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.

We study time series of solar wind large-scale structures (magnetic clouds, sheaths, slow and fast streams). These have profound importance with regard to causing disturbances in the heliospheric conditions and driving space weather on Earth. The used techniques include methods derived from information theory to determine entropy and complexity. We find that all of these techniques show stochastic fluctuations, but magnetic clouds stand out due to their coherent magnetic field.

We propose ensemble-based model predictive control (EnMPC), a novel method that improves the control of complex systems like the atmosphere by integrating control theory with data assimilation. Unlike traditional methods, which are computationally expensive, EnMPC uses ensemble simulations to efficiently handle uncertainties and optimize solutions. This approach reduces computational cost while maintaining accuracy, making it a promising step toward real-world applications in dynamic system control.

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.

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.

Vegetation patterns in semi-arid regions arise from interactions between plants and environmental factors. This study uses a numerical model to explore how vegetation responds to changes in rainfall and random disturbances. We identify key timescales that influence resilience, showing that ecosystems rely on both stable and unstable states to adapt. These findings offer insights into the resilience mechanisms that help ecosystems maintain stability under environmental stress.

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.