Editorial Type:
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Article Type:
Research Article

Linking Projected Changes in Seasonal Climate Predictability and ENSO Amplitude

Dillon J. Amaya NOAA/Physical Sciences Laboratory, Boulder, Colorado

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https://orcid.org/0000-0001-6720-3265
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Nicola Maher Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, University of Colorado Boulder Campus, Boulder, Colorado
Department of Atmospheric and Oceanic Sciences (ATOC), University of Colorado Boulder, Boulder, Colorado
The Australian National University, Canberra, Australian Capital Territory, Australia

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Clara Deser National Science Foundation National Center for Atmospheric Research, Boulder, Colorado

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Michael G. Jacox NOAA/Physical Sciences Laboratory, Boulder, Colorado
NOAA/Environmental Research Division, Southwest Fisheries Science Center, Monterey, California

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Michael A. Alexander NOAA/Physical Sciences Laboratory, Boulder, Colorado

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Matthew Newman NOAA/Physical Sciences Laboratory, Boulder, Colorado

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Juliana Dias NOAA/Physical Sciences Laboratory, Boulder, Colorado

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Jiale Lou Atmospheric and Oceanic Sciences Program, Princeton University, Princeton, New Jersey

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Online Publication:
10 Jan 2025
Print Publication:
01 Feb 2025
DOI:
https://doi.org/10.1175/JCLI-D-23-0648.1
Page(s):
675–688
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Abstract

Recent studies have shown that potential predictability and actual forecast skill have varied throughout the historical record, primarily due to natural decadal variability. In this study, we explore whether and how potential predictability is projected to change in the future as a distinct response to anthropogenic climate change. We estimate the potential predictability of El Niño–Southern Oscillation (ENSO) as well as global surface temperature, precipitation, and upper-atmospheric circulation anomalies from 1921 to 2100, within a perfect model framework, using five coupled model large ensembles. We find that historical and projected ENSO amplitude changes generate global-scale shifts in climate predictability via ENSO-driven changes in the signal-to-noise ratio of seasonal forecasts, with a 10% change in Niño-3.4 standard deviation leading to a 14% change in globally averaged forecast skill at 12-month lead. This relationship suggests that potential predictability changes across much of the globe in the coming decades could be linked to anthropogenic climate change of ENSO. However, since current models substantially disagree on the sign and intensity of projected ENSO change, the trajectory of future global predictability changes cannot yet be determined. This problem is demonstrated by widely varying predictability changes seen across the five large ensembles, with models exhibiting a robust increase, robust decrease, or no significant change in predictability, depending upon their respective projected ENSO amplitude trends. Our results highlight the need for climate model development aimed at better capturing past forced and unforced changes to ENSO variability, which is necessary (if not sufficient) to constrain projected changes to climate predictability worldwide.

© 2025 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: Dillon J. Amaya, dillon.amaya@noaa.gov

Abstract

Recent studies have shown that potential predictability and actual forecast skill have varied throughout the historical record, primarily due to natural decadal variability. In this study, we explore whether and how potential predictability is projected to change in the future as a distinct response to anthropogenic climate change. We estimate the potential predictability of El Niño–Southern Oscillation (ENSO) as well as global surface temperature, precipitation, and upper-atmospheric circulation anomalies from 1921 to 2100, within a perfect model framework, using five coupled model large ensembles. We find that historical and projected ENSO amplitude changes generate global-scale shifts in climate predictability via ENSO-driven changes in the signal-to-noise ratio of seasonal forecasts, with a 10% change in Niño-3.4 standard deviation leading to a 14% change in globally averaged forecast skill at 12-month lead. This relationship suggests that potential predictability changes across much of the globe in the coming decades could be linked to anthropogenic climate change of ENSO. However, since current models substantially disagree on the sign and intensity of projected ENSO change, the trajectory of future global predictability changes cannot yet be determined. This problem is demonstrated by widely varying predictability changes seen across the five large ensembles, with models exhibiting a robust increase, robust decrease, or no significant change in predictability, depending upon their respective projected ENSO amplitude trends. Our results highlight the need for climate model development aimed at better capturing past forced and unforced changes to ENSO variability, which is necessary (if not sufficient) to constrain projected changes to climate predictability worldwide.

