Primary Convener:
Nan Chen, University of Wisconsin-Madison

Convener:
Di Qi, Purdue University
Daniel Schertzer, Ecole Nationale des Ponts et Chaussées
Anastasios Tsonis, University of Wisconsin-Milwaukee
Shaun Lovejoy, McGill Univ
George Sugihara, Scripps Institution of Oceanography, University of California San Diego
Rui Perdigão, Interuniversity Institute for Intelligence, Complexity and Security (I3CS)
Julia Hall, Synergistic Manifolds

Early Career Convener:
Charlotte Moser, University of Wisconsin Madison

Chair:
Nan Chen, University of Wisconsin-Madison
Daniel Schertzer, Ecole Nationale des Ponts et Chaussées
Di Qi, Purdue University
Anastasios Tsonis, University of Wisconsin-Milwaukee
Rui Perdigão, Interuniversity Institute for Intelligence, Complexity and Security (I3CS)
George Sugihara, Scripps Institution of Oceanography, University of California San Diego

Nonlinear phenomena in complex multiscale turbulent dynamic systems are ubiquitous in geoscience. Modeling and forecasting these complex systems are especially challenging due to the intermittent energy transfer between unresolved subscales induced by nonlinear effects and the occurrence of extreme events. This session aims to integrate novel applied math tools with geophysical systems. The main themes will include, but not be limited to, stochastic and statistical modeling, machine learning theory and algorithms, understanding intermittency, predicting rare and extreme events, data-driven algorithms, multiscale analysis, data assimilation, and uncertainty quantification. This session also focuses on recent advances in nonlinear techniques (e.g., scaling, (multi-) fractals, complex networks, tipping points and new computational techniques) to uncover the underlying, non-trivial symmetries, information transfer across scales, causal effects and driving forces as well as their applications to data analysis and modelling of these complex systems as well as their predictability.

Index Terms
3238 Prediction
4415 Cascades
4430 Complex systems
4475 Scaling: spatial and temporal

Suggested Itineraries:
Disasters‚ Calamities and Extreme Events
Machine Learning and AI

Cross-Listed:
IN - Informatics
A - Atmospheric Sciences
GH - GeoHealth
H - Hydrology

Co-Sponsored Sessions:
JpGU： Japan Geoscience Union
EGU： European Geosciences Union
AOGS： Asia Oceania Geosciences Society

Neighborhoods:
1. Science Nexus