The Role of Atmospheric Noise in Decadal SST Variability

Edwin K. Schneider aGeorge Mason University, Fairfax, Virginia

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Benjamin P. Kirtman bRosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida

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Natalie Perlin bRosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida

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Abstract

A substantial role for atmospheric noise in simulations of decadal internal variability of SST is demonstrated by a comparison of a multicentury climate model control and a corresponding interactive ensemble (IE) simulation. The IE is designed to reduce atmospheric noise in the heat flux, wind stress, and freshwater flux at the air–sea interface. This comparison suggests that nearly all SST variability on decadal time scales is forced by internal atmospheric variability. The results are examined to determine the relative roles of atmospheric surface heat flux noise and ocean dynamics in the decadal volume-averaged heat budget of the upper ocean. The regional heat budgets in two regions, the South Pacific and the midlatitude North Atlantic, show the net atmospheric surface heat flux to be approximately in equilibrium with the ocean dynamics forcing. The IE and control results are used in the equilibrium heat budget approximation to infer the atmospheric heat flux response to SST, as well as the time series of the control atmospheric noise surface heat flux and ocean dynamics forcings for several regions. The South Pacific region SST is found to be primarily forced by the atmospheric noise surface heat flux and the North Atlantic region SST is forced by the ocean dynamics. Similar strengths for the atmospheric heat flux noise and ocean dynamics forcing, with an interdecadal atmospheric heat flux noise time scale and a centennial ocean dynamics time scale, are found for an Atlantic multidecadal variability region SST.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Edwin K. Schneider, eschnei1@gmu.edu

Abstract

A substantial role for atmospheric noise in simulations of decadal internal variability of SST is demonstrated by a comparison of a multicentury climate model control and a corresponding interactive ensemble (IE) simulation. The IE is designed to reduce atmospheric noise in the heat flux, wind stress, and freshwater flux at the air–sea interface. This comparison suggests that nearly all SST variability on decadal time scales is forced by internal atmospheric variability. The results are examined to determine the relative roles of atmospheric surface heat flux noise and ocean dynamics in the decadal volume-averaged heat budget of the upper ocean. The regional heat budgets in two regions, the South Pacific and the midlatitude North Atlantic, show the net atmospheric surface heat flux to be approximately in equilibrium with the ocean dynamics forcing. The IE and control results are used in the equilibrium heat budget approximation to infer the atmospheric heat flux response to SST, as well as the time series of the control atmospheric noise surface heat flux and ocean dynamics forcings for several regions. The South Pacific region SST is found to be primarily forced by the atmospheric noise surface heat flux and the North Atlantic region SST is forced by the ocean dynamics. Similar strengths for the atmospheric heat flux noise and ocean dynamics forcing, with an interdecadal atmospheric heat flux noise time scale and a centennial ocean dynamics time scale, are found for an Atlantic multidecadal variability region SST.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Edwin K. Schneider, eschnei1@gmu.edu
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  • Ba, J., N. S. Keenlyside, W. Park, M. Latif, E. Hawkins, and H. Ding, 2013: A mechanism for Atlantic multidecadal variability in the Kiel climate model. Climate Dyn., 41, 21332144, https://doi.org/10.1007/s00382-012-1633-4.

    • Search Google Scholar
    • Export Citation
  • Bombardi, R. J., and Coauthors, 2015: Evaluation of the CFSv2 CMIP5 decadal predictions. Climate Dyn., 44, 543557, https://doi.org/10.1007/s00382-014-2360-9.

    • Search Google Scholar
    • Export Citation
  • Bryan, F. O., R. Tomas, J. M. Dennis, D. B. Chelton, N. G. Loeb, and J. L. McClean, 2010: Frontal scale air–sea interaction in high-resolution coupled climate models. J. Climate, 23, 62776291, https://doi.org/10.1175/2010JCLI3665.1.

    • Search Google Scholar
    • Export Citation
  • Buckley, M. W., R. M. Ponte, G. Forget, and P. Heimbach, 2014: Low-frequency SST and upper-ocean heat content variability in the North Atlantic. J. Climate, 27, 49965018, https://doi.org/10.1175/JCLI-D-13-00316.1.

