SST-Forced Seasonal Simulation and Prediction Skill for Versions of the NCEP/MRF Model

SST-Forced Seasonal Simulation and Prediction Skill for Versions of the NCEP/MRF Model The feasibility of using a two-tier approach to provide guidance to operational long-lead seasonal prediction is explored. The approach includes first a forecast of global sea surface temperatures (SSTs) using a coupled general circulation model, followed by an atmospheric forecast using an atmospheric general circulation model (AGCM). For this exploration, ensembles of decade-long integrations of the AGCM driven by observed SSTs and ensembles of integrations of select cases driven by forecast SSTs have been conducted. The ability of the model in these sets of runs to reproduce observed atmospheric conditions has been evaluated with a multiparameter performance analysis.Results have identified performance and skill levels in the specified SST runs, for winters and springs over the Pacific/North America region, that are sufficient to impact operational seasonal predictions in years with major El NioSouthern Oscillation (ENSO) episodes. Further, these levels were substantially reproduced in the forecast SST runs for 1-month leads and in many instances for up to one-season leads. In fact, overall the 0- and 1-month-lead forecasts of seasonal temperature over the United States for three falls and winters with major ENSO episodes were substantially better than corresponding official forecasts. Thus, there is considerable reason to develop a dynamical component for the official seasonal forecast process. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Bulletin of the American Meteorological Society American Meteorological Society

SST-Forced Seasonal Simulation and Prediction Skill for Versions of the NCEP/MRF Model

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0477
D.O.I.
10.1175/1520-0477(1996)077<0507:SFSSAP>2.0.CO;2
Publisher site
See Article on Publisher Site

Abstract

The feasibility of using a two-tier approach to provide guidance to operational long-lead seasonal prediction is explored. The approach includes first a forecast of global sea surface temperatures (SSTs) using a coupled general circulation model, followed by an atmospheric forecast using an atmospheric general circulation model (AGCM). For this exploration, ensembles of decade-long integrations of the AGCM driven by observed SSTs and ensembles of integrations of select cases driven by forecast SSTs have been conducted. The ability of the model in these sets of runs to reproduce observed atmospheric conditions has been evaluated with a multiparameter performance analysis.Results have identified performance and skill levels in the specified SST runs, for winters and springs over the Pacific/North America region, that are sufficient to impact operational seasonal predictions in years with major El NioSouthern Oscillation (ENSO) episodes. Further, these levels were substantially reproduced in the forecast SST runs for 1-month leads and in many instances for up to one-season leads. In fact, overall the 0- and 1-month-lead forecasts of seasonal temperature over the United States for three falls and winters with major ENSO episodes were substantially better than corresponding official forecasts. Thus, there is considerable reason to develop a dynamical component for the official seasonal forecast process.

Journal

Bulletin of the American Meteorological SocietyAmerican Meteorological Society

Published: Mar 5, 1996

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