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Emergent climate change patterns originating from deep ocean warming in climate mitigation scenarios

Nature Climate Change volume 14, pages 260–266 (2024)Cite this article

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Abstract

The global oceans absorb most of the surplus heat from anthropogenic warming, but it is unclear how this heat accumulation will affect the Earth’s climate under climate mitigation scenarios. Here we show that this stored heat will be released at a much slower rate than its accumulation, resulting in a robust pattern of surface ocean warming and consequent regional precipitation. The surface ocean warming is pronounced over subpolar to polar regions and the equatorial eastern Pacific where oceans are weakly stratified to allow vigorous heat release from the deep ocean to the surface layer. We also demonstrate that this ocean warming pattern largely explains changes in the precipitation pattern, including the southward shift of the Intertropical Convergence Zone and more moistening in high latitudes. This study suggests that deep ocean warming may hinder climate recovery in some regions, even if carbon neutrality or net negative emissions are successfully achieved.

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Fig. 1: Temporal evolution and spatial pattern of ocean warming.
Fig. 2: Spatial patterns of irreversible surface climate changes.
Fig. 3: Design and results of the initial warming experiment.

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Data availability

The data used in this study are available from https://doi.org/10.6084/m9.figshare.24873216.v1 (ref. 61), and the CMIP6 archives are freely available from https://esgf-node.llnl.gov/projects/cmip6.

Code availability

The codes used in this study are available from https://doi.org/10.6084/m9.figshare.24873216.v1 (ref. 61). All figures were generated using Python with the matplotlib and basemap modules (https://matplotlib.org/, https://matplotlib.org/basemap/).

References

  1. Meyssignac, B. et al. Measuring global ocean heat content to estimate the earth energy imbalance. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00432 (2019).

  2. von Schuckmann, K. et al. Heat stored in the Earth system: where does the energy go? Earth Syst. Sci. Data 12, 2013–2041 (2020).

    Article  ADS  Google Scholar 

  3. Cheng, L. et al. Improved estimates of ocean heat content from 1960 to 2015. Sci. Adv. 3, e1601545 (2017).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  4. Easterling, D. R. & Wehner, M. F. Is the climate warming or cooling? Geophys. Res. Lett. 36, L08706 (2009).

    Article  ADS  Google Scholar 

  5. England, M. H. et al. Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus. Nat. Clim. Change 4, 222–227 (2014).

    Article  ADS  Google Scholar 

  6. Meehl, G. A., Arblaster, J. M., Fasullo, J. T., Hu, A. & Trenberth, K. E. Model-based evidence of deep-ocean heat uptake during surface-temperature hiatus periods. Nat. Clim. Change 1, 360–364 (2011).

    Article  ADS  Google Scholar 

  7. Cheng, L., Abraham, J., Hausfather, Z. & Trenberth, K. E. How fast are the oceans warming? Science 363, 128–129 (2019).

    Article  ADS  CAS  PubMed  Google Scholar 

  8. Cheng, L. et al. Past and future ocean warming. Nat. Rev. Earth Environ. https://doi.org/10.1038/s43017-022-00345-1 (2022).

  9. Held, I. M. et al. Probing the fast and slow components of global warming by returning abruptly to preindustrial forcing. J. Clim. 23, 2418–2427 (2010).

    Article  ADS  Google Scholar 

  10. Solomon, S., Plattner, G.-K., Knutti, R. & Friedlingstein, P. Irreversible climate change due to carbon dioxide emissions. Proc. Natl Acad. Sci. USA 106, 1704–1709 (2009).

  11. Frölicher, T. L., Winton, M. & Sarmiento, J. L. Continued global warming after CO2 emissions stoppage. Nat. Clim. Change 4, 40–44 (2014).

    Article  ADS  Google Scholar 

  12. Hoegh-Guldberg, O., Jacob, D. & Taylor, M. in IPCC Special Report on Global Warming of 1.5 °C (eds Masson-Delmotte, V. et al.) 175–181 (Cambridge Univ. Press, 2018).

  13. Luderer, G. et al. Residual fossil CO2 emissions in 1.5–2 °C pathways. Nat. Clim. Change 8, 626–633 (2018).

    Article  ADS  CAS  Google Scholar 

  14. Tong, D. et al. Committed emissions from existing energy infrastructure jeopardize 1.5 °C climate target. Nature 572, 373–377 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Welsby, D., Price, J., Pye, S. & Ekins, P. Unextractable fossil fuels in a 1.5 °C world. Nature 597, 230–234 (2021).

