Towards neural Earth system modelling by integrating artificial intelligence in Earth system science

[1]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[2]  Rachel James,et al.  Attribution of extreme weather events in Africa: a preliminary exploration of the science and policy implications , 2015, Climatic Change.

[3]  Jonathan A. Weyn,et al.  Can Machines Learn to Predict Weather? Using Deep Learning to Predict Gridded 500‐hPa Geopotential Height From Historical Weather Data , 2019, Journal of Advances in Modeling Earth Systems.

[4]  J. Hurrell,et al.  Viewing Forced Climate Patterns Through an AI Lens , 2019, Geophysical Research Letters.

[5]  Amy McGovern,et al.  Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning , 2019, Bulletin of the American Meteorological Society.

[6]  Marc Bocquet,et al.  Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model , 2020, J. Comput. Sci..

[7]  Alexander Binder,et al.  On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation , 2015, PloS one.

[8]  M. Bethge,et al.  Shortcut learning in deep neural networks , 2020, Nature Machine Intelligence.

[9]  Hui Yang,et al.  Machine learning and artificial intelligence to aid climate change research and preparedness , 2019, Environmental Research Letters.

[10]  L. Zanna,et al.  Data‐Driven Equation Discovery of Ocean Mesoscale Closures , 2020, Geophysical Research Letters.

[11]  Bettina K. Gier,et al.  Taking climate model evaluation to the next level , 2019, Nature Climate Change.

[12]  Karl E. Taylor,et al.  An overview of CMIP5 and the experiment design , 2012 .

[13]  D. Gagne,et al.  Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model , 2019, Journal of Advances in Modeling Earth Systems.

[14]  Amir Hossein Alavi,et al.  Machine learning in geosciences and remote sensing , 2016 .

[15]  G. Hegerl,et al.  Beyond equilibrium climate sensitivity , 2017 .

[16]  Jan Saynisch,et al.  Estimating global ocean heat content from tidal magnetic satellite observations , 2019, Scientific Reports.

[17]  P. O'Gorman,et al.  Using Machine Learning to Parameterize Moist Convection: Potential for Modeling of Climate, Climate Change, and Extreme Events , 2018, Journal of Advances in Modeling Earth Systems.

[18]  Ashesh Chattopadhyay,et al.  Predicting clustered weather patterns: A test case for applications of convolutional neural networks to spatio-temporal climate data , 2020, Scientific Reports.

[19]  Pierre Gentine,et al.  Deep learning to represent subgrid processes in climate models , 2018, Proceedings of the National Academy of Sciences.

[20]  Fuqing Zhang,et al.  Review of the Ensemble Kalman Filter for Atmospheric Data Assimilation , 2016 .

[21]  Edit J. Kaminsky,et al.  Neural network classification of remote-sensing data , 1995 .

[22]  Cameron Buckner,et al.  Understanding adversarial examples requires a theory of artefacts for deep learning , 2020, Nature Machine Intelligence.

[23]  A. Geer,et al.  Learning earth system models from observations: machine learning or data assimilation? , 2021, Philosophical Transactions of the Royal Society A.

[24]  Noah D. Brenowitz,et al.  Interpreting and Stabilizing Machine-Learning Parametrizations of Convection , 2020, Journal of the Atmospheric Sciences.

[25]  Jeong-Hwan Kim,et al.  Deep learning for multi-year ENSO forecasts , 2019, Nature.

[26]  Christopher Kadow,et al.  Artificial intelligence reconstructs missing climate information , 2020, Nature Geoscience.

[27]  Rich Caruana,et al.  Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere , 2020 .

[28]  Nils Wedi,et al.  Assessing the scales in numerical weather and climate predictions: will exascale be the rescue? , 2019, Philosophical Transactions of the Royal Society A.

[29]  Soukayna Mouatadid,et al.  WeatherBench: A Benchmark Data Set for Data‐Driven Weather Forecasting , 2020, Journal of Advances in Modeling Earth Systems.

