Debiasing deep uncertainties in climate change strategies
EUNICE seeks to develop an innovative and integrated approach to quantify, translate, and communicate in an effective manner the deep uncertainties associated with climate mitigation and adaptation strategies, advancing the foundations of mathematical modeling for climate change policy.
EUNICE combines computational and behavioral sciences to increase robustness and confidence in the quantification of profound climate uncertainties, both now and in deep time. The approach and innovations developed by EUNICE can be applied to other areas characterized by high stakes and high risks.
Learn more about the project’s mission
The three step-change approach of EUNICE
Construct
We first apply simulation and statistical methods for extending scenarios into the deep future (beyond the current century and status quo).
Consolidate
We consolidate model ensembles through machine learning and human ingenuity to eliminate statistical biases, and identify early signals of scenario plausibility.
Convert
We use decision-theoretic methods to convert model-generated maps of the future into robust recommendations and test how to communicate them effectively