references.bib

% L96 Jupyter Book

@article{christensen2022parametrization,
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  author={Christensen, Hannah and Zanna, Laure},
  year={2022},
  publisher={Oxford University Press}
}

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}

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}

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}

@article{Brajard2021,
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}

@article{Schneider2017,
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  publisher = {American Geophysical Union ({AGU})},
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  number = {24},
  author = {Tapio Schneider and Shiwei Lan and Andrew Stuart and Jo{\~{a}}o Teixeira},
  title = {Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations},
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}

@article{Russell2017,
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  month = dec,
  publisher = {Elsevier {BV}},
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  pages = {160--173},
  author = {Francis P. Russell and Peter D. D\"{u}ben and Xinyu Niu and Wayne Luk and T.N. Palmer},
  title = {Exploiting the chaotic behaviour of atmospheric models with reconfigurable architectures},
  journal = {Computer Physics Communications}
}

@article{Crommelin2008,
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  year = {2008},
  month = aug,
  publisher = {American Meteorological Society},
  volume = {65},
  number = {8},
  pages = {2661--2675},
  author = {Daan Crommelin and Eric Vanden-Eijnden},
  title = {Subgrid-Scale Parameterization with Conditional Markov Chains},
  journal = {Journal of the Atmospheric Sciences}
}

@article{Dorrestijn2013,
  doi = {10.1098/rsta.2012.0374},
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  year = {2013},
  month = may,
  publisher = {The Royal Society},
  volume = {371},
  number = {1991},
  pages = {20120374},
  author = {J. Dorrestijn and D. T. Crommelin and J. A. Biello and S. J. B\"{o}ing},
  title = {A data-driven multi-cloud model for stochastic parametrization of deep convection},
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}

@article{Law2016,
  doi = {10.1016/j.physd.2015.12.008},
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  year = {2016},
  month = jun,
  publisher = {Elsevier {BV}},
  volume = {325},
  pages = {1--13},
  author = {K.J.H. Law and D. Sanz-Alonso and A. Shukla and A.M. Stuart},
  title = {Filter accuracy for the Lorenz 96 model: Fixed versus adaptive observation operators},
  journal = {Physica D: Nonlinear Phenomena}
}

@article{Hatfield2017,
  doi = {10.1175/mwr-d-17-0132.1},
  url = {https://doi.org/10.1175/mwr-d-17-0132.1},
  year = {2017},
  month = dec,
  publisher = {American Meteorological Society},
  volume = {146},
  number = {1},
  pages = {49--62},
  author = {Sam Hatfield and Aneesh Subramanian and Tim Palmer and Peter D\"{u}ben},
  title = {Improving Weather Forecast Skill through Reduced-Precision Data Assimilation},
  journal = {Monthly Weather Review}
}

@article{Kwasniok2012,
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  url = {https://doi.org/10.1098/rsta.2011.0384},
  year = {2012},
  month = mar,
  publisher = {The Royal Society},
  volume = {370},
  number = {1962},
  pages = {1061--1086},
  author = {Frank Kwasniok},
  title = {Data-based stochastic subgrid-scale parametrization: an approach using cluster-weighted modelling},
  journal = {Philosophical Transactions of the Royal Society A: Mathematical,  Physical and Engineering Sciences}
}

@article{Chorin2015,
  doi = {10.1073/pnas.1512080112},
  url = {https://doi.org/10.1073/pnas.1512080112},
  year = {2015},
  month = jul,
  publisher = {Proceedings of the National Academy of Sciences},
  volume = {112},
  number = {32},
  pages = {9804--9809},
  author = {Alexandre J. Chorin and Fei Lu},
  title = {Discrete approach to stochastic parametrization and dimension reduction in nonlinear dynamics},
  journal = {Proceedings of the National Academy of Sciences}
}

@article{Dueben2018,
  doi = {10.5194/gmd-11-3999-2018},
  url = {https://doi.org/10.5194/gmd-11-3999-2018},
  year = {2018},
  month = oct,
  publisher = {Copernicus {GmbH}},
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  number = {10},
  pages = {3999--4009},
  author = {Peter D. Dueben and Peter Bauer},
  title = {Challenges and design choices for global weather and climate models based on machine learning},
  journal = {Geoscientific Model Development}
}

@article{Gagne2020,
  doi = {10.1029/2019ms001896},
  url = {https://doi.org/10.1029/2019ms001896},
  year = {2020},
  month = mar,
  publisher = {American Geophysical Union ({AGU})},
  volume = {12},
  number = {3},
  author = {David John Gagne and Hannah M. Christensen and Aneesh C. Subramanian and Adam H. Monahan},
  title = {Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz {\textquotesingle}96 Model},
  journal = {Journal of Advances in Modeling Earth Systems}
}

