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Merge pull request #50 from IamShubhamGupto/new/newsletters
fixed alignment and 2023 newsletters
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--- | ||
date: 2023-01-03T09:29:16+10:00 | ||
title: 'Emulating Cloud Superparameterization by Pierre Gentine' | ||
heroHeading: '' | ||
heroSubHeading: 'Using deep learning to emulate cloud superparameterization - New M²LInES-related paper' | ||
heroBackground: '' | ||
thumbnail: 'images/news/Pierrepaper1-fig1.png' | ||
images: ['images/news/Pierrepaper1-fig1.png'] | ||
link: 'https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020MS002385' | ||
--- | ||
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M²LInES PI Pierre Gentine is the senior author in this new publication on using deep learning for emulating cloud superparameterization in climate models. Research Scientist Tom Beucler is a co-author. Read their paper [here](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020MS002385) |
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--- | ||
date: 2023-05-01T09:29:16+10:00 | ||
title: 'Data-driven subgrid parameterizations by Karl Otness' | ||
heroHeading: '' | ||
heroSubHeading: 'Data-driven multiscale modeling of subgrid parameterizations in climate models' | ||
heroBackground: '' | ||
thumbnail: 'images/news/2305-Otness.png' | ||
images: ['images/news/2305-Otness.png'] | ||
link: 'https://arxiv.org/abs/2303.17496' | ||
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In this ongoing [project](https://arxiv.org/abs/2303.17496), Karl Otness and co-authors are evaluating a proof of concept multiscale approach to predicting subgrid forcings in climate models. They see encouraging preliminary results from first making a prediction in a fine-to-coarse direction which yields a lower resolution, but more confident prediction followed by a refinement to predict finer scale details. | ||
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This paper won “Best ML innovation” at the ICLR 2023 Workshop: Tackling Climate Change with Machine Learning. |
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--- | ||
date: 2023-06-01T09:29:16+10:00 | ||
title: 'Smart correction model for winter sky temperatures by Lorenzo Zampieri' | ||
heroHeading: '' | ||
heroSubHeading: 'A Machine Learning Correction Model of the Winter Clear-Sky Temperature Bias over the Arctic Sea Ice in Atmospheric Reanalyses' | ||
heroBackground: '' | ||
thumbnail: 'images/news/2306-Zampieri.png' | ||
images: ['images/news/2306-Zampieri.png'] | ||
link: 'https://journals.ametsoc.org/view/journals/mwre/151/6/MWR-D-22-0130.1.xml' | ||
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This [study](https://journals.ametsoc.org/view/journals/mwre/151/6/MWR-D-22-0130.1.xml), led by Lorenzo Zampieri, and co-authored by Marika Holland, illustrates a novel method based on machine learning for reducing the systematic surface temperature errors that characterize multiple atmospheric reanalyses in sea-ice-covered regions of the Arctic under clear-sky conditions. The corrected reanalysis temperature can be employed to support polar research activities, and in particular to better simulate the evolution of the interacting sea ice and ocean system within numerical models. |
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--- | ||
date: 2023-07-01T09:29:16+10:00 | ||
title: 'Parameterizing vertical mixing coefficients in the Ocean by Akash Sane' | ||
heroHeading: '' | ||
heroSubHeading: 'Parameterizing vertical mixing coefficients in the Ocean' | ||
heroBackground: '' | ||
thumbnail: 'images/news/2307-Sane.png' | ||
images: ['images/news/2307-Sane.png'] | ||
link: 'https://arxiv.org/abs/2306.09045' | ||
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Small scale vertical mixing processes in the upper ocean region cannot be resolved by ocean models due to the lack of computing resources, and are thus parameterized. Existing models have a few ad hoc approximations which cause uncertainty in climate change simulations. In this breakthrough [preprint](https://arxiv.org/abs/2306.09045), Aakash Sane and co-authors - Brandon Reichl, Alistair Adcroft, and Laure Zanna - replace such an approximation using data driven neural networks (NN), leading to improved physics in an ocean model simulation and a reduction of model bias in the Tropics. Moreover, the NN implementation in MOM6 is stable, and does not violate any conservation laws. |
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