Climate modeling in the era of AI | Laure Zanna | 9am PT Tues, Oct 28, 2025

[Grigory Bronevetsky]

Modeling Talks

Climate modeling in the era of AI Laure Zanna, NYU Tues, October 28, 2025 | 9am PT Meet | Youtube Stream Hi all, The presentation will be via Meet and all questions will be addressed there. If you cannot attend live, the event will be recorded and can be found afterward at https://sites.google.com/modelingtalks.org/entry/climate-modeling-in-the-era-of-ai More information on previous and future talks: https://sites.google.com/modelingtalks.org/entry/home Abstract: While AI has been disrupting conventional weather forecasting, we are only beginning to witness the impact of AI on long-term climate simulations. The fidelity and reliability of climate models has been limited by computing capabilities. These limitations lead to inaccurate representations of key processes such as convection, cloud, or mixing or restrict the ensemble size of climate predictions. Therefore, these issues are a significant hurdle in enhancing climate simulations and their predictions. Here, I will discuss a new generation of climate models with AI representations of unresolved ocean physics, learned from high-fidelity simulations, and their impact on reducing biases in climate simulations. The simulations are performed with operational ocean model components. I will further demonstrate the potential of AI to accelerate climate predictions and increase their reliability through the generation of fully AI-driven emulators, which can reproduce decades of climate model output in seconds with high accuracy. Bio: Laure Zanna is a physical oceanographer and climate physicist in the Department of Mathematics at the Courant Institute and the Center for Data Science, NYU. She holds the Joseph B. Keller and Herbert B. Keller Professorship in Applied Mathematics. Her research focuses on understanding, simulating and predicting the role of the ocean in climate on local and global scales. She combines theory, numerical simulations, statistics, and machine learning to tackle a wide range of problems in fluid dynamics and climate, including turbulence, multiscale modeling, ocean heat and carbon uptake, and sea level. Since 2020, she is leading M²LInES, an international collaboration sponsored by Schmidt Sciences dedicated to improving climate models using scientific machine learning. In 2020, Prof Zanna received the Nicholas P. Fofonoff Award from the American Meteorological Society “for exceptional creativity in the development and application of new concepts in ocean and climate dynamics”, and was the 2022 WHOI Geophysical Fluid Dynamics principal lecturer.

[Grigory Bronevetsky]

Video Recording: https://youtube.com/live/DNQY0Bsr6B4

Slides: https://drive.google.com/file/d/1PA8UjyY8kqcfwzDBxlWiS_1p_ONdTOMz/view?usp=sharing

Summary:

• Focus: global ocean simulation using AI methods

• AI weather/climate modeling is growing rapidly

• AI models provide high skill and performance

• The ocean is a critical system that evolves a lot more slowly than the atmosphere

• 70% of surface

• 4km deep

• Top 3m contains as much heat as whole atmosphere (heat battery)

• Top 50 m contains as much carbon as atmosphere

• Large currents induce atmospheric currents

• Scales span from minutes to decades

• Traditional modeling approach

• Newton’s laws of motion

• GFDL CM 2.6 Ocean simulation

• Very expensive, can resolve down to 10km

• Critical for prediction and evaluation of counter-factuals

• Latest models

• OM4 - Ocean+Sea ice: 12 years per day for 25km resolution on 4671 cores

• OM4 - Ocean+Atmosphere+land+Sea ice: 16 years per day for 25km resolution on 5535 cores

• We want

• Good/accurate simulations so you learn new insights

• Fast enough to enable exploration

• Generalize/are robust to new scenarios

• Simulations are divided into grid and sub-grid models

• Space broken up into a discrete grid

• Coarser phenomena simulated directly on the grid

• Finer-grained phenomena are modeled using approximate models that don’t directly capture the physics but are fast

• Empirical approximations

• Have free parameters that must be tuned to reduce prediction error

• Driven by external forcings (e.g. wind, radiation, CO2 concentrations)

• Simulations are imprecise and predictions differ from observations in many details

• Example: Gulf stream is too warm because its transport tends to be too slow

• Idea: use ML to learn simulation components instead of tunes empirical approximations

• Train a neural network to model sub-grid dynamics

• Loss: error in predicting the full time series of the climate system

• Not enough physical observation data, especially in the ocean

• As such, we use outputs of high-resolution numerical models as the training set

• Key idea: neural networks work much better if the data is normalized to fit into a small range

• Normalization is dynamic relative to the surrounding state of the climate

• First run a base coarse model to establish normal range of values in a given context

• Then use that to normalize the values for the neural model

• Resulting model is more accurate than case numerical simulation

• Above uses AI for sub-grid models. Can we create neural surrogates of the entire simulation?

• Samudra

• Trained on ocean time series of GDFL OM4, using historical atmospheric forcings

• 25km, 60 years

• Coarse grained to 100km, 5 day averages, 19 vertical levels

• Input: current state and time derivative

• Output: next 2 time steps

• Architecture: Autoregressive emulator, ConvNext Unet, 135M parameters

• Other architectural choices don’t make much of a difference

• Resulting model captures the spatial and temporal of the OM4 simulation

• Computational cost is orders of magnitude lower than numerical simulation

• Ongoing work: SamudrACE considering training procedure for ocean and air models

• https://allenai.org/blog/samudrace

• Previously: used ocean forcings for atmospheric model and atmospheric forcings for ocean model

• Improved to train ocean and atmosphere models separately while explicitly coupling them

• Result is 

• Separate models for ocean and atmosphere models that are coupled together

• More stable than training a unified ocean/atmosphere model because of the very different time scales of the two systems

• Some ocean trends are still poorly represented by the SamudrACE model over long time period

• Tradeoff between stability of model dynamics and sensitivity to perturbation (capturing real variability)

Access at: https://m2lines.github.io