Earth system models (ESMs) are our main tools for quantifying the physical state of the Earth and predicting how it might change in the future under ongoing anthropogenic forcing. In recent years, however, artificial intelligence (AI) methods have been increasingly used to augment or even replace classical ESM tasks, raising hopes that AI could solve some of the grand challenges of climate science. In this Perspective we survey the recent achievements and limitations of both process-based models and AI in Earth system and climate research, and propose a methodological transformation in which deep neural networks and ESMs are dismantled as individual approaches and reassembled as learning, self-validating and interpretable ESM–network hybrids. Following this path, we coin the term neural Earth system modelling. We examine the concurrent potential and pitfalls of neural Earth system modelling and discuss the open question of whether AI can bolster ESMs or even ultimately render them obsolete.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Journal of Scientific Computing Open Access 26 July 2022
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $8.25 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
Prinn, R. G. Development and application of earth system models. Proc. Natl Acad. Sci. USA 110, 3673–3680 (2013).
Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).
IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).
Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).
Lin, J. W.-B. & Neelin, J. D. Considerations for stochastic convective parameterization. J. Atmos. Sci. 59, 959–975 (2002).
Klein, R. Scale-dependent models for atmospheric flows. Annu. Rev. Fluid Mech. 42, 249–274 (2010).
Berner, J. et al. Stochastic parameterization: toward a new view of weather and climate models. Bull. Am. Meteorol. Soc. 98, 565–588 (2017).
Knutti, R. Should we believe model predictions of future climate change? Phil. Trans. R. Soc. A 366, 4647–4664 (2008).
Knutti, R., Rugenstein, M. A. & Hegerl, G. C. Beyond equilibrium climate sensitivity. Nat. Geosci. 10, 727–736 (2017).
Meehl, G. A. et al. Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models. Sci. Adv. 6, eaba1981 (2020).
Zelinka, M. D. et al. Causes of higher climate sensitivity in CMIP6 models. Geophys. Res. Lett. 47, e2019GL085782 (2020).
Lenton, T. M. et al. Tipping elements in the earth’s climate system. Proc. Natl Acad. Sci. USA 105, 1786–1793 (2008).
Boers, N., Ghil, M. & Rousseau, D.-D. Ocean circulation, ice shelf, and sea ice interactions explain Dansgaard-Oeschger cycles. Proc. Natl Acad. Sci. USA 115, E11005–E11014 (2018).
Valdes, P. Built for stability. Nat. Geosci. 4, 414–416 (2011).
Drijfhout, S. et al. Catalogue of abrupt shifts in Intergovernmental Panel on Climate Change climate models. Proc. Natl Acad. Sci. USA 112, E5777–E5786 (2015).
IPCC Special Report on Global Warming of 1.5 °C (eds Masson-Delmotte, V. et al.) (WMO, 2018); https://www.ipcc.ch/sr15
IPCC Special Report on Climate Change and Land (eds Shukla, P. et al.) (IPCC, 2019); https://www.ipcc.ch/srccl/
IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (eds Pörtner, H. et al.) (IPCC, 2019); https://www.ipcc.ch/srocc/
Otto, F. E. et al. Attribution of extreme weather events in Africa: a preliminary exploration of the science and policy implications. Climatic Change 132, 531–543 (2015).
Balsamo, G. et al. Satellite and in situ observations for advancing global earth surface modelling: a review. Remote Sens. 10, 2038 (2018).
Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).
Evensen, G. Data Assimilation: The Ensemble Kalman Filter (Springer, 2009).
Houtekamer, P. L. & Zhang, F. Review of the ensemble Kalman filter for atmospheric data assimilation. Mon. Weather Rev. 144, 4489–4532 (2016).
van Leeuwen, P. J. Nonlinear data assimilation in geosciences: an extremely efficient particle filter. Q. J. R. Meteorol. Soc. 136, 1991–1999 (2010).
van Leeuwen, P. J., Künsch, H. R., Nerger, L., Potthast, R. & Reich, S. Particle filters for high-dimensional geoscience applications: a review. Q. J. R. Meteorol. Soc. 145, 2335–2365 (2019).
Vetra-Carvalho, S. et al. State-of-the-art stochastic data assimilation methods for high-dimensional non-Gaussian problems. Tellus A 70, 1–43 (2018).
Penny, S. G. et al. Strongly coupled data assimilation in multiscale media: experiments using a quasi-geostrophic coupled model. J. Adv. Model. Earth Syst. 11, 1803–1829 (2019).
