It is shown that sheared Rossby–Khantadze waves can propagate due to inhomogeneities, taking into account the Earth's angular velocity and the magnetic field. Along with the Hall conductivity, these waves can couple in the ionospheric E region.The novelty of the present work is the consideration of magnetic field inhomogeneity, which was not considered before and reduced our considered system of equations to a 1D modified KdV equation.

We evaluated three meta-heuristic algorithms using resistivity data. An enormous solution is assessed and the best-fitted solutions are chosen. The posterior probability density function with a 68.27 % confidence interval, a mean and posterior solution, and correlation and covariance matrix were calculated for the assessment of the uncertainty, stability, and mean model. We found that vPSOGWO provides reliable and consistently better results that are correlated well with borehole information.

This paper investigates the aggregation of small, spherical, slightly buoyant, rigid particles in a simple 3D vortex flow. Our goal was to gain insights into the behaviour of slightly buoyant marine microplastics in a flow that qualitatively resembles ocean eddies. Attractors are mapped out for the steady, axisymmetric; steady, asymmetric; and nonsteady, asymmetric vortices over a range of flow and particle parameters. Simple theoretical arguments are used to interpret the results.

Current numerical weather prediction models encounter challenges in accurately representing regimes in the stably stratified atmospheric boundary layer (SBL) and the transitions between them. Stochastic modeling approaches are a promising framework to analyze when transient small-scale phenomena can trigger regime transitions. Therefore, we conducted a sensitivity analysis of the SBL to transient phenomena by augmenting a surface energy balance model with meaningful randomizations.

Across much of the ocean, and the world's lakes, less dense water (either because it is warm or fresh) overlays denser water, forming stratification. The mixing of these layers affects the distribution of heat, nutrients, plankton, sediment, and buoyancy, so it is crucial to understand. We use small-scale numerical experiments to better understand these processes, and here we propose a new analysis tool for understanding mixing within those models, looking at where two variables intersect.

Forecasting wind fields over mountains is of high importance for several applications and particularly for understanding how wind erodes and disperses snow. Forecasters rely on operational wind forecasts over mountains, which are currently only available on kilometric scales. These forecasts can also be affected by errors of diverse origins. Here we introduce a new strategy based on artificial intelligence to correct large-scale wind forecasts in mountains and increase their spatial resolution.

Improper handling of missing data and offsets will affect the accuracy of a signal of interest. The trend in GNSS belonging to GLOSS is key to getting the absolute sea level. However, this is affected by the periodic signals that are included. Although adaptive EEMD is capable of extracting periodic signals, missing data and offsets are ignored in previous work. Meanwhile, the time-varying periodic characteristics derived by adaptive EEMD are more conducive to analyzing the driving factors.

Identifying causes of specific processes is crucial in order to better understand our climate system. Traditionally, correlation analyses have been used to identify cause–effect relationships in climate studies. However, correlation does not imply causation, which justifies the need to use causal methods. We compare two independent causal methods and show that these are superior to classical correlation analyses. We also find some interesting differences between the two methods.

We consider a one-dimensional model for the Earth's temperature. We give sufficient conditions to admit three asymptotic solutions. We connect the value function (minimum value of an objective function depending on the greenhouse gas (GHG) concentration) to the global mean temperature. Then, we show that the global mean temperature is the derivative of the value function and that it is non-decreasing with respect to GHG concentration. 

Phytoplankton are key in estuaries, as they form the ecosystem's base. Despite being washed out by river flow and facing a large range of different salinities, they persist. Our Lagrangian simulation of the Elbe estuary shows that buoyancy helps them to be retained. Riverbanks and tidal flats offer refuges from strong currents. Our findings emphasize the need for careful ecosystem management in estuaries.

Different from traditional deterministic optimization algorithms, we implement the sampling method to compute the conditional nonlinear optimal perturbations (CNOPs) in the realistic and predictive coupled ocean–atmosphere model, which reduces the first-order information to the zeroth-order one, avoiding the high-cost computation of the gradient. The numerical performance highlights the importance of stochastic optimization algorithms to compute CNOPs and capture initial optimal precursors.

This work explores the effect of topography on magnetotelluric (MT) data along a synthetic model of the Roorkee–Gangotri profile (RGP). Two correction procedures were used to remove topography distortion from MT data. Flat-earth and terrain correction responses (TCRs) show that both procedures are capable of removing the topography effect. The similar topographic response and TCRs confirm that there is no need for topography correction along the RGP, as the slope angle is less than 1°.