@InProceedings{SoaresGherPezzKaya:2013:DiLoRe,
               author = "Soares, Helena Cachanhuk and Gherardi, Douglas Francisco Marcolino 
                         and Pezzi, Luciano P. and Kayano, Mary Toshie",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Differences between local and remote interannual climate forcings 
                         acting on the Brazilian Large Marine Ecosystems",
            booktitle = "Proceedings...",
                 year = "2013",
         organization = "AGU Meeting of the Americas.",
             abstract = "arge Marine Ecosystems (LMEs) are units defined based on the 
                         differences in hydrographic regimes, bathymetry, productivity and 
                         trophycally dependent populations and were established for 
                         assessment and management of marine resources and control of 
                         degradation of the coastal areas around the world. Three LME are 
                         located in the Brazilian domain, the North, East and South LMEs. 
                         In this study the influence of interannual climate variations on 
                         Brazilian LMEs are investigated. The South Atlantic is subject to 
                         local climatic modes, such as the Interhemispheric Sea Surface 
                         Temperature (SST) gradient, represented by the Tropical South 
                         Atlantic (TSA) and Tropical North Atlantic (TNA) indices and 
                         Antarctic Oscillation mode, represented by Antarctic Oscillation 
                         (AAO) index. The remote forcings considered in this work are El 
                         Niņo Southern Oscillation (ENSO) and Pacific Decadal Oscillation 
                         (PDO). Total and partial correlation (95% significance) analyses 
                         of climate indices versus SST, wind stress, sea level pressure 
                         (SLP) and outgoing long wave radiation (OLR) were calculated. The 
                         data series used were detrended and filtered to retain the 
                         interannual (2 to 7 years) variability. Correlations were carried 
                         out separately for the cold (1948/1976) and warm PDO phase 
                         (1977/2008). Results point to higher correlations between wind 
                         stress anomaly, SLP anomaly, SST anomaly (SSTA) and the Niņo 3 
                         index for a large part of the South Atlantic during the PDO warm 
                         phase than in the cold phase. The North Brazil LME region is 
                         strongly influenced by El Niņo, with a maximum positive 
                         correlation between SSTA and Niņo 3 found with 7 months lag and a 
                         positive correlation between this index and wind stress with a 
                         maximum time lag of 2 months. The East LME unit appears to be 
                         influenced in a very different way in its southern and northern 
                         portion, suggesting that management actions for the adaptation or 
                         mitigation for possible climate variability changes needs to 
                         consider this difference. The AAO is negatively correlated with 
                         SSTA between 20° and 35°S, being the sole climate index showing 
                         significant correlations in this area. Another aspect observed is 
                         that in the North region of the basin the correlation between AAO 
                         and SSTA seems to be intensified by the TSA interaction. The next 
                         step of this work will be to use the Regional Ocean Modeling 
                         System (ROMS) with a biogeochemical component to evaluate the 
                         impacts of climate variability on the LMEs biological 
                         productivity.",
  conference-location = "Cancun",
      conference-year = "2013",
                label = "lattes: 9331105406437396 4 SoaresGherPezzKaya:2013:DiLoRe",
             language = "en",
           targetfile = "soares_differences.pdf",
        urlaccessdate = "12 maio 2024"
}