From 7a584dd5d448c8481008a6126a61110f34bfc40f Mon Sep 17 00:00:00 2001 From: johannag126 Date: Tue, 7 May 2024 19:07:18 +0000 Subject: [PATCH] deploy: 9327ebe78b21b9798babc615c480e72dfca3793b --- images/news/2405Bushuk.png | Bin 0 -> 749456 bytes images/news/2405Chapman.png | Bin 0 -> 730347 bytes index.xml | 2 +- news/2405bushuk/index.html | 13 +++++++++++++ news/2405chapman/index.html | 13 +++++++++++++ news/index.html | 2 +- news/index.xml | 2 +- news/newsletters/index.html | 2 +- publications/index.html | 4 ++-- sitemap.xml | 2 +- tags/newsletters/index.html | 2 +- tags/newsletters/index.xml | 2 +- 12 files changed, 35 insertions(+), 9 deletions(-) create mode 100644 images/news/2405Bushuk.png create mode 100644 images/news/2405Chapman.png create mode 100644 news/2405bushuk/index.html create mode 100644 news/2405chapman/index.html diff --git a/images/news/2405Bushuk.png b/images/news/2405Bushuk.png new file mode 100644 index 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University, Affiliate NOAA-GFDL in Ocean divisionImproving coupled climate modelshttps://m2lines.github.io/research/research3/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/research/research3/Predicting future climate conditions on earth, and in particular, the impacts of climate change, would not be possible without climate models. Climate models are complex mathematical representations of the physical processes happening in each of the climate system components: ocean, atmosphere, sea-ice and land surface, as well as their interactions with each other. Climate models divide the globe into three dimensional grid cells representing a specific location and elevation. The resolution of the models, their “level of detail”, is dependent on the size chosen for the grid cells: the smaller the grid size the higher the resolution.Carlos Fernandez-Grandahttps://m2lines.github.io/team/carlosferndandezgranda/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/carlosferndandezgranda/NYU, Courant Institute + Center for Data ScienceJoan Brunahttps://m2lines.github.io/team/joan-bruna/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/joan-bruna/NYU, Courant Institute + Computer Science + Center for Data ScienceRyan Abernatheyhttps://m2lines.github.io/team/ryanabernathy/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/ryanabernathy/Lamont/Columbia UniversityBrandon Reichlhttps://m2lines.github.io/team/brandonreichl/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/brandonreichl/NOAA-GFDL in Ocean divisionFeiyu Luhttps://m2lines.github.io/team/feiyulu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/feiyulu/Princeton University, Affiliate NOAA-GFDL in Seasonal-decadal divisionJudith Bernerhttps://m2lines.github.io/team/judithberner/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/judithberner/National Center for Atmospheric ResearchMarika Hollandhttps://m2lines.github.io/team/marikaholland/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/marikaholland/National Center for Atmospheric ResearchMitch Bushukhttps://m2lines.github.io/team/mitchbushuk/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/mitchbushuk/UCAR, Affiliate NOAA-GFDL in Ocean divisionPierre Gentinehttps://m2lines.github.io/team/pierregentine/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/pierregentine/Columbia UniversityPaul O'Gormanhttps://m2lines.github.io/team/paulogorman/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/paulogorman/MITJulie Deshayeshttps://m2lines.github.io/team/juliedeshayes/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliedeshayes/CNRS-IPSLJulien Le Sommerhttps://m2lines.github.io/team/julienlesommer/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/julienlesommer/L&rsquo;Institut des Géosciences de l&rsquo;Environnement (IGE)Arthur Guillauminhttps://m2lines.github.io/team/arthurguillaumin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/arthurguillaumin/Queen Mary University of LondonJanni Yuvalhttps://m2lines.github.io/team/janniyuval/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/janniyuval/Google ResearchAlex Connollyhttps://m2lines.github.io/team/alexconnolly/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alexconnolly/Columbia UniversityJulius Buseckehttps://m2lines.github.io/team/juliusbusecke/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliusbusecke/Columbia UniversityAakash Sanehttps://m2lines.github.io/team/aakashsane/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/aakashsane/Princeton University, Affiliate NOAA-GFDL in Ocean DivisionAndrew Rosshttps://m2lines.github.io/team/former/andrewross/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/andrewross/NYU, Courant InstituteLorenzo Zampierihttps://m2lines.github.io/team/lorenzozampieri/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/lorenzozampieri/National Center for Atmospheric ResearchCem Gultekinhttps://m2lines.github.io/team/cemgultekin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/cemgultekin/NYU, Courant InstituteTarun Vermahttps://m2lines.github.io/team/tarunverma/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/tarunverma/Princeton UniversityDhruv Balwadahttps://m2lines.github.io/team/dhruvbalwada/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/dhruvbalwada/Lamont/Columbia UniversityCheng Zhanghttps://m2lines.github.io/team/chengzhang/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/chengzhang/Princeton UniversityAnastasiia Gorbunovahttps://m2lines.github.io/team/former/anastasiiagorbunova/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/anastasiiagorbunova/L&rsquo;Institut des Géosciences de l&rsquo;Environnement (IGE)Pavel Perezhoginhttps://m2lines.github.io/team/pavelperezhogin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/pavelperezhogin/NYU, Courant InstituteWill Chapmanhttps://m2lines.github.io/team/willchapman/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/willchapman/National Center for Atmospheric ResearchWill Gregoryhttps://m2lines.github.io/team/willgregory/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/willgregory/Princeton University, Affiliate NOAA-GFDLAbigail Bodnerhttps://m2lines.github.io/team/abigailbodner/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/abigailbodner/NYU, Courant InstituteAndrew Brettinhttps://m2lines.github.io/team/andrewbrettin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/andrewbrettin/NYU, Courant InstituteMario Herrero-Gonzalezhttps://m2lines.github.io/team/former/marioherrerogonzalez/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/marioherrerogonzalez/Columbia UniversityKarl Otnesshttps://m2lines.github.io/team/karlotness/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/karlotness/NYU, Courant InstituteMohamed Aziz Bhourihttps://m2lines.github.io/team/azizbhouri/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/azizbhouri/Columbia UniversityHugo Frezathttps://m2lines.github.io/team/former/hugofrezat/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/hugofrezat/L’Institut des Géosciences de l’Environnement (IGE)Adrien Burqhttps://m2lines.github.io/team/former/adrienburq/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/adrienburq/Columbia UniversityFabrizio Falascahttps://m2lines.github.io/team/fabriziofalasca/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/fabriziofalasca/NYU, Courant InstituteAdam Subelhttps://m2lines.github.io/team/adamsubel/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/adamsubel/NYU, Courant InstituteAlexis Bargehttps://m2lines.github.io/team/alexisbarge/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alexisbarge/CNRS-IGEMaud Tissothttps://m2lines.github.io/team/maudtissot/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/maudtissot/CNRS-IPSLElizabeth Yankovskyhttps://m2lines.github.io/team/former/elizabethyankovsky/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/elizabethyankovsky/NYU, Courant InstituteSara Shamekhhttps://m2lines.github.io/team/sarashamekh/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/sarashamekh/Columbia UniversityChris Dupuishttps://m2lines.github.io/team/former/chrisdupuis/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/chrisdupuis/Lamont/Columbia UniversityJ Emmanuel Johnsonhttps://m2lines.github.io/team/former/jemanjohnson/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/jemanjohnson/L’Institut des Géosciences de l’Environnement (IGE)Chris Pedersenhttps://m2lines.github.io/team/chrispedersen/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/chrispedersen/NYU, Courant InstituteEmily Newsomhttps://m2lines.github.io/team/emilynewsom/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/emilynewsom/NYU, Courant InstituteEtienne Meunierhttps://m2lines.github.io/team/etiennemeunier/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/etiennemeunier/CNRS-IPSLMaria Prat Colomerhttps://m2lines.github.io/team/former/mariapratcolomer/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/mariapratcolomer/NYU, Courant InstituteNora Loosehttps://m2lines.github.io/team/noraloose/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/noraloose/Princeton UniversityDavid Kammhttps://m2lines.github.io/team/davidkamm/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/davidkamm/CNRS-IPSLPatricia Luchttps://m2lines.github.io/team/patricialuc/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/patricialuc/Columbia UniversityKelsey Everardhttps://m2lines.github.io/team/kelseyeverard/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/kelseyeverard/NYU, Courant InstituteMatias Ortizhttps://m2lines.github.io/team/former/matiasortiz/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/matiasortiz/NYU, Courant InstituteShubham Guptahttps://m2lines.github.io/team/shubham/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/shubham/NYU, Tandon School of EngineeringSurya Dheeshjithhttps://m2lines.github.io/team/suryadheeshjith/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/suryadheeshjith/NYU, Courant InstituteLeo Liuhttps://m2lines.github.io/team/former/leoliu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/leoliu/NYU, Courant InstituteShantanu Acharyahttps://m2lines.github.io/team/former/shantanu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/shantanu/NYU, Courant InstituteGriffin Mooershttps://m2lines.github.io/team/griffinmooers/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/griffinmooers/MITFriedrich Ginnoldhttps://m2lines.github.io/team/former/friedrichginnold/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/friedrichginnold/ETH ZürichPrani Nallurihttps://m2lines.github.io/team/praninalluri/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/praninalluri/Columbia UniversityRenaud Falgahttps://m2lines.github.io/team/renaudfalga/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/renaudfalga/NYU, Courant InstituteJisu Hanhttps://m2lines.github.io/team/jisuhan/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/jisuhan/Columbia UniversityJulia Simpsonhttps://m2lines.github.io/team/juliasimpson/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliasimpson/Columbia UniversityJiarong Wuhttps://m2lines.github.io/team/jiarongwu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/jiarongwu/NYU Ocean, Coupled Physics, Ocean Surface WavesYongquan Quhttps://m2lines.github.io/team/yongquanqu-/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/yongquanqu-/Columbia University -Atmosphere, Machine Learning, Data AssimilationAdvances in Machine Learning Techniques for Sea Ice Applicationshttps://m2lines.github.io/news/2404zampieri/Wed, 03 Apr 2024 09:29:16 +1000https://m2lines.github.io/news/2404zampieri/Forty-two international researchers, including Lorenzo Zampieri, were invited to Gwangmyeong (South Korea) to discuss recent advances in sea ice modelling to help guide the development of a state-of-the-art Korean sea ice modelling program under the leadership of the Korean Polar Research Institute (KOPRI). The workshop produced a report , which highlights 1) the opportunity to use AI techniques in support of various sea ice modelling tasks both for short-term and climate predictions and 2) the need for strengthened collaborations among international groups to connect existing “research bubbles” and facilitate the dissemination of ideas.Joint Parameter and Parameterization Inference by Yongquanhttps://m2lines.github.io/news/2404qu/Tue, 02 Apr 2024 09:29:16 +1000https://m2lines.github.io/news/2404qu/In this article , Yongquan Qu, Mohamed Aziz Bhouri, and Pierre Gentine, tackle the joint inference and uncertainty quantification of poorly known physical parameters and machine learning parametrizations, for sub-grid scale dynamics. Achieved through a Bayesian framework enabled by differentiable programming, this method not only offers accurate parameter estimates, but also enables skillful forecasting, each accompanied by quantified uncertainty. This work is accepted at the ICLR 2024 Workshop on AI4Differential Equations In Science.Building Ocean Climate Emulators by Adamhttps://m2lines.github.io/news/2404adam/Mon, 01 Apr 2024 09:29:16 +1000https://m2lines.github.io/news/2404adam/AI can accelerate climate modeling by generating computationally cheap emulators of existing simulations. The design of climate emulators is an exciting and open challenge in AI+Climate. In this work , PhD student Adam Subel and Laure Zanna focus on two fundamental questions to facilitate the creation of ocean emulators: 1) the role of the atmosphere in improving the decadal skill of the emulator and 2) the representation of variables with distinct timescales (e.Stress-testing the coupled behavior of hybrid physics-machine simulations by Bhourihttps://m2lines.github.io/news/2402bhouri/Sat, 03 Feb 2024 09:29:16 +1000https://m2lines.github.io/news/2402bhouri/Machine Learning (ML)-based parameterizations of atmospheric convection have long been hailed as a promising alternative, with the potential to yield higher accuracy at a fraction of the cost of more explicit simulations. This work , co-authored by Mohamed Aziz Bhouri, investigates the coupled out-of-distribution extrapolation capabilities in &ldquo;online&rdquo; testing of different ML-based parameterization designs of atmospheric convection. Their results show that these design decisions are not enough to obtain satisfactory generalization benefits.Stochastic Optimal Control Matching by Joanhttps://m2lines.github.io/news/2402joan/Fri, 02 Feb 2024 09:29:16 +1000https://m2lines.github.io/news/2402joan/Joan Bruna is a co-author in this preprint , introducing Stochastic Optimal Control Matching (SOCM), a novel Iterative Diffusion Optimization (IDO) technique for stochastic optimal control. Stochastic optimal control’s goal is to drive the behavior of noisy systems. In this work, the control is learned via a least squares problem by trying to fit a matching vector field. The key idea underlying SOCM is the path-wise reparameterization trick, a novel technique that is of independent interest, e.Restratification Effect of Mesoscale Eddies by Noorahttps://m2lines.github.io/news/2402noora/Thu, 01 Feb 2024 09:29:16 +1000https://m2lines.github.io/news/2402noora/Nora Loose is lead author of this new paper - part of the Climate Process Team funded by NSF and NOAA - which compares two parameterizations (“GM90” and “GL90”) for the restratification effect of mesoscale eddies. The authors conclude that the less commonly used GL90 parameterization is a promising alternative to the popular GM90 scheme for isopycnal coordinate models, where it is more consistent with theory, computationally more efficient, easier to implement, and numerically more stable.Data-driven equation discovery ocean model by Pavelhttps://m2lines.github.io/news/2401pavel/Tue, 02 Jan 2024 09:29:16 +1000https://m2lines.github.io/news/2401pavel/Pavel Perezhogin is lead author of this new M²LInES preprint , describing the implementation, in the GFDL MOM6 ocean model, of a data-driven equation-discovery parameterization of mesoscale eddies. This scale-aware parametrization improved biases in the mean flow and energetics for a range of resolutions and in different ocean configurations. The work was done in collaboration with Cheng Zhang, Alistair Adcroft, Carlos Fernandez-Granda and Laure Zanna.Learning Machine Learning with Lorenz-96https://m2lines.github.io/news/2401l96/Mon, 01 Jan 2024 09:29:16 +1000https://m2lines.github.io/news/2401l96/The M²LInES team is proud to share our Jupyter Book on Learning Machine Learning with Lorenz-96 . This educational tool provides a computationally accessible framework to understand how machine learning techniques can tackle climate science problems, including emulators, parameterizations, data