Assessment of the CFSv2 real-time seasonal forecasts for 2011-2012

1. Assessment of the CFSv2 real-time seasonal forecasts for 2011-2012 Wanqiu Wang, Mingyue Chen, and Arun Kumar CPC/NCEP/NOAA

2. Summary • CFSv2 produce larger amplitude of Nino34 ENSO index and delayed transition between ENSO phases (Slide 6). Corrections with variance adjustment and PDF mapping improve the forecasts (slides 7/8/9). However, forecasted positive ENSO index from Apr-Jun 2012 remained too strong after the corrections. Forecast errors may related to the initialization with the OI SST analysis for this specific period and can not be corrected with any statistical correction. • CFSv2 failed to reproduce Indian dipole mode index (DMI) anomaly in 2012. Prediction of MDR index anomaly was quite realistic for the past two years except for the forecasted anomalies in Jan-Jun 2012 which were too weak (slide 6) • CFSv2 has moderate rainfall skill for tropical SST ( average ACC ~ 0.4-0.57) and rainfall (average ACC ~0.3-0.39) • CFSv2 has moderate skill for northern hemisphere land surface T2m (average ACC ~0.17-0.27) and Z200 (average ACC ~ 0.20-0.28). Forecast T2m for MAM 2012 over North America was very good (ACC~0.8) (slides 12 and 21). But precipitation skill over northern hemisphere land is very low (average ACC ~ 0.05) • CFSv2 successfully forecasted the record-low September 2012 sea ice extent (3.61 X106km2) over the Arctic region from Aug initial condition (3.70 X106km2) after a bias correction (slide 24). The forecast from Jul initial condition is also reasonably good (4.4 X106km2). However a bias correction based on the forecast of the most recent years (2008-2011) must be used to get these optimal forecast. Because of the discontinuous jumps in the initial sea ice extent in the CFSR (slide 23), model climatology can not be defined with the entire hindcast period .

3. Relevance Diagnostics/monitoring of CFS real-time forecasts • Real-time skill assessment • Improve forecast through post-processing • Impact of initial condition • Systematic errors

4. Outline • SST indices • Spatial maps • Anomaly correlation skill • Prediction of sea ice extent minimum

5. 1. SST indicies

6. SST indices Nino34 Nino34 • Stronger amplitude of both positive and negative phases • Delayed transition of ENSO phases at longer lead-time DMI • Good forecast for 2010 negative DMI. • Failed to reproduce positive DMI in 2012 MDR • Underestimate the amplitude of warm anomalies during Jan-Jul 2011 DMI MDR

7. CFSv2 Nino34 SST raw anomalies

8. CFSv2 Nino34 SST with variance correction • The correction improved the amplitude for most initial months, except for IC=Apr-Jun 2012 • Observed anomalies are better encompassed by the corrected forecast ensemble (e.g., IC=Aug-Oct 2011)

9. CFSv2 Nino34 SST with PDF correction • The correction improved the amplitude for most initial months, except for IC=Apr-Jun 2012 • Observed anomalies are better encompassed by the corrected forecast ensemble (e.g., IC=Aug-Oct 2011) • The PDF corrected forecast is similar to the variance corrected

10. 2. Spatial maps Anomaly = Total – Clim1999-2010 CFSv2 forecast is at a lead of 20 days or so. For example, forecast for Jun-Jul-Aug is from initial conditions of May 1-10th. Impacts of atmospheric initial conditions should be largely removed.

11. Forecast for MAM 2012 • Weak anomalies in the Tropical Pacific. Relatively larger SST anomalies in the Tropical Atlantic. • Weak precipitation response in the Pacific. • The model captured the overall precipitation pattern.

12. Forecast for MAM 2012 • Forecast Z200 response in the Tropical Pacific is weak. • Very good forecast for North America region.

13. Forecast for JJA 2012 • CFSv2 captured warm SST anomalies in the Tropical Pacific. • CFSv2 failed to reproduce the cold anomalies. • CFSv2 produced reasonable global precipitation pattern.

14. Forecast for JJA 2012 • CFSv2 produced warm T2m anomalies in the northern hemisphere but the amplitude is too weak. • CFSv2 Z200 anomalies are above normal over most of the globe while the observed anomalies showed larger spatial variability.

15. Forecast for SON 2012 • CFSv2 captured warm SST anomalies in the Tropical Pacific. • Cold anomalies in CFSv2 are generally weaker than the observed. • CFSv2 produced reasonable global precipitation pattern.

16. Forecast for SON 2012 • T2m anomalies in CFSv2 are too weak. • CFSv2 Z200 anomalies are above normal over most of the globe while the observed anomalies showed larger spatial variability.

17. Forecast for DJF 2012/2013 • The observed cold SST anomalies in the Tropical Pacific were not well captured in the CFSv2. • CFSv2 SST anomalies are generally too warm. • The below normal precipitation anomalies in the central Pacific in CFSv2 are too strong.

18. Forecast for DJF 2012/2013 • CFSv2 captured overall T2m pattern in North America but failed to produced the observed anomalies in Eurasian continent. • CFSv2 captured the positive Z200 anomalies in polar regions but failed to reproduce the variability in the tropics and mid-latitudes.

19. 3. Anomaly correlation skill

20. Pattern correlation over tropical oceans 20S-20N AvgSST: 0.57 AvgPrec: 0.39 AvgSST: 0.42 AvgPrec: 0.30 AvgSST: 0.40 AvgPrec: 0.33

21. Pattern correlation over NH 20N-80N AvgNH: 0.17 AvgNA: 0.27 • T2m skill for NA is better than that for Eurasian continent starting FMA 2012 • Forecast of T2m for MAM 2012 is quite successful • Very low precipitation skill for most of the period • Moderate PNA skill after SON 2011. AvgNH: 0.05 AvgNA: 0.05 AvgNH: 0.20 AvgPNA: 0.28

22. 4. Prediction of sea ice extent minimum

23. Difference in initial total Sea Ice Extent (NCEP-NSIDC) Discontinuity at 1997 and 2008

24. CFSv2 forecast of Sep 2012 sea ice extent Aug fcst 3.70 Obs=3.61 (initial dates are the 1st-10th of each month) • There is are positive biases in CFSv2 forecast of September sea ice extent minimum • The biases varies with time due to changes in initial sea ice errors • Forecast errors are largely reduced with a correction based on1997-2010 mean bias • A correction based on the bias of more recent period reduces the errors further