[30]  Joanna Staneva,et al.  Data assimilation of ocean wind waves using Neural Networks. A case study for the German Bight , 2015 .

[31]  Xiaomeng Huang,et al.  A Moist Physics Parameterization Based on Deep Learning , 2020, Journal of Advances in Modeling Earth Systems.

[32]  Albert Y. Zomaya,et al.  Temporal Convolutional Networks for the Advance Prediction of ENSO , 2020, Scientific Reports.

[33]  Imme Ebert-Uphoff,et al.  Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability , 2019, Journal of Advances in Modeling Earth Systems.

[34]  Vladimir M. Krasnopolsky,et al.  Complex hybrid models combining deterministic and machine learning components for numerical climate modeling and weather prediction , 2006, Neural Networks.

[35]  Dimitrios I. Fotiadis,et al.  Artificial neural networks for solving ordinary and partial differential equations , 1997, IEEE Trans. Neural Networks.

[36]  Di Qi,et al.  Using machine learning to predict extreme events in complex systems , 2019, Proceedings of the National Academy of Sciences.

[37]  D. Gagne,et al.  Machine Learning the Warm Rain Process , 2020, Journal of Advances in Modeling Earth Systems.

[38]  Bernhard Schölkopf,et al.  Inferring causation from time series in Earth system sciences , 2019, Nature Communications.

[39]  Jürgen Kurths,et al.  Complex networks reveal global pattern of extreme-rainfall teleconnections , 2019, Nature.

[40]  Ronald G. Prinn,et al.  Development and application of earth system models , 2012, Proceedings of the National Academy of Sciences.

[41]  Lars Nerger,et al.  State-of-the-art stochastic data assimilation methods for high-dimensional non-Gaussian problems , 2018 .

[42]  Axel Seifert,et al.  Potential and Limitations of Machine Learning for Modeling Warm‐Rain Cloud Microphysical Processes , 2020, Journal of Advances in Modeling Earth Systems.

[43]  E. Ott,et al.  A Machine Learning‐Based Global Atmospheric Forecast Model , 2020, Geophysical Research Letters.

[44]  Markus Reichstein,et al.  Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks , 2020, Global change biology.

[45]  V. Balaji,et al.  Climbing down Charney’s ladder: machine learning and the post-Dennard era of computational climate science , 2020, Philosophical Transactions of the Royal Society A.

[46]  C. Hill,et al.  Elucidating ecological complexity: Unsupervised learning determines global marine eco-provinces. , 2020, Science advances.

[47]  J. Leinonen,et al.  Reconstruction of Cloud Vertical Structure With a Generative Adversarial Network , 2019, Geophysical Research Letters.

[48]  Christopher Irrgang,et al.  Machine Learning‐Based Prediction of Spatiotemporal Uncertainties in Global Wind Velocity Reanalyses , 2020, Journal of Advances in Modeling Earth Systems.

[49]  Rupert Klein,et al.  Scale-Dependent Models for Atmospheric Flows , 2010 .

[50]  Nils Thuerey,et al.  Data‐Driven Medium‐Range Weather Prediction With a Resnet Pretrained on Climate Simulations: A New Model for WeatherBench , 2020, Journal of Advances in Modeling Earth Systems.

[51]  Stephan Rasp,et al.  Training a convolutional neural network to conserve mass in data assimilation , 2020, Nonlinear Processes in Geophysics.

[52]  Wolfgang Lucht,et al.  Tipping elements in the Earth's climate system , 2008, Proceedings of the National Academy of Sciences.

[53]  Janni Yuval,et al.  Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions , 2020, Nature Communications.

[54]  Paul D. Williams,et al.  Stochastic Parameterization: Towards a new view of Weather and Climate Models , 2015, 1510.08682.

[55]  K. Taylor,et al.  Causes of Higher Climate Sensitivity in CMIP6 Models , 2020, Geophysical Research Letters.

[56]  Joachim Denzler,et al.  Deep learning and process understanding for data-driven Earth system science , 2019, Nature.

