A Relative Sea Surface Temperature Index for Classifying ENSO Events in a Changing Climate

Michelle L. L’Heureux aNOAA/NWS/NCEP/Climate Prediction Center, College Park, Maryland

Search for other papers by Michelle L. L’Heureux in
Current site
Google Scholar
PubMed
Close
,
Michael K. Tippett bDepartment of Applied Physics and Applied Mathematics, Columbia University, New York, New York

Search for other papers by Michael K. Tippett in
Current site
Google Scholar
PubMed
Close
,
Matthew C. Wheeler cBureau of Meteorology, Melbourne, Victoria, Australia

Search for other papers by Matthew C. Wheeler in
Current site
Google Scholar
PubMed
Close
,
Hanh Nguyen cBureau of Meteorology, Melbourne, Victoria, Australia

Search for other papers by Hanh Nguyen in
Current site
Google Scholar
PubMed
Close
,
Sugata Narsey cBureau of Meteorology, Melbourne, Victoria, Australia

Search for other papers by Sugata Narsey in
Current site
Google Scholar
PubMed
Close
,
Nathaniel Johnson dNOAA/OAR/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

Search for other papers by Nathaniel Johnson in
Current site
Google Scholar
PubMed
Close
,
Zeng-Zhen Hu aNOAA/NWS/NCEP/Climate Prediction Center, College Park, Maryland

Search for other papers by Zeng-Zhen Hu in
Current site
Google Scholar
PubMed
Close
,
Andrew B. Watkins cBureau of Meteorology, Melbourne, Victoria, Australia

Search for other papers by Andrew B. Watkins in
Current site
Google Scholar
PubMed
Close
,
Chris Lucas cBureau of Meteorology, Melbourne, Victoria, Australia

Search for other papers by Chris Lucas in
Current site
Google Scholar
PubMed
Close
,
Catherine Ganter cBureau of Meteorology, Melbourne, Victoria, Australia

Search for other papers by Catherine Ganter in
Current site
Google Scholar
PubMed
Close
,
Emily Becker eUniversity of Miami/Cooperative Institute for Marine and Atmospheric Studies, Miami, Florida

Search for other papers by Emily Becker in
Current site
Google Scholar
PubMed
Close
,
Wanqiu Wang aNOAA/NWS/NCEP/Climate Prediction Center, College Park, Maryland

Search for other papers by Wanqiu Wang in
Current site
Google Scholar
PubMed
Close
, and
Tom Di Liberto fNOAA Office of Communications, Washington, D. C.

Search for other papers by Tom Di Liberto in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

El Niño–Southern Oscillation (ENSO) is often characterized through the use of sea surface temperature (SST) departures from their climatological values, as in the Niño-3.4 index. However, this approach is problematic in a changing climate when the climatology itself is varying. To address this issue, van Oldenborgh et al. proposed a relative Niño-3.4 SST index, which subtracts the tropical mean SST anomaly from the Niño-3.4 index and multiplies by a scaling factor. We extend their work by providing a simplified calculation procedure for the scaling factor, and confirm that the relative index demonstrates reduced sensitivity to climate change and multidecadal variability. In particular, we show in three observational SST datasets that the relative index provides a more consistent classification of historical El Niño and La Niña oceanic conditions that is more robust across climatological periods compared to the nonrelative index. Forecast skill of the relative Niño-3.4 index in the North American Multimodel Ensemble (NMME) and ACCESS-S2 is slightly reduced for targets during the first half of the year because subtracting the tropical mean removes a source of additional skill. For targets in the second half of the year, the relative and nonrelative indices are equally skillful. Observed ENSO teleconnections in 200-hPa geopotential height and precipitation during key seasons are sharper and explain more variability over Australia and the contiguous United States when computed with the relative index. Overall, the relative Niño-3.4 index provides a more robust option for real-time monitoring and forecasting ENSO in a changing climate.

