2017/10/27 by Gregory G. Garner, Klaus Keller, Garner, Gregory G. +1
Earth and Planetary Sciences · Environmental Science · Physics and Astronomy · #Atmospheric and Environmental Gas Dynamics #Atmospheric and Oceanic Physics (physics.ao-ph) #Climate variability and models #Data Analysis #FOS: Physical sciences #Meteorological Phenomena and Simulations #Statistics and Probability (physics.data-an) #physics.ao-ph #physics.data-an
paper · pdf · doi:10.48550/arxiv.1710.10277
19 pages, 6 figures, submitted to PLOS ONE on 27 October 2017
arxiv created 2017/10/27 · openalex publication_date 2017/10/27 · arxiv updated 2017/10/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.