    • Search Google Scholar
    • Export Citation
  • Cane, M. A., A. C. Clement, L. N. Murphy, and K. Bellomo, 2017: Low-pass filtering, heat flux, and Atlantic multidecadal variability. J. Climate, 30, 75297553, https://doi.org/10.1175/JCLI-D-16-0810.1.

    • Search Google Scholar
    • Export Citation
  • Chen, H., E. K. Schneider, B. P. Kirtman, and I. Colfescu, 2013: Evaluation of weather noise and its role in climate model simulations. J. Climate, 26, 37663784, https://doi.org/10.1175/JCLI-D-12-00292.1.

    • Search Google Scholar
    • Export Citation
  • Chen, H., E. K. Schneider, and Z. Wu, 2016: Mechanisms of internally generated decadal-to-multidecadal variability of SST in the Atlantic Ocean in a coupled GCM. Climate Dyn., 46, 15171546, https://doi.org/10.1007/s00382-015-2660-8.

    • Search Google Scholar
    • Export Citation
  • Clement, A., K. Bellomo, L. N. Murphy, M. A. Cane, T. Mauritsen, G. Rädel, and B. Stevens, 2015: The Atlantic multidecadal oscillation without a role for ocean circulation. Science, 350, 320324, https://doi.org/10.1126/science.aab3980.

    • Search Google Scholar
    • Export Citation
  • Clement, A., M. A. Cane, L. N. Murphy, K. Bellomo, T. Mauritsen, and B. Stevens, 2016: Response to comment on “The Atlantic multidecadal oscillation without a role for ocean circulation.” Science, 352, 1527, https://doi.org/10.1126/science.aaf2575.

    • Search Google Scholar
    • Export Citation
  • Colfescu, I., and E. K. Schneider, 2017: Internal atmospheric noise characteristics in 20th century coupled atmosphere-ocean model simulations. Climate Dyn., 49, 22052217, https://doi.org/10.1007/s00382-016-3440-9.

    • Search Google Scholar
    • Export Citation
  • Colfescu, I., and E. K. Schneider, 2020: Decomposition of the Atlantic multidecadal variability in a historical climate simulation. J. Climate, 33, 42294254, https://doi.org/10.1175/JCLI-D-18-0180.1.

    • Search Google Scholar
    • Export Citation
  • Danabasoglu, G., S. G. Yeager, Y.-O. Kwon, J. J. Tribbia, A. S. Phillips, and J. W. Hurrell, 2012: Variability of the Atlantic meridional overturning circulation in CCSM4. J. Climate, 25, 51535172, https://doi.org/10.1175/JCLI-D-11-00463.1.

    • Search Google Scholar
    • Export Citation
  • Delworth, T. L., and F. Zeng, 2016: The impact of the North Atlantic oscillation on climate through its influence on the Atlantic meridional overturning circulation. J. Climate, 29, 941962, https://doi.org/10.1175/JCLI-D-15-0396.1.

    • Search Google Scholar
    • Export Citation
  • Delworth, T. L., S. Manabe, and R. J. Stouffer, 1993: Interdecadal variations of the thermohaline circulation in a coupled ocean–atmosphere model. J. Climate, 6, 19932011, https://doi.org/10.1175/1520-0442(1993)006<1993:IVOTTC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Delworth, T. L., F. Zeng, L. Zhang, R. Zhang, G. A. Vecchi, and X. Yang, 2017: The central role of ocean dynamics in connecting the North Atlantic oscillation to the extratropical component of the Atlantic multidecadal oscillation. J. Climate, 30, 37893805, https://doi.org/10.1175/JCLI-D-16-0358.1.

    • Search Google Scholar
    • Export Citation
  • Deser, C., A. Phillips, V. Bourdette, and H. Tang, 2012: Uncertainty in climate change projections: The role of internal variability. Climate Dyn., 38, 527546, https://doi.org/10.1007/s00382-010-0977-x.

    • Search Google Scholar
    • Export Citation
  • Dong, B., and R. T. Sutton, 2005: Mechanism of interdecadal thermohaline circulation variability in a coupled ocean–atmosphere GCM. J. Climate, 18, 11171135, https://doi.org/10.1175/JCLI3328.1.

