On February 03 2022, SpaceX launched a new group of satellites for its Starlink constellation. This launch simultaneously released 49 satellites into orbits between 200 km and 250 km height. The launches occurred during a geomagnetic storm that was followed by a second storm. There was an immediate loss of 32 satellites. The satellite losses may have been caused by an unusually high level of atmospheric drag (unexplained by current theory or modeling) or a high level of satellite collisions.

We explored a simplified climate model based on Earth's energy budget. One advantage of such models is that they are easier to study mathematically. Using a mathematical technique known as boundary integral methods, we present a new way to solve these climate models. This method is particularly useful for modeling climates very different from Earth's current state, such as those on other planets or during past ice ages.

Artificial intelligence (AI) has recently shown promising results in ENSO (El Niño–Southern Oscillation) forecasting, outperforming traditional models. Yet AI models deliver accurate predictions without showing the underlying mechanisms. Our study examines a specific AI model, the reservoir computer (RC). Our results show that the RC is less sensitive to initial perturbations than the traditional Zebiak–Cane (ZC) model. This reduced sensitivity can explain the RC's superior skills.

We present an alternative statistical methodology to detect whether the Atlantic Ocean’s circulation system is approaching a tipping point. Our approach separates natural variability from real early warning signals of tipping, reducing false alarms. When applied to a proxy of the Atlantic Ocean’s circulation strength, we find significant signs that the system is undergoing destabilization, suggesting that it may be approaching a tipping point, which could have major impacts on global climate patterns.

Sea ice is crucial in the climate system, especially in the Marginal Ice Zone (MIZ). As the MIZ expands, understanding its dynamics is essential for predicting climate impacts. This paper addresses the largely overlooked role of clouds, developing an idealized atmosphere–ocean–ice model with cloud effects, tackling both simulation and data assimilation. The results imply the potential of integrating idealized models with data assimilation for understanding Arctic dynamics and predictions.

 Unequal-weighted ensemble prediction (UWE) using outputs of the dynamic–statistic prediction is presented, and its possible impact factors are also analysed. Results indicate that the UWE has shown promise in improving the prediction skill of summer precipitation in China on account of the fact that UWE can overcome the shortcomings of the structural inadequacy of individual dynamic–statistic predictions, reducing formulation uncertainties and resulting in more stable and accurate predictions.

We investigate the stability characteristics of lightning discharge channels through their differential resistance properties. Our analysis reveals that lightning channels exhibit bistable behavior, transitioning between low- and high-conductivity states depending on channel length and electric-field conditions. This work provides new insights into lightning channel dynamics and could contribute to improved lightning protection strategies.

A derivation of discrete dynamical equations for the dry atmosphere without dissipative processes based on the least action principle is presented. For a given set of generally irregularly spaced grid points and a given mode of interpolation, through the minimization of action, the algorithm ensures maximal closeness of the evolution of the discrete system to the motion of the continuous atmosphere. The spatial resolution can be adjusted while executing calculations.

We propose a novel method for the estimation of ocean surface flow properties in terms of its energy and spatial and temporal scales. The method relies on flow observations collected either at a fixed location or along the flow, as would be inferred from the trajectory of freely drifting platforms. The accuracy of the method is quantified in several experimental configurations. We innovatively demonstrate that freely drifting platforms, even in isolation, can be used to capture flow properties.

This work introduces and evaluates two new tandem data assimilation techniques. The first uses two synchronised forward model runs before a single adjoint model run to consistently increase the precision of the parameter estimation. The second uses a lower-resolution model with adjoint equations to drive a higher-resolution target model through data assimilation with no loss in precision compared to data assimilation without tandem methods.

We have developed a systematic approach to study the climate system at multiple scales using climate networks, which have been previously used to study correlations between time series in space at only a single scale. This new approach is used to upscale precipitation climate networks to study the Indian summer monsoon and to analyze strong dependencies between spatial regions, which change with changing scales.

We identify spatially coherent air streams into atmospheric blockings, which are important weather phenomena. By adapting mathematical methods to the atmosphere, we confirm previous findings. Our work shows that spatially coherent air streams featuring cloud formation correlate with strengthening of the blocking. The developed framework also allows for statements about the spatial behavior of the air parcels as a whole and indicates that blockings reduce the dispersion of the air parcels.

We used a simple coupled model and a data assimilation method to find the correct initialisation for climate predictions. We aim to clarify when weakly or strongly coupled data assimilation (WCDA or SCDA) is best, depending on the system's dynamical characteristics (spatio-temporal) and data coverage. We found that WCDA is better in full data coverage. When we have a partially observed system, SCDA is better. This result depends on the temporal and spatial scale of the observed quantity.

Climate prediction is challenging due to systematic errors in traditional climate models. We addressed this by training a machine learning model to correct these errors and then integrating it with the traditional climate model to form an AI-physics hybrid model. Our study demonstrates that the hybrid model outperforms the original climate model on both short-term and long-term predictions of the atmosphere and ocean.