© 2025 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: Dillon J. Amaya, dillon.amaya@noaa.gov

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  • Barnett, T. P., and R. Preisendorfer, 1987: Origins and levels of monthly and seasonal forecast skill for United States surface air temperatures determined by canonical correlation analysis. Mon. Wea. Rev., 115, 1825–1850, https://doi.org/10.1175/1520-0493(1987)115<1825:OALOMA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Barnston, A. G., and M. K. Tippett, 2017: Do statistical pattern corrections improve seasonal climate predictions in the North American multimodel ensemble models? J. Climate, 30, 8335–8355, https://doi.org/10.1175/JCLI-D-17-0054.1.

    • Search Google Scholar
    • Export Citation

  • Barnston, A. G., M. K. Tippett, M. L. L’Heureux, S. Li, and D. G. DeWitt, 2012: Skill of real-time seasonal ENSO model predictions during 2002–11: Is our capability increasing? Bull. Amer. Meteor. Soc., 93, 631–651, https://doi.org/10.1175/BAMS-D-11-00111.1.

    • Search Google Scholar
    • Export Citation

  • Becker, E., H. van den Dool, and Q. Zhang, 2014: Predictability and forecast skill in NMME. J. Climate, 27, 5891–5906, https://doi.org/10.1175/JCLI-D-13-00597.1.

    • Search Google Scholar
    • Export Citation

  • Berner, J., H. M. Christensen, and P. D. Sardeshmukh, 2020: Does ENSO regularity increase in a warming climate? J. Climate, 33, 1247–1259, https://doi.org/10.1175/JCLI-D-19-0545.1.

    • Search Google Scholar
    • Export Citation

  • Beverley, J. D., M. Newman, and A. Hoell, 2023: Rapid development of systematic ENSO-related seasonal forecast errors. Geophys. Res. Lett., 50, e2022GL102249, https://doi.org/10.1029/2022GL102249.

    • Search Google Scholar
    • Export Citation

  • Burger, F. A., J. G. John, and T. L. Frölicher, 2020: Increase in ocean acidity variability and extremes under increasing atmospheric CO2. Biogeosciences, 17, 4633–4662, https://doi.org/10.5194/bg-17-4633-2020.

    • Search Google Scholar
    • Export Citation

  • Burger, F. A., J. Terhaar, and T. L. Frölicher, 2022: Compound marine heatwaves and ocean acidity extremes. Nat. Commun., 13, 4722, https://doi.org/10.1038/s41467-022-32120-7.

    • Search Google Scholar
    • Export Citation

  • Cai, W., and Coauthors, 2021: Changing El Niño–Southern Oscillation in a warming climate. Nat. Rev. Earth Environ., 2, 628–644, https://doi.org/10.1038/s43017-021-00199-z.

    • Search Google Scholar
    • Export Citation

  • Callahan, C. W., C. Chen, M. Rugenstein, J. Bloch-Johnson, S. Yang, and E. J. Moyer, 2021: Robust decrease in El Niño/Southern Oscillation amplitude under long-term warming. Nat. Climate Change, 11, 752–757, https://doi.org/10.1038/s41558-021-01099-2.

    • Search Google Scholar
    • Export Citation

  • Capotondi, A., and Coauthors, 2015: Understanding ENSO diversity. Bull. Amer. Meteor. Soc., 96, 921–938, https://doi.org/10.1175/BAMS-D-13-00117.1.

    • Search Google Scholar
    • Export Citation

  • Cash, B. A., and N. J. Burls, 2019: Predictable and unpredictable aspects of U.S. West Coast rainfall and El Niño: Understanding the 2015/16 event. J. Climate, 32, 2843–2868, https://doi.org/10.1175/JCLI-D-18-0181.1.

    • Search Google Scholar
    • Export Citation

  • Chen, D., M. A. Cane, A. Kaplan, S. E. Zebiak, and D. Huang, 2004: Predictability of El Niño over the past 148 years. Nature, 428, 733–736, https://doi.org/10.1038/nature02439.

    • Search Google Scholar
    • Export Citation

  • Delworth, T. L., and Coauthors, 2020: SPEAR: The next generation GFDL modeling system for seasonal to multidecadal prediction and projection. J. Adv. Model. Earth Syst., 12, e2019MS001895, https://doi.org/10.1029/2019MS001895.