Browne, P. A., de Rosnay, P., Zuo, H., Bennett, A. & Dawson, A. Weakly coupled ocean-atmosphere data assimilation in the ECMWF NWP system. Remote Sens. 11, 234 (2019).
Voulodimos, A., Doulamis, N., Doulamis, A. & Protopapadakis, E. Deep learning for computer vision: a brief review. Comput. Intell. Neurosci. 2018, 7068349 (2018).
Brown, T. B. et al. Language models are few-shot learners. Preprint at https://arxiv.org/abs/2005.14165 (2020).
Loh, E. Medicine and the rise of the robots: a qualitative review of recent advances of artificial intelligence in health. BMJ Lead. 2, 59–63 (2018).
Girasa, R. in Artificial Intelligence as a Disruptive Technology 3–21 (Springer, 2020).
Bauer, P. et al. The digital revolution of Earth-system science. Nat. Comput. Sci. 1, 104–113 (2021).
Lary, D. J., Alavi, A. H., Gandomi, A. H. & Walker, A. L. Machine learning in geosciences and remote sensing. Geosci. Front. 7, 3–10 (2016).
Salcedo-Sanz, S. et al. Machine learning information fusion in Earth observation: a comprehensive review of methods, applications and data sources. Inf. Fusion 63, 256–272 (2020).
Dawson, M., Olvera, J., Fung, A. & Manry, M. Inversion of surface parameters using fast learning neural networks. In Proc. IGARSS ’92 International Geoscience and Remote Sensing Symposium Vol. 2, 910–912 (IEEE, 1992); http://ieeexplore.ieee.org/document/578294
Miller, D. M., Kaminsky, E. J. & Rana, S. Neural network classification of remote-sensing data. Comput. Geosci. 21, 377–386 (1995).
Serpico, S. B., Bruzzone, L. & Roli, F. An experimental comparison of neural and statistical non-parametric algorithms for supervised classification of remote-sensing images. Pattern Recogn. Lett. 17, 1331–1341 (1996).
Hsieh, W. W. & Tang, B. Applying neural network models to prediction and data analysis in meteorology and oceanography. Bull. Am. Meteorol. Soc. 79, 1855–1870 (1998).
Knutti, R., Stocker, T. F., Joos, F. & Plattner, G. K. Probabilistic climate change projections using neural networks. Clim. Dynam. 21, 257–272 (2003).
Arcomano, T. et al. A machine learning-based global atmospheric forecast model. Geophys. Res. Lett. 47, e2020GL087776 (2020).
Weyn, J. A., Durran, D. R. & Caruana, R. Can machines learn to predict weather? Using deep learning to predict gridded 500-hPa geopotential height from historical weather data. J. Adv. Model. Earth Syst. 11, 2680–2693 (2019).
Weyn, J. A., Durran, D. R. & Caruana, R. Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere. J. Adv. Model. Earth Syst. 12, e2020MS002109 (2020).
Chantry, M., Hatfield, S., Duben, P., Polichtchouk, I. & Palmer, T. Machine learning emulation of gravity wave drag in numerical weather forecasting. Preprint at https://arxiv.org/abs/2101.08195 (2021).
Gettelman, A. et al. Machine learning the warm rain process. J. Adv. Model. Earth Syst. 13, e2020MS002268 (2021).
Rasp, S. & Thuerey, N. Data-driven medium-range weather prediction with a Resnet pretrained on climate simulations: a new model for WeatherBench. J. Adv. Model. Earth Syst. 13, e2020MS002405 (2021).
Palmer, T. A vision for numerical weather prediction in 2030. Preprint at https://arxiv.org/abs/2007.04830 (2020).
Neumann, P. et al. Assessing the scales in numerical weather and climate predictions: will exascale be the rescue? Phil. Trans. R. Soc. A 377, 20180148 (2019).
Kurth, T. et al. Exascale deep learning for climate analytics. In SC18: International Conference for High Performance Computing, Networking, Storage and Analysis 649–660 (IEEE, 2018).
Boers, N. et al. Complex networks reveal global pattern of extreme-rainfall teleconnections. Nature 566, 373–377 (2019).
Ham, Y.-g, Kim, J.-h & Luo, J.-j Deep learning for multi-year ENSO forecasts. Nature 573, 568–572 (2019).
Yan, J., Mu, L., Wang, L., Ranjan, R. & Zomaya, A. Y. Temporal convolutional networks for the advance prediction of ENSO. Sci. Rep. 10, 8055 (2020).
Kadow, C., Hall, D. M. & Ulbrich, U. Artificial intelligence reconstructs missing climate information. Nat. Geosci. 13, 408–413 (2020).