assimilation, and uncertainty quantification. It is for all climate scientists wanting to learn or test machine learning algorithms or for machine learning experts to learn about climate modeling or develop new algorithms.ClimSim awarded Best Paper Award at NeurIPShttps://m2lines.github.io/news/2312climsim/Tue, 12 Dec 2023 09:29:16 +1000https://m2lines.github.io/news/2312climsim/ClimSim has been awarded Best Paper Award at NeurIPS ! The open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators, emerged out of the Science and Technology Center LEAP, received contributions from numerous M²LInES members. Congratulations to all authors!Will Gregory's latest article featured in Advance Science Newshttps://m2lines.github.io/news/2312willgfeature/Fri, 01 Dec 2023 09:29:16 +1000https://m2lines.github.io/news/2312willgfeature/The latest M²LInES article, led by Will Gregory (Princeton) is being featured in Advance Science News. The article details how Will and other colleagues, including M²LInES members Mitch Bushuk, Alistair Adcroft, and Laure Zanna, are using Machine Learning to enhance predictions of Arctic sea ice loss. Read the article by Giorgio Graffino hereOceanBench by J. Emmanuel Johnsonhttps://m2lines.github.io/news/2311johnson/Wed, 01 Nov 2023 09:29:16 +1000https://m2lines.github.io/news/2311johnson/OceanBench is a suite of tools for designing experiments relevant to the oceanography community like interpolation, forecasting and estimation. Lead author J. Emmanuel Johnson and co-authors, including Anastasiia Gorbunova and Julien Le Sommer, demonstrate its use within a benchmark setting for sea surface height interpolation, using general circulation model simulations and real satellite observations.UNetKF by Feiyu Luhttps://m2lines.github.io/news/2310lu/Tue, 03 Oct 2023 09:29:16 +1000https://m2lines.github.io/news/2310lu/This study by Feiyu Lu incorporates deep learning methods to compliment and improve the ensemble Kalman filter data assimilation algorithm. More specifically, a convolutional neural network is trained to predict the error statistics of a model ensemble, which will reduce the computational costs of the ensemble Kalman filter. This approach is tested in a quasi-geostrophic dynamic model to demonstrate its feasibility in more advanced weather and climate applications.Multi-fidelity climate model parameterization by Mohamed Aziz Bhourihttps://m2lines.github.io/news/2310bhouri/Mon, 02 Oct 2023 09:29:16 +1000https://m2lines.github.io/news/2310bhouri/In this preprint , Mohamed Aziz Bhouri and co-authors, including Pierre Gentine, developed a probabilistic multi-fidelity atmospheric parameterization for heat and moisture tendency. They implemented it on CESM CAM5 and SPCAM5 data and showed its capabilities to well extrapolate to unseen warmer climate simulations while returning trustworthy uncertainty quantification.Benefits of Deterministic and Stochastic Tendency Adjustments in a Climate Modelhttps://m2lines.github.io/news/2310chapman/Sun, 01 Oct 2023 09:29:16 +1000https://m2lines.github.io/news/2310chapman/Stochastic and deterministic tendency adjustments are applied to multiple experiments in the community atmosphere model version 6. The tendency adjustments are gleaned from the DART data assimilation system and a linear relaxation towards ERA-interim reanalysis. In this preprint , Will Chapman and Judith Berner show that the tendency adjustment significantly enhances the representation of the background state climatology for key prognostic and surface variables. Additionally, they show that stochastic tendencies are essential to accurately represent model variability of major atmospheric modes when making tendency adjustments. Blog Post - A New Generation of Climate Modelshttps://m2lines.github.io/news/2309blogpost/Mon, 11 Sep 2023 09:29:16 +1000https://m2lines.github.io/news/2309blogpost/M²LInES can now demonstrate the impact of data-driven Machine Learning parameterizations in global models! 🌎You can read about new gen climate models with AI in our blog postNew climate simulation model ensemble by Marika Hollandhttps://m2lines.github.io/news/2309holland/Mon, 04 Sep 2023 09:29:16 +1000https://m2lines.github.io/news/2309holland/Climate simulation uncertainties arise from internal variability, model structure, and external forcings. These uncertainty sources are difficult to disentangle across model generations because both model structure and external forcings typically change. This preprint , led by Marika Holland, presents new ensemble simulations which allow for the separation of uncertainty sources between two large ensembles (LEs) of the Community Earth System Model (CESM). Importantly, we find a strong influence of historical forcing uncertainty in differences between CESM1-LE and CESM2-LE due to aerosol effects on simulated climate.Understanding Extreme Precipitation Changes by Griffin Mooershttps://m2lines.github.io/news/2309mooers/Sun, 03 Sep 2023 09:29:16 +1000https://m2lines.github.io/news/2309mooers/Griffin Mooers is lead author in this preprint that leverages Variational Autoencoders on SPCAM5 output to examine the changes in the upper quantiles of precipitation between a control and warmed simulation. They find intense precipitation changes are mostly controlled by spatial shifts in deep convection.Reliable coarse-grained turbulent simulations by Chris Pedersonhttps://m2lines.github.io/news/2309pederson/Sat, 02 Sep 2023 09:29:16 +1000https://m2lines.github.io/news/2309pederson/In this preprint , M²LInES postdoc Chris Pedersen, along with Pavel Perezhogin, Joan Bruna, and Laure Zanna, show that the standard offline learning paradigm used to produce ML models of unresolved dynamics can be complemented with some learned time evolution of the system, and produce more stable hybrid physics and ML models.Background Pycnocline depth constrains FOHUE by Emily Newsomhttps://m2lines.github.io/news/2308newsom/Thu, 03 Aug 2023 09:29:16 +1000https://m2lines.github.io/news/2308newsom/In this paper , Emily Newsom and co-authors Laure Zanna and Jonathan Gregory, showed that the relative depth of ocean heat sequestration, which varies widely across climate models and paces the rate of surface climate warming, is strongly correlated to interior ocean stratification. Using a novel regional decomposition method, they showed that this relationship is entirely driven by mid-latitude ventilation processes and explains 70% of the variance in heat penetration depth across CMIP5/CMIP6 models.ClimSim: LEAP publicationhttps://m2lines.github.io/news/2308bhouri/Wed, 02 Aug 2023 09:29:16 +1000https://m2lines.github.io/news/2308bhouri/Several of M²LInES members and affiliates (Bhouri, Gentine, Zanna, Abernathey, Busecke, Mooers, Shamekh, and Yuval) have contributed to this LEAP paper presenting ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. The data and code are open access.Learning Atmospheric Boundary Layer Turbulence by Sara Shamekhhttps://m2lines.github.io/news/2308shamekh/Tue, 01 Aug 2023 09:29:16 +1000https://m2lines.github.io/news/2308shamekh/This preprint , by Sara Shamekh and Pierre Gentine, proposes a novel data-driven parameterization of vertical turbulent fluxes in convective boundary layer, that models the fluxes of various scalars across turbulent regimes. By incorporating a physics-based constraint this approach allows us to decompose the total vertical flux into two main modes of variability based on large scale forces that control the turbulence in the atmosphere.Parameterizing vertical mixing coefficients in the Ocean by Akash Sanehttps://m2lines.github.io/news/2307sane/Sat, 01 Jul 2023 09:29:16 +1000https://m2lines.github.io/news/2307sane/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 , 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.Smart correction model for winter sky temperatures by Lorenzo Zampierihttps://m2lines.github.io/news/2306zampieri/Thu, 01 Jun 2023 09:29:16 +1000https://m2lines.github.io/news/2306zampieri/This study , 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.Data-driven subgrid parameterizations by Karl Otnesshttps://m2lines.github.io/news/2305otness/Mon, 01 May 2023 09:29:16 +1000https://m2lines.github.io/news/2305otness/In this ongoing project , 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. +Atmosphere, Machine Learning, Data AssimilationFTORCH and FORPY libraries now integrated to CESM!https://m2lines.github.io/news/2405chapman/Thu, 02 May 2024 09:29:16 +1000https://m2lines.github.io/news/2405chapman/The M²LInES team, assisted by Jack Atkinson (ICCS) and NCAR software engineers, have integrated the FTORCH and FORPY libraries for machine learning into CESM2.1.5. To set up a machine learning-enabled CESM instance on NCAR machines, follow the steps outlined in this repository . Contact Will Chapman for questions!Predicting September Arctic Sea Ice by Dr. Bushukhttps://m2lines.github.io/news/2405bushuk/Wed, 01 May 2024 09:29:16 +1000https://m2lines.github.io/news/2405bushuk/This study , led by Mitch Bushuk, provides a comparison of September Arctic sea ice seasonal prediction skill across a diverse set of 34 dynamical and statistical prediction models, quantifying the state-of-the-art in the rapidly growing sea ice prediction research community. The authors find that both dynamical and statistical prediction models can skillfully predict September Arctic sea ice 0–3 months in advance on Pan-Arctic, regional, and local spatial scales. These results demonstrate that there are bright prospects for skillful operational seasonal predictions of Arctic sea ice and highlight a number of crucial prediction system design aspects to guide future improvements.Advances in Machine Learning Techniques for Sea Ice Applicationshttps://m2lines.github.io/news/2404zampieri/Wed, 03 Apr 2024 09:29:16 +1000https://m2lines.github.io/news/2404zampieri/Forty-two international researchers, including Lorenzo Zampieri, were invited to Gwangmyeong (South Korea) to discuss recent advances in sea ice modelling to help guide the development of a state-of-the-art Korean sea ice modelling program under the leadership of the Korean Polar Research Institute (KOPRI). The workshop produced a report , which highlights 1) the opportunity to use AI techniques in support of various sea ice modelling tasks both for short-term and climate predictions and 2) the need for strengthened collaborations among international groups to connect existing “research bubbles” and facilitate the dissemination of ideas.Joint Parameter and Parameterization Inference by Yongquanhttps://m2lines.github.io/news/2404qu/Tue, 02 Apr 2024 09:29:16 +1000https://m2lines.github.io/news/2404qu/In this article , Yongquan Qu, Mohamed Aziz Bhouri, and Pierre Gentine, tackle the joint inference and uncertainty quantification of poorly known physical parameters and machine learning parametrizations, for sub-grid scale dynamics. Achieved through a Bayesian framework enabled by differentiable programming, this method not only offers accurate parameter estimates, but also enables skillful forecasting, each accompanied by quantified uncertainty. This work is accepted at the ICLR 2024 Workshop on AI4Differential Equations In Science.Building Ocean Climate Emulators by Adamhttps://m2lines.github.io/news/2404adam/Mon, 01 Apr 2024 09:29:16 +1000https://m2lines.github.io/news/2404adam/AI can accelerate climate modeling by generating computationally cheap emulators of existing simulations. The design of climate emulators is an exciting and open challenge in AI+Climate. In this work , PhD student Adam Subel and Laure Zanna focus on two fundamental questions to facilitate the creation of ocean emulators: 1) the role of the atmosphere in improving the decadal skill of the emulator and 2) the representation of variables with distinct timescales (e.Stress-testing the coupled behavior of hybrid physics-machine simulations by Bhourihttps://m2lines.github.io/news/2402bhouri/Sat, 03 Feb 2024 09:29:16 +1000https://m2lines.github.io/news/2402bhouri/Machine Learning (ML)-based parameterizations of atmospheric convection have long been hailed as a promising alternative, with the potential to yield higher accuracy at a fraction of the cost of more explicit simulations. This work , co-authored by Mohamed Aziz Bhouri, investigates the coupled out-of-distribution extrapolation capabilities in &ldquo;online&rdquo; testing of different ML-based parameterization designs of atmospheric convection. Their results show that these design decisions are not enough to obtain satisfactory generalization benefits.Stochastic Optimal Control Matching by Joanhttps://m2lines.github.io/news/2402joan/Fri, 02 Feb 2024 09:29:16 +1000https://m2lines.github.io/news/2402joan/Joan Bruna is a co-author in this preprint , introducing Stochastic Optimal Control Matching (SOCM), a novel Iterative Diffusion Optimization (IDO) technique for stochastic optimal control. Stochastic optimal control’s goal is to drive the behavior of noisy systems. In this work, the control is learned via a least squares problem by trying to fit a matching vector field. The key idea underlying SOCM is the path-wise reparameterization trick, a novel technique that is of independent interest, e.Restratification Effect of Mesoscale Eddies by Noorahttps://m2lines.github.io/news/2402noora/Thu, 01 Feb 2024 09:29:16 +1000https://m2lines.github.io/news/2402noora/Nora Loose is lead author of this new paper - part of the Climate Process Team funded by NSF and NOAA - which compares two parameterizations (“GM90” and “GL90”) for the restratification effect of mesoscale eddies. The authors conclude that the less commonly used GL90 parameterization is a promising alternative to the popular GM90 scheme for isopycnal coordinate models, where it is more consistent with theory, computationally more efficient, easier to implement, and numerically more stable.Data-driven equation discovery ocean model by Pavelhttps://m2lines.github.io/news/2401pavel/Tue, 02 Jan 2024 09:29:16 +1000https://m2lines.github.io/news/2401pavel/Pavel Perezhogin is lead author of this new M²LInES preprint , describing the implementation, in the GFDL MOM6 ocean model, of a data-driven equation-discovery parameterization of mesoscale eddies. This scale-aware parametrization improved biases in the mean flow and energetics for a range of resolutions and in different ocean configurations. The work was done in collaboration with Cheng Zhang, Alistair Adcroft, Carlos Fernandez-Granda and Laure Zanna.Learning Machine Learning with Lorenz-96https://m2lines.github.io/news/2401l96/Mon, 01 Jan 2024 09:29:16 +1000https://m2lines.github.io/news/2401l96/The M²LInES team is proud to share our Jupyter Book on Learning Machine Learning with Lorenz-96 . This educational tool provides a computationally accessible framework to understand how machine learning techniques can tackle climate science problems, including emulators, parameterizations, data assimilation, and uncertainty quantification. It is for all climate scientists wanting to learn or test machine learning algorithms or for machine learning experts to learn about climate modeling or develop new algorithms.ClimSim awarded Best Paper Award at NeurIPShttps://m2lines.github.io/news/2312climsim/Tue, 12 Dec 2023 09:29:16 +1000https://m2lines.github.io/news/2312climsim/ClimSim has been awarded Best Paper Award at NeurIPS ! The open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators, emerged out of the Science and Technology Center LEAP, received contributions from numerous M²LInES members. Congratulations to all authors!Will Gregory's latest article featured in Advance Science Newshttps://m2lines.github.io/news/2312willgfeature/Fri, 01 Dec 2023 09:29:16 +1000https://m2lines.github.io/news/2312willgfeature/The latest M²LInES article, led by Will Gregory (Princeton) is being featured in Advance Science News. The article details how Will and other colleagues, including M²LInES members Mitch Bushuk, Alistair Adcroft, and Laure Zanna, are using Machine Learning to enhance predictions of Arctic sea ice loss. Read the article by Giorgio Graffino hereOceanBench by J. Emmanuel Johnsonhttps://m2lines.github.io/news/2311johnson/Wed, 01 Nov 2023 09:29:16 +1000https://m2lines.github.io/news/2311johnson/OceanBench is a suite of tools for designing experiments relevant to the oceanography community like interpolation, forecasting and estimation. Lead author J. Emmanuel Johnson and co-authors, including Anastasiia Gorbunova and Julien Le Sommer, demonstrate its use within a benchmark setting for sea surface height interpolation, using general circulation model simulations and real satellite observations.UNetKF by Feiyu Luhttps://m2lines.github.io/news/2310lu/Tue, 03 Oct 2023 09:29:16 +1000https://m2lines.github.io/news/2310lu/This study by Feiyu Lu incorporates deep learning methods to compliment and improve the ensemble Kalman filter data assimilation algorithm. More specifically, a convolutional neural network is trained to predict the error statistics of a model ensemble, which will reduce the computational costs of the ensemble Kalman filter. This approach is tested in a quasi-geostrophic dynamic model to demonstrate its feasibility in more advanced weather and climate applications.Multi-fidelity climate model parameterization by Mohamed Aziz Bhourihttps://m2lines.github.io/news/2310bhouri/Mon, 02 Oct 2023 09:29:16 +1000https://m2lines.github.io/news/2310bhouri/In this preprint , Mohamed Aziz Bhouri and co-authors, including Pierre Gentine, developed a probabilistic multi-fidelity atmospheric parameterization for heat and moisture tendency. They implemented it on CESM CAM5 and SPCAM5 data and showed its capabilities to well extrapolate to unseen warmer climate simulations while returning trustworthy uncertainty quantification.Benefits of Deterministic and Stochastic Tendency Adjustments in a Climate Modelhttps://m2lines.github.io/news/2310chapman/Sun, 01 Oct 2023 09:29:16 +1000https://m2lines.github.io/news/2310chapman/Stochastic and deterministic tendency adjustments are applied to multiple experiments in the community atmosphere model version 6. The tendency adjustments are gleaned from the DART data assimilation system and a linear relaxation towards ERA-interim reanalysis. In this preprint , Will Chapman and Judith Berner show that the tendency adjustment significantly enhances the representation of the background state climatology for key prognostic and surface variables. Additionally, they show that stochastic tendencies are essential to accurately represent model variability of major atmospheric modes when making tendency adjustments. Blog Post - A New Generation of Climate Modelshttps://m2lines.github.io/news/2309blogpost/Mon, 11 Sep 2023 09:29:16 +1000https://m2lines.github.io/news/2309blogpost/M²LInES can now demonstrate the impact of data-driven Machine Learning parameterizations in global models! 