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Climate variability and large marine ecosystems in the western South Atlantic317LME suggest that possible random phasing withremote forcing of SSTA is likely to produce mixedeffects in this LME.The leading EOF of monthly sea surface heightanomalies (SSHA) shown by Grodsky and Carton(2006), indicates a region of shallow thermoclinein the southwest Atlantic between 25° and 35° Scoincident with negative SST EOF scores.They interpreted the westward 10 cm interannualSSHAs as Rossby waves produced by thermoclineanomalies due to local and equatorial air-seainteractions. These waves would propagate alongthe Agulhas Eddy Corridor (AEC). The regionof shallow thermocline is coincident with a fieldof negative correlations with the AAO withouttime lag that dominates the northern half of the SBLME (Figure 4). This means that positive AAOindices are correlated with cold of SSTAs andstronger westerly circumpolar flow (positive midlatitudepressure anomalies) as previously indicatedby Thompson and Wallace (2000). Coupled oceanatmospheremodel results suggest that much of thevariability of the south Atlantic poleward of 30° Shas a direct relation with the Southern HemisphereAnnular Mode (hence, the AAO; Hall & Visbeck,2002). Positive AAO is related with increasedpoleward ocean heat transport at 30° S and areduction at 50° S, with associated 0.05° C increasein SSTA in the subtropics. The importance ofAAO as a source of large-scale interannualvariability in the tropical and Southern AtlanticOcean can be also inferred from positive correlationcenters along the northern and eastern coastand positive correlations observed between 25° Sand 35° S, 15 and 22 months ahead of the AAOfor the warm PDO phase (Figure 4). Whether thisis a result of enhanced Ekman drift and convergenceof heat it is not possible to ascertain, but theobserved correlations indicate that the AAO exertsa strong influence on the environmental conditionsalong the LMEs.It is possible that both TSA and AAO havean influence on the EB and SB LME acting indifferent ways depending on the geographic locationand time scale. This influence seems to beparticularly conspicuous during the warm PDOphase. In this preliminary report, we can only bespeculative, but this is not to say that there are noevidences for the influence of the spatial scale.Indeed, it is quite the opposite, looking at theinterplays among population dynamics, climatechange and fisheries throughout the Atlantic, it isseen that at the basin scale patterns of variations arespatially structured (Rouyer et al. 2008).The current knowledge on coupled oceanatmospheredynamics tells us that LMEs are tiedtogether by wind stress forcing, Ekman drift andheat transport. All of these are important agents thatcontrol the pelagic food-web structure, includingprimary productivity, mesozooplankton biomass andthe position of spawning habitats of pelagic fishes(Kiorboe 2008). Changes in surface currents, windstress and heat flux can have an impact on the longtermdynamics of zooplankton functional groups,leading to regime shifts in the ecosystem functioningfrom bottom-up to top-down control (Molinero et al.2008). If monitoring and management of LMEs areto become an effective means to respond to climaticimpacts on marine biodiversity and productivity,then the physical linkages between oceanatmospheredynamics and the pelagic ecosystem ona regional and basin scale have to be explicitlyconsidered.ConclusionsThe above results are preliminary findingsthat aim at exploring the spatial patterns ofcorrelation between climate indices and the SSTAalong the Brazilian LMEs at the interannual timescale. Significant correlations indicate that there is aseparation between the north and east Brazilcoasts located halfway between the boundaries ofthe EB LME. The SSTAs in the SACZ regionshowed no significant correlation with Niño 3, TNAand TSA, but are correlated with the AAO duringthe warm PDO phase. Possibly, the most evidentpattern that surfaced from the results is the influenceof the PDO phase shift causing dramatic changesin the spatial distribution of correlations. Thecorrelation patterns for the TSA and AAO seemto have a better fit with Brazilian LME duringthe warm PDO phase (1977-2007). During thisphase of the PDO, while the largest magnitudecorrelations are found in the EB and SB LME forthe TSA, they are centered in the three LME areasfor the AAO. It is, strongly recommended thecombined use of coupled ocean-climate andecological models as a means to elaborate thepossible mechanisms linking climate change andthe functioning of LMEs in Brazil. The assumptionthat LMEs delimited along the Brazilian coastcoherently respond to global climate changes, andthat these can be used to monitor their impactsshould be taken with caution. It is clear that, asfar as their dependence on SSTA is concerned,productivity and trophic relations in each of theBrazilian LMEs are likely to generate mixedresponses at the ecosystem level. This would, inturn, induce policy makers to react to a confoundedscenario of environmental change.Pan-American Journal of Aquatic Sciences (2010), 5(2): 310-319