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.
60 months from 01/12/2022 to 30/11/2027
General aims
General objectives
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 nvironmental, social and technological evaluations.
CMCC role
CMCC will provide input to two theoretical tasks of the project, namely Tasks 1A on “Normative model components and scenario logic design” and 3A on “Decision making under epistemic uncertainty”. CMCC will also provide access to the High-Performance Computing (HPC) facility. EUNICE foresees massive use of computational resources. CMCC will also be in charge of organizing one workshop, involving IPCC delegates since CMCC is the Italian focal point of the IPCC.
Activities
EUNICE is organized in a matrix of research activities. The project objectives are represented by 3 research Pillars. Each pillar is dealt with the three methodological approaches. This mapping results in 9 research tasks. In addition to the research pillars, EUNICE features the fourth pillar on project outcomes and outreach
- Pillar 1. Construction
- Task 1A. Normative model components and scenario logic design
- Task 1B. Extension of the shared socio-economic pathways (SSPs)
- Task 1C. Global sensitivity analysis on model and given-data
- Pillar 2. Consolidation
- Task 2A. Selection of informative scenario outcomes
- Task 2B. Ensemble debiasing and identification of near-term correlates
- Research Task 2C. Prediction polls of early signals of plausibility
- Pillar 3. Conversion
- Task 3A. Decision making under epistemic uncertainty
- Task 3B. Stochastic multi-objective modelling
- Task 3C. Experimental validation of scenario conversion
- Pillar 4. Project outcomes and outreach
- Task 4A. Scientific outputs of the project
- Task 4B. Outreach activities
Expected results
EUNICE aims to transform the methodological and experimental foundations of model-based climate assessments through quantification and debiasing of uncertainties in climate stabilization pathways.
By advancing the state of the art in mathematical modelling, statistics, and behavioral 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.