@misc{Simonyan2013,
  doi = {10.48550/ARXIV.1312.6034},
  url = {https://arxiv.org/abs/1312.6034},
  author = {Simonyan,  Karen and Vedaldi,  Andrea and Zisserman,  Andrew},
  keywords = {Computer Vision and Pattern Recognition (cs.CV),  FOS: Computer and information sciences,  FOS: Computer and information sciences},
  title = {Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps},
  publisher = {arXiv},
  year = {2013},
  copyright = {arXiv.org perpetual,  non-exclusive license}
}

@article{Baehrens2010,
  author  = {David Baehrens and Timon Schroeter and Stefan Harmeling and Motoaki Kawanabe and Katja Hansen and Klaus-Robert M{{\"u}}ller},
  title   = {How to Explain Individual Classification Decisions},
  journal = {Journal of Machine Learning Research},
  year    = {2010},
  volume  = {11},
  number  = {61},
  pages   = {1803--1831},
  url     = {http://jmlr.org/papers/v11/baehrens10a.html}
}

@inproceedings{Adebayo2018,
  author = {Adebayo, Julius and Gilmer, Justin and Muelly, Michael and Goodfellow, Ian and Hardt, Moritz and Kim, Been},
  booktitle = {Advances in Neural Information Processing Systems},
  editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
  pages = {},
  publisher = {Curran Associates, Inc.},
  title = {Sanity Checks for Saliency Maps},
  url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/294a8ed24b1ad22ec2e7efea049b8737-Paper.pdf},
  volume = {31},
  year = {2018}
}

@inproceedings{Ancona2018,
  title={Towards better understanding of gradient-based attribution methods for Deep Neural Networks},
  author={Marco Ancona and Enea Ceolini and Cengiz Öztireli and Markus Gross},
  booktitle={International Conference on Learning Representations},
  year={2018},
  url={https://openreview.net/forum?id=Sy21R9JAW},
}

@article{Bach2015,
  doi = {10.1371/journal.pone.0130140},
  author = {Bach, Sebastian AND Binder, Alexander AND Montavon, Grégoire AND Klauschen, Frederick AND Müller, Klaus-Robert AND Samek, Wojciech},
  journal = {PLOS ONE},
  publisher = {Public Library of Science},
  title = {On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation},
  year = {2015},
  month = {07},
  volume = {10},
  url = {https://doi.org/10.1371/journal.pone.0130140},
  pages = {1-46},
  number = {7},
}

@book{Rasmussen_Williams_2005,
  title={Gaussian Processes for Machine Learning},
  url={http://dx.doi.org/10.7551/mitpress/3206.001.0001},
  doi={10.7551/mitpress/3206.001.0001},
  publisher={The MIT Press},
  author={Rasmussen, Carl Edward and Williams, Christopher K. I.},
  year={2005}
}
@book{Bishop2006,
  author = {Bishop, Christopher M.},
  title = {Pattern Recognition and Machine Learning (Information Science and Statistics)},
  year = {2006},
  isbn = {0387310738},
  publisher = {Springer-Verlag},
  address = {Berlin, Heidelberg}
}

# m2lines

@article{Shamekh_2023,
	doi = {10.22541/essoar.168748456.60017486/v1},
	url = {https://doi.org/10.22541%2Fessoar.168748456.60017486%2Fv1},
	year = 2023,
	month = {jun},
	publisher = {Authorea,
Inc.},
	author = {Sara Shamekh and Pierre Gentine},
	title = {Learning Atmospheric Boundary Layer Turbulence}
}

@article{sane2023parameterizing,
  title={Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer using Neural Networks},
  author={Sane, Aakash and Reichl, Brandon G and Adcroft, Alistair and Zanna, Laure},
  journal={arXiv preprint arXiv:2306.09045},
  year={2023}
}

@article{Janni2023,
author = {Yuval, Janni and O'Gorman, Paul A.},
title = {Neural-Network Parameterization of Subgrid Momentum Transport in the Atmosphere},
journal = {Journal of Advances in Modeling Earth Systems},
volume = {15},
number = {4},
pages = {e2023MS003606},
keywords = {momentum parameterization, machine learning},
doi = {https://doi.org/10.1029/2023MS003606},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2023MS003606},
eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2023MS003606},
note = {e2023MS003606 2023MS003606},
year = {2023}
}

@article{perezhogin2023generative,
  title={Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model},
  author={Perezhogin, Pavel and Zanna, Laure and Fernandez-Granda, Carlos},
  journal={arXiv preprint arXiv:2302.07984},
  year={2023}
}

