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Title: The Decadal Climate Prediction Project (DCPP) contribution to CMIP6

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”,more » volcanoes), including the study of the mechanisms that determine these behaviours. Furthermore, 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.« less

Authors:
 [1];  [2];  [3];  [4];  [5];  [6];  [7];  [8];  [5];  [9];  [10];  [11];  [12];  [13];  [14];  [2]
  1. Canadian Centre for Climate Modelling and Analysis, Victoria, BC (Canada)
  2. Hadley Centre, Exeter (United Kingdom)
  3. French National Center for Scientific Research (CNRS), Toulouse (France)
  4. Catalan Institution for Research and Advanced Studies (ICREA) and Barcelona Supercomputing Center (BSC-CNS), Barcelona (Spain)
  5. National Center for Atmospheric Research, Boulder, CO (United States)
  6. Univ. of Miami, FL (United States). Rosenstiel School of Marine and Atmospheric Science
  7. Lamont Doherty Earth Observatory, Palisades, NY (United States)
  8. Univ. of Tokyo (Japan). Atmosphere and Ocean Research Inst.
  9. French National Center for Scientific Research (CNRS), Toulouse (France); National Oceanic and Atmospheric Administration (NOAA), Princeton, NJ (United States). Geophysical Fluid Dynamics Lab.
  10. Max Planck Inst. for Meteorology, Hamburg (Germany)
  11. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Program for Climate Model Diagnosis and Intercomparison (PCMDI)
  12. Pacific Climate Impacts Consortium, Victoria, BC (Canada)
  13. World Climate Research Programme, Geneva (Switzerland)
  14. Princeton Univ., NJ (United States). Atmospher and Ocean Sciences
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1395319
Grant/Contract Number:  
AC52-07NA27344
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Geoscientific Model Development (Online)
Additional Journal Information:
Journal Volume: 9; Journal Issue: 10; Journal ID: ISSN 1991-9603
Publisher:
European Geosciences Union
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Boer, George J., Smith, Douglas M., Cassou, Christophe, Doblas-Reyes, Francisco, Danabasoglu, Gokhan, Kirtman, Ben, Kushnir, Yochanan, Kimoto, Masahide, Meehl, Gerald A., Msadek, Rym, Mueller, Wolfgang A., Taylor, Karl E., Zwiers, Francis, Rixen, Michel, Ruprich-Robert, Yohan, and Eade, Rosie. The Decadal Climate Prediction Project (DCPP) contribution to CMIP6. United States: N. p., 2016. Web. doi:10.5194/gmd-9-3751-2016.
Boer, George J., Smith, Douglas M., Cassou, Christophe, Doblas-Reyes, Francisco, Danabasoglu, Gokhan, Kirtman, Ben, Kushnir, Yochanan, Kimoto, Masahide, Meehl, Gerald A., Msadek, Rym, Mueller, Wolfgang A., Taylor, Karl E., Zwiers, Francis, Rixen, Michel, Ruprich-Robert, Yohan, & Eade, Rosie. The Decadal Climate Prediction Project (DCPP) contribution to CMIP6. United States. https://doi.org/10.5194/gmd-9-3751-2016
Boer, George J., Smith, Douglas M., Cassou, Christophe, Doblas-Reyes, Francisco, Danabasoglu, Gokhan, Kirtman, Ben, Kushnir, Yochanan, Kimoto, Masahide, Meehl, Gerald A., Msadek, Rym, Mueller, Wolfgang A., Taylor, Karl E., Zwiers, Francis, Rixen, Michel, Ruprich-Robert, Yohan, and Eade, Rosie. Fri . "The Decadal Climate Prediction Project (DCPP) contribution to CMIP6". United States. https://doi.org/10.5194/gmd-9-3751-2016. https://www.osti.gov/servlets/purl/1395319.
@article{osti_1395319,
title = {The Decadal Climate Prediction Project (DCPP) contribution to CMIP6},
author = {Boer, George J. and Smith, Douglas M. and Cassou, Christophe and Doblas-Reyes, Francisco and Danabasoglu, Gokhan and Kirtman, Ben and Kushnir, Yochanan and Kimoto, Masahide and Meehl, Gerald A. and Msadek, Rym and Mueller, Wolfgang A. and Taylor, Karl E. and Zwiers, Francis and Rixen, Michel and Ruprich-Robert, Yohan and Eade, Rosie},
abstractNote = {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. Furthermore, 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.},
doi = {10.5194/gmd-9-3751-2016},
url = {https://www.osti.gov/biblio/1395319}, journal = {Geoscientific Model Development (Online)},
issn = {1991-9603},
number = 10,
volume = 9,
place = {United States},
year = {2016},
month = {1}
}

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