Taking the fingerprints of climate energy models
The landmark Paris Agreement adopted in 2015 to combat climate change sparked the quest for solutions to help reduce greenhouse gas emissions. Since then, various energy models have been created to study different emission mitigation pathways. However, these models often have different parameters, structures and objectives, and their level of detail can also vary. As a result, when used to predict the outcome of specific climate policies, they can yield very different results. How can differences in the results of such energy models be reliably quantified? Researchers supported by the EU-funded ECEMF and ENGAGE projects tackled this problem by identifying “fingerprints” of energy models that outline their unique characteristics. Their study was published in the journal ‘Nature Energy’.
Shedding new light on energy model behaviour
The research team first ran a series of diagnostic tests to compare results obtained from different energy models. The idea was to express outcomes in terms of diagnostic indicators. “The idea to create model ‘fingerprints’ rather than merely comparing such indicators individually (as done in previous literature) came to me during my own analysis of the results,” reports study lead author Dr Mark Dekker of PBL Netherlands Environmental Assessment Agency, which is part of Dutch ECEMF and ENGAGE project partner Ministry of Infrastructure and Water Management. “A unique behavior of a model in one dimension sheds new light on its behavior in another, that is why we aimed to combine many dimensions into one simple framework and ultimately succeeded,” the researcher goes on to explain in a ‘Phys.org’ news item. The team decided to quantify diagnostic indicators of energy models based on five key dimensions. These dimensions are a model’s responsiveness, its proposed mitigation strategies, energy supply, energy demand, and mitigation costs and effort. “Energy models are crucial to understanding the future of our economy and climate: they give us insights in where our future energy may come from, how it is used and in levers for policy,” explains Dr Dekker. “However, differences between these models make it difficult to navigate through these insights, for both fellow scientists and policymakers. This paper marks an important step in understanding our projections on energy by mapping where each model behaves uniquely and where they agree.” The diagnostic tests were run on eight energy models that the researchers applied to 10 greenhouse gas emission mitigation scenarios focusing on Europe. Comparing the indicators allowed the team to create comprehensive “fingerprints” that uniquely represent the energy models. “The most important practical implication of our study is that people can now place modeling studies in context, especially the ones that rely on only a single model,” states Dr Dekker. “That model’s bias or behavior is now spelled out in relation to other models. For example, the model can be usually projecting more renewable energy that [sic] other models, which is important to know when reading its projections on renewables.” The work supported by ECEMF (European Climate and Energy Modelling Forum) and ENGAGE (Exploring National and Global Actions to reduce Greenhouse gas Emissions) could help improve predictions of climate policy outcomes and guide EU climate policymaking. For more information, please see: ECEMF project website ENGAGE project website
Keywords
ECEMF, ENGAGE, energy, climate, emission, greenhouse gas emissions, energy model