🌎You can read about new gen climate models with AI in our blog postNew climate simulation model ensemble by Marika Hollandhttps://m2lines.github.io/news/2309holland/Mon, 04 Sep 2023 09:29:16 +1000https://m2lines.github.io/news/2309holland/Climate simulation uncertainties arise from internal variability, model structure, and external forcings. These uncertainty sources are difficult to disentangle across model generations because both model structure and external forcings typically change. This preprint , led by Marika Holland, presents new ensemble simulations which allow for the separation of uncertainty sources between two large ensembles (LEs) of the Community Earth System Model (CESM). Importantly, we find a strong influence of historical forcing uncertainty in differences between CESM1-LE and CESM2-LE due to aerosol effects on simulated climate.Understanding Extreme Precipitation Changes by Griffin Mooershttps://m2lines.github.io/news/2309mooers/Sun, 03 Sep 2023 09:29:16 +1000https://m2lines.github.io/news/2309mooers/Griffin Mooers is lead author in this preprint that leverages Variational Autoencoders on SPCAM5 output to examine the changes in the upper quantiles of precipitation between a control and warmed simulation. They find intense precipitation changes are mostly controlled by spatial shifts in deep convection.Reliable coarse-grained turbulent simulations by Chris Pedersonhttps://m2lines.github.io/news/2309pederson/Sat, 02 Sep 2023 09:29:16 +1000https://m2lines.github.io/news/2309pederson/In this preprint , M²LInES postdoc Chris Pedersen, along with Pavel Perezhogin, Joan Bruna, and Laure Zanna, show that the standard offline learning paradigm used to produce ML models of unresolved dynamics can be complemented with some learned time evolution of the system, and produce more stable hybrid physics and ML models.Background Pycnocline depth constrains FOHUE by Emily Newsomhttps://m2lines.github.io/news/2308newsom/Thu, 03 Aug 2023 09:29:16 +1000https://m2lines.github.io/news/2308newsom/In this paper , Emily Newsom and co-authors Laure Zanna and Jonathan Gregory, showed that the relative depth of ocean heat sequestration, which varies widely across climate models and paces the rate of surface climate warming, is strongly correlated to interior ocean stratification. Using a novel regional decomposition method, they showed that this relationship is entirely driven by mid-latitude ventilation processes and explains 70% of the variance in heat penetration depth across CMIP5/CMIP6 models.ClimSim: LEAP publicationhttps://m2lines.github.io/news/2308bhouri/Wed, 02 Aug 2023 09:29:16 +1000https://m2lines.github.io/news/2308bhouri/Several of M²LInES members and affiliates (Bhouri, Gentine, Zanna, Abernathey, Busecke, Mooers, Shamekh, and Yuval) have contributed to this LEAP paper presenting ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. The data and code are open access.Learning Atmospheric Boundary Layer Turbulence by Sara Shamekhhttps://m2lines.github.io/news/2308shamekh/Tue, 01 Aug 2023 09:29:16 +1000https://m2lines.github.io/news/2308shamekh/This preprint , by Sara Shamekh and Pierre Gentine, proposes a novel data-driven parameterization of vertical turbulent fluxes in convective boundary layer, that models the fluxes of various scalars across turbulent regimes. By incorporating a physics-based constraint this approach allows us to decompose the total vertical flux into two main modes of variability based on large scale forces that control the turbulence in the atmosphere.Parameterizing vertical mixing coefficients in the Ocean by Akash Sanehttps://m2lines.github.io/news/2307sane/Sat, 01 Jul 2023 09:29:16 +1000https://m2lines.github.io/news/2307sane/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 , 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.Smart correction model for winter sky temperatures by Lorenzo Zampierihttps://m2lines.github.io/news/2306zampieri/Thu, 01 Jun 2023 09:29:16 +1000https://m2lines.github.io/news/2306zampieri/This study , 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.Data-driven subgrid parameterizations by Karl Otnesshttps://m2lines.github.io/news/2305otness/Mon, 01 May 2023 09:29:16 +1000https://m2lines.github.io/news/2305otness/In this ongoing project , 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. This paper won “Best ML innovation” at the ICLR 2023 Workshop: Tackling Climate Change with Machine Learning.Emulating Cloud Superparameterization by Pierre Gentinehttps://m2lines.github.io/news/2301gentine/Tue, 03 Jan 2023 09:29:16 +1000https://m2lines.github.io/news/2301gentine/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 M²LInES at AGU 22https://m2lines.github.io/news/2212agu22/Tue, 06 Dec 2022 09:29:16 +1000https://m2lines.github.io/news/2212agu22/AGU M²LInES Highlights The M²LInES team is excited to present our recent scientific advances at the Fall 2022 AGU meeting in Chicago and online. Below is a summary of the talks (all times are CST). For your convenience, you can download the schedule for the talks by following this link . Machine learning for physics discovery &amp; modeling We have an extensive lineup of talks and posters from M²LInES team members and affiliates. Direct observational evidence of an oceanic dual kinetic energy cascade and its seasonalityhttps://m2lines.github.io/news/2211balwada/Thu, 03 Nov 2022 09:29:16 +1000https://m2lines.github.io/news/2211balwada/In this article , led by Dhruv Balwada, ocean drifter observations show wave and turbulence merge and split flows, simultaneously transferring energy to large and small scales. Implicit learning of convective organization explains precipitation stochasticityhttps://m2lines.github.io/news/2211shamekh/Thu, 03 Nov 2022 09:29:16 +1000https://m2lines.github.io/news/2211shamekh/Cloud and convection take many structure and degrees of organization, which are still absent in most parameterizations. But does that structure matter for precipitation? This preprint , led by Sara Shamekh, proposes an elegant method to include organization in the parameterization of precipitation that results in a significant improvement in predicting precipitation, especially extremes, at GCM scale. Ocean currents break up a tabular iceberghttps://m2lines.github.io/news/2211adcroft/Thu, 03 Nov 2022 09:29:16 +1000https://m2lines.github.io/news/2211adcroft/This article examines the mechanism that led to the breakup of massive iceberg A68 in Dec 2020. The authors, including Alistair Adcroft, demonstrate that the A68a breakup event may have been triggered by ocean-current shear, a new breakup mechanism not previously reported. Their paper was featured in the news . On Gradient Descent Convergence beyond the Edge of Stabilityhttps://m2lines.github.io/news/2211bruna/Thu, 03 Nov 2022 09:29:16 +1000https://m2lines.github.io/news/2211bruna/Joan Bruna is the senior author in this preprint on Gradient Descent (GD) convergence in unstable conditions. The study examines the conditions for such unstable convergence, focusing on simple, yet representative, learning problems. A year-round satellite sea-ice thickness record from CryoSat-2https://m2lines.github.io/news/2210bushuk/Mon, 03 Oct 2022 09:29:16 +1000https://m2lines.github.io/news/2210bushuk/This study , co-authored by Mitch Bushuk, produced the first ever year-round satellite observational record of Arctic sea ice thickness, employing a deep learning model to produce estimates of sea ice freeboard. These data span the period 2010-2020 and will be critical for better monitoring the changing Arctic climate, advancing climate model evaluation, and improving sea ice predictions. Comparing Storm Resolving Models and Climates via Unsupervised Machine Learninghttps://m2lines.github.io/news/2210gentine/Mon, 03 Oct 2022 09:29:16 +1000https://m2lines.github.io/news/2210gentine/High resolution data is overwhelming and this paper , co-authored by Pierre Gentine and Tom Beucler, tries to define an unsupervised machine learning technique to better compare Storm Resolving Climate Models (SRMs). Their analysis found that only six out of nine considered SRMs are dynamically consistent. This split among SRMs highlights the need to further investigate sub-grid parameterization choices. Semi-automatic tuning of coupled climate models with multiple intrinsic timescaleshttps://m2lines.github.io/news/2210deshayes/Mon, 03 Oct 2022 09:29:16 +1000https://m2lines.github.io/news/2210deshayes/Using the two-scale Lorenz96 model, this article evaluates the potential of History Matching to tune a climate system with multi-scale dynamics. This semi-automatic tuning was more efficient when combined with physical expertise. Julie Deshayes and V Balaji are co-authors on this study. Benchmarking of machine learning ocean subgrid parameterizations in an idealized modelhttps://m2lines.github.io/news/2208ross/Tue, 02 Aug 2022 09:29:16 +1000https://m2lines.github.io/news/2208ross/In this work , led by Andrew Ross, with Ziwei Li, Pavel Perezhogin, Carlos Fernandez-Granda and Laure Zanna, we provide a framework for systematically benchmarking the offline and online performance of physical and ML-based subgrid parameterizations. We find that the choice of filtering operator is critical for performance. To help with interpretability, we also propose a novel equation-discovery approach combining linear regression and genetic programming which generalizes better than physical and neural network parameterizations. Cloud-based framework for inter-comparing submesoscale permitting realistic ocean modelshttps://m2lines.github.io/news/2208uchida/Mon, 01 Aug 2022 09:29:16 +1000https://m2lines.github.io/news/2208uchida/This study presents a cloud-based workflow for intercomparing high-resolution ocean models and provides an assessment of one of the most used parameterizations of ocean submesoscales. Julien Le Sommer, Ryan Abernathey, and Aurelie Albert contributed to the research. Non-Linear Dimensionality Reduction with a VED to Understand Convective Processes in Climate Modelshttps://m2lines.github.io/news/2208behrens/Mon, 01 Aug 2022 09:29:16 +1000https://m2lines.github.io/news/2208behrens/Pierre Gentine and Tom Beucler are co-authors in this article , which demonstrates that Variational Encoder Decoder structures (VED) are capable of learning and accurately reproducing convective processes in an aquaplanet superparameterized climate model simulation, while enabling interpretability and better understanding of sub-grid-scale physical processes. A potential energy analysis of ocean surface mixed layers - Brandon Reichlhttps://m2lines.github.io/news/2207reichl/Tue, 05 Jul 2022 09:29:16 +1000https://m2lines.github.io/news/2207reichl/Analysis of the ocean mixed layer requires one to estimate its vertical extent, for which there are various definitions. In this paper , Brandon Reichl and co-authors, including Alistair Adcroft, show that using the potential energy anomaly to identify the mixed layer depth is practical and it offers conceptual benefits by directly linking to the physics of upper ocean vertical mixing. New preprint led by Adam Subel on transfer learninghttps://m2lines.github.io/news/2207subel/Tue, 05 Jul 2022 09:29:16 +1000https://m2lines.github.io/news/2207subel/Adam Subel is the lead author in this article , which builds a framework to understand and implement transfer learning, a powerful machine learning technique for enabling generalization between datasets from different configurations of a system or of varying quality/quantity, in multiscale physical systems. Subseasonal Earth System Prediction with CESM2 - Judith Bernerhttps://m2lines.github.io/news/2207berner/Tue, 05 Jul 2022 09:29:16 +1000https://m2lines.github.io/news/2207berner/Judith Berner is a co-author in this article , which describes the design and prediction skill of two subseasonal prediction systems based on two configurations of the Community Earth System Model, version 2 (CESM2): with the Community Atmosphere Model and with Whole Atmosphere Community Climate Model as its atmospheric component. Both systems are compared to NOAA CFSv2 and ECMWF. Deep Learning for Subgrid-Scale Turbulence Modeling in LES of the Convective Atmospheric Boundary Layerhttps://m2lines.github.io/news/gentine-abernathey/Tue, 07 Jun 2022 09:29:16 +1000https://m2lines.github.io/news/gentine-abernathey/This article , co-authored by Pierre Gentine and Ryan Abernathey, replaces the typically used physically based assumptions with deep learning in subgrid-scale modeling, in large eddy simulations of turbulence. They show that the latter performs better in a priori (offline) tests of atmospheric turbulence. The deep neural networks model also captures key statistics of turbulence in a posteriori (online) tests. Lorenzo Zampieri - A machine learning correction modelhttps://m2lines.github.io/news/zampieri2022/Tue, 07 Jun 2022 09:29:16 +1000https://m2lines.github.io/news/zampieri2022/Lorenzo Zampieri et al. illustrate in this preprint , a novel method based on ML for reducing the surface temperature errors of atmospheric reanalyses in sea-ice-covered regions of the Arctic. The corrected temperature can be employed to support polar research activities, and to better simulate the evolution of the interacting sea ice and ocean systems in numerical models. Will Gregory - Network connectivity articlehttps://m2lines.github.io/news/gregory2022/Tue, 07 Jun 2022 09:29:16 +1000https://m2lines.github.io/news/gregory2022/Will Gregory and co-authors hypothesized in this publication that regional biases in sea ice thickness may hinder the ability of CMIP6 models to reproduce an important atmospheric driver of summer sea ice variability, and hence their ability to make reliable sea ice predictions on seasonal to inter-annual time scales. 2022 CESM workshop: Info and submission deadlinehttps://m2lines.github.io/news/cesmworkshop2022/Mon, 02 May 2022 09:29:16 +1000https://m2lines.github.io/news/cesmworkshop2022/Registration is open for the 2022 CESM Workshop on June 13-16, 2022. Please visit the website to register as well as submit working group session talks and /or posters. The deadline to submit a talk / poster is Monday, May 16th. The full workshop agenda can be found here . diff --git a/news/2405bushuk/index.html b/news/2405bushuk/index.html new file mode 100644 index 00000000..0091c4f5 --- /dev/null +++ b/news/2405bushuk/index.html @@ -0,0 +1,13 @@ +Predicting September Arctic Sea Ice by Dr. Bushuk - M²LInES
    M²LInES homepage
    news

