1. 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 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 heat flux evaporationriver discharge precipitation

9. SST Density 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

15. Net Evaporation (E-P)

17. Orinoco Amazon Niger Congo

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

19. 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

20. +25,192 -6,482 +8,724 22,950

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

22. 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

26. 60 months, 2003-2007 (WHOI OA Flux) Positive values are downward fluxes Heat Flux

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

28. % 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.