European researchers, including experts from CMCC, have introduced “model fingerprints” — innovative visual tools designed to demystify intricate energy and climate models. These tools not only simplify complexities but also empower policymakers and stakeholders to navigate climate pathways with ease. This approach fosters transparency, enriches collaboration between policymakers and scientists, and elevates the credibility of climate modeling efforts.

A team of European Climate and Energy Modelling Forum (ECEMF) researchers, including researchers from CMCC, has unveiled a new method to decode intricate energy and climate models. In a recent publication in Nature Energy, they introduce “model fingerprints,” a visual tool simplifying the comprehension of complex models’ key features.

Energy models are crucial to understand emission mitigation pathways and the feasibility of climate targets. They can provide long-term scenarios of how humanity may consume and produce energy in the future. However, many differences among these models remain, making it difficult for policymakers and stakeholders to understand the pathway to climate neutrality.

To understand these differences, a team of European researchers have developed a method to decode these models using model fingerprints. The fingerprints are a visual tool which allow users to – in a single glance – obtain an overview of a model behaviour.

The researchers ran a set of extreme mitigation scenarios to fully explore the behaviour of eight models: for example, scenarios where biomass is suddenly highly limited, or electrification strongly enhanced. They measured model behaviour using diagnostic indicators across 5 categories: model responsiveness, mitigation strategies, energy supply, energy demand and mitigation costs and effort. The visual comparison of these diagnostic indicators provides the “model fingerprints”.

Project coordinator Will Usher says, “Model fingerprinting fosters transparency and collaboration between policymakers and researchers. We invite interested groups to join the model comparison exercise run by ECEMF and create their own model fingerprint.”

“Computer models are complex to grasp for the wider public, while everyone is affected by the energy transition. This paper marks a step towards understanding these models a bit better. In particular, this paper indicates that studies with only single models should always be perceived in the context of the larger model ensemble.” says lead author Mark Dekker of PBL (the Netherlands Environmental Assessment Agency).

Johannes Emmerling of RFF‐CMCC European Institute on Economics and the Environment (EIEE), and an author of the paper says: “The energy transition requires a lot of quantification of associated technologies, costs, and additional factors. Models help to simulate the large number of elements to inform governing the transitions. Yet, all models are different, and even have their own “model fingerprint”, as we show in this paper. This can allow to quickly evaluate a certain model’s prediction with its inherent characteristics and compare them with other models, in order to get a robust assessment based on multiple models.”

“We provided one of the models, the WITCH model, as a contribution to this research,” adds EIEE’s Laurent Drouet, another author. “It’s crucial to understand how different models like WITCH react under extreme scenarios. By comparing its ‘fingerprint’ with others, we can highlight its unique features, strengths, and potential areas for refinement. This collective exercise not only strengthens the credibility of modelling efforts but also fosters collaborative improvements in the broader scientific community.”

The ECEMF consortium, led by KTH Royal Institute of Technology, comprises 15 partners from 9 countries, including CMCC, IIASA, PIK, TU Wien, e-think, TNO, Fraunhofer ISI, E3M, PBL, Artelys, Comillas, TU Delft, University of Melbourne, and IOS-PIB. ECEMF has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 101022622.