Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6

Abstract. Partitioning uncertainty in projections of future climate change into contributions from internal variability, model response uncertainty and emissions scenarios has historically relied on making assumptions about forced changes in the mean and variability. With the advent of multiple single-model initial-condition large ensembles (SMILEs), these assumptions can be scrutinized, as they allow a more robust separation between sources of uncertainty. Here, the framework from Hawkins and Sutton (2009) for uncertainty partitioning is revisited for temperature and precipitation projections using seven SMILEs and the Coupled Model Intercomparison Project CMIP5 and CMIP6 archives. The original approach is…

• UD
  U.S. Department of Energy
• SR
  Sight Research UK

  Awards: NE/N018591/1, ncas10014
• NC
  National Centre for Atmospheric Science
• EC
  European Commission
• ET
  Eidgenössische Technische Hochschule Zürich
• NE
  Natural Environment Research Council

  Awards: NE/N018591/1, ncas10014
• DO
  Division of Atmospheric and Geospace Sciences

  Award: AGS-0856145, Amendment 87