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Multi-month forecasts of marine heatwaves and ocean acidification extremes

Nature Geoscience volume 17, pages 1261–1267 (2024)Cite this article

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Abstract

Marine heatwaves and ocean acidification extreme events are periods during which temperature and acidification reach statistically extreme levels (90th percentile), relative to normal variability, potentially endangering ecosystems. As the threats from marine heatwaves and ocean acidification extreme events grow with climate change, there is need for skilful predictions of events months to years in advance. Previous work has demonstrated that climate models can predict marine heatwaves up to 12 months in advance in key regions, but forecasting of ocean acidification extreme events has been difficult due to the complexity of the processes leading to extremes and sparse observations. Here we use the Community Earth System Model Seasonal-to-Multiyear Large Ensemble to make predictions of marine heatwaves and two forms of ocean acidification extreme events, as defined by anomalies in hydrogen ion concentration and aragonite saturation state. We show that the ensemble skilfully predicts marine heatwaves and ocean acidification extreme events as defined by aragonite saturation state up to 1 year in advance. Predictive skill for ocean acidification extremes as defined by hydrogen ion concentration is lower, probably reflecting mismatch between model and observed state. Skill is highest in the eastern Pacific, reflecting the predictable contribution of El Niño/Southern Oscillation to regional variability. A forecast generated in late 2023 during the 2023–2024 El Niño event finds high likelihood for widespread marine heatwaves and ocean acidification extreme events in 2024.

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Fig. 1: Forecast skill for MHW, OAX (Ωa), and OAX ([H+]).
Fig. 2: Dominant modes of tropical Pacific variability in SMYLE FOSI from principal component analysis.
Fig. 3: Gain in SEDI forecast skill from forecasts initialized during ENSO events (El Niño or La Niña) relative to forecasts initialized during neutral ENSO conditions.
Fig. 4: CESM SMYLE forecasts of Niño3.4, MHW and OAX initialized in November 2023.

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Data availability

The CESM Seasonal to Multiyear Large Ensemble and SMYLE FOSI are available at https://doi.org/10.26024/pwma-re41 (ref. 39). OceanSODA-ETHZ can be accessed at https://doi.org/10.25921/m5wx-ja34 (ref. 61).

Code availability

All figures were generated with the open-source software Python. Code used in processing and analysing CESM SMYLE output relative to SMYLE FOSI and OceanSODA-ETHZ can be found at https://doi.org/10.5281/zenodo.12103992 (ref. 71).

References

  1. Doney, S. C., Fabry, V. J., Feely, R. A. & Kleypas, J. A. Ocean acidification: the other CO2 problem. Annu Rev. Mar. Sci. 1, 169–192 (2009).

    Google Scholar 

  2. Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences 10, 6225–6245 (2013).

    Google Scholar 

  3. Kwiatkowski, L. & Orr, J. C. Diverging seasonal extremes for ocean acidification during the twenty-first century. Nat. Clim. Chang. 8, 141–145 (2018).

    CAS  Google Scholar 

  4. Heinze, C. et al. Reviews and syntheses: abrupt ocean biogeochemical change under human-made climatic forcing—warming, acidification, and deoxygenation. Preprint at Biogeosci. Discuss. https://doi.org/10.5194/bg-2023-182 (2023).

  5. Mogen, S. C. et al. Ocean biogeochemical signatures of the North Pacific Blob. Geophys. Res. Lett. https://doi.org/10.1029/2021GL096938 (2022).

  6. Frölicher, T. L. & Laufkötter, C. Emerging risks from marine heat waves. Nat. Commun. 9, 650 (2018).

    Google Scholar 

  7. Kohlman, C. et al. The 2019 marine heatwave at Ocean Station Papa: a multi-disciplinary assessment of ocean conditions and impacts on marine ecosystems. J. Geophys. Res. Oceans https://doi.org/10.1029/2023JC020167 (2024).

