Assessment of the CFSv2 real-time seasonal forecasts for 2016

1. Assessment of the CFSv2 real-time seasonal forecasts for 2016 Wanqiu Wang, Mingyue Chen, and Arun Kumar CPC/NCEP/NOAA

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

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

4. 1. SST indicies

5. SST indices Nino34 • Delayed transition of ENSO phases at longer lead-time DMI • Failed to reproduce positive DMI in 2012 and 2015 at 3-6 month lead • Good forecast for 2016 negative DMI. MDR • Too warm for Jan-Sep 2014 for 3 and 6 month lead. Too cold after Oct 2014 till early 2015. • False warm anomalies during spring 2016 Nino34 DMI MDR

6. CFSv2 Nino34 SST raw anomalies

7. CFSv2 Nino34 SST with PDF correction • Slight improvement in ensemble mean and spread with PDF correction.

8. Recent Cold Biases in Tropical North Atlantic (updated on March 11, 2016) • A cold bias emerged in the equatorial Atlantic around Mar 2015. • It reached about -6 degree at 55m depth since Nov 2015. Courtesy Yan Xue

9. 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.

10. Forecast for MAM 2016 • Stronger SSTA amplitude in the eastern Pacific. • False negative SSTA in equatorial Atlantic (due to errors in CFSR initial conditions).

11. Forecast for MAM 2016 • ENSO response in T2m anomalies in North America. Most of forecasted warm anomalies over Eurasia also verified..CFSv2 failed to produce observed negative anomalies in northeast US and southeast Canada. • For Z200, CFSv2 failed to forecast negative anomalies around north pole. Forecasted tropical and mid-latitude positive Z200 anomalies are consistent with the observed.

12. Forecast for JJA 2016 • Stronger cold SSTs along central and eastern Pacific • Stronger below normal rainfall response in the same areas.

13. Forecast for JJA 2016 • Weaker T2m warm anomalies in mid-high latitudes in CFSv2. • CFSv2 underestimated Z200 positive anomalies in the tropics from the Maritime continent to central Pacific

14. Forecast for SON 2016 • CFSv2 captured the overall SST anomaly pattern in the Pacific, but is generally too warm in the tropical Atlantic. • CFSv2 produced a reasonable anomaly pattern from the eastern Indian .Ocean to the Pacific. The forecast system did not reproduce the observed dryness in South America.

15. Forecast for SON 2016 • CFSv2 failed to forecast the cold anomalies in the central Eurasian continent and missed the warm anomalies in central and eastern US. • CFSv2 did not capture the observed negative Z200 anomalies in polar regions..

16. Forecast for DJF 2016/2017 • Slight overestimate of cold SST anomalies in the eastern Pacific. • CFSv2 failed to capture the dryness in central Indian Ocean and in South America.

17. Forecast for DJF 2016/2017 • CFSv2 produced a reasonable T2m anomaly pattern in North America except for Greenland. CFSv2 failed to produce the observed T2m anomaly pattern in Eurasian continent. • CFSv2 failed to capture the observed Z200 negative anomalies in the Arctic region.

18. 3. Anomaly correlation skill

19. Pattern correlation over tropical Pacific 20S-20N • Tropical Pacific SST and rainfall correlations during 2016 are relatively high compared to the previous periods. • The observed amplitude is relatively large in 2016 t. • Nino34 SST from the CFSv2 is quite accurate.

20. Pattern correlation over tropical Indian Ocean 20S-20N • SST skill in the fist half of 2016 is high and decreased in the second half of the year. • Rainfall skill is largely less than 0.4.

21. Pattern correlation over tropical Atlantic 20S-20N • SST correlation increased from below 0.4 in the first half of the year to above 0.4 in the second half of the year, yet rainfall correlation dropped below zero after JJA 2016. • The model produced reasonable MDR SST anomalies after JJA 2016.

22. Pattern correlation over NH 20N-80N • T2m overall skill over NA was reasonable for 2016. • Forecast skill over NA is generally higher than that over Eurasia. • Z200 skill is reasonable during 2015. • Evolution of precipitation skill is more fluctuating and lower than T2m and Z200.

23. 4. Artic September sea ice extent

24. SIPN June report (https://www.arcus.org/sipn/sea-ice-outlook/2016/june) CFSv2 Obs=4.72 CFSv2 value was from CPC experimental sea ice forecast system

25. SIPN July report (https://www.arcus.org/sipn/sea-ice-outlook/2016/july) CFSv2 Obs=4.72 CFSv2 value was from CPC experimental sea ice forecast system

26. SIPN Augustreport (https://www.arcus.org/sipn/sea-ice-outlook/2016/august) CFSv2 Obs=4.72 CFSv2 value was from CPC experimental sea ice forecast system

27. CFSv2 predicted sea ice extent for September 2016 (106 km2) Obs=4.63 • CFSv2 raw data contains large errors • Bias correction based on 1997-2010 hindcasts helps but corrected sea ice extent is still too large for March to August forecasts • Bias correction based on more recent years (2008-2015) further reduced the error but forecasts from May to Jul remain too large. • CFSv2 forecast sea ice extent from August initial condition became less than that observed.

28. Differences in sea ice extent between CFSR and NASA Team analysis (106 km2) • Significant jumps in 1997 and 2008 • The resulting time-dependent systematic bias in forecast is difficult to remove • The differences were changeable after 2008. Systematic forecast errors still exist even with a shorter period for bias correction

29. Differences in sea ice volume between CFSR and PIOMAS analysis (103 km3) • Changeable differences depending on year and month • The larger SIV during 2014 summer may be another reason (in addition of SIE, slide 27) for the predicted SIE (slide 26). Courtesy of Thomas Collow