    • Search Google Scholar
    • Export Citation
  • Fan, M., and E. K. Schneider, 2012: Observed decadal North Atlantic tripole SST variability. Part I: Weather noise forcing and coupled response. J. Atmos. Sci., 69, 3550, https://doi.org/10.1175/JAS-D-11-018.1.

    • Search Google Scholar
    • Export Citation
  • Frankignoul, C., and K. Hasselmann, 1977: Stochastic climate models. Part II: Application to sea-surface temperature anomalies and thermocline variability. Tellus, 29A, 289305, https://doi.org/10.3402/tellusa.v29i4.11362.

    • Search Google Scholar
    • Export Citation
  • Frankignoul, C., and E. Kestenare, 2002: The surface heat flux feedback. Part I: Estimates from observations in the Atlantic and the North Pacific. Climate Dyn., 19, 633647, https://doi.org/10.1007/s00382-002-0252-x.

    • Search Google Scholar
    • Export Citation
  • Frankignoul, C., A. Czaja, and B. L’Heveder, 1998: Air–sea feedback in the North Atlantic and surface boundary conditions for ocean models. J. Climate, 11, 23102324, https://doi.org/10.1175/1520-0442(1998)011<2310:ASFITN>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gates, W. L., and Coauthors, 1999: An overview of the results of the Atmospheric Model Intercomparison Project (AMIP I). Bull. Amer. Meteor. Soc., 80, 2956, https://doi.org/10.1175/1520-0477(1999)080<0029:AOOTRO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gent, P. R., and Coauthors, 2011: The Community Climate System Model version 4. J. Climate, 24, 49734991, https://doi.org/10.1175/2011JCLI4083.1.

    • Search Google Scholar
    • Export Citation
  • Gulev, S. K., M. Latif, N. Keenlyside, W. Park, and K. P. Koltermann, 2013: North Atlantic Ocean control on surface heat flux on multidecadal timescales. Nature, 499, 464467, https://doi.org/10.1038/nature12268.

    • Search Google Scholar
    • Export Citation
  • Hasselmann, K., 1976: Stochastic climate models Part I. Theory. Tellus, 28, 473485, https://doi.org/10.1111/j.2153-3490.1976.tb00696.x.

    • Search Google Scholar
    • Export Citation
  • Keenlyside, N. S., M. Latif, J. Jungclaus, L. Kornblueh, and E. Roeckner, 2008: Advancing decadal-scale climate prediction in the North Atlantic sector. Nature, 453, 8488, https://doi.org/10.1038/nature06921.

    • Search Google Scholar
    • Export Citation
  • Kim, W. M., S. G. Yeager, and G. Danabasoglu, 2018: Key role of internal ocean dynamics in Atlantic multidecadal variability during the last half century. Geophys. Res. Lett., 45, 13 44913 457, https://doi.org/10.1029/2018GL080474.

    • Search Google Scholar
    • Export Citation
  • Kim, W. M., S. G. Yeager, and G. Danabasoglu, 2020: Atlantic multidecadal variability and associated climate impacts initiated by ocean thermohaline dynamics. J. Climate, 33, 13171334, https://doi.org/10.1175/JCLI-D-19-0530.1.

    • Search Google Scholar
    • Export Citation
  • Kirtman, B. P., and J. Shukla, 2002: Interactive coupled ensemble: A new coupling strategy for CGCMs. Geophys. Res. Lett., 29, 1367, https://doi.org/10.1029/2002GL014834.

    • Search Google Scholar
    • Export Citation
  • Kirtman, B. P., D. M. Straus, D. Min, E. K. Schneider, and L. Siqueira, 2009: Toward linking weather and climate in the interactive ensemble NCAR climate model. Geophys. Res. Lett., 36, L13705, https://doi.org/10.1029/2009GL038389.

    • Search Google Scholar
    • Export Citation
  • Kirtman, B. P., E. K. Schneider, D. M. Straus, D. Min, and R. Burgman, 2011: How weather impacts the forced climate response. Climate Dyn., 37, 23892416, https://doi.org/10.1007/s00382-011-1084-3.

