This study examines the minimum ensemble size for accurate geophysical forecasting using a method called the ensemble Kalman filter. We reformulate accuracy via observation noise-dependency to classify filter performance qualitatively. Through numerical experiments with a chaotic model, we link the minimum ensemble size for the accuracy to system's instability and propose an effective ensemble downsizing method that ensures both stability and accuracy.

Recent wildfire activity in Canada has been rising the public demand for fast, yet accurate operational air quality forecasts along with related forecast uncertainties. This study explores the potential of an efficient process-based approach to estimate forecast uncertainties of operational air quality models. Our results show its potential for understanding how uncertainties of operational forecast evolve over time and an improved use of sparse observation signals at remote wildfire regions.

The recharge oscillator model has served as a simple yet powerful toy model for modelling the El Niño–Southern Oscillation. Whereas the addition of Gaussian noise is sufficient to capture the observed overall statistical features, it fails to resolve dynamical signatures associated with major El Niño events. Here we propose to employ a conditional non-Gaussian noise model that better captures the effect of large westerly wind bursts and their effect on major warming events.

The risk of climate change triggering tipping points is a concern. While Earth System Models (ESMs) predict these points, their timing often varies. Equations of nonlinear dynamical systems can model tipping, hysteresis, and inertia, enabling comparison with ESMs. We propose a method to map tipping timing estimates onto a dynamical system that simulates a full hysteresis loop. We focus on solutions to the equation under overshoot forcing in global temperature and different inertia levels.

Deep learning has been used to discover new data assimilation methods to track chaotic dynamical systems. Strikingly, these data assimilation networks can match the accuracy of ensemble-based methods using only a single state forecast. This paper first investigates the reasons for this efficacy, and then shows that their performance in more nonlinear regimes matches that of the most accurate scalable data assimilation methods, with greater efficiency and robustness.