43 citations · 271 across the 28 of their papers we have counts for
30 papers
Multi-model ocean oxygen fields predicted by conditional diffusion models
Linus Vogt, Laure Zanna
Dissolved oxygen is important for the ocean's ecosystems and biogeochemical cycles. Yet, Earth System Models (ESMs) vary in their simulations of the present-day and future ocean ox…
ClimateBench v2.0: Probabilistic Climate Model Benchmarking
Duncan Watson-Parris, Willa Tobin, Aytaç Paçal +32
We present ClimateBench v2, a standardized protocol for evaluating climate models on diagnostics expected to be informative for their skill in projecting mid-century regional tempe…
Paleoclimate Boundary Conditions as an Out-of-Sample Test for the Forced Response of Ocean Climate Emulators
Adam Subel, Laure Zanna
AI weather emulators benefit from clear objectives and metrics, which have led to the rapid development of models that outperform traditional benchmarks. In contrast, long-term cli…
Samudra 2: Scaling Ocean Emulators across Resolutions
Yuan Yuan, Jesse Rusak, Alexander Merose +5
Ocean general circulation models (OGCMs) are essential to climate science but computationally expensive, limiting ensemble size and forcing scenarios. Neural emulators promise orde…
Towards bridging the gap between data-driven and theoretical turbulence closures in stratified flows
Laure Zanna, Pavel Perezhogin
Turbulence closure models are essential for solving the equations of motion in realistic systems, where fully resolving all relevant scales of motion is computationally infeasible.…
Agents' Last Exam
Yiyou Sun, Xinyang Han, Weichen Zhang +378
Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional d…
Calibration of a neural network ocean closure for improved mean state and variability
Pavel Perezhogin, Alistair Adcroft, Laure Zanna
Global ocean models exhibit biases in the mean state and variability, particularly at coarse resolution, where mesoscale eddies are unresolved. To address these biases, parameteriz…
FloeNet: A mass-conserving global sea ice emulator that generalizes across climates
William Gregory, Mitchell Bushuk, James Duncan +8
We introduce FloeNet, a machine-learning emulator trained on the Geophysical Fluid Dynamics Laboratory global sea ice model, SIS2. FloeNet is a mass-conserving model, emulating 6-h…
Physics constraints and response validation in discrete-time reduced-order modeling: from idealized turbulent systems to climate dynamics
Fabrizio Falasca, Laure Zanna
A central challenge across science and engineering is to build data-driven reduced-order models of turbulent dynamical systems that reproduce stationary statistics, predict respons…
A fluctuation-dissipation theorem perspective on radiative responses to temperature perturbations
Fabrizio Falasca, Aurora Basinski-Ferris, Laure Zanna +1
Radiative forcing drives warming in the Earth system, leading to changes in sea surface temperatures (SSTs) and associated radiative feedbacks. The link between changes in the top-…
Transfer Learning for Emulating Ocean Climate Variability across forcing
Surya Dheeshjith, Adam Subel, Shubham Gupta +4
With the success of machine learning (ML) applied to climate reaching further every day, emulators have begun to show promise not only for weather but for multi-year time scales in…
Building Ocean Climate Emulators
Adam Subel, Laure Zanna
The current explosion in machine learning for climate has led to skilled, computationally cheap emulators for the atmosphere. However, the research for ocean emulators remains nasc…
A Data-Driven Approach for Parameterizing Submesoscale Vertical Buoyancy Fluxes in the Ocean Mixed Layer
Abigail Bodner, Dhruv Balwada, Laure Zanna
Parameterizations of O(1-10)km submesoscale flows in General Circulation Models (GCMs) represent the effects of unresolved vertical buoyancy fluxes in the ocean mixed layer. These…
A stable implementation of a data-driven scale-aware mesoscale parameterization
Pavel Perezhogin, Cheng Zhang, Alistair Adcroft +2
Ocean mesoscale eddies are often poorly represented in climate models, and therefore, their effects on the large scale circulation must be parameterized. Traditional parameterizati…
Machine learning for online sea ice bias correction within global ice-ocean simulations
William Gregory, Mitchell Bushuk, Yongfei Zhang +2
In this study we perform online sea ice bias correction within a GFDL global ice-ocean model. For this, we use a convolutional neural network (CNN) which was developed in a previou…
Reliable coarse-grained turbulent simulations through combined offline learning and neural emulation
Christian Pedersen, Laure Zanna, Joan Bruna +1
Integration of machine learning (ML) models of unresolved dynamics into numerical simulations of fluid dynamics has been demonstrated to improve the accuracy of coarse resolution s…
Background Pycnocline depth constrains Future Ocean Heat Uptake Efficiency
Emily Newsom, Laure Zanna, Jonathan Gregory
The Ocean Heat Uptake Efficiency (OHUE) quantifies the ocean's ability to mitigate surface warming through deep heat sequestration. Despite its importance, the main controls on OHU…
A data-driven framework for dimensionality reduction and causal inference in climate fields
Fabrizio Falasca, Pavel Perezhogin, Laure Zanna
We propose a data-driven framework to simplify the description of spatiotemporal climate variability into few entities and their causal linkages. Given a high-dimensional climate f…
Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer using Neural Networks
Aakash Sane, Brandon G. Reichl, Alistair Adcroft +1
Vertical mixing parameterizations in ocean models are formulated on the basis of the physical principles that govern turbulent mixing. However, many parameterizations include ad ho…
ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation
Sungduk Yu, Zeyuan Hu, Akshay Subramaniam +44
Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing critical processes like thunderst…
Discovering Causal Relations and Equations from Data
Gustau Camps-Valls, Andreas Gerhardus, Urmi Ninad +7
Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the p…
Deep learning of systematic sea ice model errors from data assimilation increments
William Gregory, Mitchell Bushuk, Alistair Adcroft +2
Data assimilation is often viewed as a framework for correcting short-term error growth in dynamical climate model forecasts. When viewed on the time scales of climate however, the…
Implementation and Evaluation of a Machine Learned Mesoscale Eddy Parameterization into a Numerical Ocean Circulation Model
Cheng Zhang, Pavel Perezhogin, Cem Gultekin +3
We address the question of how to use a machine learned parameterization in a general circulation model, and assess its performance both computationally and physically. We take one…
Data-driven multiscale modeling for correcting dynamical systems
Karl Otness, Laure Zanna, Joan Bruna
We propose a multiscale approach for predicting quantities in dynamical systems which is explicitly structured to extract information in both fine-to-coarse and coarse-to-fine dire…