Project Objectives

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.

The aim of EUNICE (Euníkē: “eu”, good, and “níkē”, victory) is to correct misspecification and biases of ensembles of climate-energy-economy models studying climate stabilization and develop ways to validate scenarios’ insights. EUNICE seeks to transform the methodological and experimental foundations of model-based climate assessments through quantification and debiasing of uncertainties in climate stabilization pathways.

Uncertainty should not and cannot be used for weakening current action: its explorations need to be accompanied by the enhanced predictive and explanatory power of scenarios, discriminating the vast ensembles on their plausibility and ensuring they are adequately understood.

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.

The project has three main objectives resulting in three major research pillars:

  1. Construction: To expand scenarios into the deep future and quantify their uncertainties
  2. Consolidation: To eliminate scenarios’ biases and allow them tracking near-term disruptions.
  3. Conversion: To devise robust recommendations and experimentally validate them.