    Article  ADS  CAS  PubMed  Google Scholar 

  16. Boucher, O. et al. Reversibility in an Earth System model in response to CO2 concentration changes. Environ. Res. Lett. 7, 024013 (2012).

  17. Garbe, J., Albrecht, T., Levermann, A., Donges, J. F. & Winkelmann, R. The hysteresis of the Antarctic ice sheet. Nature 585, 538–544 (2020).

  18. Jackson, L. C., Schaller, N., Smith, R. S., Palmer, M. D. & Vellinga, M. Response of the Atlantic meridional overturning circulation to a reversal of greenhouse gas increases. Clim. Dyn. 42, 3323–3336 (2014).

    Article  Google Scholar 

  19. Sgubin, G., Swingedouw, D., Drijfhout, S., Hagemann, S. & Robertson, E. Multimodel analysis on the response of the AMOC under an increase of radiative forcing and its symmetrical reversal. Clim. Dyn. 45, 1429–1450 (2015).

    Article  Google Scholar 

  20. Chadwick, R., Wu, P., Good, P. & Andrews, T. Asymmetries in tropical rainfall and circulation patterns in idealised CO2 removal experiments. Clim. Dyn. 40, 295–316 (2013).

    Article  Google Scholar 

  21. Wu, P., Ridley, J., Pardaens, A., Levine, R. & Lowe, J. The reversibility of CO2-induced climate change. Clim. Dyn. 45, 745–754 (2015).

    Article  Google Scholar 

  22. Bouttes, N., Gregory, J. M. & Lowe, J. A. The reversibility of sea level rise. J. Clim. 26, 2502–2513 (2013).

    Article  ADS  Google Scholar 

  23. Ehlert, D. & Zickfeld, K. Irreversible ocean thermal expansion under carbon dioxide removal. Earth Syst. Dyn. 9, 197–210 (2018).

    Article  ADS  Google Scholar 

  24. Oh, J., An, S., Shin, J. & Kug, J. Centennial memory of the Arctic Ocean for future Arctic climate recovery in response to a carbon dioxide removal. Earths Future 10, e2022EF002804 (2022).

  25. An, S. et al. Global cooling hiatus driven by an AMOC overshoot in a carbon dioxide removal scenario. Earths Future 9, e2021EF002165 (2021).

  26. Song, S.-Y. et al. Asymmetrical response of summer rainfall in East Asia to CO2 forcing. Sci. Bull. 67, 213–222 (2022).

    Article  CAS  Google Scholar 

  27. Kug, J.-S. et al. Hysteresis of the intertropical convergence zone to CO2 forcing. Nat. Clim. Change 12, 47–53 (2022).

    Article  ADS  CAS  Google Scholar 

  28. Kim, S.-K. et al. Widespread irreversible changes in surface temperature and precipitation in response to CO2 forcing. Nat. Clim. Change 12, 834–840 (2022).

    Article  ADS  CAS  Google Scholar 

  29. Pathirana, G. et al. Increase in convective extreme El Niño events in a CO2 removal scenario. Sci. Adv. 9, eadh2412 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Schwinger, J., Asaadi, A., Steinert, N. J. & Lee, H. Emit now, mitigate later? Earth system reversibility under overshoots of different magnitudes and durations. Earth Syst. Dyn. 13, 1641–1665 (2022).

    Article  ADS  Google Scholar 

  31. Li, X., Zickfeld, K., Mathesius, S., Kohfeld, K. & Matthews, J. B. R. Irreversibility of marine climate change impacts under carbon dioxide removal. Geophys. Res. Lett. 47, e2020GL088507 (2020).

  32. Mathesius, S., Hofmann, M., Caldeira, K. & Schellnhuber, H. J. Long-term response of oceans to CO2 removal from the atmosphere. Nat. Clim. Change 5, 1107–1113 (2015).

    Article  ADS  CAS  Google Scholar 

  33. Wu, P., Wood, R., Ridley, J. & Lowe, J. Temporary acceleration of the hydrological cycle in response to a CO2 rampdown. Geophys. Res. Lett. 37, L12705 (2010).

