期刊
JOURNAL OF HYDROMETEOROLOGY
卷 17, 期 9, 页码 2511-2535出版社
AMER METEOROLOGICAL SOC
DOI: 10.1175/JHM-D-16-0027.1
关键词
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资金
- Natural Sciences and Engineering Research Council of Canada's (NSERC) Climate Change and Atmospheric Research (CCAR) program through the Canadian Sea Ice and Snow Evolution (CanSISE) Network
- NSERC Visiting Fellowship
This paper examines potential predictability (PP) and actual skill for snow water equivalent (SWE) in the Canadian Seasonal to Interannual Prediction System (CanSIPS). A significant PP is found for SWE, with potentially predictable variance over 50% of the total variance at up to a 5-month lead in mid-to high latitudes in forecasts initialized after snow onset. Much, though not all, of this PP stems from a tendency for SWE anomalies to persist through the snow season. Although the spring melt acts as a PP barrier regardless of initialization date, in some regions significant PP reemerges in the following snow season. This is due primarily to ENSO teleconnections that are modeled realistically by CanSIPS, particularly in northwestern North America. Actual skill of CanSIPS in forecasting SWE is assessed using several verification datasets. Highest skills are obtained using a blend of five such datasets, consistent with the hypothesis that skill scores are degraded by errors in the verification data as well as by forecast errors, and that observational errors can be reduced by blending multiple datasets, much as forecast errors can be reduced by averaging across different models. Actual skill for SWE is comparable to, though generally lower than, that implied by PP. This is due in part to the similar autocorrelation properties of the forecast and observed SWE anomalies, which provide skill through anomaly persistence, combined with reasonably accurate initialization of SWE by CanSIPS. Long-lead skill across snow seasons is found to be linked to ENSO, particularly in western North America, much as for PP.
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