arXiv subjects

Charlotte Moser

Publications and source records attributed to Charlotte Moser.

5 recordsLinked to original sources

arXiv2026

Mechanisms and Pathways of Extreme Events in Partially-Observed Stochastic Dynamical Systems

Extreme events occur across the natural, engineering, and socioeconomic sciences, where rare but high-impact episodes can lead to disproportionate consequences that pose major challenges for prediction and risk management. Existing studies have mainly focused on the statistics, sampling, forecasting, and attribution of extremes from observable variables. In this paper, we develop a mathematical framework for studying the mechanisms and pathways of extreme events in partially-observed stochastic dynamical systems with hidden variables. By integrating data assimilation with information-theoretic and trajectory-based diagnostics, we infer latent precursor dynamics from observations, quantify their uncertainty, and determine how their influence propagates toward observed extreme events. Conditional Gaussian models provide a tractable analytical setting for deriving closed-form diagnostics, while the framework extends through numerical methods. The analysis proceeds from two complementary perspectives. From a trajectory-wise viewpoint, we compare filtering and smoothing distributions to identify the onset of hidden precursors and quantify temporal influence. From a statistical viewpoint, we construct event-conditioned hidden-state distributions to identify sensitive triggering directions, latent pathways, and multiple classes of extreme-event mechanisms through clustering. Three numerical examples illustrate the methodology. In an intermittent stochastic model, hidden damping dynamics emerge before observed bursts, where discrepancies between the filter and smoother provide an onset diagnostic. In a stochastic model with damping and forcing, separate damping-induced, forcing-driven, and mixed pathways to extremes are identified. In a nonlinear topographic-flow model, distinct mechanisms and pathways for blocking and unblocking patterns associated with observed extreme events are revealed.

math.DS↗

arXiv2025

Bridging Idealized and Operational Models: An Explainable AI Framework for Earth System Emulators

Computer models are indispensable tools for understanding the Earth system. While high-resolution operational models have achieved many successes, they exhibit persistent biases, particularly in simulating extreme events and statistical distributions. In contrast, coarse-grained idealized models isolate fundamental processes and can be precisely calibrated to excel in characterizing specific dynamical and statistical features. However, different models remain siloed by disciplinary boundaries. By leveraging the complementary strengths of models of varying complexity, we develop an explainable AI framework for Earth system emulators. It bridges the model hierarchy through a reconfigured latent data assimilation technique, uniquely suited to exploit the sparse output from the idealized models. The resulting bridging model inherits the high resolution and comprehensive variables of operational models while achieving global accuracy enhancements through targeted improvements from idealized models. Crucially, the mechanism of AI provides a clear rationale for these advancements, moving beyond black-box correction to physically insightful understanding in a computationally efficient framework that enables effective physics-assisted digital twins and uncertainty quantification. We demonstrate its power by significantly correcting biases in CMIP6 simulations of El Ni\~no spatiotemporal patterns, leveraging statistically accurate idealized models. This work also highlights the importance of pushing idealized model development and advancing communication between modeling communities.

cs.LG↗

arXiv2025

A Mathematical Framework for Quantifying Nonlinear Uncertainty Propagation in Eddy Identification Criteria

Ocean eddies are swirling mesoscale features that play a fundamental role in oceanic transport and mixing. Eddy identification relies on diagnostic criteria that are inherently nonlinear functions of the flow variables. However, estimating the ocean flow field is subject to uncertainty due to its turbulent nature and the use of sparse and noisy observations. This uncertainty interacts with nonlinear diagnostics, complicating its quantification and limiting the accuracy of eddy identification. In this paper, an analytically tractable mathematical and computational framework for studying eddy identification is developed. It aims to address how uncertainty interacts with the nonlinearity in the eddy diagnostics and how the uncertainty in the eddy diagnostics is reduced when additional information from observations is incorporated. The framework employs a simple stochastic model for the flow field that mimics turbulent dynamics, allowing closed-form solutions for assessing uncertainty in eddy statistics. It also leverages a nonlinear, yet analytically tractable, data assimilation scheme to incorporate observations, facilitating the study of uncertainty reduction in eddy identification, which is quantified rigorously using information theory. Applied to the Okubo-Weiss (OW) parameter, a widely used eddy diagnostic criterion, the framework leads to three key results. First, closed formulae reveal inhomogeneous spatial patterns in the OW uncertainty despite homogeneous flow field uncertainty. Second, it shows a close link between local minima of the OW expectation (eddy centers) and local maxima of its uncertainty. Third, it reveals a practical information barrier: the reduction in uncertainty in diagnostics asymptotically saturates, limiting the benefit of additional observations.

physics.geo-ph↗

arXiv2025

A Simple Intermediate Coupled MJO-ENSO Model: Multiscale Interactions and ENSO Complexity

The Madden-Julian Oscillation (MJO) and the El Ni\~no-Southern Oscillation (ENSO) are two dominant modes of tropical climate variability, each with profound global weather impacts. While their individual dynamics have been widely studied, their coupled interactions, particularly in the context of ENSO complexity, including spatial diversity (Central Pacific vs. Eastern Pacific events), temporal evolution (single-year and multi-year events), and intensity variations (moderate to extreme events), have received limited attention in modeling studies. In this paper, a simple intermediate coupled MJO-ENSO model is developed to address critical gaps in understanding their bidirectional feedback and its role in modulating ENSO complexity. The model integrates multiscale processes, bridging intraseasonal (MJO), interannual (ENSO), and decadal (Walker circulation) variability. Key mechanisms include: (1) interannual SST modulating MJO through latent heat and background states, (2) MJO-induced wind forcing triggering diverse ENSO events, and (3) decadal variability modulating the strength and occurrence frequency of Eastern Pacific and Central Pacific events. Effective stochastic parameterizations are incorporated to improve the characterization of multiscale MJO-ENSO interactions and the emergence of intermittency and extremes. The model captures several crucial observed MJO and ENSO features, including non-Gaussian statistics, seasonal cycles, energy spectra, and spatial event patterns. It also reproduces critical MJO-ENSO interactions: warm pool edge extension, convective activity adjustments that modulate SST, and ENSO's dependence on MJO-driven easterly and westerly wind anomalies. The model provides a useful tool to analyze long-term variations. It also advances the understanding of ENSO extreme events and their remote impacts, as well as seasonal forecasting and climate resilience.

physics.ao-ph↗

arXiv2024

A Stochastic Conceptual Model for the Coupled ENSO and MJO

Understanding the interactions between the El Nino-Southern Oscillation (ENSO) and the Madden-Julian Oscillation (MJO) is essential to studying climate variabilities and predicting extreme weather events. Here, we develop a stochastic conceptual model for describing the coupled ENSO-MJO phenomenon. The model adopts a three-box representation of the interannual ocean component to characterize the ENSO diversity. For the intraseasonal atmospheric component, a low-order Fourier representation is used to describe the eastward propagation of the MJO. We incorporate a simple decadal process to account for modulations in the background state that influence the predominant types of El Nino events. In addition to dynamical coupling through wind forcing and latent heat, state-dependent noise is introduced to characterize the statistical interactions among these multiscale processes, improving the simulation of extreme events. The model successfully reproduces the observed non-Gaussian statistics of ENSO diversity and MJO spectra while capturing the interactions between wind, MJO, and ENSO.

physics.ao-ph↗