When tails wag the decision: The role of distributional tails on climate impacts on decision-relevant time-scales

TL;DR

This paper highlights how ignoring the tails of climate projection distributions can underestimate risks and lead to overconfident decisions, emphasizing the importance of accounting for deep uncertainties in climate modeling.

Contribution

It demonstrates and quantifies the impact of ensemble sampling limitations on the representation of distribution tails in climate projections.

Findings

Ensemble methods may cut off distribution tails, missing low-probability high-impact events.

Neglecting distribution tails can lead to overconfident risk assessments.

Accounting for tails improves decision-making under deep uncertainty.

Abstract

Assessing and managing risks in a changing climate requires projections that account for decision-relevant uncertainties. These deep uncertainties are often approximated by ensembles of Earth-system model runs that sample only a subset of the known uncertainties. Here we demonstrate and quantify how this approach can cut off the tails of the distributions of projected climate variables such as sea-level rise. As a result, low-probability high-impact events that may drive risks can be under-represented. Neglecting the tails of this deep uncertainty may lead to overconfident projections and poor decisions when high reliabilities are important.