    Article  ADS  Google Scholar 

  34. Long, S.-M., Xie, S.-P., Zheng, X.-T. & Liu, Q. Fast and slow responses to global warming: sea surface temperature and precipitation patterns. J. Clim. 27, 285–299 (2014).

    Article  ADS  Google Scholar 

  35. Long, S. M. et al. Effects of ocean slow response under low warming targets. J. Clim. 33, 477–496 (2020).

    Article  ADS  Google Scholar 

  36. Xie, S.-P. et al. Global warming pattern formation: sea surface temperature and rainfall. J. Clim. 23, 966–986 (2010).

    Article  ADS  Google Scholar 

  37. Zhou, S., Huang, P., Xie, S.-P., Huang, G. & Wang, L. Varying contributions of fast and slow responses cause asymmetric tropical rainfall change between CO2 ramp-up and ramp-down. Sci. Bull. 67, 1702–1711 (2022).

    Article  Google Scholar 

  38. Li, G. et al. Increasing ocean stratification over the past half-century. Nat. Clim. Change 10, 1116–1123 (2020).

    Article  ADS  Google Scholar 

  39. Morrison, A. K., Waugh, D. W., Hogg, A. M., Jones, D. C. & Abernathey, R. P. Ventilation of the Southern Ocean pycnocline. Annu. Rev. Mar. Sci. 14, 405–430 (2022).

  40. Shi, J.-R., Xie, S.-P. & Talley, L. D. Evolving relative importance of the Southern Ocean and North Atlantic in anthropogenic ocean heat uptake. J. Clim. 31, 7459–7479 (2018).

    Article  ADS  Google Scholar 

  41. Liu, W., Lu, J., Xie, S.-P. & Fedorov, A. Southern Ocean heat uptake, redistribution, and storage in a warming climate: the role of meridional overturning circulation. J. Clim. 31, 4727–4743 (2018).

    Article  ADS  Google Scholar 

  42. Huguenin, M. F., Holmes, R. M. & England, M. H. Drivers and distribution of global ocean heat uptake over the last half century. Nat. Commun. 13, 4921 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  43. An, S.-I. et al. General circulation and global heat transport in a quadrupling CO2 pulse experiment. Sci. Rep. 12, 11569 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  44. Gregory, J. & Webb, M. Tropospheric adjustment induces a cloud component in CO2 forcing. J. Clim. 21, 58–71 (2008).

    Article  ADS  Google Scholar 

  45. Bourgeois, T., Goris, N., Schwinger, J. & Tjiputra, J. F. Stratification constrains future heat and carbon uptake in the Southern Ocean between 30° S and 55° S. Nat. Commun. 13, 340 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  46. Bronselaer, B. & Zanna, L. Heat and carbon coupling reveals ocean warming due to circulation changes. Nature 584, 227–233 (2020).

    Article  ADS  CAS  PubMed  Google Scholar 

  47. Gent, P. R. & Mcwilliams, J. C. Isopycnal mixing in ocean circulation models. J. Phys. Oceanogr. 20, 150–155 (1990).

    Article  ADS  Google Scholar 

  48. Wu, P., Jackson, L., Pardaens, A. & Schaller, N. Extended warming of the northern high latitudes due to an overshoot of the Atlantic meridional overturning circulation. Geophys. Res. Lett. 38, L24704 (2011).

    Article  ADS  Google Scholar 

  49. Redi, M. H. Oceanic isopycnal mixing by coordinate rotation. J. Phys. Oceanogr. 12, 1154–1158 (1982).

    Article  ADS  Google Scholar 

  50. Stuecker, M. F. et al. Polar amplification dominated by local forcing and feedbacks. Nat. Clim. Change 8, 1076–1081 (2018).

    Article  ADS  CAS  Google Scholar 

  51. Mechoso, C. R. et al. Can reducing the incoming energy flux over the Southern Ocean in a CGCM improve its simulation of tropical climate? Geophys. Res. Lett. 43, 11,057–11,063 (2016).

    Article  Google Scholar 

  52. Kim, H., Kang, S. M., Kay, J. E., Xie, S.-P. & Hartmann, D. Subtropical clouds key to Southern Ocean teleconnections to the tropical Pacific. Proc. Natl Acad. Sci. USA 119, e2200514119 (2022).

  53. Schneider, T., Bischoff, T. & Haug, G. H. Migrations and dynamics of the intertropical convergence zone. Nature 513, 45–53 (2014).

