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The digital revolution of Earth-system science


Computational science is crucial for delivering reliable weather and climate predictions. However, despite decades of high-performance computing experience, there is serious concern about the sustainability of this application in the post-Moore/Dennard era. Here, we discuss the present limitations in the field and propose the design of a novel infrastructure that is scalable and more adaptable to future, yet unknown computing architectures.

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Fig. 1: Typical production workflow in operational numerical weather prediction.
Fig. 2: Comparison between observed and simulated satellite imagery.
Fig. 3: Conceptual view of an efficient software infrastructure for the Earth-system digital twin.
Fig. 4: Expected contribution of main system developments necessary to achieve key science and computing technology performance goals.


  1. Cook, J. et al. Quantifying the consensus on anthropogenic global warming in the scientific literature. Environ. Res. Lett. 8, 024024 (2013).

    Google Scholar 

  2. Wallemacq, P., Below, R. & McLean, D. Economic Losses, Poverty and Disasters: 1998–2017 (UNISDR, CRED, 2018).

  3. Weather, Climate and Catastrophe Insight Report GDM05083 (AON, 2019).

  4. Franco, E. et al. The Global Risks Report 2020 (World Economic Forum, 2020).

  5. Bauer, P., Thorpe, A. & Brunet, G. The quiet revolution of numerical weather prediction. Nature 525, 47–55 (2015).

    Google Scholar 

  6. Hausfather, Z., Drake, H. F., Abbott, T. & Schmidt, G. A. Evaluating the performance of past climate model projections. Geophys. Res. Lett. 47, e2019GL085378 (2020).

    Google Scholar 

  7. Sillmann, J. et al. Understanding, modeling and predicting weather and climate extremes: challenges and opportunities. Weather Clim. Extremes 18, 65–74 (2017).

    Google Scholar 

  8. Asch, M. et al. Big data and extreme-scale computing: pathways to convergence-toward a shaping strategy for a future software and data ecosystem for scientific inquiry. Int. J. High Perform. Comput. Appl. 32, 435–479 (2018).

    Google Scholar 

  9. Khan, H. N., Hounshell, D. A. & Fuchs, E. R. Science and research policy at the end of Moore’s law. Nat. Electron. 1, 14–21 (2018).

    Google Scholar 

  10. Platzman, G. W. The ENIAC computations of 1950—gateway to numerical weather prediction. Bull. Amer. Meteorol. Soc. 60, 302–312 (1979).

    Google Scholar 

  11. Lynch, P. The Emergence of Numerical Weather Prediction: Richardson’s Dream (Cambridge Univ. Press, 2006).

  12. Leutbecher, M. & Palmer, T. N. Ensemble forecasting. J. Comput. Phys. 227, 3515–3539 (2008).

    MathSciNet  MATH  Google Scholar 

  13. Zhu, Y., Toth, Z., Wobus, R., Richardson, D. & Mylne, K. The economic value of ensemble-based weather forecasts. Bull. Amer. Meteorol. Soc. 83, 73–84 (2002).

    Google Scholar 

  14. Palmer, T. & Stevens, B. The scientific challenge of understanding and estimating climate change. Proc. Natl Acad. Sci. USA 116, 24390–24395 (2019).

    Google Scholar 

  15. Brunet, G. et al. Collaboration of the weather and climate communities to advance subseasonal-to-seasonal prediction. Bull. Amer. Meteorol. Soc. 91, 1397–1406 (2010).

    Google Scholar 

  16. Stevens, B. et al. DYAMOND: the dynamics of the atmospheric general circulation modeled on non-hydrostatic domains. Prog. Earth Planet. Sci. 6, 61 (2019).

    Google Scholar 

  17. Wedi, N.P. et al. A baseline for global weather and climate simulations at 1 km resolution. J. Adv. Model. Earth Syst. 12, e2020MS002192 (2020).

  18. Schulthess, T. C. et al. Reflecting on the goal and baseline for exascale computing: a roadmap based on weather and climate simulations. Comput. Sci. Eng. 21, 30–41 (2018).