  8. Jacox, M. G. et al. Global seasonal forecasts of marine heatwaves. Nature 604, 486–490 (2022).

    CAS  Google Scholar 

  9. Hobday, A. J. et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr. 141, 227–238 (2016).

    Google Scholar 

  10. Holbrook, N. J. et al. A global assessment of marine heatwaves and their drivers. Nat. Commun. 10, 2624 (2019).

    Google Scholar 

  11. Di Lorenzo, E. & Mantua, N. Multi-year persistence of the 2014/15 North Pacific marine heatwave. Nat. Clim. Chang. 6, 1042–1047 (2016).

    Google Scholar 

  12. Amaya, D. J., Miller, A. J., Xie, S.-P. & Kosaka, Y. Physical drivers of the summer 2019 North Pacific marine heatwave. Nat. Commun. 11, 1903 (2020).

    CAS  Google Scholar 

  13. Scannell, H. et al. Spatiotemporal evolution of marine heatwaves globally. Preprint at ESS Open Archive https://doi.org/10.22541/essoar.169008275.57053412/v1 (2023).

  14. Guo, X. et al. Threat by marine heatwaves to adaptive large marine ecosystems in an eddy-resolving model. Nat. Clim. Chang. 12, 179–186 (2022).

    Google Scholar 

  15. Wyatt, A. M., Resplandy, L. & Marchetti, A. Ecosystem impacts of marine heat waves in the northeast Pacific. Biogeosciences 19, 5689–5705 (2022).

    CAS  Google Scholar 

  16. Rühmkorff, S. et al. Marine heatwaves and upwelling shape stress responses in a keystone predator. Proc. R. Soc. B 290, 20222262 (2023).

    Google Scholar 

  17. Welch, H. et al. Impacts of marine heatwaves on top predator distributions are variable but predictable. Nat. Commun. 14, 5188 (2023).

    CAS  Google Scholar 

  18. Smale, D. A. et al. Marine heatwaves threaten global biodiversity and the provision of ecosystem services. Nat. Clim. Chang. 9, 306–312 (2019).

    Google Scholar 

  19. Fragkopoulou, E. et al. Marine biodiversity exposed to prolonged and intense subsurface heatwaves. Nat. Clim. Chang. https://doi.org/10.1038/s41558-023-01790-6 (2023).

  20. Hauri, C. et al. Spatiotemporal variability and long-term trends of ocean acidification in the California Current System. Biogeosciences 10, 193–216 (2013).

    Google Scholar 

  21. Hauri, C., Friedrich, T. & Timmermann, A. Abrupt onset and prolongation of aragonite undersaturation events in the Southern Ocean. Nat. Clim. Chang. 6, 172–176 (2016).

    CAS  Google Scholar 

  22. Burger, F. A., John, J. G. & Frölicher, T. L. Increase in ocean acidity variability and extremes under increasing atmospheric CO2. Biogeosciences 17, 4633–4662 (2020).

    CAS  Google Scholar 

  23. Burger, F. A. & Frölicher, T. L. Drivers of surface ocean acidity extremes in an Earth system model. Global Biogeochem. Cycles 37, e2023GB007785 (2023).

    CAS  Google Scholar 

  24. Desmet, F., Gruber, N., Köhn, E. E., Münnich, M. & Vogt, M. Tracking the space–time evolution of ocean acidification extremes in the California Current System and Northeast Pacific. J. Geophys. Res. Oceans https://doi.org/10.1029/2021JC018159 (2022).

  25. Wong, J., Münnich, M. & Gruber, N. Column-compound extremes in the global ocean. AGU Adv. AGU Adv. 5, e2023AV001059 (2024).

    Google Scholar 

  26. Mucci, A. The solubility of calcite and aragonite in seawater at various salinities, temperatures, and one atmosphere total pressure. Am. J. Sci. 283, 780–799 (1983).

    CAS  Google Scholar 

  27. Ries, J. B., Cohen, A. L. & McCorkle, D. C. Marine calcifiers exhibit mixed responses to CO2-induced ocean acidification. Geology 37, 1131–1134 (2009).

    CAS  Google Scholar 

  28. Bednaršek, N. et al. Systematic review and meta-analysis toward synthesis of thresholds of ocean acidification impacts on calcifying pteropods and interactions with warming. Front Mar. Sci. 6, 227 (2019).