    Predicting September Arctic Sea Ice: A Multi-Model Seasonal Skill Comparison

    This study +, led by Mitch Bushuk, provides a comparison of September Arctic sea ice seasonal prediction skill across a diverse set of 34 dynamical and statistical prediction models, quantifying the state-of-the-art in the rapidly growing sea ice prediction research community. The authors find that both dynamical and statistical prediction models can skillfully predict September Arctic sea ice 0–3 months in advance on Pan-Arctic, regional, and local spatial scales. These results demonstrate that there are bright prospects for skillful operational seasonal predictions of Arctic sea ice and highlight a number of crucial prediction system design aspects to guide future improvements.

    • This project is supported by Schmidt Futures, a philanthropic initiative founded by Eric and Wendy Schmidt that bets early on exceptional people making the world better, particularly through innovative breakthroughs in science and technology.
    \ No newline at end of file diff --git a/news/2405chapman/index.html b/news/2405chapman/index.html new file mode 100644 index 00000000..f0c63742 --- /dev/null +++ b/news/2405chapman/index.html @@ -0,0 +1,13 @@ +FTORCH and FORPY libraries now integrated to CESM! - M²LInES
    M²LInES homepage
    news

    FTORCH and FORPY libraries now integrated to CESM!

    The M²LInES team, assisted by Jack Atkinson (ICCS) and NCAR software engineers, have integrated the FTORCH and FORPY libraries for machine learning into CESM2.1.5. To set up a machine learning-enabled CESM instance on NCAR machines, follow the steps outlined in this repository +. Contact Will Chapman for questions!

    • This project is supported by Schmidt Futures, a philanthropic initiative founded by Eric and Wendy Schmidt that bets early on exceptional people making the world better, particularly through innovative breakthroughs in science and technology.
    \ No newline at end of file diff --git a/news/index.html b/news/index.html index eeca4693..8570711d 100644 --- a/news/index.html +++ b/news/index.html @@ -7,6 +7,6 @@
  1. News

    2. News

    M²LInES in the news and news from M²LInES

    Advances in Machine Learning Techniques for Sea Ice Applications

    Joint Parameter and Parameterization Inference by Yongquan

    Building Ocean Climate Emulators by Adam

    Stress-testing the coupled behavior of hybrid physics-machine simulations by Bhouri

    Stochastic Optimal Control Matching by Joan

    Restratification Effect of Mesoscale Eddies by Noora

    Data-driven equation discovery ocean model by Pavel

    Learning Machine Learning with Lorenz-96

    ClimSim awarded Best Paper Award at NeurIPS

    Will Gregory's latest article featured in Advance Science News

    OceanBench by J. Emmanuel Johnson

    UNetKF by Feiyu Lu

    Multi-fidelity climate model parameterization by Mohamed Aziz Bhouri

    Benefits of Deterministic and Stochastic Tendency Adjustments in a Climate Model

    Blog Post - A New Generation of Climate Models

    New climate simulation model ensemble by Marika Holland

    Understanding Extreme Precipitation Changes by Griffin Mooers

    Reliable coarse-grained turbulent simulations by Chris Pederson

    Background Pycnocline depth constrains FOHUE by Emily Newsom

    ClimSim: LEAP publication

    Learning Atmospheric Boundary Layer Turbulence by Sara Shamekh

    Parameterizing vertical mixing coefficients in the Ocean by Akash Sane

    Smart correction model for winter sky temperatures by Lorenzo Zampieri

    Data-driven subgrid parameterizations by Karl Otness

    Emulating Cloud Superparameterization by Pierre Gentine

    M²LInES at AGU 22

    Direct observational evidence of an oceanic dual kinetic energy cascade and its seasonality

    Implicit learning of convective organization explains precipitation stochasticity

    Ocean currents break up a tabular iceberg

    On Gradient Descent Convergence beyond the Edge of Stability

    A year-round satellite sea-ice thickness record from CryoSat-2

    Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning

    Semi-automatic tuning of coupled climate models with multiple intrinsic timescales

    Benchmarking of machine learning ocean subgrid parameterizations in an idealized model

    Cloud-based framework for inter-comparing submesoscale permitting realistic ocean models

    Non-Linear Dimensionality Reduction with a VED to Understand Convective Processes in Climate Models

    A potential energy analysis of ocean surface mixed layers - Brandon Reichl

    New preprint led by Adam Subel on transfer learning

    Subseasonal Earth System Prediction with CESM2 - Judith Berner

    Deep Learning for Subgrid-Scale Turbulence Modeling in LES of the Convective Atmospheric Boundary Layer

    Lorenzo Zampieri - A machine learning correction model

    Will Gregory - Network connectivity article

    2022 CESM workshop: Info and submission deadline

    Anastasiia Gorbunova - Eulerian spatiotemporal correlations in passive scalar turbulence

    GCM-Filters publication

    Marika Holland - Arctic sea ice sensitivity to lateral melting representation

    Balaji - Are GCMs obsolete?

    Adam Subel - Stable a posteriori LES of 2D turbulence using convolutional neural networks

    Joan Bruna - Neural Galerkin Scheme preprint

    Mitch Bushuk on the Mechanisms of Regional Arctic Sea Ice Predictability

    Dhruv Balwada and Laure Zanna article in SIAM news

    Movie about M²LInES and featured on AGU TV!

    Aakash Sane - Evaluating Coupled Climate Model Parameterizations

    Book Chapter - Isopycnal mixing by Ryan Abernathey

    Climate-Invariant Machine Learning

    Deep Probability Estimation

    Hugo Frezat - A posteriori learning of quasi-geostrophic turbulence parametrization

    Tropical precipitation clusters as islands on a rough water-vapor topography

    AGU presentations by M²LInES team members

    M²LInES Kickoff meeting

    The influence of snow on sea ice as assessed from simulations of CESM2 - Marika Holland

    Probabilistic Predictions from Deterministic Atmospheric River Forecasts with Deep Learning - Will Chapman

    Response of Northern Hemisphere Weather and Climate to Arctic Sea Ice Decline - Lorenzo Zampieri

    UN AI for Good: AI and Climate Science seminars

    Adaptive Denoising via GainTuning - Carlos Fernandez-Granda

    Impact of Sea-Ice Model Complexity on model performance by Lorenzo Zampieri

    Article by Dhruv Balwada et al. - Role of submesoscale flows on vertical fluxes

    Estimating Ocean Surface Currents With Machine Learning - Ryan Abernathey

    Seasonal Prediction and Predictability of Regional Antarctic Sea Ice - Mitch Bushuk

    Neural Networks as Geometric Chaotic Maps - by Ziwei Li

    Neural network parametrization of a subgrid process - by Janni Yuval and Paul O'Gorman

    Article in Eos by Yuval et al. - Call for Papers on Machine Learning and Earth System Modeling

    Upcoming Seminar by Laure Zanna at One World Mathematics of Climate - 7/6

    A Bayesian approach towards sea ice freeboard estimates - paper by Will Gregory

    s2s challenge

    KITP conference: Machine Learning for Climate

    2021 CESM workshop: M²LInES team members presenting on June 17th

    Call for Papers for JAMES

    Using deep learning to emulate cloud superparameterization - new paper by PI Gentine

    Neural Network emulating physical constraints - paper by PI Gentine

    New paper on deep learning parametrization by Guillaumin and Zanna

    Modelisation using neural network - paper by PI Le Sommer

    Newsletters

    • This project is supported by Schmidt Futures, a philanthropic initiative founded by Eric and Wendy Schmidt that bets early on exceptional people making the world better, particularly through innovative breakthroughs in science and technology.