    • Search Google Scholar
    • Export Citation

  • Derome, J., H. Lin, and G. Brunet, 2005: Seasonal forecasting with a simple general circulation model: Predictive skill in the AO and PNA. J. Climate, 18, 597–609, https://doi.org/10.1175/JCLI-3289.1.

    • Search Google Scholar
    • Export Citation

  • Ding, H., and M. A. Alexander, 2023: Multi-year predictability of global sea surface temperature using model-analogs. Geophys. Res. Lett., 50, e2023GL104097, https://doi.org/10.1029/2023GL104097.

    • Search Google Scholar
    • Export Citation

  • Ding, H., M. Newman, M. A. Alexander, and A. T. Wittenberg, 2018: Skillful climate forecasts of the tropical Indo-Pacific Ocean using model-analogs. J. Climate, 31, 5437–5459, https://doi.org/10.1175/JCLI-D-17-0661.1.

    • Search Google Scholar
    • Export Citation

  • Ding, H., M. Newman, M. A. Alexander, and A. T. Wittenberg, 2019: Diagnosing secular variations in retrospective ENSO seasonal forecast skill using CMIP5 model-analogs. Geophys. Res. Lett., 46, 1721–1730, https://doi.org/10.1029/2018GL080598.

    • Search Google Scholar
    • Export Citation

  • Gan, B., L. Wu, F. Jia, S. Li, W. Cai, H. Nakamura, M. A. Alexander, and A. J. Miller, 2017: On the response of the Aleutian low to greenhouse warming. J. Climate, 30, 3907–3925, https://doi.org/10.1175/JCLI-D-15-0789.1.

    • Search Google Scholar
    • Export Citation

  • Giannini, A., Y. Kushnir, and M. A. Cane, 2001: Seasonality in the impact of ENSO and the North Atlantic high on Caribbean rainfall. Phys. Chem. Earth, 26B, 143–147, https://doi.org/10.1016/S1464-1909(00)00231-8.

    • Search Google Scholar
    • Export Citation

  • Gu, D., and S. G. H. Philander, 1997: Interdecadal climate fluctuations that depend on exchanges between the tropics and extratropics. Science, 275, 805–807, https://doi.org/10.1126/science.275.5301.805.

    • Search Google Scholar
    • Export Citation

  • Gupta, A., A. C. Pandey, and A. K. Mitra, 2023: Impact of atmospheric response of ENSO and IOD on boreal summer rainfall variability over the Indian Ocean in coupled models. Climate Dyn., 61, 4107–4124, https://doi.org/10.1007/s00382-023-06796-6.

    • Search Google Scholar
    • Export Citation

  • He, S., J.-Y. Yu, S. Yang, and S.-W. Fang, 2020: ENSO’s impacts on the tropical Indian and Atlantic Oceans via tropical atmospheric processes: Observations versus CMIP5 simulations. Climate Dyn., 54, 4627–4640, https://doi.org/10.1007/s00382-020-05247-w.

    • Search Google Scholar
    • Export Citation

  • Heede, U. K., and A. V. Fedorov, 2023: Towards understanding the robust strengthening of ENSO and more frequent extreme El Niño events in CMIP6 global warming simulations. Climate Dyn., 61, 3047–3060, https://doi.org/10.1007/s00382-023-06856-x.

    • Search Google Scholar
    • Export Citation

  • Hirahara, S., M. Ishii, and Y. Fukuda, 2014: Centennial-scale sea surface temperature analysis and its uncertainty. J. Climate, 27, 57–75, https://doi.org/10.1175/JCLI-D-12-00837.1.

    • 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, 813–829, 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, 8179–8205, https://doi.org/10.1175/JCLI-D-16-0836.1.

    • 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, 865–879, https://doi.org/10.1002/joc.1169.

    • Search Google Scholar
    • Export Citation

  • Jacox, M. G., M. A. Alexander, C. A. Stock, and G. Hervieux, 2019: On the skill of seasonal sea surface temperature forecasts in the California Current System and its connection to ENSO variability. Climate Dyn., 53, 7519–7533, https://doi.org/10.1007/s00382-017-3608-y.

