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Harnessing AI and computing to advance climate modelling and prediction

There are contrasting views on how to produce the accurate predictions that are needed to guide climate change adaptation. Here, we argue for harnessing artificial intelligence, building on domain-specific knowledge and generating ensembles of moderately high-resolution (10–50 km) climate simulations as anchors for detailed hazard models.

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Fig. 1: Improving climate models and predictions by learning from observational and simulated data.

NASA (top image and satellites); K. Pressel, D. Menemenlis, C. Hill and G. Manucharyan (Clouds, ocean turbulence and sea ice images)

References

  1. Fiedler, T. et al. Nat. Clim. Change 11, 87–94 (2021).

    Article  Google Scholar 

  2. Bevacqua, E. et al. Nat. Commun. 14, 2145 (2023).

    Article  CAS  Google Scholar 

  3. Bauer, P., Stevens, B. & Hazeleger, W. Nat. Clim. Change 11, 80–83 (2021).

    Article  Google Scholar 

  4. Slingo, J. et al. Nat. Clim. Change 12, 499–503 (2022).

    Article  Google Scholar 

  5. Schneider, T. et al. Geophys. Res. Lett. 44, 12396–12417 (2017).

    Google Scholar 

  6. Schneider, T., Kaul, C. M. & Pressel, K. G. Nat. Geosci. 12, 163–167 (2019).

    Article  CAS  Google Scholar 

  7. Stevens, B. et al. Prog. Earth Planet. Sci. 6, 61 (2019).

    Article  Google Scholar 

  8. Wedi, N. P. et al. J. Adv. Model. Earth Sys. 12, e2020MS00219 (2020).

    Google Scholar 

  9. Feng, Z. et al. Geophys. Res. Lett. 50, e2022GL102603 (2023).

    Article  Google Scholar 

  10. Kovachki, N. B. & Stuart, A. M. Inverse Probl. 35, 095005 (2019).

    Article  Google Scholar 

  11. Cleary, E. et al. J. Comp. Phys. 424, 109716 (2021).

    Article  Google Scholar 

  12. Couvreux, F. et al. J. Adv. Model. Earth Sys. 13, e2020MS002217 (2021).

    Article  Google Scholar 

  13. Knutson, T. R. et al. Nat. Geosci. 3, 157–163 (2010).

    Article  CAS  Google Scholar 

  14. Oldenburg, D. et al. J. Geophys. Res. Oceans 127, e2021JC018102 (2022).

    Article  Google Scholar 

  15. Bates, P. D. et al. Water Resour. Res. 57, e2020WR02867 (2021).

    Article  Google Scholar 

  16. Feng, K. et al. Nat. Commun. 13, 4421 (2022).

    Article  CAS  Google Scholar 

  17. Shuman, J. K. et al. PNAS Nexus 1, 115 (2022).

    Article  Google Scholar 

  18. Lorenz, E. N. in The Physical Basis of Climate and Climate Modelling Vol. 16 (eds Bolin, B. et al.) 132–136 (World Meteorological Organization, 1975).

  19. Lehner, F. et al. Earth Syst. Dyn. 11, 491–508 (2020).

    Article  Google Scholar 

Download references

Acknowledgements

T.S., R.F. and A.S. acknowledge support from E. and W. Schmidt (by recommendation of Schmidt Futures) and the National Science Foundation (grant AGS-1835860). K.E. acknowledges support from the National Science Foundation (grant AGS-1906768). T.M. acknowledges support from VolkswagenStiftung (grant Az:97721). L.R.L. is supported by the Office of Science, US Department of Energy Biological and Environmental Research, as part of the Earth system model development and regional and global model analysis program areas. The Pacific Northwest National Laboratory is operated for the Department of Energy by the Battelle Memorial Institute under contract no. DE-AC05-76RLO1830. N.L. acknowledges support from the National Science Foundation (grant no. 2103754, as part of the Megalopolitan Coastal Transformation Hub). The National Center for Atmospheric Research is sponsored by the National Science Foundation. We thank M. Hell for preparing Fig. 1, and K. Pressel, D. Menemenlis, C. Hill and G. Manucharyan for providing the high-resolution visualizations of clouds, ocean flows and Arctic sea ice.

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Correspondence to Tapio Schneider.

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T.S. has an additional affiliation as a visiting researcher at Google LLC. All other authors declare no competing interests.

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Schneider, T., Behera, S., Boccaletti, G. et al. Harnessing AI and computing to advance climate modelling and prediction. Nat. Clim. Chang. 13, 887–889 (2023). https://doi.org/10.1038/s41558-023-01769-3

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