    News

    M²LInES in the news and news from M²LInES

    FTORCH and FORPY libraries now integrated to CESM!

    Predicting September Arctic Sea Ice by Dr. Bushuk

    Advances in Machine Learning Techniques for Sea Ice Applications

    Joint Parameter and Parameterization Inference by Yongquan

    Building Ocean Climate Emulators by Adam

    Stress-testing the coupled behavior of hybrid physics-machine simulations by Bhouri

    Stochastic Optimal Control Matching by Joan

    Restratification Effect of Mesoscale Eddies by Noora

    Data-driven equation discovery ocean model by Pavel

    Learning Machine Learning with Lorenz-96

    ClimSim awarded Best Paper Award at NeurIPS

    Will Gregory's latest article featured in Advance Science News

    OceanBench by J. Emmanuel Johnson

    UNetKF by Feiyu Lu

    Multi-fidelity climate model parameterization by Mohamed Aziz Bhouri

    Benefits of Deterministic and Stochastic Tendency Adjustments in a Climate Model

    Blog Post - A New Generation of Climate Models

    New climate simulation model ensemble by Marika Holland

    Understanding Extreme Precipitation Changes by Griffin Mooers

    Reliable coarse-grained turbulent simulations by Chris Pederson

    Background Pycnocline depth constrains FOHUE by Emily Newsom

    ClimSim: LEAP publication

    Learning Atmospheric Boundary Layer Turbulence by Sara Shamekh

    Parameterizing vertical mixing coefficients in the Ocean by Akash Sane

    Smart correction model for winter sky temperatures by Lorenzo Zampieri

    Data-driven subgrid parameterizations by Karl Otness

    Emulating Cloud Superparameterization by Pierre Gentine

    M²LInES at AGU 22

    Direct observational evidence of an oceanic dual kinetic energy cascade and its seasonality

    Implicit learning of convective organization explains precipitation stochasticity

    Ocean currents break up a tabular iceberg

    On Gradient Descent Convergence beyond the Edge of Stability

    A year-round satellite sea-ice thickness record from CryoSat-2

    Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning

    Semi-automatic tuning of coupled climate models with multiple intrinsic timescales

    Benchmarking of machine learning ocean subgrid parameterizations in an idealized model

    Cloud-based framework for inter-comparing submesoscale permitting realistic ocean models

    Non-Linear Dimensionality Reduction with a VED to Understand Convective Processes in Climate Models

    A potential energy analysis of ocean surface mixed layers - Brandon Reichl

    New preprint led by Adam Subel on transfer learning

    Subseasonal Earth System Prediction with CESM2 - Judith Berner

    Deep Learning for Subgrid-Scale Turbulence Modeling in LES of the Convective Atmospheric Boundary Layer

    Lorenzo Zampieri - A machine learning correction model

    Will Gregory - Network connectivity article

    2022 CESM workshop: Info and submission deadline

    Anastasiia Gorbunova - Eulerian spatiotemporal correlations in passive scalar turbulence

    GCM-Filters publication

    Marika Holland - Arctic sea ice sensitivity to lateral melting representation

    Balaji - Are GCMs obsolete?

    Adam Subel - Stable a posteriori LES of 2D turbulence using convolutional neural networks

    Joan Bruna - Neural Galerkin Scheme preprint

    Mitch Bushuk on the Mechanisms of Regional Arctic Sea Ice Predictability

    Dhruv Balwada and Laure Zanna article in SIAM news

    Movie about M²LInES and featured on AGU TV!

    Aakash Sane - Evaluating Coupled Climate Model Parameterizations

    Book Chapter - Isopycnal mixing by Ryan Abernathey

    Climate-Invariant Machine Learning

    Deep Probability Estimation

    Hugo Frezat - A posteriori learning of quasi-geostrophic turbulence parametrization

    Tropical precipitation clusters as islands on a rough water-vapor topography

    AGU presentations by M²LInES team members

    M²LInES Kickoff meeting

    The influence of snow on sea ice as assessed from simulations of CESM2 - Marika Holland

    Probabilistic Predictions from Deterministic Atmospheric River Forecasts with Deep Learning - Will Chapman

    Response of Northern Hemisphere Weather and Climate to Arctic Sea Ice Decline - Lorenzo Zampieri

    UN AI for Good: AI and Climate Science seminars

    Adaptive Denoising via GainTuning - Carlos Fernandez-Granda

    Impact of Sea-Ice Model Complexity on model performance by Lorenzo Zampieri

    Article by Dhruv Balwada et al. - Role of submesoscale flows on vertical fluxes

    Estimating Ocean Surface Currents With Machine Learning - Ryan Abernathey

    Seasonal Prediction and Predictability of Regional Antarctic Sea Ice - Mitch Bushuk

    Neural Networks as Geometric Chaotic Maps - by Ziwei Li

    Neural network parametrization of a subgrid process - by Janni Yuval and Paul O'Gorman

    Article in Eos by Yuval et al. - Call for Papers on Machine Learning and Earth System Modeling

    Upcoming Seminar by Laure Zanna at One World Mathematics of Climate - 7/6

    A Bayesian approach towards sea ice freeboard estimates - paper by Will Gregory

    s2s challenge

    KITP conference: Machine Learning for Climate

    2021 CESM workshop: M²LInES team members presenting on June 17th

    Call for Papers for JAMES

    Using deep learning to emulate cloud superparameterization - new paper by PI Gentine