@article {Zampieri2023,
      author = "Lorenzo Zampieri and Gabriele Arduini and Marika Holland and Sarah P. E. Keeley and Kristian Mogensen and Matthew D. Shupe and Steffen Tietsche",
      title = "A Machine Learning Correction Model of the Winter Clear-Sky Temperature Bias over the Arctic Sea Ice in Atmospheric Reanalyses",
      journal = "Monthly Weather Review",
      year = "2023",
      publisher = "American Meteorological Society",
      address = "Boston MA, USA",
      volume = "151",
      number = "6",
      doi = "https://doi.org/10.1175/MWR-D-22-0130.1",
      pages=      "1443 - 1458",
      url = "https://journals.ametsoc.org/view/journals/mwre/151/6/MWR-D-22-0130.1.xml"
}

@article{Guillaumin2021,
author = {Guillaumin, Arthur P. and Zanna, Laure},
title = {Stochastic-Deep Learning Parameterization of Ocean Momentum Forcing},
journal = {Journal of Advances in Modeling Earth Systems},
volume = {13},
number = {9},
pages = {e2021MS002534},
keywords = {parameterization, mesoscale, deep learning, turbulence, stochastic, Oceans},
doi = {https://doi.org/10.1029/2021MS002534},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2021MS002534},
eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2021MS002534},
note = {e2021MS002534 2021MS002534},
year = {2021}
}

# DA
@article{chapman2023benefits,
  title={Benefits of Deterministic and Stochastic Tendency Adjustments in a Climate Model},
  author={Chapman, William E and Berner, Judith},
  journal={arXiv preprint arXiv:2308.15295},
  year={2023}
}

@article{gregory2023deep,
  title={Deep learning of systematic sea ice model errors from data assimilation increments},
  author={Gregory, William and Bushuk, Mitchell and Adcroft, Alistair and Zhang, Yongfei and Zanna, Laure},
  journal={Journal of Advances in Modeling Earth Systems},
  volume={15},
  number={10},
  pages={e2023MS003757},
  year={2023},
  publisher={Wiley Online Library}
}

@misc{gregory2023machine,
      title={Machine learning for online sea ice bias correction within global ice-ocean simulations},
      author={William Gregory and Mitchell Bushuk and Yongfei Zhang and Alistair Adcroft and Laure Zanna},
      year={2023},
      eprint={2310.02488},
      archivePrefix={arXiv},
      primaryClass={physics.ao-ph}
}

# architecture
@article{Frezat2022,
author = {Frezat, Hugo and Le Sommer, Julien and Fablet, Ronan and Balarac, Guillaume and Lguensat, Redouane},
title = {A Posteriori Learning for Quasi-Geostrophic Turbulence Parametrization},
journal = {Journal of Advances in Modeling Earth Systems},
volume = {14},
number = {11},
pages = {e2022MS003124},
keywords = {parametrization, machine learning, turbulence, quasi-geostrophic},
doi = {https://doi.org/10.1029/2022MS003124},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2022MS003124},
eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2022MS003124},
note = {e2022MS003124 2022MS003124},
year = {2022}
}

@article{pedersen2023reliable,
  title={Reliable coarse-grained turbulent simulations through combined offline learning and neural emulation},
  author={Pedersen, Christian and Zanna, Laure and Bruna, Joan and Perezhogin, Pavel},
  journal={arXiv preprint arXiv:2307.13144},
  year={2023}
}

@article{otness2023data,
  title={Data-driven multiscale modeling of subgrid parameterizations in climate models},
  author={Otness, Karl and Zanna, Laure and Bruna, Joan},
  journal={arXiv preprint arXiv:2303.17496},
  year={2023}
}

@article{ross2022benchmarking,
  author = {Ross, Andrew and Li, Ziwei and Perezhogin, Pavel and Fernandez-Granda, Carlos and Zanna, Laure},
  title = {Benchmarking of machine learning ocean subgrid parameterizations in an idealized model},
  journal = {Journal of Advances in Modeling Earth Systems},
  year = {2022},
  pages = {e2022MS003258},
  doi = {https://doi.org/10.1029/2022MS003258},
  url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2022MS003258},
  eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2022MS003258},
  note = {e2022MS003258 2022MS003258}
}

#global models

@article{zhang2023implementation,
  title={Implementation and Evaluation of a Machine Learned Mesoscale Eddy Parameterization into a Numerical Ocean Circulation Model},
  author={Zhang, Cheng and Perezhogin, Pavel and Gultekin, Cem and Adcroft, Alistair and Fernandez-Granda, Carlos and Zanna, Laure},
  journal={arXiv preprint arXiv:2303.00962},
  year={2023}
}