Barnes, E. A., Hurrell, J. W., Ebert-Uphoff, I., Anderson, C. & Anderson, D. Viewing forced climate patterns through an AI lens. Geophys. Res. Lett. 46, 13389–13398 (2019).
Barnes, E. A. et al. Indicator patterns of forced change learned by an artificial neural network. J. Adv. Model. Earth Syst. 12, e2020MS002195 (2020).
Chattopadhyay, A., Hassanzadeh, P. & Pasha, S. Predicting clustered weather patterns: a test case for applications of convolutional neural networks to spatio-temporal climate data. Sci. Rep. 10, 1317 (2020).
Ramachandran, P., Zoph, B. & Le, Q. V. Searching for activation functions. Preprint at https://arxiv.org/abs/1710.05941 (2017).
Lu, Z., Hunt, B. R. & Ott, E. Attractor reconstruction by machine learning. Chaos 28, 061104 (2018).
Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533–536 (1986).
Reichstein, M. et al. Deep learning and process understanding for data-driven earth system science. Nature 566, 195–204 (2019).
Huntingford, C. et al. Machine learning and artificial intelligence to aid climate change research and preparedness. Environ. Res. Lett. 14, 124007 (2019).
Irrgang, C., Saynisch, J. & Thomas, M. Estimating global ocean heat content from tidal magnetic satellite observations. Sci. Rep. 9, 7893 (2019).
Irrgang, C., Saynisch-Wagner, J., Dill, R., Boergens, E. & Thomas, M. Self-validating deep learning for recovering terrestrial water storage from gravity and altimetry measurements. Geophys. Res. Lett. 47, e2020GL089258 (2020).
Jung, M. et al. Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the fluxcom approach. Biogeosciences 17, 1343–1365 (2020).
Tramontana, G. et al. Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks. Glob. Change Biol. 26, 5235–5253 (2020).
Bolton, T. & Zanna, L. Applications of deep learning to ocean data inference and subgrid parameterization. J. Adv. Model. Earth Syst. 11, 376–399 (2019).
Rasp, S., Pritchard, M. S. & Gentine, P. Deep learning to represent subgrid processes in climate models. Proc. Natl Acad. Sci. 115, 9684–9689 (2018).
O’Gorman, P. A. & Dwyer, J. G. Using machine learning to parameterize moist convection: potential for modeling of climate, climate change, and extreme events. J. Adv. Model. Earth Syst. 10, 2548–2563 (2018).
Gagne, D. J., Christensen, H. M., Subramanian, A. C. & Monahan, A. H. Machine learning for stochastic parameterization: generative adversarial networks in the Lorenz ’96 model. J. Adv. Model. Earth Syst. 12, e2019MS001896 (2020).
Han, Y., Zhang, G. J., Huang, X. & Wang, Y. A moist physics parameterization based on deep learning. J. Adv. Model. Earth Syst. 12, e2020MS002076 (2020).
Beucler, T., Pritchard, M., Gentine, P. & Rasp, S. Towards physically-consistent, data-driven models of convection. Preprint at http://arxiv.org/abs/2002.08525 (2020).
Yuval, J. & O’Gorman, P. A. Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions. Nat. Commun. 11, 3295 (2020).
Brenowitz, N. D. & Bretherton, C. S. Prognostic validation of a neural network unified physics parameterization. Geophys. Res. Lett. 45, 6289–6298 (2018).
Watt-Meyer, O. et al. Correcting weather and climate models by machine learning nudged historical simulations. Preprint at ESSOAr https://doi.org/10.1002/essoar.10505959.1 (2021).
Pathak, J. et al. Hybrid forecasting of chaotic processes: using machine learning in conjunction with a knowledge-based model. Chaos 28, 041101 (2018).
Krasnopolsky, V. M. & Fox-Rabinovitz, M. S. Complex hybrid models combining deterministic and machine learning components for numerical climate modeling and weather prediction. Neural Netw. 19, 122–134 (2006).
Brenowitz, N. D. et al. Machine learning climate model dynamics: offline versus online performance. Preprint at http://arxiv.org/abs/2011.03081 (2020).
Brenowitz, N. D., Beucler, T., Pritchard, M. & Bretherton, C. S. Interpreting and stabilizing machine-learning parametrizations of convection. J. Atmos. Sci. 77, 4357–4375 (2020).
Seifert, A. & Rasp, S. Potential and limitations of machine learning for modeling warm-rain cloud microphysical processes. J. Adv. Model. Earth Syst. 12, e2020MS002301 (2020).
Beucler, T., Rasp, S., Pritchard, M. & Gentine, P. Achieving conservation of energy in neural network emulators for climate modeling. Preprint at https://arxiv.org/abs/1906.06622 (2019).
