Atmosphere Ocean Science Colloquium

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Bridging Models and Data: From Traditional Assimilation to Integrating Model Hierarchies, Causal Inference, and Digital Twins

Speaker: Nan Chen, U Wisconsin - Madison

Location: Warren Weaver Hall 412

Date: Tuesday, September 16, 2025, 1 p.m.

Synopsis:

In this talk, I will present data assimilation as a crucial bridge between models and data across diverse scientific fields. I will begin with a brief review of traditional data assimilation before demonstrating its broad utility for facilitating and interacting with other areas of study and innovation. First, I will show how models of varying complexity from different communities can be integrated through data assimilation. In particular, I will illustrate how to leverage the strengths of simple conceptual models and complex operational models to create a more accurate and cohesive system, with an application to the equatorial Pacific Ocean. Second, I will introduce assimilative causal inference (ACI), a paradigm-shifting framework that uses Bayesian data assimilation to trace causes backward from observed effects, which provides a unique way to study predictability and attribution with applications in climate tipping points, model reduction, and extreme events. ACI uniquely identifies dynamic causal interactions without requiring observations of candidate causes, accommodates short datasets, and scales efficiently to high dimensions. It provides online tracking of causal roles, which may reverse intermittently, and establishes a mathematically rigorous criterion for the causal influence range, revealing how far effects propagate. Finally, I will present a nonlinear neural differential equation modeling framework that exploits generalized Koopman theory to discover a latent representation of state variables. This allows for closed-form solutions to nonlinear data assimilation and advances computationally efficient digital twins.