L96 History Matching Notebooks
Jupyter notebooks for Lorenz96 experiments.
Jupyter notebooks for Lorenz96 experiments.
Organization of the Hackathon
A monthly journal club organized in collaboration with ANR OceaniX
HRMES Mid-Term Workshop, 29 January 2021
Bio-sketch and bibliography: Anna Sommer
Bio-sketch and bibliography: Julie Deshayes
Bio-sketch and bibliography: Redouane Lguensat
Bio-sketch and bibliography: V. Balaji
Project of Anna Sommer, MOPGA Postdoc 09/05/2019-31/12/2019, CEA/LSCE/LOCEAN
Project of Redouane Lguensat, MOPGA Postdoc 16 March 2020 – present, CEA/LSCE/LOCEAN
Collaboration between Julie Deshayes (LOCEAN), Martial Mancip (Maison de la Simulation), Redouane Lguensat (IPSL), Nathan Cassereau (IDRIS) and Guillaume Gachon (ENS Lyon)
Collaboration between Redouane Lguensat (CEA/LSCE/LOCEAN) and Maike Sonnewald (GFDL/Princeton University)
Published in Comptes Rendus Géosciences, 2020
We live, it is said, in the age of data science. We show here some aspects of the evolution of simulation and data technologies and its important stakes for Earth system sciences.
Published in RSTA, 2021
Since the dawn of the computer era, we have made tremendous progress in our ability to understand and simulate the Earth system. This has been made possible by accumulation of detail, in resolution and complexity. As computing reaches certain physical limits, we are revisiting this approach using machine learning. Learning algorithms may let us derive simpler models that emulate complex ones, and deepen our understanding of the Earth system.
Published in JAMES, 2021
The North Atlantic ocean is key to climate through its role in heat transport and storage. Climate models suggest that the circulation is weakening but the physical drivers of this change are poorly constrained. Here, the root mechanisms are revealed with the explicitly transparent machine learning (ML) method Tracking global Heating with Ocean Regimes (THOR). Addressing the fundamental question of the existence of dynamical coherent regions, THOR identifies these and their link to distinct currents and mechanisms such as the formation regions of deep water masses, and the location of the Gulf Stream and North Atlantic Current. Beyond a black box approach, THOR is engineered to elucidate its source of predictive skill rooted in physical understanding. A labeled data set is engineered using an explicitly interpretable equation transform and k-means application to model data, allowing theoretical inference. A multilayer perceptron is then trained, explaining its skill using a combination of layerwise relevance propagation and theory. With abrupt CO2 quadrupling, the circulation weakens due to a shift in deep water formation regions, a northward shift of the Gulf Stream and an eastward shift in the North Atlantic Current. If CO2 is increased 1% yearly, similar but weaker patterns emerge influenced by natural variability. THOR is scalable and applicable to a range of models using only the ocean depth, dynamic sea level and wind stress, and could accelerate the analysis and dissemination of climate model data. THOR constitutes a step toward trustworthy ML called for within oceanography and beyond, as its predictions are physically tractable. Revealing the Impact of Global Heating on North Atlantic Circulation Using Transparent Machine Learning
Published in arXiv, 2022
The trustworthiness of neural networks is often challenged because they lack the ability to express uncertainty and explain their skill. This can be problematic given the increasing use of neural networks in high stakes decision-making such as in climate change applications. We address both issues by successfully implementing a Bayesian Neural Network (BNN), where parameters are distributions rather than deterministic, and applying novel implementations of explainable AI (XAI) techniques. The uncertainty analysis from the BNN provides a comprehensive overview of the prediction more suited to practitioners’ needs than predictions from a classical neural network. Using a BNN means we can calculate the entropy (i.e. uncertainty) of the predictions and determine if the probability of an outcome is statistically significant. To enhance trustworthiness, we also spatially apply the two XAI techniques of Layer-wise Relevance Propagation (LRP) and SHapley Additive exPlanation (SHAP) values. These XAI methods reveal the extent to which the BNN is suitable and/or trustworthy. Using two techniques gives a more holistic view of BNN skill and its uncertainty, as LRP considers neural network parameters, whereas SHAP considers changes to outputs. We verify these techniques using comparison with intuition from physical theory. The differences in explanation identify potential areas where new physical theory guided studies are needed.
Published in arXiv, 2022
The objective of this study is to evaluate the potential for History Matching (HM) to tune a climate system with multi-scale dynamics. By considering a toy climate model, namely, the two-scale Lorenz96 model and producing experiments in perfect-model setting, we explore in detail how several built-in choices need to be carefully tested. We also demonstrate the importance of introducing physical expertise in the range of parameters, a priori to running HM. Finally we revisit a classical procedure in climate model tuning, that consists of tuning the slow and fast components separately. By doing so in the Lorenz96 model, we illustrate the non-uniqueness of plausible parameters and highlight the specificity of metrics emerging from the coupling. This paper contributes also to bridging the communities of uncertainty quantification, machine learning and climate modeling, by making connections between the terms used by each community for the same concept and presenting promising collaboration avenues that would benefit climate modeling research.
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Invited lecture at the 42nd ORAP Forum, AI for HPC and HPC for AI
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An overview of 5 years of activity of the WGCM Infrastructure Panel.
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Invited lecture to present future evolution of activity of the WGCM Infrastructure Panel.
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Keynote presentation, EGU AS1.5/CL5.05/ESSI1.2/NP1.4/OS4.20.
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Invited lecture at the European Centre for Medium-Range Weather Forecasting.
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Invited lecture at the European Centre for Medium-Range Weather Forecasting.
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An overview of machine learning approaches in Earth system modeling.
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Dr R Pitchai Endowment Lecture, Indian Institute of Technology, Madras
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Invited talk at the Workshop on Machine Learning for Weather and Climate Modelling
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Invited talk at the Académie des Sciences in Paris, in the colloquium Face au changement climatique, le champ des possibles.
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.