Schneider, T., Lan, S., Stuart, A. & Teixeira, J. Earth system modeling 2.0: a blueprint for models that learn from observations and targeted high-resolution simulations. Geophys. Res. Lett. 44, 12396–12417 (2017).
Cintra, R. S. & Velho, H. Fd. C. Data assimilation by artificial neural networks for an atmospheric general circulation model: conventional observation. Bull. Am. Meteorological Soc. 77, 437–471 (2014).
Wahle, K., Staneva, J. & Guenther, H. Data assimilation of ocean wind waves using neural networks. a case study for the german bight. Ocean Model. 96, 117–125 (2015).
Irrgang, C., Saynisch-Wagner, J. & Thomas, M. Machine learning-based prediction of spatiotemporal uncertainties in global wind velocity reanalyses. J. Adv. Model. Earth Syst. 12, e2019MS001876 (2020).
Brajard, J., Carrassi, 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).
Ruckstuhl, Y., Janjić, T. & Rasp, S. Training a convolutional neural network to conserve mass in data assimilation. Nonlin. Processes Geophys. 28, 111–119 (2020).
Geer, A. J. Learning Earth system models from observations: machine learning or data assimilation? Phil. Trans. R. Soc. A 379, 20200089 (2021).
Runge, J. et al. Inferring causation from time series in Earth system sciences. Nat. Commun. 10, 2553 (2019).
Boers, N. et al. Prediction of extreme floods in the eastern central andes based on a complex networks approach. Nat. Commun. 5, 5199 (2014).
Qi, D. & Majda, A. J. Using machine learning to predict extreme events in complex systems. Proc. Natl Acad. Sci. USA 117, 52–59 (2020).
Sonnewald, M., Dutkiewicz, S., Hill, C. & Forget, G. Elucidating ecological complexity: unsupervised learning determines global marine eco-provinces. Sci. Adv. 6, 1–12 (2020).
Leinonen, J., Guillaume, A. & Yuan, T. Reconstruction of cloud vertical structure with a generative adversarial network. Geophys. Res. Lett. 46, 7035–7044 (2019).
Stengel, K., Glaws, A., Hettinger, D. & King, R. N. Adversarial super-resolution of climatological wind and solar data. Proc. Natl Acad. Sci. USA 117, 16805–16815 (2020).
Huber, M. & Knutti, R. Anthropogenic and natural warming inferred from changes in Earth’s energy balance. Nat. Geosci. 5, 31–36 (2012).
Zanna, L. & Bolton, T. Data-driven equation discovery of ocean mesoscale closures. Geophys. Res. Lett. 47, e2020GL088376 (2020).
Lagaris, I. E., Likas, A. & Fotiadis, D. I. Artificial neural networks for solving ordinary and partial differential equations. IEEE Trans. Neural Netw. 9, 987–1000 (1998).
Raissi, M., Perdikaris, P. & Karniadakis, G. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686–707 (2019).
Ramadhan, A. et al. Capturing missing physics in climate model parameterizations using neural differential equations. Preprint at http://arxiv.org/abs/2010.12559 (2020).
Goodfellow, I. J. et al. Generative adversarial networks. Preprint at https://arxiv.org/abs/1406.2661 (2014).
Hurrell, J. W. et al. The Community Earth System Model: a framework for collaborative research. Bull. Am. Meteorol. Soc. 94, 1339 – 1360 (2013).
Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1, 206–215 (2019).
Balaji, V. Climbing down Charney’s ladder: machine learning and the post-Dennard era of computational climate science. Phil. Trans. R. Soc. A 379, 20200085 (2021).
Sonnewald, M. et al. Bridging observation, theory and numerical simulation of the ocean using machine learning. Preprint at https://arxiv.org/abs/2104.12506 (2021).
Ethics Guidelines for Trustworthy AI (European Commission, 2019); https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai
The Biden Administration Launches AI.gov Aimed at Broadening Access to Federal Artificial Intelligence Innovation Efforts, Encouraging Innovators of Tomorrow (White House, 2021); https://www.whitehouse.gov/ostp/news-updates/2021/05/05/the-biden-administration-launches-ai-gov-aimed-at-broadening-access-to-federal-artificial-intelligence-innovation-efforts-encouraging-innovators-of-tomorrow/
Toms, B. A., Barnes, E. A. & Ebert-Uphoff, I. Physically interpretable neural networks for the geosciences: applications to Earth system variability. J. Adv. Model. Earth Syst. 12, e2019MS002002 (2020).
