Multimodel Ensemble ENSO Prediction with CCSM and CFS

Abstract

Results are described from a large sample of coupled ocean-atmosphere retrospective forecasts during 1982-98. The prediction system is based on the National Center for Atmospheric Research (NCAR) Community Climate System Model, version 3 (CCSM3.0), and a state-of-the-art ocean data assimilation system made available by the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory (GFDL). The retrospective forecasts are initialized in January, April, July, and November of each year, and ensembles of 6 forecasts are run for each initial month, yielding a total of 408 1-yr predictions. In generating the ensemble members, perturbations are added to the atmospheric initial state only. The skill of the prediction system is analyzed from both a deterministic and a probabilistic perspective, it is then compared to the operational NOAA Climate Forecast System (CFS), and the forecasts are combined with CFS to produce a multimodel prediction system. While the skill scores for each model are highly dependent on lead time and initialization month, the overall level of skill of the individual models is quite comparable. The multimodel combination (i.e., the unweighted average of the forecast), while not always the most skillful, is generally as skillful as the best model, using either deterministic or probabilistic skill metrics.

Decadal Climate Prediction: Opportunities and Challenges

“…This sensitivity is associated with the uncertainty in subgrid scale parameterized physics and model numerics. The recognition of the importance of this sensitivity has led to a number of efforts that have demonstrated that a multi-model ensemble strategy is the best current approach for adequately resolving forecast uncertainty and the forecast probability distribution in seasonal-tointerannual predictions ( [65], [66], [67], [68], [69]). Another recently proposed methodology is to use stochastic-dynamic parameterization techniques which perturb parameterizations in such a way as to improve on the benefits of a multi-model ensemble by using a single model [70].…”

Weighting of NMME temperature and precipitation forecasts across Europe

“…The NMME is based on the recognition that multimodel ensemble approaches generate better forecasts than any single model ensemble (e.g., Doblas-Reyes et al, 2005, Kirtman and Min, 2009). …”

Nonlinear dynamics approach to the predictability of the Cane–Zebiak coupled ocean–atmosphere model

“…The fundamental question regarding the predictability limits of the coupled ocean-atmosphere system has not been well established yet, although recent coupled models are able to forecast phenomena like El Niño-Southern Oscillation (ENSO) up to about one year in advance (Kirtman and Min, 2009). The atmospheric predictability has been extensively investigated in the past, and classical predictability studies (Lorenz, 1965(Lorenz, , 1969Charney et al, 1966;Smagorinsky, 1969;Shukla, 1985) have established that the instantaneous state of the atmosphere cannot be predicted beyond a few weeks.…”