    Google Scholar 

  19. Bauer, P., Stevens, B., Hazeleger, W. A digital twin of Earth for the green transition.Nat. Clim. Change (2021).

  20. Davis, N. What is the fourth industrial revolution? World Economic Forum (19 January 2016).

  21. Tao, F. & Qi, Q. Make more digital twins. Nature 573, 490–491 (2019).

    Google Scholar 

  22. Bell, G. Supercomputers: The Amazing Race (A History of Supercomputing, 1960–2020) (2014).

  23. Lynch, P. J. The origins of computer weather prediction and climate modeling. Comput. Phys. 227, 3431–3444 (2008).

    MathSciNet  MATH  Google Scholar 

  24. Bondyopadhyay, P. K. Moore’s law governs the silicon revolution. Proc. IEEE 86, 78–81 (1998).

    Google Scholar 

  25. Frank, D. J. et al. Device scaling limits of Si MOSFETs and their application dependencies. Proc. IEEE 89, 259–288 (2001).

    Google Scholar 

  26. Easterbrook, S. M. & Johns, T. C. Engineering the software for understanding climate change. Comput. Sci. Eng. 11, 65–74 (2009).

    Google Scholar 

  27. Fuhrer, O. et al. Near-global climate simulation at 1 km resolution: establishing a performance baseline on 4888 GPUs with COSMO 5.0. Geosci. Model Dev. 11, 1665–1681 (2018).

    Google Scholar 

  28. Lawrence, B. N. et al. Crossing the chasm: how to develop weather and climate models for next generation computers. Geosci. Model Dev. 11, 1799–1821 (2018).

    Google Scholar 

  29. Williamson, D. L. The evolution of dynamical cores for global atmospheric models. J. Meteorol. Soc. Jpn Ser. II 85, 241–269 (2007).

    Google Scholar 

  30. McFarlane, N. Parameterizations: representing key processes in climate models without resolving them. Wiley Interdiscip. Rev. Clim. Change 2, 482–497 (2011).

    Google Scholar 

  31. Flato, G. M. Earth system models: an overview. Wiley Interdiscip. Rev. Clim. Change 2, 783–800 (2011).

    Google Scholar 

  32. Steppeler, J., Hess, R., Schättler, U. & Bonaventura, L. Review of numerical methods for nonhydrostatic weather prediction models. Meteorol. Atmos. Phys. 82, 287–301 (2003).

    Google Scholar 

  33. Mengaldo, G. et al. Current and emerging time-integration strategies in global numerical weather and climate prediction. Arch. Comput. Meth. Eng. 26, 663–684 (2019).

    MathSciNet  Google Scholar 

  34. Teixeira, J., Reynolds, C. A. & Judd, K. Time step sensitivity of nonlinear atmospheric models: numerical convergence, truncation error growth, and ensemble design. J. Atmos. Sci. 64, 175–189 (2007).

    Google Scholar 

  35. Dueben, P. D. & Palmer, T. Benchmark tests for numerical weather forecasts on inexact hardware. Mon. Weather Rev. 142, 3809–3829 (2014).

    Google Scholar 

  36. Vána, F. et al. Single precision in weather forecasting models: an evaluation with the IFS. Mon. Weather Rev. 145, 495–502 (2017).

    Google Scholar 

  37. Hatfield, S. et al. Choosing the optimal numerical precision for data assimilation in the presence of model error. J. Adv. Model. Earth Syst. 10, 2177–2191 (2018).

    Google Scholar 

  38. Dueben, P. D. & Dawson, A. An approach to secure weather and climate models against hardware faults. J. Adv. Model. Earth Syst. 9, 501–513 (2017).

    Google Scholar 

  39. Balaji, V., Benson, R., Wyman, B. & Held, I. Coarse-grained component concurrency in Earth system modeling: parallelizing atmospheric radiative transfer in the GFDL AM3 model using the flexible modeling system coupling framework. Geosci. Model Dev. 9, 3605–3616 (2016).

    Google Scholar 

  40. Koldunov, N. V. et al. Scalability and some optimization of the Finite-volumE Sea ice–Ocean Model, Version 2.0 (FESOM2). Geosci. Model Dev. 12, 3991–4012 (2019).

    Google Scholar 

  41. Mozdzynski, G., Hamrud, M., Wedi, N., Doleschal, J. & Richardson, H. 2012 SC Companion: High Performance Computing, Networking Storage and Analysis 652–661 (2012).

  42. Sanan, P., Schnepp, S. M. & May, D. A. Pipelined, flexible Krylov subspace methods. SIAM J. Sci. Comput. 38, C441–C470 (2016).

    MathSciNet  MATH  Google Scholar 

  43. Maisonnave, E. et al. CDI-pio & XIOS I/O Servers Compatibility with HR Climate Models TR/CMGC/17/52 (CERFACS, 2017).

  44. Govett, M. W., Middlecoff, J. & Henderson, T. Running the NIM next-generation weather model on GPUs. In 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing 792–796 (2010).

