The North American Multimodel Ensemble: Phase-1 Seasonal-to-Interannual Prediction; Phase-2 toward Developing Intraseasonal Prediction

Ben P. Kirtman Rosenstiel School for Marine and Atmospheric Science, University of Miami, Miami, Florida

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Dughong Min Rosenstiel School for Marine and Atmospheric Science, University of Miami, Miami, Florida

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Johnna M. Infanti Rosenstiel School for Marine and Atmospheric Science, University of Miami, Miami, Florida

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James L. Kinter III Center for Ocean–Land–Atmosphere Studies, Calverton, Maryland

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Daniel A. Paolino Center for Ocean–Land–Atmosphere Studies, Calverton, Maryland

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Qin Zhang NOAA/National Centers for Environmental Prediction, Camp Springs, Maryland

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Huug van den Dool NOAA/National Centers for Environmental Prediction, Camp Springs, Maryland

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Suranjana Saha NOAA/National Centers for Environmental Prediction, Camp Springs, Maryland

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Malaquias Pena Mendez NOAA/National Centers for Environmental Prediction, Camp Springs, Maryland

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Emily Becker NOAA/National Centers for Environmental Prediction, Camp Springs, Maryland

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Peitao Peng NOAA/National Centers for Environmental Prediction, Camp Springs, Maryland

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Patrick Tripp NOAA/National Centers for Environmental Prediction, Camp Springs, Maryland

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Jin Huang NOAA/National Centers for Environmental Prediction, Camp Springs, Maryland

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David G. DeWitt International Research Institute for Climate and Society, Palisades, New York

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Michael K. Tippett International Research Institute for Climate and Society, Palisades, New York, and Center of Excellence for Climate Change Research, Department of Meteorology, King Abdulaziz University, Jeddah, Saudi Arabia

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Anthony G. Barnston International Research Institute for Climate and Society, Palisades, New York

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Shuhua Li International Research Institute for Climate and Society, Palisades, New York

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Anthony Rosati NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

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Siegfried D. Schubert NASA Goddard Space Flight Center, Greenbelt, Maryland

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Joseph Tribbia National Center for Atmospheric Research, Boulder, Colorado

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Kathleen Pegion CIRES, University of Colorado Boulder, Boulder, Colorado

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William J. Merryfield Environment Canada, Fredericton, New Brunswick, Canada

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The recent U.S. National Academies report, Assessment of Intraseasonal to Interannual Climate Prediction and Predictability, was unequivocal in recommending the need for the development of a North American Multimodel Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users.

The multimodel ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation and has proven to produce better prediction quality (on average) than any single model ensemble. This multimodel approach is the basis for several international collaborative prediction research efforts and an operational European system, and there are numerous examples of how this multimodel ensemble approach yields superior forecasts compared to any single model.

Based on two NOAA Climate Test bed (CTB) NMME workshops (18 February and 8 April 2011), a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data are readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC (www.cpc.ncep.noaa.gov/products/NMME/). Moreover, the NMME forecast is already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, and presents an overview of the multimodel forecast quality and the complementary skill associated with individual models.

*CURRENT AFFILIATION: NOAA/National Weather Service, Washington, D.C.

CORRESPONDING AUTHOR: Ben Kirtman, Rosenstiel School for Marine and Atmospheric Science, 4600 Rickenbacker Causeway, Miami, FL 33149, E-mail: bkirtman@rsmas.miami.edu

The recent U.S. National Academies report, Assessment of Intraseasonal to Interannual Climate Prediction and Predictability, was unequivocal in recommending the need for the development of a North American Multimodel Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users.

The multimodel ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation and has proven to produce better prediction quality (on average) than any single model ensemble. This multimodel approach is the basis for several international collaborative prediction research efforts and an operational European system, and there are numerous examples of how this multimodel ensemble approach yields superior forecasts compared to any single model.

Based on two NOAA Climate Test bed (CTB) NMME workshops (18 February and 8 April 2011), a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data are readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC (www.cpc.ncep.noaa.gov/products/NMME/). Moreover, the NMME forecast is already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, and presents an overview of the multimodel forecast quality and the complementary skill associated with individual models.

*CURRENT AFFILIATION: NOAA/National Weather Service, Washington, D.C.

CORRESPONDING AUTHOR: Ben Kirtman, Rosenstiel School for Marine and Atmospheric Science, 4600 Rickenbacker Causeway, Miami, FL 33149, E-mail: bkirtman@rsmas.miami.edu
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