    Neural Network emulating physical constraints - paper by PI Gentine

    New paper on deep learning parametrization by Guillaumin and Zanna

    Modelisation using neural network - paper by PI Le Sommer

    Newsletters

    • This project is supported by Schmidt Futures, a philanthropic initiative founded by Eric and Wendy Schmidt that bets early on exceptional people making the world better, particularly through innovative breakthroughs in science and technology.
    \ No newline at end of file diff --git a/news/index.xml b/news/index.xml index b1730fcd..ad988316 100644 --- a/news/index.xml +++ b/news/index.xml @@ -1,4 +1,4 @@ -News on M²LInEShttps://m2lines.github.io/news/Recent content in News on M²LInESHugo -- gohugo.ioen-usWed, 03 Apr 2024 09:29:16 +1000Advances in Machine Learning Techniques for Sea Ice Applicationshttps://m2lines.github.io/news/2404zampieri/Wed, 03 Apr 2024 09:29:16 +1000https://m2lines.github.io/news/2404zampieri/Forty-two international researchers, including Lorenzo Zampieri, were invited to Gwangmyeong (South Korea) to discuss recent advances in sea ice modelling to help guide the development of a state-of-the-art Korean sea ice modelling program under the leadership of the Korean Polar Research Institute (KOPRI). The workshop produced a report , which highlights 1) the opportunity to use AI techniques in support of various sea ice modelling tasks both for short-term and climate predictions and 2) the need for strengthened collaborations among international groups to connect existing “research bubbles” and facilitate the dissemination of ideas.Joint Parameter and Parameterization Inference by Yongquanhttps://m2lines.github.io/news/2404qu/Tue, 02 Apr 2024 09:29:16 +1000https://m2lines.github.io/news/2404qu/In this article , Yongquan Qu, Mohamed Aziz Bhouri, and Pierre Gentine, tackle the joint inference and uncertainty quantification of poorly known physical parameters and machine learning parametrizations, for sub-grid scale dynamics. Achieved through a Bayesian framework enabled by differentiable programming, this method not only offers accurate parameter estimates, but also enables skillful forecasting, each accompanied by quantified uncertainty. This work is accepted at the ICLR 2024 Workshop on AI4Differential Equations In Science.Building Ocean Climate Emulators by Adamhttps://m2lines.github.io/news/2404adam/Mon, 01 Apr 2024 09:29:16 +1000https://m2lines.github.io/news/2404adam/AI can accelerate climate modeling by generating computationally cheap emulators of existing simulations. The design of climate emulators is an exciting and open challenge in AI+Climate. In this work , PhD student Adam Subel and Laure Zanna focus on two fundamental questions to facilitate the creation of ocean emulators: 1) the role of the atmosphere in improving the decadal skill of the emulator and 2) the representation of variables with distinct timescales (e.Stress-testing the coupled behavior of hybrid physics-machine simulations by Bhourihttps://m2lines.github.io/news/2402bhouri/Sat, 03 Feb 2024 09:29:16 +1000https://m2lines.github.io/news/2402bhouri/Machine Learning (ML)-based parameterizations of atmospheric convection have long been hailed as a promising alternative, with the potential to yield higher accuracy at a fraction of the cost of more explicit simulations. This work , co-authored by Mohamed Aziz Bhouri, investigates the coupled out-of-distribution extrapolation capabilities in &ldquo;online&rdquo; testing of different ML-based parameterization designs of atmospheric convection. Their results show that these design decisions are not enough to obtain satisfactory generalization benefits.Stochastic Optimal Control Matching by Joanhttps://m2lines.github.io/news/2402joan/Fri, 02 Feb 2024 09:29:16 +1000https://m2lines.github.io/news/2402joan/Joan Bruna is a co-author in this preprint , introducing Stochastic Optimal Control Matching (SOCM), a novel Iterative Diffusion Optimization (IDO) technique for stochastic optimal control. Stochastic optimal control’s goal is to drive the behavior of noisy systems. In this work, the control is learned via a least squares problem by trying to fit a matching vector field. The key idea underlying SOCM is the path-wise reparameterization trick, a novel technique that is of independent interest, e.Restratification Effect of Mesoscale Eddies by Noorahttps://m2lines.github.io/news/2402noora/Thu, 01 Feb 2024 09:29:16 +1000https://m2lines.github.io/news/2402noora/Nora Loose is lead author of this new paper - part of the Climate Process Team funded by NSF and NOAA - which compares two parameterizations (“GM90” and “GL90”) for the restratification effect of mesoscale eddies. The authors conclude that the less commonly used GL90 parameterization is a promising alternative to the popular GM90 scheme for isopycnal coordinate models, where it is more consistent with theory, computationally more efficient, easier to implement, and numerically more stable.Data-driven equation discovery ocean model by Pavelhttps://m2lines.github.io/news/2401pavel/Tue, 02 Jan 2024 09:29:16 +1000https://m2lines.github.io/news/2401pavel/Pavel Perezhogin is lead author of this new M²LInES preprint , describing the implementation, in the GFDL MOM6 ocean model, of a data-driven equation-discovery parameterization of mesoscale eddies. This scale-aware parametrization improved biases in the mean flow and energetics for a range of resolutions and in different ocean configurations. The work was done in collaboration with Cheng Zhang, Alistair Adcroft, Carlos Fernandez-Granda and Laure Zanna.Learning Machine Learning with Lorenz-96https://m2lines.github.io/news/2401l96/Mon, 01 Jan 2024 09:29:16 +1000https://m2lines.github.io/news/2401l96/The M²LInES team is proud to share our Jupyter Book on Learning Machine Learning with Lorenz-96 . This educational tool provides a computationally accessible framework to understand how machine learning techniques can tackle climate science problems, including emulators, parameterizations, data assimilation, and uncertainty quantification. It is for all climate scientists wanting to learn or test machine learning algorithms or for machine learning experts to learn about climate modeling or develop new algorithms.ClimSim awarded Best Paper Award at NeurIPShttps://m2lines.github.io/news/2312climsim/Tue, 12 Dec 2023 09:29:16 +1000https://m2lines.github.io/news/2312climsim/ClimSim has been awarded Best Paper Award at NeurIPS ! The open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators, emerged out of the Science and Technology Center LEAP, received contributions from numerous M²LInES members. Congratulations to all authors!Will Gregory's latest article featured in Advance Science Newshttps://m2lines.github.io/news/2312willgfeature/Fri, 01 Dec 2023 09:29:16 +1000https://m2lines.github.io/news/2312willgfeature/The latest M²LInES article, led by Will Gregory (Princeton) is being featured in Advance Science News. The article details how Will and other colleagues, including M²LInES members Mitch Bushuk, Alistair Adcroft, and Laure Zanna, are using Machine Learning to enhance predictions of Arctic sea ice loss. Read the article by Giorgio Graffino hereOceanBench by J. Emmanuel Johnsonhttps://m2lines.github.io/news/2311johnson/Wed, 01 Nov 2023 09:29:16 +1000https://m2lines.github.io/news/2311johnson/OceanBench is a suite of tools for designing experiments relevant to the oceanography community like interpolation, forecasting and estimation. Lead author J. Emmanuel Johnson and co-authors, including Anastasiia Gorbunova and Julien Le Sommer, demonstrate its use within a benchmark setting for sea surface height interpolation, using general circulation model simulations and real satellite observations.UNetKF by Feiyu Luhttps://m2lines.github.io/news/2310lu/Tue, 03 Oct 2023 09:29:16 +1000https://m2lines.github.io/news/2310lu/This study by Feiyu Lu incorporates deep learning methods to compliment and improve the ensemble Kalman filter data assimilation algorithm. More specifically, a convolutional neural network is trained to predict the error statistics of a model ensemble, which will reduce the computational costs of the ensemble Kalman filter. This approach is tested in a quasi-geostrophic dynamic model to demonstrate its feasibility in more advanced weather and climate applications.Multi-fidelity climate model parameterization by Mohamed Aziz Bhourihttps://m2lines.github.io/news/2310bhouri/Mon, 02 Oct 2023 09:29:16 +1000https://m2lines.github.io/news/2310bhouri/In this preprint , Mohamed Aziz Bhouri and co-authors, including Pierre Gentine, developed a probabilistic multi-fidelity atmospheric parameterization for heat and moisture tendency. They implemented it on CESM CAM5 and SPCAM5 data and showed its capabilities to well extrapolate to unseen warmer climate simulations while returning trustworthy uncertainty quantification.Benefits of Deterministic and Stochastic Tendency Adjustments in a Climate Modelhttps://m2lines.github.io/news/2310chapman/Sun, 01 Oct 2023 09:29:16 +1000https://m2lines.github.io/news/2310chapman/Stochastic and deterministic tendency adjustments are applied to multiple experiments in the community atmosphere model version 6. The tendency adjustments are gleaned from the DART data assimilation system and a linear relaxation towards ERA-interim reanalysis. In this preprint , Will Chapman and Judith Berner show that the tendency adjustment significantly enhances the representation of the background state climatology for key prognostic and surface variables. Additionally, they show that stochastic tendencies are essential to accurately represent model variability of major atmospheric modes when making tendency adjustments. Blog Post - A New Generation of Climate Modelshttps://m2lines.github.io/news/2309blogpost/Mon, 11 Sep 2023 09:29:16 +1000https://m2lines.github.io/news/2309blogpost/M²LInES can now demonstrate the impact of data-driven Machine Learning parameterizations in global models! 🌎You can read about new gen climate models with AI in our blog postNew climate simulation model ensemble by Marika Hollandhttps://m2lines.github.io/news/2309holland/Mon, 04 Sep 2023 09:29:16 +1000https://m2lines.github.io/news/2309holland/Climate simulation uncertainties arise from internal variability, model structure, and external forcings. These uncertainty sources are difficult to disentangle across model generations because both model structure and external forcings typically change. This preprint , led by Marika Holland, presents new ensemble simulations which allow for the separation of uncertainty sources between two large ensembles (LEs) of the Community Earth System Model (CESM). Importantly, we find a strong influence of historical forcing uncertainty in differences between CESM1-LE and CESM2-LE due to aerosol effects on simulated climate.Understanding Extreme Precipitation Changes by Griffin Mooershttps://m2lines.github.io/news/2309mooers/Sun, 03 Sep 2023 09:29:16 +1000https://m2lines.github.io/news/2309mooers/Griffin Mooers is lead author in this preprint that leverages Variational Autoencoders on SPCAM5 output to examine the changes in the upper quantiles of precipitation between a control and warmed simulation. They find intense precipitation changes are mostly controlled by spatial shifts in deep convection.Reliable coarse-grained turbulent simulations by Chris Pedersonhttps://m2lines.github.io/news/2309pederson/Sat, 02 Sep 2023 09:29:16 +1000https://m2lines.github.io/news/2309pederson/In this preprint , M²LInES postdoc Chris Pedersen, along with Pavel Perezhogin, Joan Bruna, and Laure Zanna, show that the standard offline learning paradigm used to produce ML models of unresolved dynamics can be complemented with some learned time evolution of the system, and produce more stable hybrid physics and ML models.Background Pycnocline depth constrains FOHUE by Emily Newsomhttps://m2lines.github.io/news/2308newsom/Thu, 03 Aug 2023 09:29:16 +1000https://m2lines.github.io/news/2308newsom/In this paper , Emily Newsom and co-authors Laure Zanna and Jonathan Gregory, showed that the relative depth of ocean heat sequestration, which varies widely across climate models and paces the rate of surface climate warming, is strongly correlated to interior ocean stratification. Using a novel regional decomposition method, they showed that this relationship is entirely driven by mid-latitude ventilation processes and explains 70% of the variance in heat penetration depth across CMIP5/CMIP6 models.ClimSim: LEAP publicationhttps://m2lines.github.io/news/2308bhouri/Wed, 02 Aug 2023 09:29:16 +1000https://m2lines.github.io/news/2308bhouri/Several of M²LInES members and affiliates (Bhouri, Gentine, Zanna, Abernathey, Busecke, Mooers, Shamekh, and Yuval) have contributed to this LEAP paper presenting ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. The data and code are open access.Learning Atmospheric Boundary Layer Turbulence by Sara Shamekhhttps://m2lines.github.io/news/2308shamekh/Tue, 01 Aug 2023 09:29:16 +1000https://m2lines.github.io/news/2308shamekh/This preprint , by Sara Shamekh and Pierre Gentine, proposes a novel data-driven parameterization of vertical turbulent fluxes in convective boundary layer, that models the fluxes of various scalars across turbulent regimes. By incorporating a physics-based constraint this approach allows us to decompose the total vertical flux into two main modes of variability based on large scale forces that control the turbulence in the atmosphere.Parameterizing vertical mixing coefficients in the Ocean by Akash Sanehttps://m2lines.github.io/news/2307sane/Sat, 01 Jul 2023 09:29:16 +1000https://m2lines.github.io/news/2307sane/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 , 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.Smart correction model for winter sky temperatures by Lorenzo Zampierihttps://m2lines.github.io/news/2306zampieri/Thu, 01 Jun 2023 09:29:16 +1000https://m2lines.github.io/news/2306zampieri/This study , 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.Data-driven subgrid parameterizations by Karl Otnesshttps://m2lines.github.io/news/2305otness/Mon, 01 May 2023 09:29:16 +1000https://m2lines.github.io/news/2305otness/In this ongoing project , 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. +News on M²LInEShttps://m2lines.github.io/news/Recent content in News on M²LInESHugo -- gohugo.ioen-usThu, 02 May 2024 09:29:16 +1000FTORCH and FORPY libraries now integrated to CESM!https://m2lines.github.io/news/2405chapman/Thu, 02 May 2024 09:29:16 +1000https://m2lines.github.io/news/2405chapman/The M²LInES team, assisted by Jack Atkinson (ICCS) and NCAR software engineers, have integrated the FTORCH and FORPY libraries for machine learning into CESM2.1.5. To set up a machine learning-enabled CESM instance on NCAR machines, follow the steps outlined in this repository . Contact Will Chapman for questions!Predicting September Arctic Sea Ice by Dr. Bushukhttps://m2lines.github.io/news/2405bushuk/Wed, 01 May 2024 09:29:16 +1000https://m2lines.github.io/news/2405bushuk/This study , led by Mitch Bushuk, provides a comparison of September Arctic sea ice seasonal prediction skill across a diverse set of 34 dynamical and statistical prediction models, quantifying the state-of-the-art in the rapidly growing sea ice prediction research community. The authors find that both dynamical and statistical prediction models can skillfully predict September Arctic sea ice 0–3 months in advance on Pan-Arctic, regional, and local spatial scales. These results demonstrate that there are bright prospects for skillful operational seasonal predictions of Arctic sea ice and highlight a number of crucial prediction system design aspects to guide future improvements.Advances in Machine Learning Techniques for Sea Ice Applicationshttps://m2lines.github.io/news/2404zampieri/Wed, 03 Apr 2024 09:29:16 +1000https://m2lines.github.io/news/2404zampieri/Forty-two international researchers, including Lorenzo Zampieri, were invited to Gwangmyeong (South Korea) to discuss recent advances in sea ice modelling to help guide the development of a state-of-the-art Korean sea ice modelling program under the leadership of the Korean Polar Research Institute (KOPRI). The workshop produced a report , which highlights 1) the opportunity to use AI techniques in support of various sea ice modelling tasks both for short-term and climate predictions and 2) the need for strengthened collaborations among international groups to connect existing “research bubbles” and facilitate the dissemination of ideas.Joint Parameter and Parameterization Inference by Yongquanhttps://m2lines.github.io/news/2404qu/Tue, 02 Apr 2024 09:29:16 +1000https://m2lines.github.io/news/2404qu/In this article , Yongquan Qu, Mohamed Aziz Bhouri, and Pierre Gentine, tackle the joint inference and uncertainty quantification of poorly known physical parameters and machine learning parametrizations, for sub-grid scale dynamics. Achieved through a Bayesian framework enabled by differentiable programming, this method not only offers accurate parameter estimates, but also enables skillful forecasting, each accompanied by quantified uncertainty. This work is accepted at the ICLR 2024 Workshop on AI4Differential Equations In Science.Building Ocean Climate Emulators by Adamhttps://m2lines.github.io/news/2404adam/Mon, 01 Apr 2024 09:29:16 +1000https://m2lines.github.io/news/2404adam/AI can accelerate climate modeling by generating computationally cheap emulators of existing simulations. The design of climate emulators is an exciting and open challenge in AI+Climate. In this work , PhD student Adam Subel and Laure Zanna focus on two fundamental questions to facilitate the creation of ocean emulators: 1) the role of the atmosphere in improving the decadal skill of the emulator and 2) the representation of variables with distinct timescales (e.Stress-testing the coupled behavior of hybrid physics-machine simulations by Bhourihttps://m2lines.github.io/news/2402bhouri/Sat, 03 Feb 2024 09:29:16 +1000https://m2lines.github.io/news/2402bhouri/Machine Learning (ML)-based parameterizations of atmospheric convection have long been hailed as a promising alternative, with the potential to yield higher accuracy at a fraction of the cost of more explicit simulations. This work , co-authored by Mohamed Aziz Bhouri, investigates the coupled out-of-distribution extrapolation capabilities in &ldquo;online&rdquo; testing of different ML-based parameterization designs of atmospheric convection. Their results show that these design decisions are not enough to obtain satisfactory generalization benefits.Stochastic Optimal Control Matching by Joanhttps://m2lines.github.io/news/2402joan/Fri, 02 Feb 2024 09:29:16 +1000https://m2lines.github.io/news/2402joan/Joan Bruna is a co-author in this preprint , introducing Stochastic Optimal Control Matching (SOCM), a novel Iterative Diffusion Optimization (IDO) technique for stochastic optimal control. Stochastic optimal control’s goal is to drive the behavior of noisy systems. In this work, the control is learned via a least squares problem by trying to fit a matching vector field. The key idea underlying SOCM is the path-wise reparameterization trick, a novel technique that is of independent interest, e.Restratification Effect of Mesoscale Eddies by Noorahttps://m2lines.github.io/news/2402noora/Thu, 01 Feb 2024 09:29:16 +1000https://m2lines.github.io/news/2402noora/Nora Loose is lead author of this new paper - part of the Climate Process Team funded by NSF and NOAA - which compares two parameterizations (“GM90” and “GL90”) for the restratification effect of mesoscale eddies. The authors