  45. Thaler, F. et al. Porting the cosmo weather model to manycore CPUS. In Proc. Platform for Advanced Scientific Computing Conference 1–11 (2019).

  46. Alexander, F. et al. Exascale applications: skin in the game. Phil. Trans. R. Soc. A 378, 20190056 (2020).

    MathSciNet  Google Scholar 

  47. Zhang, S. et al. Optimizing high-resolution community Earth system model on a heterogeneous many-core supercomputing platform. Geosci. Model Dev. 13, 4809–4829 (2020).

    Google Scholar 

  48. Melvin, T. et al. A mixed finite-element, finite-volume, semi-implicit discretization for atmospheric dynamics: Cartesian geometry. Q. J. Royal Meteorol. Soc. 145, 2835–2853 (2019).

    Google Scholar 

  49. Adams, S. V. et al. LFRic: Meeting the challenges of scalability and performance portability in weather and climate models. J. Parallel Distrib. Comput. 132, 383–396 (2019).

    Google Scholar 

  50. Satoh, M. et al. Nonhydrostatic icosahedral atmospheric model (NICAM) for global cloud resolving simulations. J. Comput. Phys. 227, 3486–3514 (2008).

    MathSciNet  MATH  Google Scholar 

  51. Miyoshi, T., Kondo, K. & Imamura, T. The 10,240-member ensemble Kalman filtering with an intermediate AGCM. Geophys. Res. Lett. 41, 5264–5271 (2014).

    Google Scholar 

  52. Washington, W. M., Buja, L. & Craig, A. The computational future for climate and Earth system models: on the path to petaflop and beyond. Phil. Trans. R. Soc. A 367, 833–846 (2009).

    MathSciNet  MATH  Google Scholar 

  53. Reichstein, M. et al. Deep learning and process understanding for data-driven Earth system science. Nature 566, 195–204 (2019).

    Google Scholar 

  54. Balaji, V. et al. CPMIP: measurements of real computational performance of Earth system models in CMIP6. Geosci. Model Dev. 10, 19–34 (2017).

    Google Scholar 

  55. Tumolo, G. & Bonaventura, L. A semi-implicit, semi-Lagrangian discontinuous Galerkin framework for adaptive numerical weather prediction. Q. J. R. Meteorol. Soc. 141, 2582–2601 (2015).

    Google Scholar 

  56. Kühnlein, C. et al. FVM 1.0: a nonhydrostatic finite-volume dynamical core for the IFS. Geosci. Model Dev. 12, 651–676 (2019).

    Google Scholar 

  57. Nastrom, G., Gage, K. S. & Jasperson, W. Kinetic energy spectrum of large-and mesoscale atmospheric processes. Nature 310, 36–38 (1984).

    Google Scholar 

  58. Gander, M. J. in Multiple Shooting and Time Domain Decomposition Methods 69–113 (Springer, 2015).

  59. Hamon, F. P., Schreiber, M. & Minion, M. L. Parallel-in-time multi-level integration of the shallow-water equations on the rotating sphere. J. Comput. Phys. 407, 109210 (2020).

    MathSciNet  Google Scholar 

  60. Fisher, M. & Gürol, S. Parallelization in the time dimension of four-dimensional variational data assimilation. Q. J. R. Meteorol. Soc. 143, 1136–1147 (2017).

    Google Scholar 

  61. Duran, A. et al. OmpSs: a proposal for programming heterogeneous multi-core architectures. Parallel Proc. Lett. 21, 173–193 (2011).

    MathSciNet  Google Scholar 

  62. Weiland, M., Jackson, A., Johnson, N. & Parsons, M. Exploiting the performance benefits of storage class memory for HPC and HPDA workflows. Supercomput. Front. Innov. 5, 79–94 (2018).

    Google Scholar 

  63. Müller, A. et al. The ESCAPE project: energy-efficient scalable algorithms for weather prediction at exascale. Geosci. Model Dev. 12, 4425–4441 (2019).

    Google Scholar 

  64. Heroux, M. et al. Improving Performance via Mini-Applications SAND2009-5574 (Sandia, 2009).

  65. Yang, C. et al. 10M-core scalable fully-implicit solver for nonhydrostatic atmospheric dynamics. In SC16: Proc. International Conference for High Performance Computing, Networking, Storage and Analysis 57–68 (2016).

  66. Mozdzynski, G., Hamrud, M. & Wedi, N. A partitioned global address space implementation of the European Centre for Medium Range Weather Forecasts Integrated Forecasting System. Int. J. High Perform. Comput. Appl. 29, 261–273 (2015).

