Project description
Robust climate mitigation strategies against disruptive uncertainties
Climate change strategies need to be resilient to disruptive scientific and political uncertainties. Mathematical models can help identify robust courses of action, but computational and epistemic constraints limit their predictive capacity. The EU-funded EUNICE project aims to quantify and analyse uncertainties in the stabilisation pathways consistent with climate stabilisation. Researchers will use machine learning and simulations to explore a vast range of scenarios extending into the deep future. EUNICE will help identify robust strategies to reduce emissions and deal with abrupt climate change, reconciling long-term predictions with the fast-changing political and technological landscape of climate policy.
Objective
Mathematical models have become central tools in global environmental assessments. To serve society well, climate change stabilization assessments need to capture the uncertainties of the deep future, be statistically sound and track near-term disruptions. Up to now, conceptual, computational and data constraints have limited the quantification of uncertainties of climate stabilization pathways to a narrow set, focused on the current century. The statistical interpretation of scenarios generated by multi-model ensembles is problematic due to availability biases and model dependencies. Scenario plausibility assessments are scant. Simplified, single-objective decision criteria frameworks are used to translate decarbonization uncertainties into decision rules whose understanding is not validated.
EUNICE aims to transform the methodological and experimental foundations of model-based climate assessments through quantification and debiasing of uncertainties in climate stabilization pathways. Our approach is threefold: construct, consolidate and convert. We first apply simulation and statistical methods for extending scenarios into the deep future (beyond the current century and status quo), quantifying and attributing deep uncertainties. We consolidate model ensembles through machine learning and human ingenuity to eliminate statistical biases, pin down near-term correlates of long-term targets, and identify early signals of scenario plausibility through prediction polls. Finally, we use decision-theoretic methods to convert model-generated maps of the future into resilient recommendations and experimentally test how to communicate them effectively. By advancing the state of the art in mathematical modelling, statistics, and behavioural decision-making, we strengthen the scientific basis of climate assessments, such as those of the IPCC. The approach and insights of EUNICE can be applied to other high-stakes environmental, social and technological evaluations.
Fields of science
Keywords
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
20133 Milano
Italy