    Article  ADS  CAS  PubMed  Google Scholar 

  54. Bischoff, T. & Schneider, T. Energetic constraints on the position of the intertropical convergence zone. J. Clim. 27, 4937–4951 (2014).

    Article  ADS  Google Scholar 

  55. White, R. H. et al. Tropical precipitation and cross-equatorial heat transport in response to localized heating: basin and hemisphere dependence. Geophys. Res. Lett. 45, 11,949–11,958 (2018).

    Article  Google Scholar 

  56. Byrne, M. P., Pendergrass, A. G., Rapp, A. D. & Wodzicki, K. R. Response of the intertropical convergence zone to climate change: location, width, and strength. Curr. Clim. Change Rep. 4, 355–370 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Cai, W. et al. Increasing frequency of extreme El Niño events due to greenhouse warming. Nat. Clim. Change 4, 111–116 (2014).

    Article  ADS  Google Scholar 

  58. Hoskins, B. J. & Karoly, D. J. The steady linear response of a spherical atmosphere to thermal and orographic forcing. J. Atmos. Sci. 38, 1179–1196 (1981).

    Article  ADS  Google Scholar 

  59. Goosse, H. et al. Quantifying climate feedbacks in polar regions. Nat. Commun. 9, 1919 (2018).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  60. Keller, D. P. et al. The carbon dioxide removal model intercomparison project (CDRMIP): rationale and experimental protocol for CMIP6. Geosci. Model Dev. 11, 1133–1160 (2018).

  61. Oh, J.-H. Emergent climate change patterns originating from deep ocean warming in climate mitigation scenarios. figshare https://doi.org/10.6084/m9.figshare.24873216.v1 (2023).

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Acknowledgements

The CESM simulation and data transfer were supported by the National Supercomputing Center with supercomputing resources (KSC-2023-CHA-0001), and the National Center for Meteorological Supercomputer of the Korea Meteorological Administration (KMA) and the Korea Research Environment Open NETwork (KREONET), respectively. J.-S.K. was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (NRF-2022R1A3B1077622). S.-I.A. was supported by the National Research Foundation of Korea (NRF-2018R1A5A1024958). This is PMEL contribution no. 5451.

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Authors and Affiliations

  1. Division of Environmental Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, South Korea

    Ji-Hoon Oh, Jong-Seong Kug & Soon-Il An

  2. Institute for Convergence Research and Education in Advanced Technology, Yonsei University, Seoul, South Korea

    Jong-Seong Kug

  3. Department of Atmospheric Sciences/Irreversible Climate Change Research Center, Yonsei University, Seoul, South Korea

    Soon-Il An

  4. Department of Atmospheric Sciences, University of Hawaii, Honolulu, HI, USA

    Fei-Fei Jin

  5. NOAA/Pacific Marine Environmental Laboratory, Seattle, WA, USA

    Michael J. McPhaden

  6. Woods Hole Oceanographic Institution, Woods Hole, Falmouth, MA, USA

    Jongsoo Shin

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Contributions

J.-H.O. compiled the data, conducted analyses and simulations, prepared the figures and wrote the paper. J.-S.K. designed the research and wrote the paper. All authors discussed the results and revised the paper.

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Correspondence to Jong-Seong Kug.

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Extended data

Extended Data Fig. 1 Background barotropic steam function and Eulerian vertical velocity.

a, Background annual mean barotropic steam function. The black line indicates the barotropic steam function of 0 Sv. b, c, d, e, Background annual mean Eulerian vertical velocity (\({W}_{{eulerian}}\)) at 200 m, 700 m, 1000 m, and 2000 m, respectively.

Extended Data Fig. 2 Background eddy-induced vertical velocity.

a, b, c, d, As in Extended Data Fig. 1, but for the eddy-induced vertical velocity (\({W}_{{eddy}-{induced}}\), bolus vertical velocity). The red contour denotes the maximum mixed layer depth of 150 m (see Extended Data Fig. 3a).

Extended Data Fig. 3 Background maximum mixed layer depth (MMLD) and isopycnal diffusivity.

a, b, Maximum mixed layer depth (monthly maximum among 12 months climatology in each grid) and isopycnal diffusivity at 700 m for the CESM1 (present-day control simulation, see Methods), respectively. The contour in panels a and b indicates the MMLD of 150 m.

