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This study examines the impact of extensive hindcasts on the skill of climate prediction models and the effectiveness of systematic error correction. Results show that without error correction, there is no skill in any method. However, when error correction is applied, certain models show improved skill. The CFS model performs the best with extensive hindcasts of 21 years, while other models have less skill. Cross-validation is problematic and further years are needed to determine the systematic error correction needed.
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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