43 citations · 271 across the 28 of their papers we have counts for
21 papers · 1 filter
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…
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…
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…
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…
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…
Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model
Pavel Perezhogin, Laure Zanna, Carlos Fernandez-Granda
Subgrid parameterizations of mesoscale eddies continue to be in demand for climate simulations. These subgrid parameterizations can be powerfully designed using physics and/or data…
Exploring the non-stationarity of coastal sea level probability distributions
Fabrizio Falasca, Andrew Brettin, Laure Zanna +3
Studies agree on a significant global mean sea level rise in the 20th century and its recent 21st century acceleration in the satellite record. At regional scale, the evolution of…
Oceanic stochastic parametrizations in a seasonal forecast system
M. Andrejczuk, F. C. Cooper, S. Juricke +3
We study the impact of three stochastic parametrizations in the ocean component of a coupled model, on forecast reliability over seasonal timescales. The relative impacts of these…
Optimisation of an idealised ocean model, stochastic parameterisation of sub-grid eddies
Fenwick C. Cooper, Laure Zanna
An optimisation scheme is developed to accurately represent the sub-grid scale forcing of a high dimensional chaotic ocean system. Using a simple parameterisation scheme, the veloc…