    • Search Google Scholar
    • Export Citation

  • Kay, J. E., and Coauthors, 2015: The Community Earth System Model (CESM) large ensemble project: A community resource for studying climate change in the presence of internal climate variability. Bull. Amer. Meteor. Soc., 96, 1333–1349, https://doi.org/10.1175/BAMS-D-13-00255.1.

    • Search Google Scholar
    • Export Citation

  • Kumar, A., 2009: Finite samples and uncertainty estimates for skill measures for seasonal prediction. Mon. Wea. Rev., 137, 2622–2631, https://doi.org/10.1175/2009MWR2814.1.

    • Search Google Scholar
    • Export Citation

  • Kumar, A., P. Peng, and M. Chen, 2014: Is there a relationship between potential and actual skill? Mon. Wea. Rev., 142, 2220–2227, https://doi.org/10.1175/MWR-D-13-00287.1.

    • Search Google Scholar
    • Export Citation

  • Kumar, S., C. F. Dewes, M. Newman, and Y. Duan, 2023: Robust changes in North America’s hydroclimate variability and predictability. Earth’s Future, 11, e2022EF003239, https://doi.org/10.1029/2022EF003239.

    • Search Google Scholar
    • Export Citation

  • Lenssen, N., P. DiNezio, L. Goddard, C. Deser, Y. Kushnir, S. J. Mason, M. Newman, and Y. Okumura, 2024: Strong El Niño events lead to robust multi-year ENSO predictability. Geophys. Res. Lett., 51, e2023GL106988, https://doi.org/10.1029/2023GL106988.

    • Search Google Scholar
    • Export Citation

  • Lorenz, E. N., 1969: Atmospheric predictability as revealed by naturally occurring analogues. J. Atmos. Sci., 26, 636–646, https://doi.org/10.1175/1520-0469(1969)26<636:APARBN>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Lou, J., M. Newman, and A. Hoell, 2023: Multi-decadal variation of ENSO forecast skill since the late 1800s. npj Climate Atmos. Sci., 6, 89, https://doi.org/10.1038/s41612-023-00417-z.

    • Search Google Scholar
    • Export Citation

  • MacLeod, D., C. O’Reilly, T. Palmer, and A. Weisheimer, 2018: Flow dependent ensemble spread in seasonal forecasts of the boreal winter extratropics. Atmos. Sci. Lett., 19, e815, https://doi.org/10.1002/asl.815.

    • Search Google Scholar
    • Export Citation

  • Maher, N., and Coauthors, 2019: The Max Planck Institute Grand Ensemble: Enabling the exploration of climate system variability. J. Adv. Model. Earth Syst., 11, 2050–2069, https://doi.org/10.1029/2019MS001639.

    • 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, 413–431, https://doi.org/10.5194/esd-14-413-2023.

    • Search Google Scholar
    • Export Citation

  • McGregor, S., C. Cassou, Y. Kosaka, and A. S. Phillips, 2022: Projected ENSO teleconnection changes in CMIP6. Geophys. Res. Lett., 49, e2021GL097511, https://doi.org/10.1029/2021GL097511.

    • Search Google Scholar
    • Export Citation

  • Menary, M. B., J. Mignot, and J. Robson, 2021: Skilful decadal predictions of subpolar North Atlantic SSTs using CMIP model-analogues. Environ. Res. Lett., 16, 064090, https://doi.org/10.1088/1748-9326/ac06fb.

    • Search Google Scholar
    • Export Citation

  • Mo, K. C., and M. Ghil, 1987: Statistics and dynamics of persistent anomalies. J. Atmos. Sci., 44, 877–902, https://doi.org/10.1175/1520-0469(1987)044<0877:SADOPA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Newman, M., and P. D. Sardeshmukh, 2017: Are we near the predictability limit of tropical Indo-Pacific sea surface temperatures? Geophys. Res. Lett., 44, 8520–8529, https://doi.org/10.1002/2017GL074088.

    • Search Google Scholar
    • Export Citation

  • Newman, M., A. T. Wittenberg, L. Cheng, G. P. Compo, and C. A. Smith, 2018: The extreme 2015/16 El Niño, in the context of historical climate variability and change. Bull. Amer. Meteor. Soc., 99, S16–S20, https://doi.org/10.1175/BAMS-D-17-0116.1.

