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Subseasonal to Seasonal Prediction of Wintertime Northern Hemisphere Extratropical Cyclone Activity by S2S and NMME Models

Journal Article · · Journal of Geophysical Research: Atmospheres
DOI:https://doi.org/10.1029/2019jd031252· OSTI ID:1802547
 [1];  [2];  [2];  [2];  [3]
  1. Stony Brook Univ., NY (United States); OSTI
  2. Stony Brook Univ., NY (United States)
  3. National Oceanic and Atmospheric Administration (NOAA), College Park, MD (United States). Climate Prediction Center
+ Show Author Affiliations
In this study, the prediction of wintertime extratropical cyclone activity from subseasonal to seasonal timescale in current dynamical models' reforecasts is investigated. On seasonal time scales, the North American Multi-Model Ensemble (NMME) models show skillful predictions over the eastern North Pacific, North America and the western North Atlantic with at least 5 months lead. The prediction skill is highly related to El Nino-Southern Oscillation (ENSO), as using the ENSO-related SST pattern gives rise to prediction skill with very similar spatial pattern and amplitude. On subseasonal time scales, models in the Seasonal to Sub-seasonal Prediction (S2S) dataset have skillful predictions up to 4 weeks lead over regions from the eastern North Pacific to the western Atlantic, as well as northern Europe, the eastern Atlantic and East Asia. Generally, forecast skill improves with a larger ensemble size. The subseasonal prediction skill from the Pacific to the western Atlantic is related to ENSO, and that over eastern Atlantic, Europe and East Asia are associated with stratospheric polar vortex anomalies. Current models do not show much skill from the Madden-Julian Oscillation (MJO), as the MJO impact on extratropical cyclone activity is not well captured by the models. European Centre for Medium-Range Weather Forecasts (ECMWF) model has the highest single model subseasonal prediction skill. The prediction skill in the ECMWF model is higher than its estimated potential predictability, likely because the signal-to-noise ratio is too low in the model hindcasts.
Research Organization:
State Univ. of New York (SUNY), Albany, NY (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
Grant/Contract Number:
SC0014050
OSTI ID:
1802547
Journal Information:
Journal of Geophysical Research: Atmospheres, Journal Name: Journal of Geophysical Research: Atmospheres Journal Issue: 22 Vol. 124; ISSN 2169-897X
Publisher:
American Geophysical UnionCopyright Statement
Country of Publication:
United States
Language:
English

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