SOURCE-LINKED INTELLIGENCE

Debiasing the uncertainties of climate stabilization ensembles

CORDIS · observation · Publication date unknown

nd 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. climate change mitigation, climate emission pathways, model ensembles, uncertainty quantification, uncertainty communication, behavioural science

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recordType

award

status

SIGNED

region

EU

value

1995000

unit

EUR