Extended Data Fig. 4 Temporal evolution of the changes in AMOC strength.

Temporal evolution of the AMOC strength defined as the maximum of the ocean stream function below 500 m at 26°N. The line and shading are as in Fig. 1a.

Extended Data Fig. 5 Positive feedbacks amplifying the irreversible SST warming.

a, c, d, As in Fig. 2a, but for the low cloud fraction, sea ice concentration, and surface air temperature, respectively. b, Shortwave cloud feedback strength (SWCFS) in the present-day control simulation, which is calculated as shortwave radiation at the top of the atmosphere regressed onto the underlying SST at each grid. The positive feedback between the SST and low clouds (that is shortwave cloud feedback) can amplify the irreversible SST warming: As the amount of low clouds decreases, the amount of solar radiation reaching the surface increases, resulting in a warming effect on the local SST. This SST warming, in turn, decreases atmospheric stability, which subsequently contributes to a decrease in the amount of low clouds. The black contour in panel a and b indicates the SWCFS of 0 Wm−2K−1. It is clear that the significant reduction in low cloud fraction occurs in regions with strong cloud feedback in the present climate. This low cloud reduction pattern well explains the distribution of the surface air temperature anomaly with significant sea-ice reduction in polar oceans, indicating the positive local feedbacks.

Extended Data Fig. 6 Spatial patterns of the irreversible OHC and SST changes in CMIP6 models.

a, b, Multi-model mean in the total OHC and SST anomalies averaged over the restoring period of each CMIP6 model ACCESS-ESM1-5, CESM2, CNRM-ESM2-1, CanESM5, GFDL-ESM4, MIROC-ES2L, NorESM2-LM, UKESM1-0-LL). c, Same as in b, but the AMOC effect is linearly removed using simple linear regression between AMOC strength and SST field. Note that the length of restoring period in each CMIP6 model is different (see Methods). Hatchings indicate insignificant responses at the 95% confidence level.

Extended Data Fig. 7 Patterns of the AMOC changes in CMIP6 models.

a, b, c, d, e, f, g, h, AMOC anomalies averaged over the restoring period of each CMIP6 model ACCESS-ESM1-5, CESM2, CNRM-ESM2-1, CanESM5, GFDL-ESM4, MIROC-ES2L, NorESM2-LM, UKESM1-0-LL).

Extended Data Fig. 8 Inter-ensemble relationship between global OHC and irreversible SST pattern.

a, Magnitude of irreversible SST pattern (see Methods) against global total OHC anomaly at its peak for each of 28 ensemble members. The gray dot indicates the ensemble mean. b, As in panel a, but for global 700-2000 m integrated OHC anomaly. c, As in panel a, cl 700-2000 m integrated OHC from its peak. The p value based on two-sided student’s t test in each panel is 7.2 × 10−5, 3.3 × 10−6, and 9.3 × 10−7, respectively.

Extended Data Fig. 9 Spatial patterns of the irreversible surface climate changes in initial warming experiments.

a, b, As in Fig. 3c, d, but for the IW_be100. c, d, As in Fig. 4c, d, but for the IW_be700. e, f, As in Fig. 3c, d, but for the IW_up100. Only significant values at the 95% confidence level are shown in all panels. The numbers labeled at the upper top corner in panels are the pattern correlations between each panel’s pattern and the reference irreversible SST and PRCP patterns (Figs. 2a and 2d).

Extended Data Fig. 10 Spatial patterns of the irreversible land temperature changes.

a, As in Fig. 2a, but for the land surface air temperature (SAT). b, c, d, e, As in Fig. 3c, but for the land SAT in IW_total, IW_be100, IW_be700, and IW_up100, respectively. The blue lines in each panel show the climatological annual mean snow cover edge of 50% in the present-day simulation. Only significant values at the 95% confidence level are shown in all panels. The numbers labeled at the upper top corner in panels (b-e) are the pattern correlations between each panel’s pattern and the pattern in panel a.

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Supplementary Figs. 1–3 and discussions (two topics).

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Oh, JH., Kug, JS., An, SI. et al. Emergent climate change patterns originating from deep ocean warming in climate mitigation scenarios. Nat. Clim. Chang. 14, 260–266 (2024). https://doi.org/10.1038/s41558-024-01928-0

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