# Climbing down Charney's ladder: Machine Learning and the post-Dennard era of computational climate science

arXiv: https://arxiv.org/abs/2005.11862 · HTML: https://mlsanity.com/paper/2005.11862/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2020.

## Built on by (most cited first, 18 of 18)

- [FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](https://mlsanity.com/paper/2202.11214/) (2022)
- [Will Artificial Intelligence supersede Earth System and Climate Models?](https://mlsanity.com/paper/2101.09126/) (2021)
- [One-shot learning for solution operators of partial differential equations](https://mlsanity.com/paper/2104.05512/) (2021)
- [Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling](https://mlsanity.com/paper/2305.14452/) (2023)
- [Generative Diffusion-based Downscaling for Climate](https://mlsanity.com/paper/2404.17752/) (2024)
- [Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers](https://mlsanity.com/paper/2103.09360/) (2021)
- [Bridging observation, theory and numerical simulation of the ocean using Machine Learning](https://mlsanity.com/paper/2104.12506/) (2021)
- [Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challenges](https://mlsanity.com/paper/2306.05014/) (2023)
- [Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence](https://mlsanity.com/paper/2205.04601/) (2022)
- [Probabilistic forecasts of extreme heatwaves using convolutional neural networks in a regime of lack of data](https://mlsanity.com/paper/2208.00971/) (2022)
- [Discovery of interpretable structural model errors by combining Bayesian sparse regression and data assimilation: A chaotic Kuramoto-Sivashinsky test case](https://mlsanity.com/paper/2110.00546/) (2021)
- [Extreme heatwave sampling and prediction with analog Markov chain and comparisons with deep learning](https://mlsanity.com/paper/2307.09060/) (2023)
- [Distilling Machine Learning's Added Value: Pareto Fronts in Atmospheric Applications](https://mlsanity.com/paper/2408.02161/) (2024)
- [Wavelet-Assisted Mamba for Satellite-Derived Sea Surface Temperature Super-Resolution](https://mlsanity.com/paper/2509.24334/) (2025)
- [Semi-automatic tuning of coupled climate models with multiple intrinsic timescales: lessons learned from the Lorenz96 model](https://mlsanity.com/paper/2208.06243/) (2022)
- [Benchmarking atmospheric circulation variability in an AI emulator, ACE2, and a hybrid model, NeuralGCM](https://mlsanity.com/paper/2510.04466/) (2025)
- [Machine learning for Earth System Science (ESS): A survey, status and future directions for South Asia](https://mlsanity.com/paper/2112.12966/) (2021)
- [Machine Learning Workflows in Climate Modeling: Design Patterns and Insights from Case Studies](https://mlsanity.com/paper/2510.03305/) (2025)

## Abstract (arXiv)

The advent of digital computing in the 1950s sparked a revolution in the science of weather and climate. Meteorology, long based on extrapolating patterns in space and time, gave way to computational methods in a decade of advances in numerical weather forecasting. Those same methods also gave rise to computational climate science, studying the behaviour of those same numerical equations over intervals much longer than weather events, and changes in external boundary conditions. Several subsequent decades of exponential growth in computational power have brought us to the present day, where models ever grow in resolution and complexity, capable of mastery of many small-scale phenomena with global repercussions, and ever more intricate feedbacks in the Earth system. The current juncture in computing, seven decades later, heralds an end to what is called Dennard scaling, the physics behind ever smaller computational units and ever faster arithmetic. This is prompting a fundamental change in our approach to the simulation of weather and climate, potentially as revolutionary as that wrought by John von Neumann in the 1950s. One approach could return us to an earlier era of pattern recognition and extrapolation, this time aided by computational power. Another approach could lead us to insights that continue to be expressed in mathematical equations. In either approach, or any synthesis of those, it is clearly no longer the steady march of the last few decades, continuing to add detail to ever more elaborate models. In this prospectus, we attempt to show the outlines of how this may unfold in the coming decades, a new harnessing of physical knowledge, computation, and data.