Editorial Type:
Article
Article Type:
Research Article

Time–Space SST Variability in the Atlantic during 2013: Seasonal Cycle

Liyan Liu I. M. Systems Group, and Environmental Modeling Center, NOAA/NWS/NCEP, College Park, Maryland

Search for other papers by Liyan Liu in
Current site
Google Scholar
PubMed Close
,
Carlos Lozano Environmental Modeling Center, NOAA/NWS/NCEP, College Park, Maryland

Search for other papers by Carlos Lozano in
Current site
Google Scholar
PubMed Close
, and
Dan Iredell I. M. Systems Group, and Environmental Modeling Center, NOAA/NWS/NCEP, College Park, Maryland

Search for other papers by Dan Iredell in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Sep 2015
DOI:
https://doi.org/10.1175/JTECH-D-15-0028.1
Page(s):
1689–1705
Restricted access

Abstract

A 2-yr-long daily gridded field of sea surface temperature (SST) in the Atlantic centered for the year 2013 is projected onto orthogonal components: its mean, six harmonics of the year cycle, the slow-varying contribution, and the fast-varying contribution. The periodic function defined by the year harmonics, referred to here as the seasonal harmonic, contains most of the year variability in 2013. The seasonal harmonic is examined in its spatial and temporal distribution by describing the amplitude and phase of its maxima and minima, and other associated parameters. In the seasonal harmonic, the ratio of the duration of warming period to cooling period ranges from 0.2 to 2.0. There are also differences in the spatial patterns and dominance of the year harmonics—in general associated with regions with different insolation, oceanic, and atmospheric regimes. Empirical orthogonal functions (EOFs) of the seasonal harmonic allow for a succinct description of the seasonal evolution for the Atlantic and its subdomains. The decomposition can be applied to model output, allowing for a more incisive model validation and data assimilation. The decorrelation time scale of the rapidly varying signal is found to be nearly independent of the time of the year once four or more harmonics are used. The decomposition algorithm, here implemented for a single year cycle, can be applied to obtain a multiyear average of the seasonal harmonic.

Corresponding author address: Dr. Liyan Liu, Center for Weather and Climate Prediction, NOAA/NWS/NCEP/CPC, 5830 University Research Court, College Park, MD 20740. E-mail: liyan.liu@noaa.gov

Abstract

A 2-yr-long daily gridded field of sea surface temperature (SST) in the Atlantic centered for the year 2013 is projected onto orthogonal components: its mean, six harmonics of the year cycle, the slow-varying contribution, and the fast-varying contribution. The periodic function defined by the year harmonics, referred to here as the seasonal harmonic, contains most of the year variability in 2013. The seasonal harmonic is examined in its spatial and temporal distribution by describing the amplitude and phase of its maxima and minima, and other associated parameters. In the seasonal harmonic, the ratio of the duration of warming period to cooling period ranges from 0.2 to 2.0. There are also differences in the spatial patterns and dominance of the year harmonics—in general associated with regions with different insolation, oceanic, and atmospheric regimes. Empirical orthogonal functions (EOFs) of the seasonal harmonic allow for a succinct description of the seasonal evolution for the Atlantic and its subdomains. The decomposition can be applied to model output, allowing for a more incisive model validation and data assimilation. The decorrelation time scale of the rapidly varying signal is found to be nearly independent of the time of the year once four or more harmonics are used. The decomposition algorithm, here implemented for a single year cycle, can be applied to obtain a multiyear average of the seasonal harmonic.

Corresponding author address: Dr. Liyan Liu, Center for Weather and Climate Prediction, NOAA/NWS/NCEP/CPC, 5830 University Research Court, College Park, MD 20740. E-mail: liyan.liu@noaa.gov
Save
Cover Journal of Atmospheric and Oceanic Technology
Journal of Atmospheric and Oceanic Technology

  • Abbott, M. R., and Letelier R. M. , 1998: Decorrelation scales of chlorophyll as observed from bio-optical drifters in the California Current. Deep-Sea Res. II, 45, 1639–1667, doi:10.1016/S0967-0645(98)80011-8.