    • Search Google Scholar
    • Export Citation

  • O’Brien, J. P., and C. Deser, 2023: Quantifying and understanding forced changes to unforced modes of atmospheric circulation variability over the North Pacific in a coupled model large ensemble. J. Climate, 36, 19–37, https://doi.org/10.1175/JCLI-D-22-0101.1.

    • Search Google Scholar
    • Export Citation

  • O’Reilly, C. H., J. Heatley, D. MacLeod, A. Weisheimer, T. N. Palmer, N. Schaller, and T. Woollings, 2017: Variability in seasonal forecast skill of Northern Hemisphere winters over the twentieth century. Geophys. Res. Lett., 44, 5729–5738, https://doi.org/10.1002/2017GL073736.

    • Search Google Scholar
    • Export Citation

  • O’Reilly, C. H., T. Woollings, L. Zanna, and A. Weisheimer, 2019: An interdecadal shift of the extratropical teleconnection from the tropical Pacific during boreal summer. Geophys. Res. Lett., 46, 13 379–13 388, https://doi.org/10.1029/2019GL084079.

    • Search Google Scholar
    • Export Citation

  • Peng, Q., S.-P. Xie, and C. Deser, 2024: Collapsed upwelling projected to weaken ENSO under sustained warming beyond the twenty-first century. Nat. Climate Change, 14, 815–822, https://doi.org/10.1038/s41558-024-02061-8.

    • Search Google Scholar
    • Export Citation

  • Planton, Y. Y., and Coauthors, 2021: Evaluating climate models with the CLIVAR 2020 ENSO metrics package. Bull. Amer. Meteor. Soc., 102, E193–E217, https://doi.org/10.1175/BAMS-D-19-0337.1.

    • Search Google Scholar
    • Export Citation

  • Quan, X., M. Hoerling, J. Whitaker, G. Bates, and T. Xu, 2006: Diagnosing sources of U.S. seasonal forecast skill. J. Climate, 19, 3279–3293, https://doi.org/10.1175/JCLI3789.1.

    • 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

  • Rodgers, K. B., and Coauthors, 2021: Ubiquity of human-induced changes in climate variability. Earth Syst. Dyn., 12, 1393–1411, https://doi.org/10.5194/esd-12-1393-2021.

    • Search Google Scholar
    • Export Citation

  • Sardeshmukh, P. D., G. P. Compo, and C. Penland, 2000: Changes of probability associated with El Niño. J. Climate, 13, 4268–4286, https://doi.org/10.1175/1520-0442(2000)013<4268:COPAWE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Scaife, A. A., and D. Smith, 2018: A signal-to-noise paradox in climate science. npj Climate Atmos. Sci., 1, 28, https://doi.org/10.1038/s41612-018-0038-4.

    • Search Google Scholar
    • Export Citation

  • Shepherd, T. G., and Coauthors, 2018: Storylines: An alternative approach to representing uncertainty in physical aspects of climate change. Climatic Change, 151, 555–571, https://doi.org/10.1007/s10584-018-2317-9.

    • Search Google Scholar
    • Export Citation

  • Shi, H., and Coauthors, 2022: Global decline in ocean memory over the 21st century. Sci. Adv., 8, eabm3468, https://doi.org/10.1126/sciadv.abm3468.

    • Search Google Scholar
    • Export Citation

  • Shi, W., N. Schaller, D. MacLeod, T. N. Palmer, and A. Weisheimer, 2015: Impact of hindcast length on estimates of seasonal climate predictability. Geophys. Res. Lett., 42, 1554–1559, https://doi.org/10.1002/2014GL062829.

    • Search Google Scholar
    • Export Citation

  • Suarez, M. J., and P. S. Schopf, 1988: A delayed action oscillator for ENSO. J. Atmos. Sci., 45, 3283–3287, https://doi.org/10.1175/1520-0469(1988)045<3283:ADAOFE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • von Storch, H., and F. W. Zwiers, 1999: Statistical Analysis in Climate Research. Cambridge University Press, 484 pp.

  • Weisheimer, A., N. Schaller, C. O’Reilly, D. A. MacLeod, and T. Palmer, 2017: Atmospheric seasonal forecasts of the twentieth century: Multi-decadal variability in predictive skill of the winter North Atlantic Oscillation (NAO) and their potential value for extreme event attribution. Quart. J. Roy. Meteor. Soc., 143, 917–926, https://doi.org/10.1002/qj.2976.

