Standard methods for decomposing geophysical data assume simple diffusion, but ocean currents, floods, and atmospheric plumes spread with long-range correlations. We developed a generalized decomposition using fractional calculus that captures these behaviors. Tests show the method needs five to eight times fewer patterns to represent the same variability. This has direct implications for flood forecasting and ocean tracer analysis.

We investigated a highly significant double-logarithmic relationship between the drought duration and frequency, which has been confirmed through both mathematical derivation and empirical data analysis.

Neural networks are limited in situations which differ from the learned conditions. We propose a solution to this out of distribution problem. We derive anomalies of trained neural networks internal parameters by retraining on subsets of the same training data. Then we relate the network-parameter sensitivities to differences in the training data subsets that caused them. Finally, we extrapolate the found relations to generate networks that perform better outside the training distribution.

Shorelines often change in rhythmic ways that cannot be explained by storms or climate cycles alone. Using global satellite observations, we show that a repeating two-year shoreline pattern emerges from interactions between seasonal wave activity and delayed shoreline adjustment. This hidden interaction transfers energy across timescales, creating previously overlooked variability, which matters for understanding and anticipating future coastal change.

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.

We developed a method to plan tiny, local nudges that reduce spikes in weather-like simulations. It was motivated by the lack of reliable ways to design small interventions in unpredictable systems. The method uses many parallel forecasts to suggest where a nudge matters, then tests and refines it with repeated full-model simulations. In two models, it reduced extremes and peak regional wind speeds with similar or smaller input, enabling safer study of intervention ideas.

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.

We studied how sandy beaches evolve by tracking the shoreline and offshore sandbars from satellites over many years. By rebuilding beach behavior directly from observations, we show that beaches follow organized but chaotic motion shaped by internal feedbacks. Beyond the seasonal rhythm imposed by waves, shorelines and sandbars exchange energy through the surf zone, producing repeated erosion and recovery cycles with limited predictability, explaining why beaches remain difficult to forecast.

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.

Unlike earlier studies that assume constant ionospheric parameters, our formulation explicitly accounts for altitude-dependent variations, offering more realistic boundary conditions for modeling ion–acoustic solitons and related nonlinear plasma phenomena. We believe this study will be of interest to the journal’s readership, particularly those working on nonlinear plasma waves, planetary ionospheres, and data-driven modeling of space environments.

Recent wildfire activity in Canada has been rising the public demand for smoke forecasts. This however comes with a challenge for national weather centers to provide fast, yet accurate forecasts of these strong unexpected events. This study explores the potential of a novel approach for operational air quality forecasting during wildfire smoke episodes. It not only enables an improved use of sparse observation signals, but also provides theoretical insights how uncertainties evolve over time.

The goal of data assimilation in geoscience is to merge information from models and observations. In the presence of structures such as ocean eddies, classic assimilation schemes fail to merge those two sources, resulting in position and shape errors. Here, we propose a new scheme which assimilates only the parameters describing a simplified eddy structure. Comparison with classic schemes shows that our approach preserves the eddy parameters and reduces the computational cost.

Causal inference methods could be used to study the interaction of climate tipping elements, which may degrade abruptly due to climate change. We compare three of these methods to determine their reliability and apply two of them to the Arctic summer sea ice and the Atlantic Meridional Overturning Circulation (AMOC). Our results imply that a weaker AMOC would stabilize Arctic summer sea ice, and that a loss of Arctic summer sea would stabilize the AMOC.