    Google Scholar 

  29. Negrete-García, G., Lovenduski, N. S., Hauri, C., Krumhardt, K. M. & Lauvset, S. K. Sudden emergence of a shallow aragonite saturation horizon in the Southern Ocean. Nat. Clim. Chang. 9, 313–317 (2019).

    Google Scholar 

  30. Bednaršek, N., Harvey, C. J., Kaplan, I. C., Feely, R. A. & Možina, J. Pteropods on the edge: cumulative effects of ocean acidification, warming, and deoxygenation. Prog. Oceanogr. 145, 1–24 (2016).

    Google Scholar 

  31. Bednaršek, N. et al. El Niño-related thermal stress coupled with upwelling-related ocean acidification negatively impacts cellular to population-level responses in pteropods along the California Current System with implications for increased bioenergetic costs. Front Mar. Sci. 5, 486 (2018).

    Google Scholar 

  32. Bednaršek, N. et al. Exposure history determines pteropod vulnerability to ocean acidification along the US West Coast. Sci. Rep. 7, 4526 (2017).

    Google Scholar 

  33. Bednaršek, N. et al. Integrated assessment of ocean acidification risks to pteropods in the northern high latitudes: regional comparison of exposure, sensitivity and adaptive capacity. Front Mar. Sci. 8, 671497 (2021).

    Google Scholar 

  34. Manno, C., Peck, V. L. & Tarling, G. A. Pteropod eggs released at high \(p_{{\mathrm{CO}}_2}\) lack resilience to ocean acidification. Sci. Rep. 6, 25752 (2016).

    CAS  Google Scholar 

  35. Stock, C. A. et al. Seasonal sea surface temperature anomaly prediction for coastal ecosystems. Prog. Oceanogr. 137, 219–236 (2015).

    Google Scholar 

  36. Mogen, S. C. et al. Skillful multi-month predictions of ecosystem stressors in the surface and subsurface ocean. Earths Future 11, e2023EF003605 (2023).

    Google Scholar 

  37. Brady, R. X., Lovenduski, N. S., Yeager, S. G., Long, M. C. & Lindsay, K. Skillful multiyear predictions of ocean acidification in the California Current System. Nat. Commun. 11, 2166 (2020).

    CAS  Google Scholar 

  38. McAdam, R., Masina, S. & Gualdi, S. Seasonal forecasting of subsurface marine heatwaves. Commun. Earth Environ. 4, 225 (2023).

    Google Scholar 

  39. Yeager, S. G. et al. The Seasonal-to-Multiyear Large Ensemble (SMYLE) prediction system using the Community Earth System Model version 2. Geosci. Model Dev. 15, 6451–6493 (2022).

    CAS  Google Scholar 

  40. Jacox, M. G. et al. Seasonal-to-interannual prediction of North American coastal marine ecosystems: forecast methods, mechanisms of predictability, and priority developments. Prog. Oceanogr. 183, 102307 (2020).

    Google Scholar 

  41. Capotondi, A., Sardeshmukh, P. D., Di Lorenzo, E., Subramanian, A. C. & Miller, A. J. Predictability of US West Coast ocean temperatures is not solely due to ENSO. Sci. Rep. 9, 10993 (2019).

    Google Scholar 

  42. Capotondi, A., Newman, M., Xu, T. & Di Lorenzo, E. An optimal precursor of Northeast Pacific marine heatwaves and Central Pacific El Niño events. Geophys. Res. Lett. 49, e2021GL097350 (2022).

    Google Scholar 

  43. Ren, X., Liu, W., Capotondi, A., Amaya, D. J. & Holbrook, N. J. The Pacific Decadal Oscillation modulated marine heatwaves in the Northeast Pacific during past decades. Commun. Earth Environ. 4, 218 (2023).

    Google Scholar 

  44. Sarmiento, J. L. & Gruber, N. Ocean Biogeochemical Dynamics (Princeton Univ. Press, 2013); http://www.jstor.org/stable/10.2307/j.ctt3fgxqx

  45. Lovenduski, N. S. et al. The potential impact of nuclear conflict on ocean acidification. Geophys Res Lett. 47, e2019GL086246 (2020).