Particle-tracking simulations compute how ocean currents transport material. However, initializing these simulations is often ad hoc. Here, we explore how two different strategies (releasing particles over space or over time) compare. Specifically, we compare the variability in particle trajectories to the variability of particles computed in a 50-member ensemble simulation. We find that releasing the particles over 20 weeks gives variability that is most like that in the ensemble.

The Madden–Julian Oscillation (MJO) is a large-scale tropical wave of enhanced and suppressed rainfall, slowly moving eastward along the equator and influencing the weather and climate globally. We study the MJO using a simplified model designed to capture its large-scale features. We introduce new, more realistic model inputs, show that this enhanced model successfully replicates key characteristics of the MJO, and identify some of its limitations.

Controlling chaotic systems is a key step toward weather control. The control simulation experiment (CSE) modifies weather systems using small perturbations, as shown in studies with the Lorenz-63 model. However, the effectiveness of CSE compared to other methods is unclear. This study evaluates CSE against model predictive control (MPC). Simulations reveal that MPC achieves higher success rates with less effort under certain conditions, linking control theory and atmospheric science.

The hydrostatic approximation is the basis of most simulations of ocean and climate dynamics. It is evaluated here by using a projection method in the 4D Fourier space. The evaluation is analytic.

Turbulence is found in many natural systems. We study its statistics in terms of scaling laws of up to fourth-order normalized moments for the velocity or magnetic fields through their relative behavior. We show analytically using existing intermittency models that the physical dimension of structures becomes irrelevant. The strongest intermittent structure has a parabolic scaling as found previously and confirmed by long simulations. More analysis will be performed using simplified models. 

The contributing influence of SSWs (sudden stratospheric warming) on regional ionosphere through chaos theory is examined. We find that ionospheric chaos is more pronounced in the European sector compared to the African sector during an SSW. Evidence of orderliness behavior in regional ionosphere of African sector is observed. Finally, we notice that after the peak phase of an SSW, ionospheric chaos is found to be more pronounced.

Can multilevel Monte Carlo methods improve ensemble variational data assimilation without increasing its computational cost? By shifting part of the ensemble generation cost to coarser simulation grids, larger ensemble sizes become affordable. This gives smaller sampling errors without introducing any coarse-grid bias. Numerical experiments with a quasi-geostrophic model demonstrate the potential of the approach and highlight the challenges of operational implementation.

In recent years, there have been more and more floods due to intense precipitation, such as the July 2021 event in Belgium. Predicting precipitation is a difficult task, even just for the next few hours. This study focuses on a tool that assesses whether a given situation is stable or not (i.e., whether it is likely to stay as it is or could evolve in an unpredictable manner).

Fog can disrupt aviation by causing accidents and delays due to low visibility, yet it remains underresearched in Croatia. This study examined fog and mist at Pula Airport using 20 years of data and machine learning techniques. There is a declining trend in fog that is linked to changing weather patterns. Fog mainly occurs from October to March. These findings enhance knowledge about fog in Croatia and can improve weather forecasts, increasing safety at the airport.

Seismo-electromagnetic (SEM) signals emitted prior to major earthquakes show promise as candidates for short-term earthquake prediction. Analyzing the frequency characteristics of SEM signals provides insights into pre-earthquake processes in the focal zone, such as microfracturing and electrokinetic phenomena. The combined application of fractal and multifractal methods proves to be an effective tool for analyzing these complex signals.

Wind energy exhibits extreme variability in space and time. However, it also shows scaling properties (properties that remain similar across different times and spaces of measurement). This can be quantified using appropriate statistical tools. In this way, the scaling properties of power from a wind farm are analysed here. Since every turbine is manufactured by design for a rated power, this acts as an upper limit on the data. This bias is identified here using data and numerical simulations.

To understand the influence of rainfall on wind power production, turbine power and rainfall were measured simultaneously on an operational wind farm and analysed. The correlation between wind, wind power, air density, and other fields was obtained on various temporal scales under rainy and dry conditions. An increase in the correlation was observed with an increase in the rain; rain also influenced the correspondence between actual and expected values of power at various velocities.

We investigated how machine learning can forecast extreme vegetation responses to weather. Examining four models, no single one stood out as the best, though "echo state networks" showed minor advantages. Our results indicate that while these tools are able to generally model vegetation states, they face challenges under extreme conditions. This underlines the potential of artificial intelligence in ecosystem modeling, also pinpointing areas that need further research.

This study presents a new approach to simulating large ocean density waves that travel long distances without breaking down. This new approach ensures that these waves are depicted more accurately and realistically in our models. This is particularly useful for understanding wave behavior in lakes with distinct water layers, which can help predict natural phenomena and their effects on environments like swash zones, where waves meet the shore.