Significance Statement

The goal of this study is to further explore a relative sea surface temperature index for monitoring and prediction of El Niño–Southern Oscillation. Sea surface temperature indices are typically computed as a difference from a 30-yr climatological average, and El Niño and La Niña events occur when values exceed a certain threshold. This method is suitable when the climate is stationary. However, because of climate change and other lower-frequency variations, historical El Niño and La Niña events are reclassified depending on which climatological period is selected. A relative index is investigated to ameliorate this problem.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Michelle L. L’Heureux, michelle.lheureux@noaa.gov

Abstract

El Niño–Southern Oscillation (ENSO) is often characterized through the use of sea surface temperature (SST) departures from their climatological values, as in the Niño-3.4 index. However, this approach is problematic in a changing climate when the climatology itself is varying. To address this issue, van Oldenborgh et al. proposed a relative Niño-3.4 SST index, which subtracts the tropical mean SST anomaly from the Niño-3.4 index and multiplies by a scaling factor. We extend their work by providing a simplified calculation procedure for the scaling factor, and confirm that the relative index demonstrates reduced sensitivity to climate change and multidecadal variability. In particular, we show in three observational SST datasets that the relative index provides a more consistent classification of historical El Niño and La Niña oceanic conditions that is more robust across climatological periods compared to the nonrelative index. Forecast skill of the relative Niño-3.4 index in the North American Multimodel Ensemble (NMME) and ACCESS-S2 is slightly reduced for targets during the first half of the year because subtracting the tropical mean removes a source of additional skill. For targets in the second half of the year, the relative and nonrelative indices are equally skillful. Observed ENSO teleconnections in 200-hPa geopotential height and precipitation during key seasons are sharper and explain more variability over Australia and the contiguous United States when computed with the relative index. Overall, the relative Niño-3.4 index provides a more robust option for real-time monitoring and forecasting ENSO in a changing climate.

Significance Statement

The goal of this study is to further explore a relative sea surface temperature index for monitoring and prediction of El Niño–Southern Oscillation. Sea surface temperature indices are typically computed as a difference from a 30-yr climatological average, and El Niño and La Niña events occur when values exceed a certain threshold. This method is suitable when the climate is stationary. However, because of climate change and other lower-frequency variations, historical El Niño and La Niña events are reclassified depending on which climatological period is selected. A relative index is investigated to ameliorate this problem.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Michelle L. L’Heureux, michelle.lheureux@noaa.gov

Supplementary Materials

    • Supplemental Materials (PDF 5.1612 MB)
Save
  • Ádames, A. F., and J. M. Wallace, 2017: On the tropical atmospheric signature of El Niño. J. Atmos. Sci., 74, 19231939, https://doi.org/10.1175/JAS-D-16-0309.1.

    • Search Google Scholar
    • Export Citation
  • Back, L. E., and C. S. Bretherton, 2009: On the relationship between SST gradients, boundary layer winds, and convergence over the tropical oceans. J. Climate, 22, 41824196, https://doi.org/10.1175/2009JCLI2392.1.

    • Search Google Scholar
    • Export Citation
  • Barnston, A. G., M. Chelliah, and S. B. Goldenberg, 1997: Documentation of a highly ENSO-related SST region in the equatorial Pacific: Research note. Atmos.–Ocean, 35, 367383, https://doi.org/10.1080/07055900.1997.9649597.

    • Search Google Scholar
    • Export Citation
  • Becker, E. J., B. P. Kirtman, M. L’Heureux, Á. G. Muñoz, and K. Pegion, 2022: A decade of the North American Multimodel Ensemble (NMME): Research, application, and future directions. Bull. Amer. Meteor. Soc., 103, E973E995, https://doi.org/10.1175/BAMS-D-20-0327.1.

    • Search Google Scholar
    • Export Citation
  • Bjerknes, J., 1969: Atmospheric teleconnections from the equatorial Pacific. Mon. Wea. Rev., 97, 163172, https://doi.org/10.1175/1520-0493(1969)097<0163:ATFTEP>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chen, M., W. Shi, P. Xie, V. B. S. Silva, V. E. Kousky, R. W. Higgins, and J. E. Janowiak, 2008: Assessing objective techniques for gauge-based analyses of global daily precipitation. J. Geophys. Res., 113, D04110, https://doi.org/10.1029/2007JD009132.