    • Search Google Scholar
    • Export Citation
  • Kirtman, B. P., and Coauthors, 2012: Impact of ocean model resolution on CCSM climate simulations. Climate Dyn., 39, 13031328, https://doi.org/10.1007/s00382-012-1500-3.

    • Search Google Scholar
    • Export Citation
  • Kirtman, B. P., and Coauthors, 2013: Near-term climate change: Projections and predictability. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 953–1028.

  • Kirtman, B. P., N. Perlin, and L. Siqueira, 2017: Ocean eddies and climate predictability. Chaos, 27, 126902, https://doi.org/10.1063/1.4990034.

    • Search Google Scholar
    • Export Citation
  • Li, L., M. S. Lozier, and M. W. Buckley, 2020: An investigation of the ocean’s role in Atlantic multidecadal variability. J. Climate, 33, 30193035, https://doi.org/10.1175/JCLI-D-19-0236.1.

    • Search Google Scholar
    • Export Citation
  • Liu, Z., P. Gu, and T. L. Delworth, 2023: Strong red noise ocean forcing on Atlantic multidecadal variability assessed from surface heat flux: Theory and application. J. Climate, 36, 5580, https://doi.org/10.1175/JCLI-D-22-0063.1.

    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 1963: Deterministic nonperiodic flow. J. Atmos. Sci., 20, 130141, https://doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., and Coauthors, 2014: Decadal climate prediction: An update from the trenches. Bull. Amer. Meteor. Soc., 95, 243267, https://doi.org/10.1175/BAMS-D-12-00241.1.

    • Search Google Scholar
    • Export Citation
  • Milinski, S., N. Maher, and D. Olonscheck, 2020: How large does a large ensemble need to be? Earth Syst. Dyn., 11, 885901, https://doi.org/10.5194/esd-11-885-2020.

    • Search Google Scholar
    • Export Citation
  • O’Reilly, C. H., M. Huber, T. Woollings, and L. Zanna, 2016: The signature of low-frequency oceanic forcing in the Atlantic multidecadal oscillation. Geophys. Res. Lett., 43, 28102818, https://doi.org/10.1002/2016GL067925.

    • Search Google Scholar
    • Export Citation
  • Park, S., C. Deser, and M. A. Alexander, 2005: Estimation of the surface heat flux response to sea surface temperature anomalies over the global oceans. J. Climate, 18, 45824599, https://doi.org/10.1175/JCLI3521.1.

    • Search Google Scholar
    • Export Citation
  • Schneider, E. K., and M. Fan, 2007: Weather noise forcing of surface climate variability. J. Atmos. Sci., 64, 32653280, https://doi.org/10.1175/JAS4026.1.

    • Search Google Scholar
    • Export Citation
  • Smith, D. M., A. A. Scaife, and B. P. Kirtman, 2012: What is the current state of scientific knowledge with regard to seasonal and decadal forecasting? Environ. Res. Lett., 7, 015602, https://doi.org/10.1088/1748-9326/7/1/015602.

    • Search Google Scholar
    • Export Citation
  • Wu, Z., E. K. Schneider, and B. P. Kirtman, 2004: Causes of low frequency North Atlantic SST variability in a coupled GCM. Geophys. Res. Lett., 31, L09210, https://doi.org/10.1029/2004GL019548.

    • Search Google Scholar
    • Export Citation
  • Zhang, R., 2017: On the persistence and coherence of subpolar sea surface temperature and salinity anomalies associated with the Atlantic multidecadal variability. Geophys. Res. Lett., 44, 78657875, https://doi.org/10.1002/2017GL074342.

    • Search Google Scholar
    • Export Citation
  • Zhang, R., R. Sutton, G. Danabasoglu, T. L. Delworth, W. M. Kim, J. Robson, and S. G. Yeager, 2016: Comment on “The Atlantic multidecadal oscillation without a role for ocean circulation.” Science, 352, 1527, https://doi.org/10.1126/science.aaf1660.

    • Search Google Scholar
    • Export Citation
  • Zhang, W., and B. Kirtman, 2019: Estimates of decadal climate predictability from an interactive ensemble model. Geophys. Res. Lett., 46, 33873397, https://doi.org/10.1029/2018GL081307.

    • Search Google Scholar
    • Export Citation
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