    • Search Google Scholar
    • Export Citation

  • Weisheimer, A., D. Decremer, D. MacLeod, C. O’Reilly, T. N. Stockdale, S. Johnson, and T. N. Palmer, 2019: How confident are predictability estimates of the winter North Atlantic Oscillation? Quart. J. Roy. Meteor. Soc., 145, 140–159, https://doi.org/10.1002/qj.3446.

    • Search Google Scholar
    • Export Citation

  • Weisheimer, A., D. J. Befort, D. MacLeod, T. Palmer, C. O’Reilly, and K. Strømmen, 2020: Seasonal forecasts of the twentieth century. Bull. Amer. Meteor. Soc., 101, E1413–E1426, https://doi.org/10.1175/BAMS-D-19-0019.1.

    • Search Google Scholar
    • Export Citation

  • Weisheimer, A., M. A. Balmaseda, T. N. Stockdale, M. Mayer, S. Sharmila, H. Hendon, and O. Alves, 2022: Variability of ENSO forecast skill in 2-year global reforecasts over the 20th century. Geophys. Res. Lett., 49, e2022GL097885, https://doi.org/10.1029/2022GL097885.

    • Search Google Scholar
    • Export Citation

  • Weisheimer, A., and Coauthors, 2024: The signal-to-noise paradox in climate forecasts: Revisiting our understanding and identifying future priorities. Bull. Amer. Meteor. Soc., 105, E651–E659, https://doi.org/10.1175/BAMS-D-24-0019.1.

    • Search Google Scholar
    • Export Citation

  • Wengel, C., S.-S. Lee, M. F. Stuecker, A. Timmermann, J.-E. Chu, and F. Schloesser, 2021: Future high-resolution El Niño/Southern Oscillation dynamics. Nat. Climate Change, 11, 758–765, https://doi.org/10.1038/s41558-021-01132-4.

    • Search Google Scholar
    • Export Citation

  • Wheeler, M. C., H. Zhu, A. H. Sobel, D. Hudson, and F. Vitart, 2017: Seamless precipitation prediction skill comparison between two global models. Quart. J. Roy. Meteor. Soc., 143, 374–383, https://doi.org/10.1002/qj.2928.

    • Search Google Scholar
    • Export Citation

  • Wills, R. C. J., Y. Dong, C. Proistosecu, K. C. Armour, and D. S. Battisti, 2022: Systematic climate model biases in the large-scale patterns of recent sea-surface temperature and sea-level pressure change. Geophys. Res. Lett., 49, e2022GL100011, https://doi.org/10.1029/2022GL100011.

    • Search Google Scholar
    • Export Citation

  • Zhang, L., T. L. Delworth, X. Yang, Y. Morioka, F. Zeng, and F. Lu, 2023: Skillful decadal prediction skill over the Southern Ocean based on GFDL SPEAR Model-Analogs. Environ. Res. Commun., 5, 021002, https://doi.org/10.1088/2515-7620/acb90e.

    • Search Google Scholar
    • Export Citation

  • Zhao, M., H. H. Hendon, O. Alves, G. Liu, and G. Wang, 2016: Weakened eastern Pacific El Niño predictability in the early twenty-first century. J. Climate, 29, 6805–6822, https://doi.org/10.1175/JCLI-D-15-0876.1.

    • Search Google Scholar
    • Export Citation

  • Zheng, Y., M. Rugenstein, P. Pieper, G. Beobide-Arsuaga, and J. Baehr, 2022: El Niño–Southern Oscillation (ENSO) predictability in equilibrated warmer climates. Earth Syst. Dyn., 13, 1611–1623, https://doi.org/10.5194/esd-13-1611-2022.

    • Search Google Scholar
    • Export Citation

  • Zhou, Z.-Q., S.-P. Xie, X.-T. Zheng, Q. Liu, and H. Wang, 2014: Global warming–induced changes in El Niño teleconnections over the North Pacific and North America. J. Climate, 27, 9050–9064, https://doi.org/10.1175/JCLI-D-14-00254.1.

    • Search Google Scholar
    • Export Citation
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