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An Assessment of the CFS real-time forecasts for 2005-2011

An Assessment of the CFS real-time forecasts for 2005-2011. Wanqiu Wang, Mingyue Chen, and Arun Kumar CPC/NCEP/NOAA. Summary. CFS continues producing delayed transition between ENSO phases; PDF correction improves the forecasts (slides 6/7/8)

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An Assessment of the CFS real-time forecasts for 2005-2011

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  1. An Assessment of the CFS real-time forecasts for 2005-2011 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 (slides 6/7/8) • CFS reproduced Indian dipole mode index (DMI) variability in for 2007. But for other years, either the sign or the timing or both were erroneous; CFS forecast correct sign of MDR SST index but with weaker amplitude (slide 6) • The CFS forecasted T2m, precipitation and Z200 distributions in the tropics and over the North America similar to the observed for DJF 2010/2011, consistent with atmospheric response to tropical SST anomalies; For JJA 2010, forecast of T2m over the central and eastern United States are too cold (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 2010 • Skills of CFS forecasts during 2005-2010 • Systematic errors in the forecast

  5. 1. CFS forecast for 2010

  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. 2011 forecast is good for L0 and L3. • Bad forecast for 2005, 2008, and 2010 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 2011/2012 Obs CFS 0-mo lead CFS 1-mo lead AMIP (DJ) • Both the CFS and AMIP simulation captured observed precipitation and Z200 anomalies in the subtropics and tropics • The forecasted and simulated z200 in the mid-high latitudes are quite unrealistic. • The models failed to reproduce observed overwhelming warmth over most of the North America.

  9. Forecast for JJA 2011 Obs CFS 0-mo lead CFS 1-mo lead AMIP • CFS and AMIP simulation produced a reasonable distribution of the tropical precipitation. Tropical Z200 anomalies are qeak in both the observation and forecast. • The observed spatial pattern of T2m over NH land is much better reproduced in AMIP. The CFS did not capture the observed warm anomalies in the central eastern United States, likely due to the too wet initial soil moisture (slides 22-23).

  10. 2. CFS forecast skill • SST

  11. SST temporal correlation 2005-2011forecast 1981-2004 hindcast • Lower forecast skill tropical eastern Pacific at longer lead-time

  12. Nino34 SST temporal correlation (1981-2004) (2005-2011) 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 Beginning of the 4-year window • 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 2005-2011 forecast 1981-2004 hindcast T2M Prec Z200 • Higher Z200 skill in northern high-latitudes • Higher precipitation skill over land • Lower T2m skill in over NH land

  16. Temporal correlation 2005-2011 forecast 2005-2011 AMIP T2M Prec Z200 • Higher precipitation skill over land and in Indian Ocean • Higher Z200 skill in northern high-latitudes • Similar T2M skill.

  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 during spring and summer forecast – air/sea coupling important Atlantic • Higher SST skill between JFM2005 and FMA 2007 and after DJF 2009/2010 • Lower rainfall skill in both forecast and AMIP even when SST skill is high – 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 summers

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

  20. JJA T2m 2005 2006 2007 2008 2009 2010 2011 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 2005-2011 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. 2005-2011 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

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