    CAS  Google Scholar 

  46. Reynolds, R. W. et al. Daily high-resolution-blended analyses for sea surface temperature. J. Clim. 20, 5473–5496 (2007).

    Google Scholar 

  47. NOAA Earth System Research Laboratory. Niño3.4, https://stateoftheocean.osmc.noaa.gov/sur/pac/nino34.php (2024).

  48. Lilly, L. E. et al. Biogeochemical anomalies at two Southern California Current System moorings during the 2014–2016 Warm Anomaly–El Niño sequence. J. Geophys Res. Oceans 124, 6886–6903 (2019).

    CAS  Google Scholar 

  49. Mills, K. E. et al. in Ocean Ecosystems and Marine Resources (eds Crimmins, A. et al.) (US Global Change Research Program, 2023).

  50. NOAA Physical Sciences Laboratory. Marine Heatwaves Forecast, https://psl.noaa.gov/marine-heatwaves/#forecasts (2024).

  51. Mercator Ocean International. Marine Heatwave Bulletin, https://www.mercator-ocean.eu/en/category/mhw-bulletin/ (2024).

  52. Siedlecki, S. A. et al. Experiments with seasonal forecasts of ocean conditions for the northern region of the California Current upwelling system. Sci. Rep. 6, 27203 (2016).

    CAS  Google Scholar 

  53. Brodie, S. et al. Ecological forecasts for marine resource management during climate extremes. Nat. Commun. 14, 7701 (2023).

    CAS  Google Scholar 

  54. Moore, J. K., Doney, S. C., Kleypas, J. A., Glover, D. M. & Fung, I. Y. An intermediate complexity marine ecosystem model for the global domain. Deep Sea Res. 2 49, 403–462 (2001).

    Google Scholar 

  55. Moore, J. K., Doney, S. C. & Lindsay, K. Upper ocean ecosystem dynamics and iron cycling in a global three-dimensional model. Global Biogeochem. Cycles 18, GB4028 (2004).

  56. Moore, J. K., Lindsay, K., Doney, S. C., Long, M. C. & Misumi, K. Marine ecosystem dynamics and biogeochemical cycling in the Community Earth System Model [CESM1(BGC)]: comparison of the 1990s with the 2090s under the RCP4.5 and RCP8.5 scenarios. J. Clim. 26, 9291–9312 (2013).

    Google Scholar 

  57. Long, M. C. et al. Simulations with the Marine Biogeochemistry Library (MARBL). J. Adv. Model. Earth Syst. https://doi.org/10.1029/2021MS002647 (2021).

  58. Tsujino, H. et al. JRA-55 based surface dataset for driving ocean–sea-ice models (JRA55-do). Ocean Model. 130, 79–139 (2018).

    Google Scholar 

  59. Bakker, D. C. E. et al. A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT). Earth Syst. Sci. Data 8, 383–413 (2016).

    Google Scholar 

  60. Olsen, A. et al. GLODAPv2.2019—an update of GLODAPv2. Earth Syst. Sci. Data 11, 1437–1461 (2019).

    Google Scholar 

  61. Gregor, L. & Gruber, N. OceanSODA-ETHZ: a global gridded data set of the surface ocean carbonate system for seasonal to decadal studies of ocean acidification. Earth Syst. Sci. Data 13, 777–808 (2021).

    Google Scholar 

  62. Humphreys, M. P., Lewis, E. R., Sharp, J. D. & Pierrot, D. PyCO2SYS v1.7: marine carbonate system calculations in Python. Geosci. Model Dev. https://doi.org/10.5194/gmd-2021-159 (2022).

  63. Good, S., Embury, O., Bulgin, C. & Mittaz, J. ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Level 4 Analysis Climate Data Record Version 2.0 (CEDA, 2019); https://catalogue.ceda.ac.uk/uuid/aced40d7cb964f23a0fd3e85772f2d48

  64. Good, S. et al. The current configuration of the OSTIA system for operational production of foundation sea surface temperature and ice concentration analyses. Remote Sens. 12, 720 (2020).

