The forecast skill of numerical weather prediction (NWP) models and the intrinsic predictability can be different among weather regimes. Here, we examine the predictability of distinct Pacific‐North American weather regimes during extended boreal summer. The four identified weather regimes include Pacific trough, Arctic low, Arctic high, and Alaskan ridge. The medium range forecast skill of these regimes is quantified in the ECMWF and the National Centers for Environmental Prediction models from the TIGGE project. Based on anomaly correlation coefficient, persistence, and transition frequency, the highest forecast skill is consistently found for the Arctic high regime. Based on the instantaneous local dimension and persistence from a dynamical systems analysis, the Arctic high regime has the highest intrinsic predictability. The analysis also suggests that overall, the Pacific trough regime has the lowest intrinsic predictability. These findings are consistent with the forecast skills of the NWP models, and highlight the link between prediction skill and intrinsic predictability.
Weather regimes defined through cluster analysis concisely categorize the anomalous regional circulation pattern on any given day. Owing to their persistence and low dimensionality, regimes are increasingly used in subseasonal-to-seasonal prediction and in analysis of climate variability and change. However, a limitation of existing regime classifications for North America is their seasonal dependence, with most existing studies defining regimes for winter only. Here, we normalize the seasonal cycle in daily geopotential height variance and use empirical orthogonal function analysis combined with
Weather regimes provide a simple way of classifying daily large-scale regional weather patterns into a few predefined types. Existing methods usually define regimes for a specific season (typically winter), which limits their use, or provides only a minimal assessment of their robustness. In this study, we objectively quantify four weather regimes for use year-round over North America, while we classify near-normal conditions as No Regime. The four regimes represent persistent large-scale weather types that last for about a week and occasionally much longer. Our new classification can be applied to subseasonal-to-seasonal forecasts and climate model output to diagnose recurrent weather types across the North American continent.
- NSF-PAR ID:
- 10462918
- Publisher / Repository:
- American Meteorological Society
- Date Published:
- Journal Name:
- Journal of Climate
- Volume:
- 36
- Issue:
- 20
- ISSN:
- 0894-8755
- Format(s):
- Medium: X Size: p. 7091-7108
- Size(s):
- ["p. 7091-7108"]
- Sponsoring Org:
- National Science Foundation
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