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Spatiotemporal Patterns in Sea Surface Density in the Tropical Atlantic

Spatiotemporal Patterns in Sea Surface Density in the Tropical Atlantic. C. Hunt 1 , D. Vandemark 1 , B. Chapron 2 , N. Reul 2 , D. Wisser 1 and J. Salisbury 1 1 University of New Hampshire 2 Institut Francais dr Recherche et d’Exploitation de la Mer ASLO Session S04, San Juan

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Spatiotemporal Patterns in Sea Surface Density in the Tropical Atlantic

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  1. Spatiotemporal Patterns in Sea Surface Density in the Tropical Atlantic C. Hunt1, D. Vandemark1, B. Chapron2, N. Reul2, D. Wisser1 and J. Salisbury1 1University of New Hampshire 2Institut Francais dr Recherche et d’Exploitation de la Mer ASLO Session S04, San Juan February 18, 2011

  2. What’s new? • Previous large-scale density estimates were usually from models (i.e. Hycom) or climatologies (i.e. WOA) • Two new satellites this year will provide L-band microwave SSS • The ARGO float network can now provide a broad-scale validation set to couple with remotely-sensed SSS • However, a less sensitive microwave sensor has been aboard AMSR-E for 7+ years (Reul et al. 2009)

  3. AMSR-E SSS Background • C-band (6.9 GHz) and X-band (10.7 GHz) • Corrected with AVHRR-AMSR SST, water vapor, cloud liquid water, and surface winds • Caveats: • C- and X-bands much less sensitive to SSS than L-band (factor of 10-20) • Better sensitivity with warmer waters • Still much more sensitive to SST than SSS

  4. Having said all that… • Data are monthly, 1-degree data gridded down to .25-degree • Five years: 2003-2007 • 20°N20°S • 70°W15°E • Density calculated using UNESCO 1983 polynomial (sw_dens0.m)

  5. Mean SST image +

  6. 60-Month mean, 2003-2007 • Lower density near rivers and across ITCZ • Higher density intrusions from South and possibly North Equatorial Currents

  7. Density changes the most in areas of lowest mean density

  8. Factors influencing density Density salinity temperature evaporation river discharge precipitation heat flux

  9. Density SST r=-0.9569

  10. High negative correlation poleward, and through Benguela-SEC • Low to no correlation around equator and ITCZ

  11. Strong correlation along coasts, especially Amazon, Niger and Congo outflows, and through ITCZ

  12. SSS and SST influence on density • SSS more significant in river plumes, along coasts and along the ITCZ • SST more significant in South and North Equatorial Currents • So, now let’s look at density and its relation to: • Heat Flux • Precipitation/Evaporation • River Discharge

  13. Precipitation

  14. Net Evaporation (E-P)

  15. Orinoco Niger Amazon Congo

  16. r=-0.7596 r=-0.54 r=0.2778 r=-0.411

  17. Does Density Help? • Quick and dirty PCA of three data combinations: • Density, chl, cdom • SSS, chl, cdom • SST, chl, cdom • Recorded % variance represented by PC1 and PC2 for each data combination • Subtracted PC1%var-den – PC1%var-sss • and PC1%var-den – PC1%var-sst

  18. +25,192 -6,482 +8,724 22,950

  19. Now what? • Comparison to SSS climatologies (WOA, etc) • Comparison to ARGO float network • PCA and harmonic analyses

  20. Some stuff with npp, chl, cdom? • Get CDOM and NPP images (will they be the right coordinates?) • Do a PCA with SST, CDOM and NPP • Do again with density, CDOM, and NPP • Hopefully the density PCA will explain more of the variance that the SST one! • Also, see how the SSS papers use PCA

  21. Heat Flux • 60 months, 2003-2007 (WHOI OA Flux) • Positive values are downward fluxes

  22. Despite some areas of strong heat flux gradients, not much in the way of good correlations

  23. % REFERENCES: • % Unesco 1983. Algorithms for computation of fundamental properties of • % seawater, 1983. _Unesco Tech. Pap. in Mar. Sci._, No. 44, 53 pp. • % • % Millero, F.J. and Poisson, A. • % International one-atmosphere equation of state of seawater. • % Deep-Sea Res. 1981. Vol28A(6) pp625-629.

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