    Google Scholar 

  65. Ma, D., Gregor, L. & Gruber, N. Four decades of trends and drivers of global surface ocean acidification. Global Biogeochem. Cycles 37, e2023GB007765 (2023).

    CAS  Google Scholar 

  66. Amaya, D. J. et al. Marine heatwaves need clear definitions so coastal communities can adapt. Nature 616, 29–32 (2023).

    CAS  Google Scholar 

  67. Hauri, C. et al. More than marine heatwaves: a new regime of heat, acidity, and low oxygen compound extreme events in the Gulf of Alaska. AGU Adv. 5, e2023AV001039 (2024).

    Google Scholar 

  68. Gruber, N., Boyd, P. W., Frölicher, T. L. & Vogt, M. Biogeochemical extremes and compound events in the ocean. Nature 600, 395–407 (2021).

    CAS  Google Scholar 

  69. Marshall, A. G. et al. Intra-seasonal drivers of extreme heat over Australia in observations and POAMA-2. Clim. Dyn. 43, 1915–1937 (2014).

    Google Scholar 

  70. Ferro, C. A. T. & Stephenson, D. B. Extremal dependence indices: improved verification measures for deterministic forecasts of rare binary events. Weather Forecast. 26, 699–713 (2011).

    Google Scholar 

  71. Mogen, S. Marine extreme forecasting in CESM SMYLE (multi-month forecasts of marine heatwaves and ocean acidification extremes). Zenodo https://doi.org/10.5281/zenodo.12103992 (2024).

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Acknowledgements

S.M. and N.S.L. were supported by the National Science Foundation (OCE 1752724) and the National Oceanic and Atmospheric Administration (NA20OAR4310405). M.P.B. was supported by the National Oceanic and Atmospheric Administration (NA20OAR4310405). S.J.Y. and N.R. acknowledge support from the Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling Program of the US Department of Energy’s Office of Biological and Environmental Research (BER) under award number DE-SC0022070. W.K. and S.J.Y. acknowledge the support of NOAA Climate Program Office’s CVP programme under grant no. NA20OAR4310408. A.C. was supported by the NOAA Climate Program Office’s MAPP programme, DOE Award no. DE-SC0023228, and NASA Physical Oceanography grant no. 80NSSC21K0556. This work also was supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation (NSF) under Cooperative Agreement no. 1852977. We thank the National Center for Atmospheric Research Earth System Working Group for their development of invaluable software tools used in processing CESM SMYLE: https://github.com/CESM-ESPWG/ESP-Lab. We are grateful for helpful feedback from D. Amaya.

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Authors and Affiliations

  1. Department of Atmospheric and Oceanic Sciences and Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO, USA

    Samuel C. Mogen & Nicole S. Lovenduski

  2. NSF National Center for Atmospheric Research, Climate and Global Dynamics Lab, Boulder, CO, USA

    Stephen G. Yeager, Who Kim & Nan Rosenbloom

  3. Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA

    Antonietta Capotondi

  4. National Oceanic and Atmospheric Administration Physical Sciences Laboratory, Boulder, CO, USA

    Antonietta Capotondi & Michael G. Jacox

  5. National Oceanic and Atmospheric Administration Southwest Fisheries Science Center, Monterey, CA, USA

    Michael G. Jacox, Stephen Bograd, Elliot L. Hazen & Mercedes Pozo Buil

  6. Department of Earth, Environmental, and Planetary Sciences, Brown University, Providence, RI, USA

    Emanuele Di Lorenzo

  7. Institute of Marine Sciences, University of California Santa Cruz, Monterey, CA, USA

    Mercedes Pozo Buil

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  1. Samuel C. Mogen
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Contributions

S.M. and N.S.L. designed the study. S.M. performed the analysis. All authors contributed to interpretation of the results. S.M. and N.S.L. wrote the manuscript with input from all authors.

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Correspondence to Samuel C. Mogen.

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Extended data

Extended Data Fig. 1 Different skill metrics (SEDI, Forecast Accuracy, Brier Skill Score) at lead-time 3.5 months.