    Google Scholar 

  67. Schulthess, T. C. Programming revisited. Nat. Phys. 11, 369–373 (2015).

    Google Scholar 

  68. Deconinck, W. et al. Atlas: a library for numerical weather prediction and climate modelling. Comput. Phys. Comm. 220, 188–204 (2017).

    Google Scholar 

  69. Trèmolet, Y. The Joint Effort for Data Assimilation Integration (JEDI). JCSDA Q. 66, 1–5 (2020).

    Google Scholar 

  70. Hill, C., DeLuca, C., Balaji, V., Suarez, M. & da Silva, A. The architecture of the Earth system modeling framework. Comput. Sci. Eng. 6, 18–28 (2004).

    Google Scholar 

  71. Smart, S., Quintino, T. & Raoult, B. A high-performance distributed object-store for exascale numerical weather prediction and climate. In Proc. Platform for Advanced Scientific Computing Conference 1–11 (2019).

  72. Bertagna, L. et al. HOMMEXX 1.0: a performance-portable atmospheric dynamical core for the energy exascale Earth system model. Geosci. Model Dev. 12, 1423–1441 (2019).

    Google Scholar 

  73. Edwards, H. C. & Sunderland, D. Kokkos array performance-portable manycore programming model. In Proc. 2012 International Workshop on Programming Models and Applications for Multicores and Manycores 1–10 (2012).

  74. Gysi, T., Osuna, C., Fuhrer, O., Bianco, M. & Schulthess, T. C. STELLA: a domain-specific tool for structured grid methods in weather and climate models. In Proc. International Conference for High Performance Computing, Networking, Storage and Analysis 1–12 (2015).

  75. Sønderby, C. K. et al. MetNet: a neural weather model for precipitation forecasting. Preprint at (2020).

  76. Ham, Y.-G., Kim, J.-H. & Luo, J.-J. Deep learning for multi-year ENSO forecasts. Nature 573, 568–572 (2019).

    Google Scholar 

  77. Rasp, S. et al. WeatherBench: a benchmark data set for data‐driven weather forecasting. J. Adv. Model. Earth Syst. 12, e2020MS002203, (2020).

  78. Prudden, R. et al. A review of radar-based nowcasting of precipitation and applicable machine learning techniques. Preprint at (2020).

  79. Bonavita, M., Laloyaux, P. Machine learning for model error inference and correction. Earth Space Sci. Open Arch. (2020).

  80. Brajard, J., Carassi, A., Bocquet, M. & Bertino, L. Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model. J. Comput. Sci. 44, 101171 (2020).

    MathSciNet  Google Scholar 

  81. Chevallier, F., Morcrette, J.-J., Chéruy, F. & Scott, N. Use of a neural-network-based long-wave radiative-transfer scheme in the ECMWF atmospheric model. Q. J. R. Meteorol. Soc. 126, 761–776 (2000).

    Google Scholar 

  82. Krasnopolsky, V. M., Fox-Rabinovitz, M. S. & Chalikov, D. V. New approach to calculation of atmospheric model physics: accurate and fast neural network emulation of longwave radiation in a climate model. Mon. Weather Rev. 133, 1370–1383 (2005).

    Google Scholar 

  83. Schneider, T., Lan, S., Stuart, A. & Teixeira, J. Middle atmosphere dynamical sources of the semiannual oscillation in the thermosphere and ionosphere. Geophys. Res. Lett. 44, 12–21 (2017).

    Google Scholar 

  84. Kurth, T. et al. Exascale deep learning for climate analytics. In SC18: International Conference for High Performance Computing, Networking, Storage and Analysis 649–660 (2018).

  85. Vandal, T. et al. DeepSD: Generating high resolution climate change projections through single image super-resolution. In Proc. 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1663–1672 (2017).

  86. Rasp, S. & Lerch, S. Neural networks for postprocessing ensemble weather forecasts. Mon. Weather Rev. 146, 3885–3900 (2018).

    Google Scholar 

  87. Grönquist, P. et al. Deep learning for post-processing ensemble weather forecasts. Preprint at (2020)

  88. Chui, M. Notes from the AI frontier: Applications and value of deep learning. McKinsey (17 April 2018).

  89. Black, D. HPC Market Update from Hyperion Research. insideHPC (2019).

  90. Klöwer, M., Dueben, P.D., Palmer, T.N. J. Adv. Model. Earth Syst., e2020MS002246, (10 September 2020).

  91. Brenowitz, N. D. & Bretherton, C. S. Bretherton, prognostic validation of a neural network unified physics parameterization. Geophys. Res. Lett. 45, 6289–6298 (2018).

