistent visualisation of key decisions, especially climate-driven ones, by case-study leads.
• To identify the points/nodes in the decision trees where SECLI-FIRM partners can provide and use improved climate information and where the value of adopting this information can be assessed.
• To assess how best to embed the probabilistic format of seasonal forecasts in current decision-making processes.
SECLI-FIRM developed a purpose-built comprehensive dataset with more than ten independent seasonal forecasts systems on a platform which allowed partners to have a common and unique workplace where they could closely interact. This dataset greatly assisted with the scientific developments in SECLI-FIRM.
Scientific research in SECLI-FIRM explored a variety of approaches for extracting the most signal from the forecast for each case study. Approaches range from tailored downscaling, to the use of weather regimes, to the exploitation of large-scale climatic drivers (such as the North Atlantic Oscillation) to the tuning of multi-model combinations, including by means of machine learning models. Aside from having extensively documented these results in public project reports, several of these have been, or are being, published in journals such as the MDPI Climate Special Issue "Seasonal Forecasting Climate Services for the Energy Industry".
Some of the conclusions from this work are:
• The optimal selection of models is different depending on the region/phenomenon. However, by using independence information it is possible to reduce the number of models and data to produce the optimized forecasts.
• Downscaling is one of the most requested features of seasonal forecasts. To get local forecasts is best to downscale the large scale of the dynamical model rather the local features. Clearly, availability of observations is key to optimising local forecasts
• More advanced post-processing methods (e.g. using non-linear statistical models) generally require more data / statistics than the historical records typically available
• "Classic" daily weather types aren't very useful for seasonal forecasts (due to a lack of skill - they often work in "perfect forecast" conditions). However, a more tailored approach was shown to be useful
• The average skill of weather regimes forecast is not good enough to improve the downscaling in general. But in case of good predictability (window of opportunity), it improves its quality for wind and precipitation in mountainous areas
The co-development of the individual Case Studies into trial climate services is another key learning of SECLI-FIRM towards the development of the services, including early conversations through to the presentation of the forecasts in forms useful to the users, to the write up of deliverables. The use of diverse approaches to communicating the seasonal forecasts, from the sharing of plots with a commentary to online visualisation tools (e.g. Teal, https://tealtool.earth) is a strength of the SECLI-FIRM trial climate services.
SECLI-FIRM has shown how tailoring seasonal forecasts to specific case studies can enhance its predictive power. Crucially, the interaction, or co-production, at the core of SECLI-FIRM activities, have given users sufficient confidence in the benefit of using seasonal forecasts, particularly as a long-term strategy, as in the case of ENEL.
Some of the realized and expected SECLI-FIRM impact include:
• Strong advances in the two-way interaction (science-industry), which is critical in co-production; industry Case Study users gained strong insights into how seasonal forecasts can become an integratal part of their decision-making process;
• Development of a variety of advanced seasonal forecast processing, which are already utilised in practice (e.g. multi-model combination for Teal climate service), or are object of further investigations (e.g. exploration of novel approaches based on weather patterns using SECLI-FIRM results as a benchmark).
• Commercial exploitation of trial climate service Teal, including some of the scientific results about the use of seasonal forecasting as well as co-development learnings, via the establishment of a start-up company, Inside Climate Service srl
• Strong participation in the SECLI-FIRM Summer School, with overwhelming positive feedback from students, and buy in from several non-partner organisations as well as consistent users’ interest in SECLI-FIRM run webinars, stakeholder workshop, final conference.
• Strong advances in the two-way interaction (science-industry), which is critical in co-production; industry Case Study users gained strong insights into how seasonal forecasts can become an integratal part of their decision-making process;
• Development of a variety of advanced seasonal forecast processing, which are already utilised in practice (e.g. multi-model combination for Teal climate service), or are object of further investigations (e.g. exploration of novel approaches based on weather patterns using SECLI-FIRM results as a benchmark).
• Commercial exploitation of trial climate service Teal, including some of the scientific results about the use of seasonal forecasting as well as co-development learnings, via the establishment of a start-up company, Inside Climate Service srl
• Strong participation in the SECLI-FIRM Summer School, with overwhelming positive feedback from students, and buy in from several non-partner organisations as well as consistent users’ interest in SECLI-FIRM run webinars, stakeholder workshop, final conference.