    • Search Google Scholar
    • Export Citation
  • Chiang, J. C. H., and A. H. Sobel, 2002: Tropical tropospheric temperature variations caused by ENSO and their influence on the remote tropical climate. J. Climate, 15, 26162631, https://doi.org/10.1175/1520-0442(2002)015<2616:TTTVCB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • DelSole, T., and M. K. Tippett, 2014: Comparing forecast skill. Mon. Wea. Rev., 142, 46584678, https://doi.org/10.1175/MWR-D-14-00045.1.

    • Search Google Scholar
    • Export Citation
  • Evans, A., D. Jones, R. Smalley, and S. Lellyett, 2020: An enhanced gridded rainfall analysis scheme for Australia. Bureau Research Rep. 041, 35 pp., http://nla.gov.au/nla.obj-2786078795.

  • Gamble, F., G. Beard, A. Watkins, D. Jones, C. Ganter, V. Webb, and A. Evans, 2017: Tracking the El Niño-Southern Oscillation in real-time: A staged communication approach to event onset. J. South. Hemisphere Earth Syst. Sci., 67, 6478, https://doi.org/10.22499/3.6702.001.

    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

    • Search Google Scholar
    • Export Citation
  • Horel, J. D., and J. M. Wallace, 1981: Planetary-scale atmospheric phenomena associated with the southern oscillation. Mon. Wea. Rev., 109, 813829, https://doi.org/10.1175/1520-0493(1981)109<0813:PSAPAW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Huang, B., and Coauthors, 2017: Extended Reconstructed Sea Surface Temperature, version 5 (ERSSTv5): Upgrades, validations, and intercomparisons. J. Climate, 30, 81798205, https://doi.org/10.1175/JCLI-D-16-0836.1.

    • Search Google Scholar
    • Export Citation
  • Huang, B., C. Liu, V. Banzon, E. Freeman, G. Graham, B. Hankins, T. Smith, and H.-M. Zhang, 2021: Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) version 2.1. J. Climate, 34, 29232939, https://doi.org/10.1175/JCLI-D-20-0166.1.

    • Search Google Scholar
    • Export Citation
  • Ishii, M., A. Shouji, S. Sugimoto, and T. Matsumoto, 2005: Objective analyses of sea-surface temperature and marine meteorological variables for the 20th century using ICOADS and the Kobe Collection. Int. J. Climatol., 25, 865879, https://doi.org/10.1002/joc.1169.

    • Search Google Scholar
    • Export Citation
  • Izumo, T., J. Vialard, M. Lengaigne, and I. Suresh, 2020: Relevance of relative sea surface temperature for tropical rainfall interannual variability. Geophys. Res. Lett., 47, e2019GL086182, https://doi.org/10.1029/2019GL086182.

    • Search Google Scholar
    • Export Citation
  • Johnson, N. C., and S.-P. Xie, 2010: Changes in the sea surface temperature threshold for tropical convection. Nat. Geosci., 3, 842845, https://doi.org/10.1038/ngeo1008.

    • Search Google Scholar
    • Export Citation
  • Johnson, N. C., and Y. Kosaka, 2016: The impact of eastern equatorial Pacific convection on the diversity of boreal winter El Niño teleconnection patterns. Climate Dyn., 47, 37373765, https://doi.org/10.1007/s00382-016-3039-1.

    • Search Google Scholar
    • Export Citation
  • Keeling, C. D., R. B. Bacastow, A. E. Bainbridge, C. A. Ekdahl Jr., P. R. Guenther, L. S. Waterman, and J. F. S. Chin, 1976: Atmospheric carbon dioxide variations at Mauna Loa Observatory, Hawaii. Tellus, 28, 538551, https://doi.org/10.1111/j.2153-3490.1976.tb00701.x.

    • Search Google Scholar
    • Export Citation
  • Kirtman, B. P., and Coauthors, 2014: The North American Multimodel Ensemble: Phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction. Bull. Amer. Meteor. Soc., 95, 585601, https://doi.org/10.1175/BAMS-D-12-00050.1.

    • Search Google Scholar
    • Export Citation
  • Lee, S., M. L’Heureux, A. T. Wittenberg, R. Seager, P. A. O’Gorman, and N. C. Johnson, 2022: On the future zonal contrasts of equatorial Pacific climate: Perspectives from observations, simulations, and theories. npj Climate Atmos. Sci., 5, 82, https://doi.org/10.1038/s41612-022-00301-2.

