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 Hampshire2Institut Francais dr Recherche et d’Exploitation de la Mer

ASLO Session S04, San JuanFebruary 18, 2011

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)

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

Having said all that…

• Data are monthly, 1-degree data gridded down to .25-degree

• Five years: 2003-2007

• 20°N20°S

• 70°W15°E

• Density calculated using UNESCO 1983 polynomial (sw_dens0.m)

Mean SST image

+

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

Currents

• Density changes the most in areas of lowest mean density

Factors influencing density

Density

salinity temperature

heat fluxevaporation river dischargeprecipitation

SST Density

y = -3.9619x + 4082.1

15

17

19

21

23

25

27

1024 1024.5 1025 1025.5 1026 1026.5

Density (kg/m3)

SS

T (

deg

C)

r=-0.9569

• High negative correlation poleward, and through Benguela-SEC

• Low to no correlation around equator and ITCZ

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

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

Precipitation

Net Evaporation (E-P)

Orinoco

AmazonNiger

Congo

r=-0.54

r=-0.7596

r=0.2778 r=-0.411

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

• +25,192• -6,482

• +8,724• 22,950

Now what?

• Comparison to SSS climatologies (WOA, etc)

• Comparison to ARGO float network

• PCA and harmonic analyses

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

• 60 months, 2003-2007 (WHOI OA Flux)

• Positive values are downward fluxes

Heat Flux

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

• % 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.