    Google Scholar 

  92. Rasp, S., Pritchard, M. S. & Gentine, P. Deep learning to represent subgrid processes in climate models. Proc. Natl Acad. Sci. USA 115, 9684–9689 (2018).

    Google Scholar 

  93. Gysi, T. et al. Domain-specific multi-level IR rewriting for GPU. Preprint at (2020).

  94. Ben-Nun, T., de Fine Licht, J., Ziogas, A. N., Schneider, T. & Hoefler, T. Stateful dataflow multigraphs: a data-centric model for performance portability on heterogeneous architectures. In Proc. International Conference for High Performance Computing, Networking, Storage and Analysis 1–14 (2019).

  95. Hruska, J. As chip design costs skyrocket, 3nm process node is in jeopardy. ExtremeTech (22 June 2018).

  96. Unat, D. et al. Trends in data locality abstractions for HPC systems. IEEE Trans. Parallel Distrib. Syst. 28, 3007–3020 (2017).

    Google Scholar 

  97. Horowitz, M. 1.1 computing’s energy problem (and what we can do about it). In IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC) 10–14 (2014).

  98. Gysi, T., Grosser, T., Hoefler, T. Modesto: Data-centric analytic optimization of complex stencil programs on heterogeneous architectures. In Modesto: Proceedings of the 29th ACM on International Conference on Supercomputing 177–186 (2015).

  99. de Fine Licht, J. et al. StencilFlow: Mapping Large Stencil Programs to Distributed Spatial Computing Systems. Preprint at (2020)

  100. Piz Daint. Swiss National Supercomputing Centre (2020).

  101. EuroHPC supercomputer systems. European Commission (2020).

  102. Girolamo, S. D., Schmid, P., Schulthess, T. & Hoefler, T. SimFS: a simulation data virtualizing file system interface. In Proc. 33st IEEE International Parallel & Distributed Processing Symposium (IPDPS’19) (2019).

  103. Yang, C., Wu, H., Huang, Q., Li, Z. & Li, J. Using spatial principles to optimize distributed computing for enabling the physical science discoveries. Proc. Natl Acad. Sci. USA 108, 5498–5503 (2011).

    Google Scholar 

  104. Jia, Z., Maggioni, M., Staiger, B. & Scarpazza, D. P. Dissecting the NVIDIA Volta GPU architecture via microbenchmarking. Preprint at (2018).

  105. Tsai, Y. M., Cojean, T. & Anzt, H. Evaluating the performance of NVIDIA’s A100 ampere GPU for sparse linear algebra computations. Preprint at (2020).

  106. Voskuilen, G. R., Gimenez, A., Peng, I., Moore, S. & Gokhale, M. Milestone M1 Report: HBM2/3 Evaluation on Many-core CPU WBS 2.4, Milestone ECP-MT-1000 SAND2018-6370R (SANDIA, 2018).

  107. Buehner, M., Houtekamer, P., Charette, C., Mitchell, H. L. & He, B. Intercomparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP. Part I: description and single-observation experiments. Mon. Weather Rev. 138, 1550–1566 (2010).

    Google Scholar 

  108. Blayo, É., Bocquet, M., Cosme, E. & Cugliandolo, L. F. Advanced Data Assimilation for Geosciences: Lecture Notes of the Les Houches School of Physics (Oxford Univ. Press, 2014).

  109. Palmer, T. Short-term tests validate long-term estimates of climate change. Nature 582, 185–186 (2020).

    Google Scholar 

  110. Voosen, P. Europe is building a ‘digital twin’ of Earth to revolutionize climate forecasts. Science (1 October 2020)

  111. Palmer, T., Stevens, B. & Bauer, P. We Need an International Center for Climate Modeling. Scientific American (18 December 2019)

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The authors are grateful to P. Lopez for providing Fig. 2, M. Fielding and M. Janiskova for the illustrations of simulation-observation fusion in Box 1, and to the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and NASA for providing the satellite data used to produce Fig. 2 and the figure in Box 1.

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P.B. conceived and organized the manuscript. P.B., P.D., T.H., T.Q., T.S. and N.W. contributed to the writing and revision of the manuscript.

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Peer review information Nature Computational Science thanks Jana Sillmann and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Fernando Chirigati was the primary editor on this Perspective and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Bauer, P., Dueben, P.D., Hoefler, T. et al. The digital revolution of Earth-system science. Nat Comput Sci 1, 104–113 (2021).

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