Will Artificial Intelligence supersede Earth System and Climate Models?
arXiv:2101.09126 · doi:10.1038/s42256-021-00374-3
Abstract
We outline a perspective of an entirely new research branch in Earth and climate sciences, where deep neural networks and Earth system models are dismantled as individual methodological approaches and reassembled as learning, self-validating, and interpretable Earth system model-network hybrids. Following this path, we coin the term "Neural Earth System Modelling" (NESYM) and highlight the necessity of a transdisciplinary discussion platform, bringing together Earth and climate scientists, big data analysts, and AI experts. We examine the concurrent potential and pitfalls of Neural Earth System Modelling and discuss the open question whether artificial intelligence will not only infuse Earth system modelling, but ultimately render them obsolete.
Perspective paper submitted to Nature Machine Intelligence, 23 pages, 3 figures
References in corpus (26)
- Artificial Neural Networks for Solving Ordinary and Partial Differential Equations
- Shortcut Learning in Deep Neural Networks
- Deep learning to represent sub-grid processes in climate models
- Detecting causal associations in large nonlinear time series datasets
- WeatherBench: A benchmark dataset for data-driven weather forecasting
- Stochastic Parameterization: Towards a new view of Weather and Climate Models
- Attractor Reconstruction by Machine Learning
- Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations
- Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability
- Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
- Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model
- Using machine learning to parameterize moist convection: potential for modeling of climate, climate change and extreme events
- Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere
- Machine Learning Information Fusion in Earth Observation: A Comprehensive Review of Methods, Applications and Data Sources
- Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model
- Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model
- A test case for application of convolutional neural networks to spatio-temporal climate data: Re-identifying clustered weather patterns
- Data-driven medium-range weather prediction with a Resnet pretrained on climate simulations: A new model for WeatherBench
- Learning dominant physical processes with data-driven balance models
- Indicator patterns of forced change learned by an artificial neural network
- Interpreting and Stabilizing Machine-learning Parametrizations of Convection
- Machine Learning Climate Model Dynamics: Offline versus Online Performance
- Climbing down Charney's ladder: Machine Learning and the post-Dennard era of computational climate science
- Identifying Opportunities for Skillful Weather Prediction with Interpretable Neural Networks
- Data Assimilation by Artificial Neural Networks for an Atmospheric General Circulation Model: Conventional Observation
- Objective discovery of dominant dynamical processes with intelligible machine learning
Cited by in corpus (8)
- Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers
- When Geoscience Meets Foundation Models: Towards General Geoscience Artificial Intelligence System
- Earth System Data Cubes: Avenues for advancing Earth system research
- Differentiable Programming for Earth System Modeling
- Equation-free surrogate modeling of geophysical flows at the intersection of machine learning and data assimilation
- Reconstructing Historical Climate Fields With Deep Learning
- Downscaling GRACE-derived ocean bottom pressure anomalies using self-supervised data fusion
- Implicit Neural Representations for Simultaneous Reduction and Continuous Reconstruction of Multi-Altitude Climate Data