The prediction of wintertime extratropical cyclone activity (ECA) on subseasonal time scales by models participating in the Subseasonal Experiment (SubX) and the Seasonal to Subseasonal Prediction (S2S) is assessed. Consistent with a previous study that investigated the S2S models, the SubX models have skillful predictions of ECA over regions from central North Pacific across North America to western North Atlantic, as well as East Asia and northern and southern part of easternNorth Atlantic at 3-4weeks lead time. SubX provides daily mean data,while S2S provides instantaneous data at 0000 UTC each day. This leads to different variance of ECA. Different S2S and SubX models have different reforecast initialization times and reforecast time periods. These factors can all lead to differences in prediction skill. To fairly compare the prediction skill between different models, we develop a novel way to evaluate the prediction of individual model across the two ensembles by comparing every model to the Climate Forecast System, version 2 (CFSv2), as CFSv2 has 6-hourly output and forecasts initialized every day. Among the S2S and SubX models, the European Centre forMedium-Range Weather Forecasts model exhibits the best prediction skill, followed by CFSv2. Our results also suggest that while the prediction skill is sensitive to forecast lead time, including forecasts up to 4 days old into the ensemble may still be useful for weeks 3-4 predictions of ECA.
Bibliographical noteFunding Information:
Acknowledgments. The authors thank three anonymous reviewers for comments that help to improve this paper. This research has been conducted as part of the NOAA MAPP S2S Prediction Task Force and supported by NOAA Grant NA16OAR4310070 while the first author was at Stony Brook University. The ERA-Interim reanalysis data, SubX data, and S2S data are cited in the reference list. The Niño-3.4 index is available from the NOAA ESRL website (https://www.esrl. noaa.gov/psd/gcos_wgsp/Timeseries/Nino34/).
© 2021 American Meteorological Society.
- Forecast verification/skill
- Intraseasonal variability