Forecast skill calculated by three different metrics at lead-time 3.5 months. SEDI (as in Fig. 1) (row 1; a,b,c), Forecast Accuracy (row 2; d,e,f), and Brier Skill Score (BSS) (row 3: g,h,i) for marine heatwaves (column 1), ocean acidification extremes (Ωa), and ocean acidification extremes ([H+]).

Extended Data Fig. 2 Number, duration, and intensity of extremes in CESM SMYLE and observations.

Number per month (row 1; a,b,c), duration (row 2; d,e,f), and intensity (row 3; g,h,i) of the average extreme event in observations and CESM SMYLE for marine heatwaves (column 1), ocean acidification extremes (Ωa) (column 2), and ocean acidification extremes ([H+]) (column 3) at each location.

Extended Data Fig. 3 Correlation between historically observed and modelled variability and extremes.

Correlation coefficient between historical (column 1) variability of (a) sea surface temperature, (c) Ωa, (e) [H+] and (column 2) extremes (b) marine heatwaves, (d) ocean acidification extremes (Ωa),(f) ocean acidification extremes ([H+]) in SMYLE FOSI and observations (OceanSODA-ETHZ). Higher correlation coefficients indicate more similar historical variability or extreme events.

Extended Data Fig. 4 Model skill (CESM SMYLE relative to observations) and model predictability (CESM SMYLE relative to SMYLE FOSI).

Comparison of (column 1) model skill (CESM SMYLE relative to observations) and (column 2) model predictability (CESM SMYLE relative to SMYLE FOSI) for 20 ensemble members from CESM SMYLE at 1.5 (a-b), 3.5 (c-d), 6.5 (e-f), and 10.5 month lead-time (g-h). Skill scores range from -1 to 1, with SEDI score close to -1 being unskillful, SEDI score of 0 being no better than random forecasts, and SEDI score of 1 being perfect skill.

Extended Data Fig. 5 Decomposition and drivers of extremes in aragonite saturation state.

Decomposition of Ωa to determine drivers of extreme events. (row 1) Decomposition of [CO3]2- into drivers of changes during extremes relative to all times, including effects of (a) temperature, (b) salinity, (c) DIC, (d) Alkalinity. (row 2) Changes to tendency terms of DIC during extremes relative to all times, including: (e) total DIC tendency, (f) circulation tendency, (g) air-sea CO2 flux tendency, and (h) biological tendency.

Extended Data Fig. 6 Example forecast of aragonite saturation state extreme events from the 1999 La Niña.

Forecasts of Ωa initialized during the August 1999 La Niña event. Anomalies (color) and extremes (hatching) in (column 1) from an interpolated observational product (OceanSODA-ETHZ), and (column 2) CESM SMYLE forecasts (a,b) 1.5, (c,d) 3.5, and (e,f) 5.5 months after initialization. Extreme events are defined in observations (below the 10th percentile) and in CESM SMYLE (below the 10th percentile in a minimum of 50% of ensemble members).

Extended Data Fig. 7 Example forecast of aragonite saturation state extremes in the tropical Pacific from 2004-2012.

Example timeseries of Ωa anomalies in the central tropical Pacific (0.5∘N, 138.5∘W) for (black) SMYLE FOSI and (grey) two November CESM SMYLE initializations (2006a and 2009; with ensemble spread represented) from 2006-2012. Occurrence of extreme events are indicated for (red lines) SMYLE FOSI and (bar plot) CESM SMYLE (as a percentage of ensemble members).

Extended Data Fig. 8 Magnitude of variability associated with trend (per decade), climatology, interannual variability, and extreme event anomaly for aragonite saturation state.

Relative magnitude of anomalies associated with (row 1; a,b) trend (per decade), (row 2; c,d) seasonal climatology, (row 3; e,f) interannual variability, and (row 4; g,h) mean strength of anomaly to generate extreme event for temperature and Ωa.

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Mogen, S.C., Lovenduski, N.S., Yeager, S.G. et al. Multi-month forecasts of marine heatwaves and ocean acidification extremes. Nat. Geosci. 17, 1261–1267 (2024). https://doi.org/10.1038/s41561-024-01593-0

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