Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling

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William D. Collins, Cecilia M. Bitz, Maurice L. Blackmon, Gordon B. Bonan, Christopher S. Bretherton, James A. Carton, Ping Chang, Scott C. Doney, James J. Hack, Thomas B. Henderson, Jeffrey T. Kiehl, William G. Large, Daniel S. McKenna, Benjamin D. Santer, and Richard D. Smith · 2006

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Water in the atmosphere

Bjorn Stevens and Sandrine Bony · 2009

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Moist static energy budget of MJO-like disturbances in the atmosphere of a zonally symmetric aquaplanet

Joseph Allan Andersen and Zhiming Kuang · 2012

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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude

Original

Tijmen Tieleman, Geoffrey E. Hinton, Nitish Srivastava, and Kevin Swersky · 2012

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Using Ensemble of Neural Networks to Learn Stochastic Convection Parameterizations for Climate and Numerical Weather Prediction Models from Data Simulated by a Cloud Resolving Model

Vladimir M Krasnopolsky, Michael S Fox-Rabinovitz, and Alexei A Belochitski · 2013

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Climate Change 2013 - The Physical Science Basis

IPCC · 2014

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Spread in model climate sensitivity traced to atmospheric convective mixing

Steven C. Sherwood, Sandrine Bony, and Jean Louis Dufresne · 2014

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Intercomparison of methods of coupling between convection and large-scale circulation: 1. Comparison over uniform surface conditions

C. L. Daleu, R. S. Plant, S. J. Woolnough, S. Sessions, M. J. Herman, A. Sobel, S. Wang, D. Kim, A. Cheng, G. Bellon, P. Peyrille, F. Ferry, P. Siebesma, and L. van Ulft · 2015

Cited alongside, same era.

Intercomparison of methods of coupling between convection and large-scale circulation: 2. Comparison over nonuniform surface conditions

Climate goals and computing the future of clouds

Tapio Schneider, João Teixeira, Christopher S. Bretherton, Florent Brient, Kyle G. Pressel, Christoph Schär, and A. Pier Siebesma · 2017

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Prognostic Validation of a Neural Network Unified Physics Parameterization

Noah Domino Brenowitz and Christopher Stephen Bretherton · 2018

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Could Machine Learning Break the Convection Parameterization Deadlock?

P. Gentine, M. Pritchard, S. Rasp, G. Reinaudi, and G. Yacalis · 2018

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Using machine learning to represent subgrid moist convection: potential for modeling of climate, climate change and extreme events

Paul A O’Gorman and John G Dwyer · 2018

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Deep learning to represent sub-grid processes in climate models

Original

Stephan Rasp, Michael S Pritchard, and Pierre Gentine · 2018

Later among the works it cites.