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Assessment of the CFSv2 real-time seasonal forecasts for 2018

This study evaluates the skill and performance of the CFSv2 real-time seasonal forecasts for 2018, focusing on SST indices, spatial maps, anomaly correlation skill, and prediction of sea ice extent. It also highlights systematic errors and the impact of initial conditions, while discussing potential improvements through post-processing methods.

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Assessment of the CFSv2 real-time seasonal forecasts for 2018

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  1. Assessment of the CFSv2 real-time seasonal forecasts for 2018 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 2018 at 3-6 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 • Failed to produce anomaly drop during spring 2018 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 2018 • Stronger SSTA amplitude in Atlantic subtropics. • Unrealistic wetness in the far western Pacific tropics.

  10. Forecast for MAM 2018 • Failed to produce observed cold anomalies in western Russia and northeast North America • Failed to produce corresponding observed Z200 anomalies.

  11. Forecast for JJA 2018 • Reasonable SST anomalies in the tropics but unrealistic negative anomalies in the high-latitudes, possiblly related to sea ice errors. • Rainfall anomaly pattern over most of the tropics is reasonable.

  12. Forecast for JJA 2018 • More reasonable T2m over Eurasia than over North America. • Similar comparison in Z200

  13. Forecast for SON 2018 • Warmer eastern Indian Ocean SST. • Larger rainfall anomalies in central equatorial Indian Ocean

  14. Forecast for SON 2018 • CFSv2 missed cold T2m anomalies over central Eurasia and North America. • CFSv2 missed corresponding Z200 anomalies.

  15. Forecast for DJF 2018/2019 • Wider positive SST anomaly regions. • Tropical rainfall pattern looks reasonable over most regions.

  16. Forecast for DJF 2018/2019 • CFSv2 did not produce observed T2m pattern, especially for the cold anomalies. • 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 2018 is generally below 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 with rainfall correlations being about average of other years. • The observed amplitude is relatively small in 2018. • 2018 Nino34 SST from the CFSv2 is reasonable.

  20. Pattern correlation over tropical Atlantic 20S-20N • SST correlation is between 0 and 0.6 in 2018. • Rainfall prediction skill is low. • No good relationship between SST and rainfall amplitude • Observed 2018 MRD index was weak and CFSv2 forecast skill is low.

  21. Pattern correlation over NH 20N-80N • T2m overall skill over NA was high in early 2018 but low for the rest of the year. • Forecast skill over NH is generally low. • Precipitation skill is about average of other years. • Z200 skill in 2017 is higher than precipitation andT2m.

  22. 4. Artic September sea ice extent

  23. CFSv2 predicted sea ice extent for September 2018 (106 km2) Obs=4.71 • 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 (2008-2017) 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, 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.

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