The fellowship ground the use of IAMs for formulating climate policies, as they should need to be robust towards the uncertain futures. Considering the emissions scenario ensemble used in the latest Assessment Report, uncertainty is displayed by the deviation in the model outputs.
The fellowship project has shown that the sources of uncertainty could be bigger than those used displayed in the IAMs outputs. This means that deviations in IAMs outputs could be bigger than those used in the Sixth Assessment Report. (AR6) In addition, the fellowship project demonstrated that when the IAMs outputs, used in the form of scenario ensembles, become detrimental to the policy formulation, processes should be in place to bridge such knowledge gap. In addition, the project has demonstrated a need for more sophisticated tools than those currently used for analysing emissions scenarios ensemble. These tools should not be meant as purely quantitative software packages but more broadly should become inter-disciplinary platforms for modellers, stakeholders, and decision-makers.
To achieve the project scope, two deliverables were released, and a workshop organised.
In the first deliverable, a mapping of the uncertainty space of the emissions scenario ensemble against alternative lines of evidence was performed. Socio-economic inputs which are critical determinants of emissions, such as population and Gross Domestic Product (GDP) , were analysed. The analysis showed that the emissions scenarios ensemble used for AR6, focuses primarily around a “Business-As-Usual”. In addition, a critical assessment of the methods available to describe emissions scenarios ensembles was performed. The literature review highlighted that the analysis of IAMs outputs is based on simplified scenario post-processing methods, which are limited in the way they can disentangle the origin of similarities and variations in the model responses.
The second deliverable was the development of online platforms which use descriptive and quantitative tools (i.e. critical analyses of alternative lines of evidence, post-processing scenario analyses, machine learning) to characterize the emissions scenario ensemble in terms of suitability for policy formulation (auditing). These tools included dashboard platforms for non-experts, who could be more guided during the explanation of the model response differences . Among the tools delivered, more complex online platforms for experts were generated to assess the cross-dependencies between input and selected outputs .
In addition to papers, a key dissemination event was a final workshop, which brought together philosophers, modellers, economists, and other stakeholders relevant in the context of climate policy, to discuss about quantitative (trajectories) and qualitative (narratives) used to characterise uncertainty.