conclude that the less commonly used GL90 parameterization is a promising alternative to the popular GM90 scheme for isopycnal coordinate models, where it is more consistent with theory, computationally more efficient, easier to implement, and numerically more stable.Data-driven equation discovery ocean model by Pavelhttps://m2lines.github.io/news/2401pavel/Tue, 02 Jan 2024 09:29:16 +1000https://m2lines.github.io/news/2401pavel/Pavel Perezhogin is lead author of this new M²LInES preprint , describing the implementation, in the GFDL MOM6 ocean model, of a data-driven equation-discovery parameterization of mesoscale eddies. This scale-aware parametrization improved biases in the mean flow and energetics for a range of resolutions and in different ocean configurations. The work was done in collaboration with Cheng Zhang, Alistair Adcroft, Carlos Fernandez-Granda and Laure Zanna.Learning Machine Learning with Lorenz-96https://m2lines.github.io/news/2401l96/Mon, 01 Jan 2024 09:29:16 +1000https://m2lines.github.io/news/2401l96/The M²LInES team is proud to share our Jupyter Book on Learning Machine Learning with Lorenz-96 . This educational tool provides a computationally accessible framework to understand how machine learning techniques can tackle climate science problems, including emulators, parameterizations, data assimilation, and uncertainty quantification. It is for all climate scientists wanting to learn or test machine learning algorithms or for machine learning experts to learn about climate modeling or develop new algorithms.ClimSim awarded Best Paper Award at NeurIPShttps://m2lines.github.io/news/2312climsim/Tue, 12 Dec 2023 09:29:16 +1000https://m2lines.github.io/news/2312climsim/ClimSim has been awarded Best Paper Award at NeurIPS ! The open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators, emerged out of the Science and Technology Center LEAP, received contributions from numerous M²LInES members. Congratulations to all authors!Will Gregory's latest article featured in Advance Science Newshttps://m2lines.github.io/news/2312willgfeature/Fri, 01 Dec 2023 09:29:16 +1000https://m2lines.github.io/news/2312willgfeature/The latest M²LInES article, led by Will Gregory (Princeton) is being featured in Advance Science News. The article details how Will and other colleagues, including M²LInES members Mitch Bushuk, Alistair Adcroft, and Laure Zanna, are using Machine Learning to enhance predictions of Arctic sea ice loss. Read the article by Giorgio Graffino hereOceanBench by J. Emmanuel Johnsonhttps://m2lines.github.io/news/2311johnson/Wed, 01 Nov 2023 09:29:16 +1000https://m2lines.github.io/news/2311johnson/OceanBench is a suite of tools for designing experiments relevant to the oceanography community like interpolation, forecasting and estimation. Lead author J. Emmanuel Johnson and co-authors, including Anastasiia Gorbunova and Julien Le Sommer, demonstrate its use within a benchmark setting for sea surface height interpolation, using general circulation model simulations and real satellite observations.UNetKF by Feiyu Luhttps://m2lines.github.io/news/2310lu/Tue, 03 Oct 2023 09:29:16 +1000https://m2lines.github.io/news/2310lu/This study by Feiyu Lu incorporates deep learning methods to compliment and improve the ensemble Kalman filter data assimilation algorithm. More specifically, a convolutional neural network is trained to predict the error statistics of a model ensemble, which will reduce the computational costs of the ensemble Kalman filter. This approach is tested in a quasi-geostrophic dynamic model to demonstrate its feasibility in more advanced weather and climate applications.Multi-fidelity climate model parameterization by Mohamed Aziz Bhourihttps://m2lines.github.io/news/2310bhouri/Mon, 02 Oct 2023 09:29:16 +1000https://m2lines.github.io/news/2310bhouri/In this preprint , Mohamed Aziz Bhouri and co-authors, including Pierre Gentine, developed a probabilistic multi-fidelity atmospheric parameterization for heat and moisture tendency. They implemented it on CESM CAM5 and SPCAM5 data and showed its capabilities to well extrapolate to unseen warmer climate simulations while returning trustworthy uncertainty quantification.Benefits of Deterministic and Stochastic Tendency Adjustments in a Climate Modelhttps://m2lines.github.io/news/2310chapman/Sun, 01 Oct 2023 09:29:16 +1000https://m2lines.github.io/news/2310chapman/Stochastic and deterministic tendency adjustments are applied to multiple experiments in the community atmosphere model version 6. The tendency adjustments are gleaned from the DART data assimilation system and a linear relaxation towards ERA-interim reanalysis. In this preprint , Will Chapman and Judith Berner show that the tendency adjustment significantly enhances the representation of the background state climatology for key prognostic and surface variables. Additionally, they show that stochastic tendencies are essential to accurately represent model variability of major atmospheric modes when making tendency adjustments. Blog Post - A New Generation of Climate Modelshttps://m2lines.github.io/news/2309blogpost/Mon, 11 Sep 2023 09:29:16 +1000https://m2lines.github.io/news/2309blogpost/M²LInES can now demonstrate the impact of data-driven Machine Learning parameterizations in global models! 🌎You can read about new gen climate models with AI in our blog postNew climate simulation model ensemble by Marika Hollandhttps://m2lines.github.io/news/2309holland/Mon, 04 Sep 2023 09:29:16 +1000https://m2lines.github.io/news/2309holland/Climate simulation uncertainties arise from internal variability, model structure, and external forcings. These uncertainty sources are difficult to disentangle across model generations because both model structure and external forcings typically change. This preprint , led by Marika Holland, presents new ensemble simulations which allow for the separation of uncertainty sources between two large ensembles (LEs) of the Community Earth System Model (CESM). Importantly, we find a strong influence of historical forcing uncertainty in differences between CESM1-LE and CESM2-LE due to aerosol effects on simulated climate.Understanding Extreme Precipitation Changes by Griffin Mooershttps://m2lines.github.io/news/2309mooers/Sun, 03 Sep 2023 09:29:16 +1000https://m2lines.github.io/news/2309mooers/Griffin Mooers is lead author in this preprint that leverages Variational Autoencoders on SPCAM5 output to examine the changes in the upper quantiles of precipitation between a control and warmed simulation. They find intense precipitation changes are mostly controlled by spatial shifts in deep convection.Reliable coarse-grained turbulent simulations by Chris Pedersonhttps://m2lines.github.io/news/2309pederson/Sat, 02 Sep 2023 09:29:16 +1000https://m2lines.github.io/news/2309pederson/In this preprint , M²LInES postdoc Chris Pedersen, along with Pavel Perezhogin, Joan Bruna, and Laure Zanna, show that the standard offline learning paradigm used to produce ML models of unresolved dynamics can be complemented with some learned time evolution of the system, and produce more stable hybrid physics and ML models.Background Pycnocline depth constrains FOHUE by Emily Newsomhttps://m2lines.github.io/news/2308newsom/Thu, 03 Aug 2023 09:29:16 +1000https://m2lines.github.io/news/2308newsom/In this paper , Emily Newsom and co-authors Laure Zanna and Jonathan Gregory, showed that the relative depth of ocean heat sequestration, which varies widely across climate models and paces the rate of surface climate warming, is strongly correlated to interior ocean stratification. Using a novel regional decomposition method, they showed that this relationship is entirely driven by mid-latitude ventilation processes and explains 70% of the variance in heat penetration depth across CMIP5/CMIP6 models.ClimSim: LEAP publicationhttps://m2lines.github.io/news/2308bhouri/Wed, 02 Aug 2023 09:29:16 +1000https://m2lines.github.io/news/2308bhouri/Several of M²LInES members and affiliates (Bhouri, Gentine, Zanna, Abernathey, Busecke, Mooers, Shamekh, and Yuval) have contributed to this LEAP paper presenting ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. The data and code are open access.Learning Atmospheric Boundary Layer Turbulence by Sara Shamekhhttps://m2lines.github.io/news/2308shamekh/Tue, 01 Aug 2023 09:29:16 +1000https://m2lines.github.io/news/2308shamekh/This preprint , by Sara Shamekh and Pierre Gentine, proposes a novel data-driven parameterization of vertical turbulent fluxes in convective boundary layer, that models the fluxes of various scalars across turbulent regimes. By incorporating a physics-based constraint this approach allows us to decompose the total vertical flux into two main modes of variability based on large scale forces that control the turbulence in the atmosphere.Parameterizing vertical mixing coefficients in the Ocean by Akash Sanehttps://m2lines.github.io/news/2307sane/Sat, 01 Jul 2023 09:29:16 +1000https://m2lines.github.io/news/2307sane/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 , 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.Smart correction model for winter sky temperatures by Lorenzo Zampierihttps://m2lines.github.io/news/2306zampieri/Thu, 01 Jun 2023 09:29:16 +1000https://m2lines.github.io/news/2306zampieri/This study , 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.Data-driven subgrid parameterizations by Karl Otnesshttps://m2lines.github.io/news/2305otness/Mon, 01 May 2023 09:29:16 +1000https://m2lines.github.io/news/2305otness/In this ongoing project , 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. This paper won “Best ML innovation” at the ICLR 2023 Workshop: Tackling Climate Change with Machine Learning.Emulating Cloud Superparameterization by Pierre Gentinehttps://m2lines.github.io/news/2301gentine/Tue, 03 Jan 2023 09:29:16 +1000https://m2lines.github.io/news/2301gentine/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 M²LInES at AGU 22https://m2lines.github.io/news/2212agu22/Tue, 06 Dec 2022 09:29:16 +1000https://m2lines.github.io/news/2212agu22/AGU M²LInES Highlights The M²LInES team is excited to present our recent scientific advances at the Fall 2022 AGU meeting in Chicago and online. Below is a summary of the talks (all times are CST). For your convenience, you can download the schedule for the talks by following this link . Machine learning for physics discovery &amp; modeling We have an extensive lineup of talks and posters from M²LInES team members and affiliates. Direct observational evidence of an oceanic dual kinetic energy cascade and its seasonalityhttps://m2lines.github.io/news/2211balwada/Thu, 03 Nov 2022 09:29:16 +1000https://m2lines.github.io/news/2211balwada/In this article , led by Dhruv Balwada, ocean drifter observations show wave and turbulence merge and split flows, simultaneously transferring energy to large and small scales. Implicit learning of convective organization explains precipitation stochasticityhttps://m2lines.github.io/news/2211shamekh/Thu, 03 Nov 2022 09:29:16 +1000https://m2lines.github.io/news/2211shamekh/Cloud and convection take many structure and degrees of organization, which are still absent in most parameterizations. But does that structure matter for precipitation? This preprint , led by Sara Shamekh, proposes an elegant method to include organization in the parameterization of precipitation that results in a significant improvement in predicting precipitation, especially extremes, at GCM scale. Ocean currents break up a tabular iceberghttps://m2lines.github.io/news/2211adcroft/Thu, 03 Nov 2022 09:29:16 +1000https://m2lines.github.io/news/2211adcroft/This article examines the mechanism that led to the breakup of massive iceberg A68 in Dec 2020. The authors, including Alistair Adcroft, demonstrate that the A68a breakup event may have been triggered by ocean-current shear, a new breakup mechanism not previously reported. Their paper was featured in the news . On Gradient Descent Convergence beyond the Edge of Stabilityhttps://m2lines.github.io/news/2211bruna/Thu, 03 Nov 2022 09:29:16 +1000https://m2lines.github.io/news/2211bruna/Joan Bruna is the senior author in this preprint on Gradient Descent (GD) convergence in unstable conditions. The study examines the conditions for such unstable convergence, focusing on simple, yet representative, learning problems. A year-round satellite sea-ice thickness record from CryoSat-2https://m2lines.github.io/news/2210bushuk/Mon, 03 Oct 2022 09:29:16 +1000https://m2lines.github.io/news/2210bushuk/This study , co-authored by Mitch Bushuk, produced the first ever year-round satellite observational record of Arctic sea ice thickness, employing a deep learning model to produce estimates of sea ice freeboard. These data span the period 2010-2020 and will be critical for better monitoring the changing Arctic climate, advancing climate model evaluation, and improving sea ice predictions. Comparing Storm Resolving Models and Climates via Unsupervised Machine Learninghttps://m2lines.github.io/news/2210gentine/Mon, 03 Oct 2022 09:29:16 +1000https://m2lines.github.io/news/2210gentine/High resolution data is overwhelming and this paper , co-authored by Pierre Gentine and Tom Beucler, tries to define an unsupervised machine learning technique to better compare Storm Resolving Climate Models (SRMs). Their analysis found that only six out of nine considered SRMs are dynamically consistent. This split among SRMs highlights the need to further investigate sub-grid parameterization choices. Semi-automatic tuning of coupled climate models with multiple intrinsic timescaleshttps://m2lines.github.io/news/2210deshayes/Mon, 03 Oct 2022 09:29:16 +1000https://m2lines.github.io/news/2210deshayes/Using the two-scale Lorenz96 model, this article evaluates the potential of History Matching to tune a climate system with multi-scale dynamics. This semi-automatic tuning was more efficient when combined with physical expertise. Julie Deshayes and V Balaji are co-authors on this study. Benchmarking of machine learning ocean subgrid parameterizations in an idealized modelhttps://m2lines.github.io/news/2208ross/Tue, 02 Aug 2022 09:29:16 +1000https://m2lines.github.io/news/2208ross/In this work , led by Andrew Ross, with Ziwei Li, Pavel Perezhogin, Carlos Fernandez-Granda and Laure Zanna, we provide a framework for systematically benchmarking the offline and online performance of physical and ML-based subgrid parameterizations. We find that the choice of filtering operator is critical for performance. To help with interpretability, we also propose a novel equation-discovery approach combining linear regression and genetic programming which generalizes better than physical and neural network parameterizations. Cloud-based framework for inter-comparing submesoscale permitting realistic ocean modelshttps://m2lines.github.io/news/2208uchida/Mon, 01 Aug 2022 09:29:16 +1000https://m2lines.github.io/news/2208uchida/This study presents a cloud-based workflow for intercomparing high-resolution ocean models and provides an assessment of one of the most used parameterizations of ocean submesoscales. Julien Le Sommer, Ryan Abernathey, and Aurelie Albert contributed to the research. Non-Linear Dimensionality Reduction with a VED to Understand Convective Processes in Climate Modelshttps://m2lines.github.io/news/2208behrens/Mon, 01 Aug 2022 09:29:16 +1000https://m2lines.github.io/news/2208behrens/Pierre Gentine and Tom Beucler are co-authors in this article , which demonstrates that Variational Encoder Decoder structures (VED) are capable of learning and accurately reproducing convective processes in an aquaplanet superparameterized climate model simulation, while enabling interpretability and better understanding of sub-grid-scale physical processes. A potential energy analysis of ocean surface mixed layers - Brandon Reichlhttps://m2lines.github.io/news/2207reichl/Tue, 05 Jul 2022 09:29:16 +1000https://m2lines.github.io/news/2207reichl/Analysis of the ocean mixed layer requires one to estimate its vertical extent, for which there are various definitions. In this paper , Brandon Reichl and co-authors, including Alistair Adcroft, show that using the potential energy anomaly to identify the mixed layer depth is practical and it offers conceptual benefits by directly linking to the physics of upper ocean vertical mixing. New preprint led by Adam Subel on transfer learninghttps://m2lines.github.io/news/2207subel/Tue, 05 Jul 2022 09:29:16 +1000https://m2lines.github.io/news/2207subel/Adam Subel is the lead author in this article , which builds a framework to understand and implement transfer learning, a powerful machine learning technique for enabling generalization between datasets from different configurations of a system or of varying quality/quantity, in multiscale physical systems. Subseasonal Earth System Prediction with CESM2 - Judith Bernerhttps://m2lines.github.io/news/2207berner/Tue, 05 Jul 2022 09:29:16 +1000https://m2lines.github.io/news/2207berner/Judith Berner is a co-author in this article , which describes the design and prediction skill of two subseasonal prediction systems based on two configurations of the Community Earth System Model, version 2 (CESM2): with the Community Atmosphere Model and with Whole Atmosphere Community Climate Model as its atmospheric component. Both systems are compared to NOAA CFSv2 and ECMWF. Deep Learning for Subgrid-Scale Turbulence Modeling in LES of the Convective Atmospheric Boundary Layerhttps://m2lines.github.io/news/gentine-abernathey/Tue, 07 Jun 2022 09:29:16 +1000https://m2lines.github.io/news/gentine-abernathey/This article , co-authored by Pierre Gentine and Ryan Abernathey, replaces the typically used physically based assumptions with deep learning in subgrid-scale modeling, in large eddy simulations of turbulence. They show that the latter performs better in a priori (offline) tests of atmospheric turbulence. The deep neural networks model also captures key statistics of turbulence in a posteriori (online) tests. Lorenzo Zampieri - A machine learning correction modelhttps://m2lines.github.io/news/zampieri2022/Tue, 07 Jun 2022 09:29:16 +1000https://m2lines.github.io/news/zampieri2022/Lorenzo Zampieri et al. illustrate in this preprint , a novel method based on ML for reducing the surface temperature errors of atmospheric reanalyses in sea-ice-covered regions of the Arctic. The corrected temperature can be employed to support polar research activities, and to better simulate the evolution of the interacting sea ice and ocean systems in numerical models. Will Gregory - Network connectivity articlehttps://m2lines.github.io/news/gregory2022/Tue, 07 Jun 2022 09:29:16 +1000https://m2lines.github.io/news/gregory2022/Will Gregory and co-authors hypothesized in this publication that regional biases in sea ice thickness may hinder the ability of CMIP6 models to reproduce an important atmospheric driver of summer sea ice variability, and hence their ability to make reliable sea ice predictions on seasonal to inter-annual time scales. 2022 CESM workshop: Info and submission deadlinehttps://m2lines.github.io/news/cesmworkshop2022/Mon, 02 May 2022 09:29:16 +1000https://m2lines.github.io/news/cesmworkshop2022/Registration is open for the 2022 CESM Workshop on June 13-16, 2022. Please visit the website to register as well as submit working group session talks and /or posters. The deadline to submit a talk / poster is Monday, May 16th. The full workshop agenda can be found here . diff --git a/news/newsletters/index.html b/news/newsletters/index.html index c3cc3c11..4cffbc39 100644 --- a/news/newsletters/index.html +++ b/news/newsletters/index.html @@ -9,6 +9,6 @@

