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An Assessment of the CFS real-time forecasts for 2005-2009

Evaluation of CFS forecasts for 2005-2009, highlighting skill and errors in predicting ENSO, SST, and atmospheric conditions. Detailed analysis of systematic biases and impact of initial conditions on forecast accuracy.

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An Assessment of the CFS real-time forecasts for 2005-2009

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  1. An Assessment of the CFS real-time forecasts for 2005-2009 Wanqiu Wang, Mingyue Chen, and Arun Kumar CPC/NCEP/NOAA

  2. Summary • CFS continues producing delayed transition between ENSO phases; PDF correction improves the forecasts, especially for those initialized after Apr 2009 (slides 6/7/8) • CFS reproduced Indian dipole mode index (DMI) variability in for 2007-2009, but failed for 2005; CFS forecast correct sign of MDR SST index but with weaker amplitude (slide 6) • The CFS produced T2m, precipitation and Z200 distributions similar to the observed for DJF 2009/2010, including the negative AO phase, but with weaker amplitude of Z200 over the NH polar region and of T2m negative anomalies in NH; For JJA 2009, forecast of T2m is reasonably good although CFS did not produce the observed precipitation and Z200 distributions (slides 9 &10) • ENSO has been in a low variability and low predictability regime during the last few years (slides 12-14) • The CFS forecast shows better precipitation skill over land compared to hindcast (slide 16) • The CFS produces a cold bias in northern extratropics during warm seasons due to wet initial soil moisture in R2, lowering T2m forecast skill T2m (slides 16-19, 21-23) • There exists a mean cold bias over the globe during the forecast period (slide 24)

  3. Relevance Diagnostics/monitoring of CFS real-time forecasts • Real-time skill against the hindcast • Long-term skill variability • Impact of initial condition • Systematic errors

  4. Outline • CFS forecast for 2009 • Skills of CFS forecasts during 2005-2009 • Systematic errors in the forecast

  5. 1. CFS forecast for 2009

  6. SST indices Nino34 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 DMI MDR

  7. See http://origin.cpc.ncep.noaa.gov/products/people/wwang/cfs_fcst/PDFcorrection.html for an explanation of the PDF correction

  8. Forecast for DJF 2009/2010 Obs CFS 0-mo lead CFS 1-mo lead AMIP • Both the CFS and AMIP simulation captured observed precipitation and Z200 anomalies in the tropics • The models also captured the observed positive Z200 anomalies corresponding to negative AO phase, but with weaker amplitude • The models reproduced observed T2m distribution, but with weaker amplitude for the negative anomalies in the northern hemisphere.

  9. Forecast for JJA 2009 Obs CFS 0-mo lead CFS 1-mo lead AMIP • CFS and AMIP simulation did not produce a reasonable distribution of the observed precipitation and Z200 anomalies • The CFS reproduced a T2m pattern similar to the observed but with wider areas of negative anomalies over the Eurasia continent; the AMIP simulation failed to produce the observed negative T2m anomalies over central North America.

  10. 2. CFS forecast skill • SST

  11. SST temporal correlation 2005-2009forecast 1981-2004 hindcast • Lower forecast skill tropical eastern Pacific at longer lead-time

  12. Nino34 SST temporal correlation (1981-2004) (2005-2009) Why is Nino3.4 forecast skill at longer lead time not as good ?

  13. Statistics for sliding 4-year windows Global mean correlation Nino34 correlation Nino34 STDV • Most of the real time forecast period is in a low predictability regime • The skill depends on amplitude of tropical interannual variability

  14. 2. CFS forecast skill • Atmospheric fields

  15. Temporal correlation 2005-2009 forecast 1981-2004 hindcast T2M Prec Z200 • Higher Z200 skill in northern high-latitudes • Higher precipitation skill over land • Lower skill in over eastern Europe Russia and North America

  16. Temporal correlation 2005-2009 forecast 2005-2009 AMIP T2M Prec Z200 • Higher precipitation skill over land and in Indian Ocean • Higher Z200 skill in northern high-latitudes • Similar T2M skill, except over Russia around 100E/60N

  17. 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 JFM2005 and FMA 2007 • Lower skill in both forecast and AMIP – low predictability

  18. Pattern correlation over N.H. land 20N-80N • Higher CFS precipitation skill in 2005-2008 • Good CFS and AMIP skill during 2007/2008 La Nino winter • Lower T2M skill during all 5 summers

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

  20. JJA T2m 2005 2006 2007 2008 2009 1-mo-lead Forecast Observation CFS keeps producing negative anomalies in central or eastern North America where observed anomalies are more changeable from year to year.

  21. JJA T2M and May soil moisture 2005-2009 average Obs JJA T2M CFS JJA T2M • Errors in forecast T2m appear to be related to initial wet SM anomalie AMIP JJA T2M R2 May SM

  22. May soil moisture over North America from R2 40N-60N average • Initial soil moisture during the forecast period remains well above normal

  23. 2005-2009 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

  24. Summary • CFS continues producing delayed transition between ENSO phases; PDF correction improves the forecasts, especially for those initialized after Apr 2009 (slides 6/7/8) • CFS reproduced Indian dipole mode index (DMI) variability in for 2007-2009, but failed for 2005; CFS forecast correct sign of MDR SST index but with weaker amplitude (slide 6) • The CFS produced T2m, precipitation and Z200 distributions similar to the observed for DJF 2009/2010, including the negative AO phase, but with weaker amplitude of Z200 over the NH polar region and of T2m negative anomalies in NH; For JJA 2009, forecast of T2m is reasonably good although CFS did not produce the observed precipitation and Z200 distributions (slides 9 &10) • ENSO has been in a low variability and low predictability regime during the last few years (slides 12-14) • The CFS forecast shows better precipitation skill over land compared to hindcast (slide 16) • The CFS produces a cold bias in northern extratropics during warm seasons due to wet initial soil moisture in R2, lowering T2m forecast skill T2m (slides 16-19, 21-23) • There exists a mean cold bias over the globe during the forecast period (slide 24)

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