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

2 - 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 , 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) Summary

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

4 Outline 1. CFS forecast for Skills of CFS forecasts during Systematic errors in the forecast

5 1. CFS forecast for 2009

6 Nino34 DMI MDR 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

7

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

9 Forecast for DJF 2009/2010  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. ObsCFS 0-mo leadAMIPCFS 1-mo lead

10 Forecast for JJA 2009 ObsCFS 0-mo leadAMIPCFS 1-mo lead  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.

11 2. CFS forecast skill -SST

forecast SST temporal correlation hindcast  Lower forecast skill tropical eastern Pacific at longer lead-time

13 Nino34 SST temporal correlation Why is Nino3.4 forecast skill at longer lead time not as good ? ( ) ( )

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

15 2. CFS forecast skill -Atmospheric fields

16 Temporal correlation forecast hindcast Higher Z200 skill in northern high-latitudes Higher precipitation skill over land Lower skill in over eastern Europe Russia and North America T2M Prec Z200

forecast AMIP Temporal correlation  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 T2M Prec Z200

18 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 JFM2005 and FMA 2007  Lower skill in both forecast and AMIP – low predictability 20S-20N

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

20 3. Systemetic errors - Cold summers - Mean bias

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

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

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

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

25 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 , 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)