Articles | Volume 9, issue 10
Geosci. Model Dev., 9, 3751–3777, 2016

Special issue: Coupled Model Intercomparison Project Phase 6 (CMIP6) Experimental...

Geosci. Model Dev., 9, 3751–3777, 2016

Model experiment description paper 25 Oct 2016

Model experiment description paper | 25 Oct 2016

The Decadal Climate Prediction Project (DCPP) contribution to CMIP6

George J. Boer1, Douglas M. Smith2, Christophe Cassou3, Francisco Doblas-Reyes4, Gokhan Danabasoglu5, Ben Kirtman6, Yochanan Kushnir7, Masahide Kimoto8, Gerald A. Meehl5, Rym Msadek3,12, Wolfgang A. Mueller9, Karl E. Taylor10, Francis Zwiers11, Michel Rixen13, Yohan Ruprich-Robert14, and Rosie Eade2 George J. Boer et al.
  • 1Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria BC, Canada
  • 2Met Office, Hadley Centre, Exeter, UK
  • 3Centre National de la Recherche Scientifique (CNRS)/CERFACS, CECI, UMR 5318 Toulouse, France
  • 4Institució Catalana de Recerca i Estudis Avançats (ICREA) and Barcelona Supercomputing Center (BSC-CNS), Barcelona, Spain
  • 5National Center for Atmospheric Research (NCAR), Boulder, CO, USA
  • 6Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, USA
  • 7Lamont Doherty Earth Observatory, Palisades, NY, USA
  • 8Atmosphere and Ocean Research Institute, University of Tokyo, Tokyo, Japan
  • 9Max-Planck-Institute for Meteorology, Hamburg, Germany
  • 10Program for Climate Model Diagnosis and Intercomparison (PCMDI), Lawrence Livermore National Laboratory, Livermore, CA, USA
  • 11Pacific Climate Impacts Consortium, Victoria BC, Canada
  • 12Geophysical Fluid Dynamics Laboratory/NOAA, Princeton, NJ, USA
  • 13World Climate Research Programme, Geneva, Switzerland
  • 14Atmosphere and Ocean Sciences, Princeton University, Princeton, NJ, USA

Abstract. The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability, and variability. The DCPP makes use of past experience in simulating and predicting decadal variability and forced climate change gained from the fifth Coupled Model Intercomparison Project (CMIP5) and elsewhere. It builds on recent improvements in models, in the reanalysis of climate data, in methods of initialization and ensemble generation, and in data treatment and analysis to propose an extended comprehensive decadal prediction investigation as a contribution to CMIP6 (Eyring et al., 2016) and to the WCRP Grand Challenge on Near Term Climate Prediction (Kushnir et al., 2016). The DCPP consists of three components. Component A comprises the production and analysis of an extensive archive of retrospective forecasts to be used to assess and understand historical decadal prediction skill, as a basis for improvements in all aspects of end-to-end decadal prediction, and as a basis for forecasting on annual to decadal timescales. Component B undertakes ongoing production, analysis and dissemination of experimental quasi-real-time multi-model forecasts as a basis for potential operational forecast production. Component C involves the organization and coordination of case studies of particular climate shifts and variations, both natural and naturally forced (e.g. the “hiatus”, volcanoes), including the study of the mechanisms that determine these behaviours. Groups are invited to participate in as many or as few of the components of the DCPP, each of which are separately prioritized, as are of interest to them.

The Decadal Climate Prediction Project addresses a range of scientific issues involving the ability of the climate system to be predicted on annual to decadal timescales, the skill that is currently and potentially available, the mechanisms involved in long timescale variability, and the production of forecasts of benefit to both science and society.

Short summary
The Decadal Climate Prediction Project (DCPP) investigates our ability to skilfully predict climate variations from a year to a decade ahead by means of a series of retrospective forecasts. Quasi-real-time forecasts are also produced for potential users. In addition, the DCPP investigates how perturbations such as volcanoes affect forecasts and, more broadly, what new information can be learned about the mechanisms governing climate variations by means of case studies of past climate behaviour.