Transcript Spatiotemporal Patterns in Sea Surface Density in the Tropical Atlantic.

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
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°N20°S
• 70°W15°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
precipitation
evaporation
temperature
river discharge
heat flux
Density
SST
27
25
SST (degC)
y = -3.9619x + 4082.1
23
r=-0.9569
21
19
17
15
1024
1024.5
1025
1025.5
Density (kg/m3)
1026
1026.5
• 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
Niger
Amazon
Congo
r=-0.7596
r=-0.54
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
Heat Flux
• 60 months, 2003-2007 (WHOI OA Flux)
• Positive values are downward fluxes
• 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) pp625629.