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Over the last couple of years, machine learning parameterizations have emerged as a potential way to improve the representation of sub-grid processes in Earth System Models (ESMs).
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Khairoutdinov, M. F. and Randall, D. A.: A cloud resolving model as a cloud parameterization in the NCAR Community Climate System Model: Preliminary results, Geophysical Research Letters, 28, 3617–3620, 10.1029/2001GL013552 , URL http://doi.wiley.com/10.1029/2001GL013552 , 2001
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Crommelin, D. and Vanden-Eijnden, E.: Subgrid-Scale Parameterization with Conditional Markov Chains, Journal of the Atmospheric Sciences, 65, 2661–2675, 10.1175/2008JAS2566.1 , URL http://journals.ametsoc.org/doi/abs/10.1175/2008JAS2566.1 , 2008
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Saffari, A., Leistner, C., Santner, J., Godec, M., and Bischof, H.: On-line Random Forests, in: 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, pp. 1393–1400, IEEE, 10.1109/ICCVW.2009.5457447 , URL http://ieeexplore.ieee.org/document/5457447/ , 2009
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Krasnopolsky, V. M., Fox-Rabinovitz, M. S., and Belochitski, A. A.: 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, Advances in Artificial Neural Systems, 2013, 1–13, 10.1155/2013/485913 , URL https://www.hindawi.com/archive/2013/485913/ , 2013
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Berner, J., Fossell, K. R., Ha, S.-Y., Hacker, J. P., and Snyder, C.: Increasing the Skill of Probabilistic Forecasts: Understanding Performance Improvements from Model-Error Representations, Monthly Weather Review, 143, 1295–1320, 10.1175/MWR-D-14-00091.1 , URL http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-14-00091.1 , 2015
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Nielsen, M. A.: Neural Networks and Deep Learning, Determination Press, URL http://neuralnetworksanddeeplearning.com , 2015
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Hourdin, F., Mauritsen, T., Gettelman, A., Golaz, J.-C., Balaji, V., Duan, Q., Folini, D., Ji, D., Klocke, D., Qian, Y., Rauser, F., Rio, C., Tomassini, L., Watanabe, M., and Williamson, D.: The Art and Science of Climate Model Tuning, Bulletin of the American Meteorological Society, 98, 589–602, 10.1175/BAMS-D-15-00135.1 , URL http://journals.ametsoc.org/doi/10.1175/BAMS-D-15-00135.1 , 2017
2017
Cited alongside, same era.
Brenowitz, N. D. and Bretherton, C. S.: Prognostic Validation of a Neural Network Unified Physics Parameterization, Geophysical Research Letters, 45, 6289–6298, 10.1029/2018GL078510 , URL http://doi.wiley.com/10.1029/2018GL078510 , 2018
2018
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Dueben, P. D. and Bauer, P.: Challenges and design choices for global weather and climate models based on machine learning, Geosci. Model Dev., 10.5194/gmd-2018-148 , URL https://www.geosci-model-dev-discuss.net/gmd-2018-148/gmd-2018-148.pdf , 2018
2018
Cited alongside, same era.
2019
Closest in time.
Bocquet, M., Brajard, J., Carrassi, A., and Bertino, L.: Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models, Nonlinear Processes in Geophysics, 26, 143–162, 10.5194/npg-26-143-2019 , URL https://www.nonlin-processes-geophys.net/26/143/2019/ , 2019
2019
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Bolton, T. and Zanna, L.: Applications of Deep Learning to Ocean Data Inference and Subgrid Parameterization, Journal of Advances in Modeling Earth Systems, 11, 376–399, 10.1029/2018MS001472 , URL http://doi.wiley.com/10.1029/2018MS001472 , 2019
2019
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Brenowitz, N. D. and Bretherton, C. S.: Spatially Extended Tests of a Neural Network Parametrization Trained by Coarse-graining, Journal of Advances in Modeling Earth Systems, p. 2019MS001711, 10.1029/2019MS001711 , URL https://onlinelibrary.wiley.com/doi/abs/10.1029/2019MS001711 , 2019
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Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G., and Yacalis, G.: Could Machine Learning Break the Convection Parameterization Deadlock?, Geophysical Research Letters, 45, 5742–5751, 10.1029/2018GL078202 , URL http://doi.wiley.com/10.1029/2018GL078202 , 2018
2018
Cited alongside, same era.