[57]  Michael Ghil,et al.  Ocean circulation, ice shelf, and sea ice interactions explain Dansgaard–Oeschger cycles , 2018, Proceedings of the National Academy of Sciences.

[58]  Imme Ebert-Uphoff,et al.  Evaluation, Tuning, and Interpretation of Neural Networks for Working with Images in Meteorological Applications , 2020 .

[59]  Christopher Irrgang,et al.  Self‐Validating Deep Learning for Recovering Terrestrial Water Storage From Gravity and Altimetry Measurements , 2020, Geophysical Research Letters.

[60]  Cynthia Rudin,et al.  Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead , 2018, Nature Machine Intelligence.

[61]  Paul J. Valdes,et al.  Built for stability , 2011 .

[62]  N Marwan,et al.  Prediction of extreme floods in the eastern Central Andes based on a complex networks approach , 2014, Nature Communications.

[63]  J. Thepaut,et al.  The ERA5 global reanalysis , 2020, Quarterly Journal of the Royal Meteorological Society.

[64]  F. Joos,et al.  Probabilistic climate change projections using neural networks , 2003 .

[65]  Markus Reichstein,et al.  Constraining Uncertainty in Projected Gross Primary Production With Machine Learning , 2020, Journal of Geophysical Research: Biogeosciences.

[66]  Thomas Bolton,et al.  Applications of Deep Learning to Ocean Data Inference and Subgrid Parameterization , 2019, Journal of Advances in Modeling Earth Systems.

[67]  Johnny Wei-Bing Lin,et al.  Considerations for Stochastic Convective Parameterization , 2002 .

[68]  Reto Knutti,et al.  Should we believe model predictions of future climate change? , 2008, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[69]  T. Yoshida,et al.  Strongly Coupled Data Assimilation in Multiscale Media: Experiments Using a Quasi‐Geostrophic Coupled Model , 2019, Journal of Advances in Modeling Earth Systems.

[70]  W. Collins,et al.  The Community Earth System Model: A Framework for Collaborative Research , 2013 .

[71]  M. Scheffer,et al.  Catalogue of abrupt shifts in Intergovernmental Panel on Climate Change climate models , 2015, Proceedings of the National Academy of Sciences.

[72]  Andrew Glaws,et al.  Adversarial super-resolution of climatological wind and solar data , 2020, Proceedings of the National Academy of Sciences.

[73]  Reto Knutti,et al.  Anthropogenic and natural warming inferred from changes in Earth’s energy balance , 2012 .

[74]  Paris Perdikaris,et al.  Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations , 2019, J. Comput. Phys..

[75]  William W. Hsieh,et al.  Applying Neural Network Models to Prediction and Data Analysis in Meteorology and Oceanography. , 1998 .

[76]  Lorenzo Bruzzone,et al.  An experimental comparison of neural and statistical non-parametric algorithms for supervised classification of remote-sensing images , 1996, Pattern Recognit. Lett..

[77]  Michelle Girvan,et al.  Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model , 2018, Chaos.

[78]  Torsten Hoefler,et al.  The digital revolution of Earth-system science , 2021, Nature Computational Science.

[79]  Sancho Salcedo-Sanz,et al.  Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources , 2020, Inf. Fusion.

[80]  P. Leeuwen Nonlinear data assimilation in geosciences: an extremely efficient particle filter , 2010 .

[81]  E. Loh Medicine and the rise of the robots: a qualitative review of recent advances of artificial intelligence in health , 2018, BMJ Leader.

[82]  Veronika Eyring,et al.  Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization , 2015 .

[83]  Noah D. Brenowitz,et al.  Prognostic Validation of a Neural Network Unified Physics Parameterization , 2018, Geophysical Research Letters.

[84]  Russell G. Death,et al.  An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data , 2004 .

[85]  Roland Potthast,et al.  Particle filters for applications in geosciences , 2018, 1807.10434.

[86]  Carl Wunsch,et al.  Unsupervised Learning Reveals Geography of Global Ocean Dynamical Regions , 2019, Earth and space science.