Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling

Abstract

Artificial neural-networks have the potential to emulate cloud processes with higher accuracy than the semi-empirical emulators currently used in climate models. However, neural-network models do not intrinsically conserve energy and mass, which is an obstacle to using them for long-term climate predictions. Here, we propose two methods to enforce linear conservation laws in neural-network emulators of physical models: Constraining (1) the loss function or (2) the architecture of the network itself. Applied to the emulation of explicitly-resolved cloud processes in a prototype multi-scale climate model, we show that architecture constraints can enforce conservation laws to satisfactory numerical precision, while all constraints help the neural-network better generalize to conditions outside of its training set, such as global warming.

arXiv commentICML 2019 Workshop. Climate Change: How Can AI Help? 3 pages, 3 figures, 1 table

References in corpus (1)

• Deep learning to represent sub-grid processes in climate models

Cited by in corpus (16)

• WeatherBench: A benchmark dataset for data-driven weather forecasting
• Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems
• Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
• A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic
• On closures for reduced order models $-$ A spectrum of first-principle to machine-learned avenues
• DC3: A learning method for optimization with hard constraints
• Model fusion with physics-guided machine learning
• Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles
• Coupled online learning as a way to tackle instabilities and biases in neural network parameterizations
• Differentiable Programming for Earth System Modeling
• BAMCAFE: A Bayesian Machine Learning Advanced Forecast Ensemble Method for Complex Turbulent Systems with Partial Observations
• Emergence of Concepts in DNNs?
• Using Machine Learning at Scale in HPC Simulations with SmartSim: An Application to Ocean Climate Modeling
• Machine learning structure preserving brackets for forecasting irreversible processes
• Using Machine Learning for Model Physics: an Overview
• MC-LSTM: Mass-Conserving LSTM