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    • This project is supported by Schmidt Futures, a philanthropic initiative founded by Eric and Wendy Schmidt that bets early on exceptional people making the world better, particularly through innovative breakthroughs in science and technology.

    Links to our past newsletters are below.

    2024

    2023

    2022

    2021

    • This project is supported by Schmidt Futures, a philanthropic initiative founded by Eric and Wendy Schmidt that bets early on exceptional people making the world better, particularly through innovative breakthroughs in science and technology.
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    Publications

    Research work and relevant papers by our team

    M²LInES research publications

    If you are interested in understanding how M²LInES is using machine learning to improve climate models, we have developed an educational JupyterBook Learning Machine Learning for Climate modeling with Lorenz 96 -. This JupyterBook describes the key research themes in M²LInES, through the use of a simple climate model and machine learning algorithms. You can run the notebooks yourself, contribute to the development of the JupyterBook or let us know what you think on GitHub https://github.com/m2lines/L96_demo.

    2024

    Adam Subel, Laure Zanna
    Building Ocean Climate Emulators
    ICLR 2024 Workshop: Tackling Climate Change with Machine Learning. DOI: 10.48550/arXiv.2402.04342

    William Gregory, Mitchell Bushuk, Yongfei Zhang, Alistair Adcroft, Laure Zanna
    Machine Learning for Online Sea Ice Bias Correction Within Global Ice-Ocean Simulations
    Geophysical Research Letters 2024. DOI: 10.1029/2023GL106776

    2023

    Pavel Perezhogin, Cheng Zhang, Alistair Adcroft, Carlos Fernandez-Granda, Laure Zanna
    Implementation of a data-driven equation-discovery mesoscale parameterization into an ocean mode
    James 2023. DOI: 10.48550/arXiv.2311.02517

    Will Chapman and Judith Berner
    Benefits of Deterministic and Stochastic Tendency Adjustments in a Climate Model
    ArXiv 2023. DOI: 10.48550/arXiv.2308.15295

    Christian Pedersen, Laure Zanna, Joan Bruna, Pavel Perezhogin
    Reliable coarse-grained turbulent simulations through combined offline learning and neural emulation
    ICML 2023 Workshop on Synergy of Scientific and Machine Learning Modeling DOI: 10.48550/arXiv.2307.13144

    Emily Newsom, Laure Zanna, Jonathan Gregory
    Background Pycnocline depth constrains Future Ocean Heat Uptake Efficiency
    AGU Geophysical Research Letters 2023. DOI: 10.1029/2023GL105673

    Fabrizio Falasca, Pavel Perezhogin, Laure Zanna
    A data-driven framework for dimensionality reduction and causal inference in climate fields
    ArXiv 2023. DOI: 10.48550/arXiv.2306.14433

    Sara Shamekh and Pierre Gentine
    Learning Atmospheric Boundary Layer Turbulence
    JAMES 2023. DOI: 10.22541/essoar.168748456.60017486/v1

    Aakash Sane, Brandon G. Reichl, Alistair Adcroft, Laure Zanna
    Parameterizing vertical mixing coefficients in the Ocean -Surface Boundary Layer using Neural Networks
    JAMES 2023. DOI: 10.1029/2023MS003890

    Karan Jakhar, Yifei Guan, Rambod Mojgani, Ashesh Chattopadhyay, Pedram Hassanzadeh, Laure Zanna
    Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challenges.
    ESS Open Archive. 2023. DOI: 10.22541/essoar.168677212.21341231/v1

    Gustau Camps-Valls, Andreas Gerhardus, Urmi Ninad, Gherardo Varando, Georg Martius,
    Emili Balaguer-Ballester, Ricardo Vinuesa, Emiliano Diaz, Laure Zanna, Jakob Runge
    Discovering Causal Relations and Equations from Data.
    Physics Reports 2023. DOI: 10.1016/j.physrep.2023.10.005

    Rei Chemke and Janni Yuval
    Human-induced weakening of the Northern Hemisphere tropical circulation
    Nature. 2023. DOI: 10.1038/s41586-023-05903-1

    William Gregory, Mitchell Bushuk, Alistair Adcroft, Yongfei Zhang, Laure Zanna
    Deep learning of systematic sea ice model errors from data assimilation increments
    JAMES 2023. DOI: 10.1029/2023MS003757

    Janni Yuval and Paul A. O’Gorman
    Neural-Network Parameterization of Subgrid Momentum Transport in the Atmosphere
    JAMES 2023. DOI: 10.1029/2023MS003606

    Karl Otness, Laure Zanna, Joan Bruna
    Data-driven multiscale modeling of subgrid parameterizations in climate models
    arXiv:2303.17496. Preprint submitted to ICLR Workshop on Climate Change AI. 2023. DOI: 10.48550/arXiv.2303.17496

    Fabrizio Falasca, Andrew Brettin, Laure Zanna, Stephen M. Griffies, Jianjun Yin, Ming Zhao
    Exploring the non-stationarity of coastal sea level probability distributions
    arxiv.org:2211.04608. Preprint submitted to EDS. 2023. DOI: 10.48550/arXiv.2211.04608

    Pavel Perezhogin, Laure Zanna, Carlos Fernandez-Granda
    Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model
    JAMES. 2023. DOI: 10.1029/2023MS003681

    Pavel Perezhogin, Andrey Glazunov
    Subgrid Parameterizations of Ocean Mesoscale Eddies Based on Germano Decomposition
    JAMES. 2023. DOI: 10.1029/2023MS003771

    Andrew Ross, Ziwei Li, Pavel Perezhogin, Carlos Fernandez-Granda, Laure Zanna
    Benchmarking of machine learning ocean subgrid parameterizations in an idealized model
    JAMES. 2023. DOI: 10.1029/2022MS003258

    Cheng Zhang, Pavel Perezhogin, Cem Gultekin, Alistair Adcroft, Carlos Fernandez-Granda, Laure Zanna
    Implementation and Evaluation of a Machine Learned Mesoscale Eddy Parameterization into a
    Numerical Ocean Circulation Model
    JAMES 2023. DOI: 10.1029/2023MS003697

    Qiyu Xiao, Dhruv Balwada, C. Spencer Jones, Mario Herrero-Gonzalez, K. Shafer Smith, Ryan Abernathey
    Reconstruction of Surface Kinematics from Sea Surface Height Using Neural Networks
    JAMES. 2023. DOI: 10.1029/2022MS003258

    Takaya Uchida, Dhruv Balwada, Quentin Jamet, William K. Dewar, Bruno Deremble,
    Thierry Penduff, Julien Le Sommer
    Cautionary tales from the mesoscale eddy transport tensor
    ScienceDirect 2023. DOI: 10.1016/j.ocemod.2023.102172

    Adam Subel, Yifei Guan, Ashesh Chattopadhyay, Pedram Hassanzadeh
    Explaining the physics of transfer learning in data-driven turbulence modeling
    PNAS NEXUS 2023. DOI: 10.1093/pnasnexus/pgad015

    Lorenzo Zampieri, Gabriele Arduini, Marika Holland, Sarah Keeley, Kristian S. Mogensen,
    Matthew D. Shupe, Steffen Tietsche
    A Machine Learning Correction Model of the Winter Clear-Sky Temperature Bias over the Arctic Sea Ice in Atmospheric Reanalyses
    AMS Journals, Monthy Weather Review: volume151, issue6 DOI: 10.1175/MWR-D-22-0130.1


    2022

    Joan Bruna, Benjamin Peherstorfer, Eric Vanden-Eijnden
    Neural Galerkin Scheme with Active Learning for High-Dimensional Evolution Equations
    Journal of Computational Physics DOI: 10.1016/j.jcp.2023.112588

    Peidong Wang, Janni Yuval, Paul A. O'Gorman
    Non-local parameterization of atmospheric subgrid processes with neural networks
    JAMES 2022. DOI: 10.1029/2022MS002984

    Sara Shamekh, Kara D Lamb, Yu Huang, Pierre Gentine
    Implicit learning of convective organization explains precipitation stochasticity
    In review. 2022. DOI: 10.1002/essoar.10512517.1

    Hannah Christensen and Laure Zanna
    Parametrization in Weather and Climate Models
    Oxford Research Encyclopedia of Climate Science. 2022. DOI: 10.1093/acrefore/9780190228620.013.826

    Sheng Liu, Aakash Kaku, Haoxiang Huang, Laure Zanna, Weicheng Zhu, Narges Razavian,
    Matan Leibovich, Sreyas Mohan, Boyang Yu, Jonathan Niles-Weed, Carlos Fernandez-Granda
    Deep Probability Estimation
    Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13746-13781, 2022.
    DOI: 10.48550/arXiv.2111.10734

    Nora Loose, Ryan Abernathey, Ian Grooms, Julius Busecke, Arthur Guillaumin,
    Elizabeth Yankovsky, Gustavo Marques, Jacob Steinberg, Andrew Slavin Ross, Hemant Khatri,
    Scott Bachman, Laure Zanna, Paige Martin
    GCM-Filters: A Python Package for Diffusion-based Spatial Filtering of Gridded Data
    Journal of Open Source Software 7(70), p.3947. 2022. DOI: 10.21105/joss.03947

    Hugo Frezat, Julien Le Sommer, Ronan Fablet, Guillaume Balarac, Redouane Lguensat
    A posteriori learning for quasi-geostrophic turbulence parametrization
    JAMES. 2022. DOI: 10.1029/2022MS003124

    Lei Chen and Joan Bruna
    On Gradient Descent Convergence beyond the Edge of Stability
    ArXiv 2022 DOI: 10.48550/arXiv.2206.04172

    Mohamed Aziz Bhouri and Pierre Gentine
    History-Based, Bayesian, Closure for Stochastic Parameterization: Application to Lorenz '96
    ArXiv 2022. DOI: 10.48550/arXiv.2210.14488

    Publications Archive

    2021 -

    • This project is supported by Schmidt Futures, a philanthropic initiative founded by Eric and Wendy Schmidt that bets early on exceptional people making the world better, particularly through innovative breakthroughs in science and technology.

    Publications Archive

    2021 -

    • This project is supported by Schmidt Futures, a philanthropic initiative founded by Eric and Wendy Schmidt that bets early on exceptional people making the world better, particularly through innovative breakthroughs in science and technology.
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