    • Search Google Scholar
    • Export Citation

  • Alexander, M. A., and Penland C. , 1996: Variability in a mixed layer ocean model driven by stochastic atmospheric forcing. J. Climate, 9, 2424–2442, doi:10.1175/1520-0442(1996)009<2424:VIAMLO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Armstrong, E. M., and Vazquez-Cuervo J. , 2001: A new global satellite-based sea surface temperature climatology. Geophys. Res. Lett., 28, 4199–4202, doi:10.1029/2001GL013316.

    • Search Google Scholar
    • Export Citation

  • Bingham, C., Godfrey M. D. , and Tukey J. W. , 1967: Modern techniques of power spectrum estimation. IEEE Trans. Audio Electroacoust., 15, 55–66, doi:10.1109/TAU.1967.1161895.

    • Search Google Scholar
    • Export Citation

  • Box, G. E. P., and Jenkins G. M. , 1976: Time Series Analysis: Forecasting and Control. Holden-Day, 575 pp.

  • Burls, N. J., Reason C. J. C. , Penven P. , and Philander S. G. , 2011: Similarities between the tropical Atlantic seasonal cycle and ENSO: An energetics perspective. J. Geophys. Res., 116, C11010, doi:10.1029/2011JC007164.

    • Search Google Scholar
    • Export Citation

  • Casey, K. S., Brandon T. B. , Cornillon P. , and Evans R. , 2010: The past, present and future of the AVHRR Pathfinder SST program. Oceanography from Space: Revisited, V. Barale, J. F. R. Gower, and L. Alberotanza, Eds., Springer, 273–287.

    • Search Google Scholar
    • Export Citation

  • Chang, Y.-S., Rosati A. J. , Zhang S. , and Harrison M. J. , 2009: Objective analysis of monthly temperature and salinity for the world ocean in the 21st century: Comparison with World Ocean Atlas and application to assimilation validation. J. Geophys. Res., 114, C02014, doi:10.1029/2008JC004970.

    • Search Google Scholar
    • Export Citation

  • Chao, Y., Li Z. , Kindle J. C. , Paduan J. D. , and Chavez F. P. , 2003: A high-resolution surface vector wind product for coastal oceans: Blending satellite scatterometer measurements with regional mesoscale atmospheric model simulations. Geophys. Res. Lett., 30, 1013, doi:10.1029/2002GL015729.

    • Search Google Scholar
    • Export Citation

  • Craddock, J. M., 1973: Problems and prospects for eigenvector analysis in meteorology. J. Roy. Stat. Soc., 22D, 133–145, doi:10.2307/2987365.

    • Search Google Scholar
    • Export Citation

  • Deser, C., Alexander M. A. , Xie S.-P. , and Phillips A. S. , 2010: Sea surface temperature variability: Patterns and mechanisms. Annu. Rev. Mar. Sci., 2, 115–143, doi:10.1146/annurev-marine-120408-151453.

    • Search Google Scholar
    • Export Citation

  • Gallegos-Garcia, A., Emery W. J. , Reid R. O. , and Magaard L. , 1981: Frequency-wavenumber spectra of sea surface temperature and wind-stress curl in the eastern North Pacific. J. Phys. Oceanogr.,11, 1059–1077, doi:10.1175/1520-0485(1981)011<1059:FWSOSS>2.0.CO;2.

  • Giannakis, D., and Majda A. J. , 2012: Limits of predictability in the North Pacific sector of a comprehensive climate model. Geophys. Res. Lett., 39, L24602, doi:10.1029/2012GL054273.

    • Search Google Scholar
    • Export Citation

  • Huybers, P., and Curry W. , 2006: Links between annual, Milankovitch and continuum temperature variability. Nature, 441, 329–332, doi:10.1038/nature04745.

    • Search Google Scholar
    • Export Citation

  • Knudsen, P., Andersen O. B. , and Knudsen T. , 1996: ATSR sea surface temperature data in a global analysis with TOPEX/POSEIDON altimetry. Geophys. Res. Lett., 23, 821–824, doi:10.1029/96GL00618.

    • Search Google Scholar
    • Export Citation

  • Legaard, K. R., and Thomas A. C. , 2007: Spatial patterns of intraseasonal variability of chlorophyll and sea surface temperature in the California Current. J. Geophys. Res.,112, C09006, doi:10.1029/2007JC004097.

  • Locarnini, R. A., and Coauthors, 2013: Temperature. Vol. 1, World Ocean Atlas 2013, S. Levitus and A. Mishonov, Ed., NOAA Atlas NESDIS 73, 40 pp.

