Surface Wind Dynamics: Insights from Satellite Retrievals & Model Comparisons

1. Oceanic Surface Wind in Satellite Retrievals, NCEP Reanalyses, GFS, and CFS Sarah Marquardt, Wanqiu Wang, and Pingping Xie

2. Background • Surface wind is a critical variable in the coupling between atmosphere and ocean. It directly affects • Latent and sensible heat fluxes • Momentum flux • Substantial SST errors in the coupled models, such as CFS, are related to errors in surface wind • Validation of coupled atmosphere-ocean models needs • Global coverage of surface wind observations • Diagnosis of the reliability of observations

3. CFS 4-mo lead SST forecast mean error (K) • Is the cold bias in equatorial Pacific due to erroneous surface winds? • What are the impacts of surface winds on the southeastern Pacific warm bias in addition to the known excessive local solar radiation?

4. Objectives • Estimate uncertainty of the surface wind fields in satellite retrievals • Examine the dependability of NCEP GDAS, CDAS1, CDAS2 • Diagnose errors in surface wind fields in NCEP GFS and CFS

5. Data (10-m wind) • Satellite retrievals (2000-2007) • RSS QuikScat wind Version 3a • JPL QuikScat wind Product 109 • RSS SSMI wind Version 6 • NCDC Blended Winds Version 1.2 • In situ observations (2000-2004) • FSU in situ wind FSU3 Version 1 • Reanalyses (2000-2007) • GDAS (2002-2007) • CDAS1 • CDAS2 • NCEP model simulations (Version 2003) • GFS (2000-2007) • CFS 0 mo. lead, init. 21st 1999-2006

6. Uncertainty in satellite retrievals

7. Annual mean wind speed (m/s) • Spatial distribution of all observations is very similar • NCDC winds are slightly faster in the tropics, SSMI winds are slightly slower in N.H. • Spread among retrievals is generally less than 0.4 m/s except for west coast of continents

8. QuikScat vs FSU moored buoy • Bias and rms error are smaller and correlation is higher in the tropics • - QuikScat wind speed mean error is generally less than 1 m/s in the tropics but can be over 1 m/s too high near the extratropics

9. Uncertainty of Observations 2000-2004 Satellite – FSU Moored Buoys • Bias and rms error are smaller and correlation is higher in the tropics • Mean wind speed error is about 0.5 m/s (9% of total) in the tropics and about 1.6 m/s outside the tropics • All satellite retrievals are comparable. RSS QuikScat slightly better in the tropics, SSMI is better in NH

10. Dependability of NCEP reanalyses OBS = Mean of RSS, JPL, and SSMI

11. Annual mean wind speed (m/s) • GDAS wind matches observations very well • CDAS1 is too slow in the lower latitudes and CDAS2 is too fast in mid to high latitudes

12. Seasonal mean wind vector (m/s) • GDAS wind matches observations very well • CDAS1 (CDAS2) wind too slow (fast) in southeastern Pacific • CDAS1 and CDAS2 have wind direction errors along the coast

13. Seasonal mean wind speed (m/s) FMA ASO • All three NCEP products are too fast near the coast • Slow bias along the coast in CDAS1 and CDAS2

14. Zonal mean wind speed (m/s)

15. Surface wind errors in NCEP models OBS = Mean of RSS, JPL, and SSMI

16. Annual mean wind speed (m/s) • GFS and CFS winds are very similar • Both models are too weak north and south of the equator in the Pacific but too strong in eastern equatorial Pacific • GFS is too weak in the extratropics

17. Seasonal mean wind vector (m/s) • Some errors in wind direction exist along 30S in GFS in FMA • Wind direction is accurate along the coast in FMA but inaccurate in JJA. Model winds are easterly instead of southerly.

18. Seasonal mean wind (m/s) FMA ASO • GFS and CFS winds are too strong along the equator throughout the year • Wind differences are largest in ASO, the same months where the cold SST bias is largest in the CFS forecast

19. Wind effects on SST • Too strong easterlies persist through the 1 mo lead forecast • Cold SST bias increases in size and magnitude • Evolution of equatorial CFS wind speed indicates a cancellation of errors: SST induced westerlies cancel too strong easterlies

20. CFS 4-mo lead SST forecast mean error (K) • Too strong easterlies in GFS likely the reason for cold SST bias in the CFS forecast for the months of ASON • Impact of wind errors on SEP warm bias is not clear

21. Vector Correlation • Equation described by Crosby et al (1993) • CDAS1, CDAS2, GDAS: Vector correlation generally over 0.6, lowest correlations occur over the cold tongues of the eastern tropical Pacific and Atlantic • GFS: Vector correlation is highest in the tropics

22. Summary • Uncertainty of time mean wind speed in satellite observations is about 0.5 m/s in the tropics and 1 m/s in the extratropics • GDAS winds are very accurate • CDAS1 is too slow in the tropics and CDAS2 is too fast in the mid latitudes; both have wind direction errors along the west coasts of South America and Africa • GFS and CFS winds are too strong in the Southeast Pacific and the eastern equatorial Pacific; there are wind direction errors along the west coast of South America. • Too strong easterlies in ASO in the GFS are associated with the eastern equatorial cold SST bias in the CFS forecast for ASON

23. Additional Figures

24. Vector Correlation = Tr Covariance matrices of datasets 1 and 2 Covariance matrix of dataset 1 • Equation described by Crosby et al (1993)

26. RMS differences of annual mean climatology • RMS error between observations and GDAS is about 0.3 m/s • RMS errors are largest in the tropics and extratropics

27. Seasonal mean wind pseudo stress • GFS and CFS wind stress is too strong near the coast between equator and 25S • GFS is too strong in ASO along the equator while CFS is too weak • The too weak CFS wind speed in the equatorial Pacific is associated with the southeastern Pacific SST warm bias • Too strong easterlies in GFS likely the reason for cold SST bias in the CFS forecast for the months of ASON