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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

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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.