  • Lorenz, E. N., 1970: Climatic change as a mathematical problem. J. Appl. Meteor., 9, 325–329, doi:10.1175/1520-0450(1970)009<0325:CCAAMP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Lumpkin, R., and Garraffo Z. , 2005: Evaluating the decomposition of tropical Atlantic drifter observations. J. Atmos. Oceanic Technol., 22, 1403–1415, doi:10.1175/JTECH1793.1.

    • Search Google Scholar
    • Export Citation

  • Mesias, J. M., Bisagni J. J. , and Brunner A. , 2007: A high-resolution satellite-derived sea surface temperature climatology for the western North Atlantic Ocean. Cont. Shelf Res., 27, 191–207, doi:10.1016/j.csr.2006.10.002.

    • Search Google Scholar
    • Export Citation

  • Molinari, R. L., Garraffo Z. , and Snowden D. , 2008: Differences between observed and a coupled simulation of North Atlantic sea surface currents and temperature. J. Geophys. Res., 113, C09011, doi:10.1029/2008JC004848.

    • Search Google Scholar
    • Export Citation

  • Namias, J. 1969: Seasonal interactions between the North Pacific Ocean and the atmosphere during the 1960's. Mon. Wea. Rev.,97, 173-192, doi:10.1175/1520-0493(1969)097<0173:SIBTNP>2.3.CO;2.

  • Petrie, B., Ouellet M. , and Chassé J. , 2003: The annual cycle of sea-surface temperature and spatial scales of its anomalies in Canadian Atlantic waters. AZMP Bull., 3, 13–17.

    • Search Google Scholar
    • Export Citation

  • Podesta, G. P., Brown O. B. , and Evans R. H. , 1991: The annual cycle of satellite-derived sea surface temperature in the southwestern Atlantic Ocean. J. Climate, 4, 457–467, doi:10.1175/1520-0442(1991)004<0457:TACOSD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Purser, R. J., Wu W.-S. , Parrish D. F. , and Roberts N. M. , 2003a: Numerical aspects of the application of recursive filters to variational statistical analysis. Part I: Spatially homogeneous and isotropic Gaussian covariances. Mon. Wea. Rev., 131, 1524–1535, doi:10.1175//1520-0493(2003)131<1524:NAOTAO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Purser, R. J., Wu W.-S. , Parrish D. F. , and Roberts N. M. , 2003b: Numerical aspects of the application of recursive filters to variational statistical analysis. Part II: Spatially inhomogeneous and anisotropic general covariances. Mon. Wea. Rev., 131, 1536–1548, doi:10.1175//2543.1.

    • Search Google Scholar
    • Export Citation

  • Reynolds, R. W., and Chelton D. B. , 2010: Comparisons of daily sea surface temperature analyses for 2007–08. J. Climate, 23, 3545–3562, doi:10.1175/2010JCLI3294.1.

    • Search Google Scholar
    • Export Citation

  • Reynolds, R. W., Rayner N. A. , Smith T. M. , Stokes D. C. , and Wang W. , 2002: An improved in situ and satellite SST analysis for climate. J. Climate, 15, 1609–1625, doi:10.1175/1520-0442(2002)015<1609:AIISAS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Reynolds, R. W., Smith T. M. , Liu C. , Chelton D. B. , Casey K. S. , and Schlax M. G. , 2007: Daily high-resolution blended analyses for sea surface temperature. J. Climate, 20, 5473–5496, doi:10.1175/2007JCLI1824.1.

    • Search Google Scholar
    • Export Citation

  • Yamenis, T., and Stammer D. , 2004: Time and space scales in the AMSR-E SST fields. ECCO Report Series 35, 24 pp. [Available online at http://www.ecco-group.org/pdfs/reports/report_35.pdf.]

  • Vazquez, J., Perry K. , and Kilpatrick K. , 1998: NOAA/NASA AVHRR Oceans Pathfinder sea surface temperature data set user’s reference manual. Version 4.0, JPL Publ. D-14070. [Available online at ftp://podaac-ftp.jpl.nasa.gov/allData/avhrr/L3/pathfinder_v51/docs/usr_gde4_0_toc.html.]

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 174 62 3
PDF Downloads 107 26 0