The Decadal Climate Prediction Project (DCPP) contribution to CMIP6

George J. Boer, Douglas M. Smith, Christophe Cassou, Francisco Doblas-Reyes, Gokhan Danabasoglu, Ben Kirtman, Yochanan Kushnir, Masahide Kimoto, Gerald A. Meehl, Rym Msadek, Wolfgang A. Mueller, Karl E. Taylor, Francis Zwiers, Michel Rixen, Yohan Ruprich-Robert, Rosie Eade

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)3751-3777
Number of pages27
JournalGeoscientific Model Development
Volume9
Issue number10
DOIs
StatePublished - Oct 25 2016

Fingerprint

climate prediction
Climate
Prediction
Forecast
timescale
Time Scales
Multi-model
climate
prediction
Annual
hiatus
project
Coupled Model
volcano
Volcanoes
Climate Change
Predictability
Initialization
Climate change
Forecasting

ASJC Scopus subject areas

  • Modeling and Simulation
  • Earth and Planetary Sciences(all)

Cite this

Boer, G. J., Smith, D. M., Cassou, C., Doblas-Reyes, F., Danabasoglu, G., Kirtman, B., ... Eade, R. (2016). The Decadal Climate Prediction Project (DCPP) contribution to CMIP6. Geoscientific Model Development, 9(10), 3751-3777. https://doi.org/10.5194/gmd-9-3751-2016

The Decadal Climate Prediction Project (DCPP) contribution to CMIP6. / 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.

In: Geoscientific Model Development, Vol. 9, No. 10, 25.10.2016, p. 3751-3777.

Research output: Contribution to journalArticle

Boer, GJ, Smith, DM, Cassou, C, Doblas-Reyes, F, Danabasoglu, G, Kirtman, B, Kushnir, Y, Kimoto, M, Meehl, GA, Msadek, R, Mueller, WA, Taylor, KE, Zwiers, F, Rixen, M, Ruprich-Robert, Y & Eade, R 2016, 'The Decadal Climate Prediction Project (DCPP) contribution to CMIP6', Geoscientific Model Development, vol. 9, no. 10, pp. 3751-3777. https://doi.org/10.5194/gmd-9-3751-2016
Boer GJ, Smith DM, Cassou C, Doblas-Reyes F, Danabasoglu G, Kirtman B et al. The Decadal Climate Prediction Project (DCPP) contribution to CMIP6. Geoscientific Model Development. 2016 Oct 25;9(10):3751-3777. 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 ; Eade, Rosie. / The Decadal Climate Prediction Project (DCPP) contribution to CMIP6. In: Geoscientific Model Development. 2016 ; Vol. 9, No. 10. pp. 3751-3777.
@article{7025901cdd3b4565866f2a7e4845308c,
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{"}, 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.",
author = "Boer, {George J.} and Smith, {Douglas M.} and Christophe Cassou and Francisco Doblas-Reyes and Gokhan Danabasoglu and Ben Kirtman and Yochanan Kushnir and Masahide Kimoto and Meehl, {Gerald A.} and Rym Msadek and Mueller, {Wolfgang A.} and Taylor, {Karl E.} and Francis Zwiers and Michel Rixen and Yohan Ruprich-Robert and Rosie Eade",
year = "2016",
month = "10",
day = "25",
doi = "10.5194/gmd-9-3751-2016",
language = "English (US)",
volume = "9",
pages = "3751--3777",
journal = "Geoscientific Model Development",
issn = "1991-959X",
publisher = "Copernicus Gesellschaft mbH",
number = "10",

}

TY - JOUR

T1 - The Decadal Climate Prediction Project (DCPP) contribution to CMIP6

AU - Boer, George J.

AU - Smith, Douglas M.

AU - Cassou, Christophe

AU - Doblas-Reyes, Francisco

AU - Danabasoglu, Gokhan

AU - Kirtman, Ben

AU - Kushnir, Yochanan

AU - Kimoto, Masahide

AU - Meehl, Gerald A.

AU - Msadek, Rym

AU - Mueller, Wolfgang A.

AU - Taylor, Karl E.

AU - Zwiers, Francis

AU - Rixen, Michel

AU - Ruprich-Robert, Yohan

AU - Eade, Rosie

PY - 2016/10/25

Y1 - 2016/10/25

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84993929563&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84993929563&partnerID=8YFLogxK

U2 - 10.5194/gmd-9-3751-2016

DO - 10.5194/gmd-9-3751-2016

M3 - Article

AN - SCOPUS:84993929563

VL - 9

SP - 3751

EP - 3777

JO - Geoscientific Model Development

JF - Geoscientific Model Development

SN - 1991-959X

IS - 10

ER -