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This paper examines the effectiveness of extensive hindcasts in improving climate prediction models, particularly focusing on the skill of models post systematic error correction (SEC). Analyzing monthly temperature data from 1981 to 2001, the study reveals that without SEC, skill is lacking across models. With SEC, only a few demonstrate skill, while the Climate Forecast System (CFS) shows significant improvement with extensive hindcasting, indicating model dependency on the duration and quality of hindcasts for accurate forecasting.
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How extensive (long) should hindcasts be? Huug van den Dool Climate Prediction Center, NCEP/NWS/NOAA Suranjana Saha Environmental Modeling Center, NCEP/NWS/NOAA
Explained Variance (%) Feb 1981-2001; lead 3 (Nov starts); monthly T2m (US, CD data) Explained Variance=Square of Anom Correlation SEC : Systematic Error Correction; EW: Equal Weights CFS=CFS, USA; EC=ECMWF; PLA=Max Planck Inst, Germany; METF=MeteoFrance, France; UKM=UKMetOffice; INGV=INGV, Italy, LOD=LODYC, France; CERF=CERFACS, France
Anomaly Correlation (%) Feb 1981-2001; lead 3 (Nov starts); monthly T2m (US, CD data) WITH SEC21 WITH SEC8 SEC8-SEC21 Need more years to determine the SEC where/when the inter annual standard deviation is large SEC : Systematic Error Correction
CONCLUSIONS • Without SEC (systematic error correction) there is no skill by any method (for presumably the best month: Feb) • With SEC (1st moment only), there is skill by only a few models (5 out of 8 are still useless) • MME not good when quality of models varies too much • MME3 works well, when using just three good models
CONCLUSIONS (contd) • CFS improves the most from extensive hindcasts (21 years noticeably better than 8) and has the most skill. Other models have far less skill with all years included. • Cross validation (CV) is problematic (leave 3 years out when doing 8 year based SEC?) • Need more years to determine the SEC where/when the inter annual standard deviation is large
15-member CFS reforecasts 15-member CFS reforecasts