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Relationship between sea surface temperature anomalies in the Southwestern Atlantic Continental Shelf and atmospheric variability on intraseasonal timescales

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Abstract

The intraseasonal (IS) variability of the sea surface temperature (SST) in the Southwestern Atlantic Continental Shelf (SWACS, 45–33° S—70–50° W), and its relationship with that in the atmosphere, was studied for the austral warm season. SST satellite data (11-km resolution NOAA CoastWatch Program) and data of different atmospheric variables (Reanalysis1 NCEP/NCAR and ERA-Interim) were used. Data were filtered using a 10–90 day filter to isolate the IS variability. A Principal Component analysis was applied then to the filtered SST anomalies (SSTA) and the activity of the leading modes was described through the corresponding temporal series. The first three modes are significant. EOF1 (25.7% of variance) exhibits SSTA of opposite sign to the north/south of 42° S. EOF2 (9.0%) and EOF3 (5.1%) are related with centers of SSTA of opposite sign located off the Uruguayan coast and in the middle shelf. Composites of SSTA and of key atmospheric variables were made considering the days in which the main modes were active. They show that the SSTA described by the three modes are associated with distinctive regional sea level pressure anomalies that, in turn, seem to be related to atmospheric Rossby wave trains extending from the Australia area towards South America. The corresponding atmospheric wave sources vary depending on the mode. These results show, therefore, that the SSTA in the SWACS exhibit significant IS variability that is, in part, locally and remotely influenced by atmospheric anomalies oscillating on similar timescales. These ocean–atmosphere teleconnections could help to improve ocean predictability at those timescales in the future.

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Availability of data and material

The SST data that support the findings of this study are available in NOAA CoastWatch Program, NOAA NESDIS Office of Satellite Data Processing and Distribution, and NASA’s Goddard Space Flight Center, OceanColor Web at https://coastwatch.pfeg.noaa.gov/infog/BA_ssta_las.html. The relevant atmospheric variables data that support the findings of this study are available in National Center for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis dataset (Kalnay et al. 1996) through the IRI Data Library and from the ERA Interim reanalysis from the European Centre for Medium-Range Weather Forecasts (Dee et al. 2011) at https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim. Outgoing Longwave Radiation (OLR) data, considered as a proxy of atmospheric convection, were obtained from the National Oceanic and Atmospheric Administration (NOAA) gridded dataset at https://psl.noaa.gov/data/gridded/data.interp_OLR.html. The datasets analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

The code used during the current study is available from the corresponding author on reasonable request.

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Acknowledgements

SST data were provided by the NOAA CoastWatch Program, NOAA NESDIS Office of Satellite Data Processing and Distribution, and NASA’s Goddard Space Flight Center, OceanColor Web (https://coastwatch.pfeg.noaa.gov/infog/BA_ssta_las.html). Data of relevant atmospheric variables were obtained from the National Center for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis dataset (Kalnay et al. 1996) through the IRI Data Library and from the ERA Interim reanalysis from the European Centre for Medium-Range Weather Forecasts (Dee et al. 2011). Outgoing Longwave Radiation (OLR) data, considered as a proxy of atmospheric convection, were obtained from the National Oceanic and Atmospheric Administration (NOAA) gridded dataset. This work is a contribution to the ANPCyT (National Agency for Scientific and Technological Research of Argentina) 2015-1934 and the UBACYT (Secretariat of Science and Technology—University of Buenos Aires) 20020190100200BA Projects. Dr. Luz Clara, Dr. Vera, Dr. Alvarez and Dr. Simionato’s salaries were paid by the CONICET (National Council of Scientific and Technical Research of Argentina). Dr. Jaureguizar’s salary was paid by the Scientific Research Commission (CIC) of Buenos Aires Province. INIDEP (National Institute for Fisheries Research and Development) contribution #2254.

Funding

This research was supported by the ANPCyT 2015-1934 and the UBACYT 20020190100200BA Projects. Dr. Luz Clara, Dr. Vera, Dr. Alvarez and Dr. Simionato’s salaries were paid by the CONICET. Dr. Jaureguizar’s salary was paid by the CIC-Provincia de Buenos Aires.

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Authors and Affiliations

  1. Instituto de Investigaciones Marinas y Costeras - Consejo Nacional de Investigaciones Científicas y Técnicas (IIMyC - CONICET), Mar del Plata, Buenos Aires, Argentina

    Moira Luz Clara

  2. Instituto Nacional de Investigación y Desarrollo Pesquero (INIDEP), Mar del Plata, Buenos Aires, Argentina

    Moira Luz Clara

  3. Instituto Franco-Argentino Para el Estudio del Clima y Sus Impactos (UMI-IFAECI/CNRS), Buenos Aires, Argentina

    Moira Luz Clara, Mariano S. Alvarez, Carolina Vera & Claudia G. Simionato

  4. Centro de Investigaciones del Mar y la Atmósfera (CONICET-UBA) and Departamento de Ciencias de la Atmósfera y los Océanos (FCEN-UBA), Buenos Aires, Argentina

    Mariano S. Alvarez, Carolina Vera & Claudia G. Simionato

  5. Comisión de Investigaciones Científicas de la Provincia de Buenos Aires (CIC), La Plata, Buenos Aires, Argentina

    Andrés J. Jaureguizar

  6. Instituto Argentino de Oceanografía (IADO-CONICET), Bahía Blanca, Buenos Aires, Argentina

    Andrés J. Jaureguizar

  7. Universidad Provincial del Sudoeste (UPSO)-Subsede Coronel Pringles, Coronel Pringles, Buenos Aires, Argentina

    Andrés J. Jaureguizar

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  1. Moira Luz Clara
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  2. Mariano S. Alvarez
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Luz Clara, M., Alvarez, M.S., Vera, C. et al. Relationship between sea surface temperature anomalies in the Southwestern Atlantic Continental Shelf and atmospheric variability on intraseasonal timescales. Clim Dyn 59, 1539–1554 (2022). https://doi.org/10.1007/s00382-021-06058-3

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