Merwin Monteiro, J., McGibbon, J., and Caballero, R.: Sympl (v. 0.4.0) and climt (v. 0.15.3) - Towards a flexible framework for building model hierarchies in Python, Geoscientific Model Development, 11, 3781–3794, 10.5194/gmd-11-3781-2018 , URL https://doi.org/10.5194/gmd-11-3781-2018 , 2018
2018
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O’Gorman, P. A. and Dwyer, J. G.: Using Machine Learning to Parameterize Moist Convection: Potential for Modeling of Climate, Climate Change, and Extreme Events, Journal of Advances in Modeling Earth Systems, 10, 2548–2563, 10.1029/2018MS001351 , URL http://doi.wiley.com/10.1029/2018MS001351 , 2018
2018
Cited alongside, same era.
Pathak, J., Hunt, B., Girvan, M., Lu, Z., and Ott, E.: Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach, Physical Review Letters, 120, 24 102, 10.1103/PhysRevLett.120.024102 , URL https://doi.org/10.1103/PhysRevLett.120.024102 , 2018
2018
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Rasp, S. and Lerch, S.: Neural Networks for Postprocessing Ensemble Weather Forecasts, Monthly Weather Review, 146, 3885–3900, 10.1175/MWR-D-18-0187.1 , URL http://journals.ametsoc.org/doi/10.1175/MWR-D-18-0187.1 , 2018
2018
Cited alongside, same era.
Rasp, S., Pritchard, M. S., and Gentine, P.: Deep learning to represent subgrid processes in climate models., Proceedings of the National Academy of Sciences of the United States of America, 115, 9684–9689, 10.1073/pnas.1810286115 , URL http://www.ncbi.nlm.nih.gov/pubmed/30190437http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC6166853 , 2018
2018
Cited alongside, same era.
Subramanian, A. C., Gagne, D. J., I., Christensen, H. M., and Monahan, A. H.: Evaluating Generative Adversarial Network Stochastic Parameterizations of the Lorenz ’96 Model at Climate and Weather Scales, American Geophysical Union, Fall Meeting 2018, URL http://adsabs.harvard.edu/abs/2018AGUFMIN14A..02S , 2018
2018
Cited alongside, same era.
Schneider, T., Lan, S., Stuart, A., and Teixeira, J.: Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations, Geophysical Research Letters, 44, 396–12, 10.1002/2017GL076101 , URL http://doi.wiley.com/10.1002/2017GL076101 , 2017a
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Schneider, T., Teixeira, J., Bretherton, C. S., Brient, F., Pressel, K. G., Schär, C., and Siebesma, A. P.: Climate goals and computing the future of clouds, Nature Climate Change, 7, 3–5, 10.1038/nclimate3190 , URL http://www.nature.com/articles/nclimate3190 , 2017b
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2019
Closest in time.
Bretherton, C. S., McCoy, I. L., Mohrmann, J., Wood, R., Ghate, V., Gettelman, A., Bardeen, C. G., Albrecht, B. A., and Zuidema, P.: Cloud, aerosol, and boundary layer structure across the northeast Pacific stratocumulus-cumulus transition as observed during CSET, Monthly Weather Review, 147, 2083–2103, 10.1175/MWR-D-18-0281.1 , 2019
2019
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2019
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Palmer, T. N.: Stochastic weather and climate models, Nature Reviews Physics, p. 1, 10.1038/s42254-019-0062-2 , URL http://www.nature.com/articles/s42254-019-0062-2 , 2019
2019
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Scher, S. and Messori, G.: Generalization properties of neural networks trained on Lorenzsystems, Nonlinear Processes in Geophysics Discussions, pp. 1–19, 10.5194/npg-2019-23 , URL https://www.nonlin-processes-geophys-discuss.net/npg-2019-23/ , 2019
2019
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2020
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