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An Assessment of the CFS real-time forecasts for 2005-2008

An Assessment of the CFS real-time forecasts for 2005-2008. Wanqiu Wang, Mingyue Chen, and Arun Kumar CPC/NCEP/NOAA. Outline. CFS forecast skill and comparison with hindcast Comparison with potential predictability based on AMIP simulations Systematic errors in the forecast - Cold summers

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An Assessment of the CFS real-time forecasts for 2005-2008

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  1. An Assessment of the CFS real-time forecasts for 2005-2008 Wanqiu Wang, Mingyue Chen, and Arun Kumar CPC/NCEP/NOAA

  2. Outline • CFS forecast skill and comparison with hindcast • Comparison with potential predictability based on AMIP simulations • Systematic errors in the forecast • - Cold summers • - Mean bias

  3. 1. CFS forecast skill • SST forecast

  4. Seasonal SST indices Nino34 • Persists and amplifies existing anomalies • Delayed transition of ENSO phases at longer lead-time DMI • More realistic DMI for 2007 & 2006 • Bad forecast for 2005 & 2008 MDR • Amplitude too weak Nino34 DMI MDR

  5. SST temporal correlation 2005-2008 forecast 1981-2004 hindcast • Better overall skill over the globe • Lower skill tropical eastern Pacific at longer lead-time

  6. SST temporal correlation Why is global forecast skill better? Global • Shorter lead time • Larger ensemble size • Better initial conditions • Long-term trend Nino3.4 Why is Nino3.4 forecast skill at longer lead time not as good ?

  7. Statistics for sliding 3-year windows Correlation Heidke skill Stdv • Most of the real time forecast period is in a low predictability regime • The skill depends on amplitude of interannual variability

  8. 1. CFS forecast skill • Atmospheric fields

  9. Temporal correlation 2005-2008 forecast 1981-2004 hindcast T2M Prec Z200 • Higher Z200 skill; higher precipitation skill over land • Higher T2M skill over eastern Australia and central South America • Lower skill in over eastern Europe Russia and central North America

  10. 2. Comparison with AMIP simulations

  11. Temporal correlation 2005-2008 forecast 2005-2008 AMIP T2M Prec Z200 • Higher precipitation skill over land and in Indian Ocean • Comparable Z200 skill • Similar T2M skill, except over Russia and central North America

  12. Pattern correlation over tropical ocean 20S-20N Pacific • Higher skill compared to IO and ATL oceans • Comparable between CFS forecast and AMIP • Seasonal variation Indian Ocean • Higher skill in CFS forecast – air/sea coupling important Atlantic • Higher SST skill between FMA 2007 • Lower skill in both forecast and AMIP – low predictability

  13. Pattern correlation over N.H. land 20N-80N • Higher forecast precipitation skill • Good skill during 2007/2008 La Nino winter • Lower T2M skill during all 4 summers

  14. 3. Systemetic errors • Cold summers • Mean bias

  15. JJA T2m 2005 2006 2007 2008 1-mo-lead Forecast Observation

  16. JJA T2M and May soil moisture 2005-2008 average Obs JJA T2M CFS JJA T2M • Initial wet SM anomalies  cold T2m • Is the initial SM realistic? AMIP JJA T2M R2 May SM

  17. May soil moisture over North America 40N-60N average • Large discrepancy among analyses • R2 SM wettest in 2005-2008 compared to its own history and compared to RR and LB

  18. 2005-2008 mean bias 2-month-lead forecast • Cold T2m and SST, and negative Z200 bias • Possible causes: • Lack of increasing greenhouse gases • Lack of realistic sea ice coverage • Initial soil moisture • …

  19. Summary • Overall, SST forecast skill over the globe is higher than hindcast skill - Use of hindcast skill mask may result in a loss of useful forecast • ENSO has been in a low variability and low predictability regime during the last few years • The CFS forecast shows better precipitation skill over land compared to hindcast • The CFS produces a cold bias in northern extratropics during warm season due to wet initial soil moisture in R2 • There exists a mean cold bias over the globe during the forecast period

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