Dynamical Seasonal Prediction

By J. Shukla, Jeffrey L. Anderson, D. Baumhefner, Č. Branković, Yehui Chang, Eugenia Kalnay, L. Marx, Tim N. Palmer, D. Paolino, J. Ploshay and 4 more. 2000

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Scale Interactions and Atmospheric Predictability: An Updated Perspective (2004, methodology): “…used in these experiments is the NCAR Community Climate Model Version 3 (CCM3; Kiehl et al. 1998), which has well-documented capabilities as an atmospheric climate model and a medium-range forecast tool (Shukla et al. 2000; Baumhefner et al. 2003, unpublished manuscript; see also the appendix).”
Prediction of the Madden–Julian Oscillation: A Review (2018, background): “…focusing on intraseasonal prediction, with a particular emphasis on multimodel ensemble approach that has been proven to be one of the most effective ways to improve the seasonal prediction (e.g., Palmer et al. 2000, 2004; Shukla et al. 2000) andMJOprediction (Kang andKim 2010; Fu et al. 2013).”
Multimodel Simulation of Water Flow in a Field Soil Using Pedotransfer Functions (2009, methodology): “To deal with these uncertainties, multimodel prediction emerged as a popular technique in climate prediction (Barnston et al., 2003; Palmer et al., 2000, 2004; Shukla et al., 2000).”
The rationale behind the success of multi-model ensembles in seasonal forecasting — II. Calibration and combination (2005, background): “There is substantial evidence (e.g. Shukla et al., 2000) of the fact that different single models have very different performances, the ranking of model skills changing as a function of lead time, variable, region, etc.”