Kaiser, B. E., Saenz, J. A., Sonnewald, M. & Livescu, D. Objective discovery of dominant dynamical processes with intelligible machine learning. Preprint at https://arxiv.org/abs/2106.12963 (20201).
McGovern, A. et al. Making the black box more transparent: understanding the physical implications of machine learning. Bull. Am. Meteorological Soc. 100, 2175 – 2199 (2019).
Ebert-Uphoff, I. & Hilburn, K. Evaluation, tuning and interpretation of neural networks for working with images in meteorological applications. B. Am. Meteorol. Soc. 101, E2149–E2170 (2020).
Sonnewald, M. & Lguensat, R. Revealing the impact of global heating on North Atlantic circulation using transparent machine learning. Preprint at ESSOAr https://doi.org/10.1002/essoar.10506146.1 (2021).
Beucler, T., Ebert-Uphoff, I., Rasp, S., Pritchard, M. & Gentine, P. Machine learning for clouds and climate (invited chapter for the AGU geophysical monograph series ‘clouds and climate’). Preprint at ESSOAr https://doi.org/10.1002/essoar.10506925.1 (2021).
Olden, J. D., Joy, M. K. & Death, R. G. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecol. Model. 178, 389–397 (2004).
Bach, S. et al. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 10, e0130140 (2015).
Barnes, E. A., Mayer, K., Toms, B., Martin, Z. & Gordon, E. Identifying opportunities for skillful weather prediction with interpretable neural networks. Preprint at https://arxiv.org/abs/2012.07830 (2020).
Sonnewald, M., Wunsch, C. & Heimbach, P. Unsupervised learning reveals geography of global ocean dynamical regions. Earth Space Sci. 6, 784–794 (2019).
Callaham, J. L., Koch, J. V., Brunton, B. W., Kutz, J. N. & Brunton, S. L. Learning dominant physical processes with data-driven balance models. Nat. Commun. 12, 1016 (2021).
Runge, J., Nowack, P., Kretschmer, M., Flaxman, S. & Sejdinovic, D. Detecting and quantifying causal associations in large nonlinear time series datasets. Sci. Adv. 5, eaau4996 (2019).
Eyring, V. et al. Taking climate model evaluation to the next level. Nat. Clim. Change 9, 102–110 (2019).
Schlund, M. et al. Constraining uncertainty in projected gross primary production with machine learning. J. Geophys. Res. Biogeosci. 125, e2019JG005619 (2020).
Rasp, S. et al. WeatherBench: a benchmark dataset for data-driven weather forecasting. J. Adv. Model. Earth Syst. 12, e2020MS002203 (2020).
Geirhos, R. et al. Shortcut learning in deep neural networks. Nat. Mach. Intell. 2, 665–673 (2020).
Buckner, C. Understanding adversarial examples requires a theory of artefacts for deep learning. Nat. Mach. Intell. 2, 731–736 (2020).
Alvarez-Melis, D. & Jaakkola, T. S. On the robustness of interpretability methods. Preprint at https://arxiv.org/abs/1806.08049 (2018).
Rolnick, D. et al. Tackling climate change with machine learning. Preprint at http://arxiv.org/abs/1906.05433 (2019).
This study was funded by the Helmholtz Association and by the Initiative and Networking Fund of the Helmholtz Association through the project Advanced Earth System Modelling Capacity (ESM). N.B. acknowledges funding by the Volskwagen foundation and the European Union’s Horizon 2020 research and innovation program under grant agreement number 820970 (TiPES, contribution #121). E.A.B. was supported, in part, by the US National Science Foundation under grant number AGS-1749261. M.S. acknowledges funding from the Cooperative Institute for Modeling the Earth System, Princeton University, under award number NA18OAR4320123 and from the National Oceanic and Atmospheric Administration, US Department of Commerce. The statements, findings, conclusions, and recommendations are those of the authors and do not necessarily reflect the views of Princeton University, the National Oceanic and Atmospheric Administration or the US Department of Commerce.
The authors declare no competing interests.
Peer review information Nature Machine Intelligence thanks the anonymous reviewers for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Irrgang, C., Boers, N., Sonnewald, M. et al. Towards neural Earth system modelling by integrating artificial intelligence in Earth system science. Nat Mach Intell 3, 667–674 (2021). https://doi.org/10.1038/s42256-021-00374-3
Nature Climate Change (2022)
Nature Reviews Earth & Environment (2022)
A Hybrid Neural Network Model for ENSO Prediction in Combination with Principal Oscillation Pattern Analyses
Advances in Atmospheric Sciences (2022)
Journal of Scientific Computing (2022)