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Assessment of the CFSv2 real-time seasonal forecasts for 2017

Assessment of the CFSv2 real-time seasonal forecasts for 2017. Wanqiu Wang, Mingyue Chen, and Arun Kumar CPC/NCEP/NOAA. Relevance. Diagnostics/monitoring of CFS real-time forecasts. Real-time skill assessment Improve forecast through post-processing Impact of initial condition

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Assessment of the CFSv2 real-time seasonal forecasts for 2017

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  1. Assessment of the CFSv2 real-time seasonal forecasts for 2017 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. • Failed to reproduce positive DMI in spring 2017 at 6 month lead 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. 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.

  9. Forecast for MAM 2017 • Stronger SSTA amplitude in the eastern Pacific. • Unrealistic rainfall pattern in the tropics.

  10. Forecast for MAM 2017 • Model produces an ENSO response in T2m anomalies in North America which does not appear in the observation. Model also failed to predict the strong warm anomalies in the Eurasia. • For Z200, CFSv2 captured the tropical positive anomalies but failed to produce the observed anomalies in mid-high latitudes.

  11. Forecast for JJA 2017 • Reasonable SST anomalies in the tropics but unrealistic negative anomalies in the high-latitudes, possible related to sea ice errors. • Rainfall anomaly pattern in the Indo-West Pacific is incorrect..

  12. Forecast for JJA 2017 • CFSv2 forecast ensemble mean T2m warm anomalies are weak and do not show a good resemblance to the observed pattern. • For Z200, CFSv2 captured the tropical positive anomalies but failed to produce the observed anomalies in mid-high latitudes

  13. Forecast for SON 2017 • Negative SST anomalies in the Indian Ocean and Pacific are too weak, but the rainfall anomaly pattern looks quite reasonable.

  14. Forecast for SON 2017 • CFSv2 forecasted T2m warm anomaly patterns was not shown in the observations. • CFSv2 did capture the observed Z200 wave patterns in the mid-high latitudes.

  15. Forecast for DJF 2017/2018 • Stronger SST anomaly amplitude in the tropics. • Reasonable rainfall pattern in the Pacific but incorrect rainfall anomaly sign in the Indian Ocean.

  16. Forecast for DJF 2017/2018 • 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 anomaly pattern in the mid-high latitudes.

  17. 3. Anomaly correlation skill

  18. Pattern correlation over tropical Indian Ocean 20S-20N • SST skill in 2017 is about 0.4. • Rainfall skill is low • DMI amplitude is lower than observed.

  19. Pattern correlation over tropical Pacific 20S-20N • Tropical Pacific SST correlation in 2017 reasonably high but rainfall correlations during spring and summer 2017 is low. • The observed amplitude is relatively small in 2017. • 2017 Nino34 SST from the CFSv2 is reasonable.

  20. Pattern correlation over tropical Atlantic 20S-20N • SST correlation is between 0 and 0.4 in 2017. • Rainfall prediction skill is low. • SST and rainfall varibility is weak • MRD index in CFSv2 is reasonable.

  21. Pattern correlation over NH 20N-80N • T2m overall skill over NA in 2017 was not high and fluctuating. • Forecast skill over NA is generally higher than that over Eurasia. • Precipitation skill is also very low. • Z200 skill in 2017 is higher than precipitation andT2m but changeable with time. • Evolution of precipitation skill is more fluctuating and lower than T2m and Z200.

  22. 4. Artic September sea ice extent

  23. 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 the corrected sea ice extent is still too large except for the forecast from August 2017 • Bias correction based on more recent years (2008-2016) further reduced the error for March to July forecasts but overcorrected the forecast from August, which is probably related to the year-to-year bias change the initial state (slide 24), making it difficult to make a reliable bias correction.

  24. 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. • Use 2008-2016 mean bias would result in an over-correction for 2017 forecast.

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