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

Ben P. Kirtman, Dughong Min, Johnna M. Infanti, James L. Kinter, Daniel A. Paolino, Qin Zhang, Huug van den Dool, Suranjana Saha, Malaquias Pena Mendez, Emily Becker, Peitao Peng, Patrick Tripp (+17 others)
2014 Bulletin of The American Meteorological Society - (BAMS)  
A fter more than three decades of research into the origins of seasonal climate predictability and the development of dynamical model-based seasonal prediction systems, the continuing relatively deliberate pace of progress has inspired two notable changes in prediction strategy, largely based on multiinstitutional international collaborations. One change in strategy is the inclusion of quantitative information regarding uncertainty (i.e., probabilistic prediction) in forecasts and probabilistic
more » ... measures of forecast quality in the verifications (e.g., Palmer et al. 2000; Goddard et al. 2001; Kirtman 2003; Palmer et al. 2004 ; DeWitt 2005; Hagedorn et al. 2005; Doblas-Reyes et al. 2005; Saha et al. 2006; among many others). The other change is the recognition that a multimodel ensemble strategy is a viable approach for adequately resolving forecast uncertainty (Palmer et al. 2004; Hagedorn et al. 2005; Doblas-Reyes et al. 2005; Palmer et al. 2008), although other techniques such as perturbed physics ensembles (currently in use at the Met Office for their operational system) or stochastic physics (e.g., Berner et al. 2008) have been developed and appear to be quite promising. The first change in prediction strategy naturally follows from the fact that climate variability includes a chaotic or irregular component, and, because of this, forecasts must include a quantitative assessment of this uncertainty. More importantly, the climate prediction community now understands that the potential utility of climate forecasts is based on end-user decision support (Palmer et al. 2000; Morse et al. 2005; Challinor et al. 2005) , which requires probabilistic forecasts that include quantitative information regarding forecast uncertainty. The second change in prediction strategy follows from the first, because, given our current modeling capabilities, a multimodel strategy is a practical and relatively simple approach for quantifying forecast uncertainty due to uncertainty in model formulation, although it is likely that the uncertainty is not fully resolved. More recently, there has been a growing interest in forecast information on time scales beyond 10 days but less than a season. For example, the National Centers for Environmental Prediction Climate Prediction Center (NCEP/CPC) in the United 585 APRIL 2014 AMERICAN METEOROLOGICAL SOCIETY |
doi:10.1175/bams-d-12-00050.1 fatcat:idh5jlbesjgirot2ghqtzfhlde