Thursday, 11 January 2018: 2:30 PM
406 (Hilton) (Austin, Texas)
While dynamical models rely on the forcing of climate modes of variability for predictability of subseasonal-to-seasonal climate, these models may not correctly represent teleconnection patterns in temperature and precipitation forecasts. For example, the relationship between the Arctic Oscillation (AO) and North American air temperature or precipitation is not consistently reproduced by the member models of the North American Multi-Model Ensemble (NMME) or of the Subseasonal Prediction Experiment (SubX). Statistical post-processing can lead to improvements in forecast skill. Calibration models developed from dynamical model hindcasts and observations are used to statistically correct dynamical model forecast fields. Bridging models use dynamical model forecasts of relevant climate indices (e.g., ENSO or AO) as predictors of North American temperature and precipitation. Bridging and calibration models are first developed separately and then may be combined. Specifically, we apply the Calibration, Bridging, and Merging (CBaM) framework described by Schepen et al. (2014) and Schepen et al. (2016). Seasonal and lead-dependent differences between calibration and bridging are examined. Forecast skill is assessed using attributes diagrams as well as Heidke and Brier skill scores for probabilistic forecasts of above and below normal air temperature and precipitation. We find that although calibration generally yields skillful forecasts, bridging improves upon calibrated model skill for some regions and seasons.
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