  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.