    • Search Google Scholar
    • Export Citation
  • L’Heureux, M. L., and Coauthors, 2017: Observing and predicting the 2015/16 El Niño. Bull. Amer. Meteor. Soc., 98, 13631382, https://doi.org/10.1175/BAMS-D-16-0009.1.

    • Search Google Scholar
    • Export Citation
  • Li, X., Z.-Z. Hu, R. Ding, and Y. Liu, 2023: Which ENSO index best represents its global influences? Climate Dyn., 61, 48994913, https://doi.org/10.1007/s00382-023-06804-9.

    • Search Google Scholar
    • Export Citation
  • Maher, N., and Coauthors, 2023: The future of the El Niño–Southern Oscillation: Using large ensembles to illuminate time-varying responses and inter-model differences. Earth Syst. Dyn., 14, 413431, https://doi.org/10.5194/esd-14-413-2023.

    • Search Google Scholar
    • Export Citation
  • Nguyen, H., C. Lucas, M. Wheeler, and A. Watkins, 2022: Summary of a workshop on ENSO/IOD alert systems for a warming world held 16–17 August 2022. Bureau Research Rep. 072, 12 pp., http://nla.gov.au/nla.obj-3136569121.

  • NOAA CPC, 2023: Cold and warm ENSO episodes by season. NOAA, accessed 30 June 2023, https://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php.

  • Ramsay, H. A., and A. H. Sobel, 2011: Effects of relative and absolute sea surface temperature on tropical cyclone potential intensity using a single-column model. J. Climate, 24, 183193, https://doi.org/10.1175/2010JCLI3690.1.

    • Search Google Scholar
    • Export Citation
  • Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670.

    • Search Google Scholar
    • Export Citation
  • Risbey, J. S., M. J. Pook, P. C. McIntosh, M. C. Wheeler, and H. H. Hendon, 2009: On the remote drivers of rainfall variability in Australia. Mon. Wea. Rev., 137, 32333253, https://doi.org/10.1175/2009MWR2861.1.

    • Search Google Scholar
    • Export Citation
  • Sobel, A. H., I. M. Held, and C. S. Bretherton, 2002: The ENSO signal in tropical tropospheric temperature. J. Climate, 15, 27022706, https://doi.org/10.1175/1520-0442(2002)015<2702:TESITT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Tippett, M. K., and M. L. L’Heureux, 2020: Low-dimensional representations of Niño 3.4 evolution and the spring persistence barrier. npj Climate Atmos. Sci., 3, 24, https://doi.org/10.1038/s41612-020-0128-y.

    • Search Google Scholar
    • Export Citation
  • Turkington, T., B. Timbal, and R. Rahmat, 2019: The impact of global warming on sea surface temperature based El Niño-Southern Oscillation monitoring indices. Int. J. Climatol., 39, 10921103, https://doi.org/10.1002/joc.5864.

    • Search Google Scholar
    • Export Citation
  • van Oldenborgh, G. J., H. Hendon, T. Stockdale, M. L’Heureux, E. C. de Perez, R. Singh, and M. van Aalst, 2021: Defining El Niño indices in a warming climate. Environ. Res. Lett., 16, 044003, https://doi.org/10.1088/1748-9326/abe9ed.

    • Search Google Scholar
    • Export Citation
  • Vecchi, G. A., and B. J. Soden, 2007: Global warming and the weakening of the tropical circulation. J. Climate, 20, 43164340, https://doi.org/10.1175/JCLI4258.1.

    • Search Google Scholar
    • Export Citation
  • Wedd, R., and Coauthors, 2022: ACCESS-S2: The upgraded Bureau of Meteorology multi-week to seasonal prediction system. J. South. Hemisphere Earth Syst. Sci., 72, 218242, https://doi.org/10.1071/ES22026.

    • Search Google Scholar
    • Export Citation
  • WMO, 2017: World Meteorological Organization guidelines on the calculation of climate normals. WMO-1203, 18 pp., https://library.wmo.int/doc_num.php?explnum_id=4166.

  • Xie, P., and P. A. Arkin, 1997: Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc., 78, 25392558, https://doi.org/10.1175/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 7312 7312 2003
Full Text Views 880 880 82
PDF Downloads 879 879 73