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

2. 2 Outline 1. CFS forecast skill and comparison with hindcast 2. Comparison with potential predictability based on AMIP simulations 3. Systematic errors in the forecast - Cold summers - Mean bias

3. 3 1. CFS forecast skill - SST forecast

4. 4 Nino34 DMI MDR 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

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

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

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

8. 8 1. CFS forecast skill - Atmospheric fields

9. 9 Temporal correlation 2005-2008 forecast1981-2004 hindcast 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 T2M Prec Z200

10. 10 2. Comparison with AMIP simulations

11. 11 2005-2008 forecast2005-2008 AMIP Temporal correlation  Higher precipitation skill over land and in Indian Ocean  Comparable Z200 skill  Similar T2M skill, except over Russia and central North America T2M Prec Z200

12. 12 Pattern correlation over tropical ocean 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 20S-20N

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

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

15. 15 JJA T2m 2005200620072008 Observation 1-mo-lead Forecast

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

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

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

19. 19 - 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 Summary