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Decadal Climate Prediction: An Update from the Trenches

  • Meehl, Gerald A. National Center for Atmospheric Research,* Boulder, Colorado
  • Goddard, Lisa International Research Institute for Climate and Society, Palisades, New York
  • Boer, George Canadian Centre for Climate Modeling and Analysis, Victoria, British Columbia, Canada
  • Burgman, Robert Florida International University, Miami, Florida
  • Branstator, Grant National Center for Atmospheric Research,* Boulder, Colorado
  • Cassou, Christophe Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique, Toulouse, France
  • Corti, Susanna European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom, and Institute of Atmospheric Sciences and Climate, Italian National Research Council, Bologna, Italy
  • Danabasoglu, Gokhan National Center for Atmospheric Research,* Boulder, Colorado
  • Doblas-Reyes, Francisco Catalan Institute of Climate Sciences, Barcelona, Spain
  • Hawkins, Ed National Centre for Atmospheric Science, University of Reading, Reading, United Kingdom
  • Karspeck, Alicia National Center for Atmospheric Research,* Boulder, Colorado
  • Kimoto, Masahide University of Tokyo, Kashiwa, Japan
  • Kumar, Arun Climate Prediction Center, National Oceanographic and Atmospheric Administration/National Centers for Environmental Prediction, College Park, Maryland
  • Matei, Daniela Max Planck Institute for Meteorology, Hamburg, Germany
  • Mignot, Juliette Institut Pierre Simon Laplace des Sciences de l'Environment, Paris, France, and University of Bern, Bern, Switzerland
  • Msadek, Rym Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey
  • Navarra, Antonio Centro Euromediterraneo sui Cambiamenti Climatici, and Istituto Nazionale di Geofisica e Vulcanologia, Bologna, Italy
  • Pohlmann, Holger Max Planck Institute for Meteorology, Hamburg, Germany
  • Rienecker, Michele National Aeronautics and Space Administration Goddard Space Flight Center, Greenbelt, Maryland
  • Rosati, Tony Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey
  • Schneider, Edwin George Mason University, Fairfax, Virginia, and COLA, Calverton, Maryland
  • Smith, Doug Met Office Hadley Centre, Exeter, United Kingdom
  • Sutton, Rowan National Centre for Atmospheric Science, University of Reading, Reading, United Kingdom
  • Teng, Haiyan National Center for Atmospheric Research,* Boulder, Colorado
  • van Oldenborgh, Geert Jan Koninklijk Nederlands Meteorologisch Instituut, De Bilt, Netherlands
  • Vecchi, Gabriel Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey
  • Yeager, Stephen National Center for Atmospheric Research,* Boulder, Colorado
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  • 2014-2-1
Published in:
  • Bulletin of the American Meteorological Society. - American Meteorological Society. - 2014, vol. 95, no. 2, p. 243-267
Atmospheric Science
English This paper provides an update on research in the relatively new and fast-moving field of decadal climate prediction, and addresses the use of decadal climate predictions not only for potential users of such information but also for improving our understanding of processes in the climate system. External forcing influences the predictions throughout, but their contributions to predictive skill become dominant after most of the improved skill from initialization with observations vanishes after about 6–9 years. Recent multimodel results suggest that there is relatively more decadal predictive skill in the North Atlantic, western Pacific, and Indian Oceans than in other regions of the world oceans. Aspects of decadal variability of SSTs, like the mid-1970s shift in the Pacific, the mid-1990s shift in the northern North Atlantic and western Pacific, and the early-2000s hiatus, are better represented in initialized hindcasts compared to uninitialized simulations. There is evidence of higher skill in initialized multimodel ensemble decadal hindcasts than in single model results, with multimodel initialized predictions for near-term climate showing somewhat less global warming than uninitialized simulations. Some decadal hindcasts have shown statistically reliable predictions of surface temperature over various land and ocean regions for lead times of up to 6–9 years, but this needs to be investigated in a wider set of models. As in the early days of El Niño–Southern Oscillation (ENSO) prediction, improvements to models will reduce the need for bias adjustment, and increase the reliability, and thus usefulness, of decadal climate predictions in the future.
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  • English
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green
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https://sonar.ch/global/documents/9652
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