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  • Author or Editor: Siegfried D. Schubert x
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Hailan Wang, Siegfried D. Schubert, Randal D. Koster, and Yehui Chang
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Siegfried D. Schubert, Richard B. Rood, and James Pfaendtner

The Data Assimilation Office at NASA's Goddard Space Flight Center is currently producing a multiyear gridded global atmospheric dataset for use in climate research, including tropospheric chemistry applications. The data, which are being made available to the scientific community, are well suited for climate research since they are produced by a fixed assimilation system designed to minimize the spinup in the hydrological cycle. By using a nonvarying system, the variability due to algorithm change is eliminated and geophysical variability can be more confidently isolated.

The analysis incorporates rawinsonde reports, satellite retrievals of geopotential thickness, cloud-motion winds, and aircraft, ship, and rocketsonde reports. At the lower boundary, the assimilating atmospheric general circulation model is constrained by the observed sea surface temperature and soil moisture derived from observed surface air temperature and precipitation fields. The available output data include all prognostic variables and a large number of diagnostic quantities such as heating rates, precipitation, surface fluxes, cloud fraction, and the height of the planetary boundary layer. These variables were chosen to assure a complete budget of the energy and moisture cycles. The assimilated data should also be useful for estimating transport by cumulus processes. The analysis increments (observation minus first guess) and the estimated analysis errors are provided to help the user assess the quality of the data. All quantities are made available every 6 h at the full resolution of the assimilating general circulation model. Selected surface quantities are made available every 3 h.

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Ben P. Kirtman, Dughong Min, Johnna M. Infanti, James L. Kinter III, Daniel A. Paolino, Qin Zhang, Huug van den Dool, Suranjana Saha, Malaquias Pena Mendez, Emily Becker, Peitao Peng, Patrick Tripp, Jin Huang, David G. DeWitt, Michael K. Tippett, Anthony G. Barnston, Shuhua Li, Anthony Rosati, Siegfried D. Schubert, Michele Rienecker, Max Suarez, Zhao E. Li, Jelena Marshak, Young-Kwon Lim, Joseph Tribbia, Kathleen Pegion, William J. Merryfield, Bertrand Denis, and Eric F. Wood

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., and in graphical format from CPC ( 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.

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