The objective of this study is to explore the application of high-order numerical methods in ionosphere-plasmasphere modeling. Specifically, the nodal discontinuous Galerkin (DG) method is chosen to solve the multifluid dynamical equations along the magnetic field lines. A general curvilinear magnetic field-line-following coordinate system is also used in the model. Numerical simulations with different combinations of number of elements (K) and polynomial orders (N) show the converging results, indicating the robustness of the algorithms and implementation. The model also captures the dawn terminator effect very well in the He+ field.

As well as strong upward propagation of planetary waves from the troposphere, the state of the stratospheric mean flow has been recognized as a key factor for the occurrence of stratospheric sudden warmings (SSWs). The modification of the mean flow to a suitable state for an SSW occurrence is called “vortex preconditioning”. Recently, increasing attention has been paid to the role of gravity waves (GWs) in the preconditioning mechanism. However, because of the limited availability of datasets covering the whole neutral atmosphere, much uncertainty still exists in the role of GWs in the preconditioning. The aim of this study is to investigate the mechanism of modification of the mean flow in the stratosphere and mesosphere before SSWs from a climatological viewpoint and elucidate the role of GWs in it. We use two state-of-the-art datasets covering the whole neutral atmosphere: a 17-year medium-resolution reanalysis dataset and the output data from hindcast simulations performed with a GW-permitting general circulation model. It is shown that the second principal component of the zonal-mean zonal wind in the stratosphere and mesosphere tends to show a maximum prior to an SSW, characterizing preconditioning. GW forcing alters the structure of the upper part of the jet and contributes to the preconditioning along with planetary waves. Comparison of GW forcing between the reanalysis and GW-permitting model suggests that the magnitude of parameterized GW forcing is approximately half that of the GW forcing in the polar upper stratosphere where the forcing is responsible for the preconditioning.

A document by Zeeshan Shariff. Click on the document to view its contents.

Seismic velocity inversion of upper crustal structures in deep sea is challenging, as the unique condition of thick water column often limits the applicability of conventional inversion techniques. For example, while dispersion curves of guided P waves are commonly utilized for estimating subsurface P-wave velocity in land and shallow water environments, these waves have remained in obscurity in deep-water environments due to interference from subseafloor reflections, making them difficult to observe and, until now, unobserved. In this work, we report the first observation of guided P waves in deep water from multichannel seismic data and successfully extract multimode dispersion curves. The dispersive modes of deep water guided P waves are controlled by layered medium beneath the seafloor. Analyzing the dispersive modes of deep water guided P waves provides a tool for subsurface structural inversion in deep water environments.

A document by Hirohiko Masunaga. Click on the document to view its contents.

The North Icelandic Jet (NIJ) is a significant contributor to the lower limb of the Atlantic Meridional Overturning Circulation, supplying one-third to one-half of the overflow water transport through Denmark Strait, including the densest portion. Dense water produced in the Nordic Seas is banked up along the slope north of Iceland and can flow relatively unimpeded across the Denmark Strait sill as part of the NIJ. Major uncertainties remain regarding the mechanisms contributing to the emergence of the NIJ northeast of Iceland. This study investigates these mechanisms using a novel setup with a high-resolution, idealized model for the north Icelandic slope. Initially, we set up a channel model along the slope north of Iceland with differing slope geometry, no external forcing, and horizontally uniform initial and boundary conditions based on observations. Subsequently, we iteratively impose highly idealized inflows and outflows as boundary conditions in the west, emulating the North Icelandic Irminger Current (NIIC) inflow and dense NIJ outflow through Denmark Strait. The model consistently replicates key features of the NIJ, such as its mid-depth intensified core associated with diverging isopycnals away from the slope. Our results corroborate that a steeper slope, a stronger NIIC-like current, and stronger cross-slope density gradients promote instabilities closely related to the emerging NIJ-like current. Furthermore, the variability of the models’ NIJ-like current is largely associated with passing eddies, which might explain some of the observed occupations where the NIJ features a double-core structure.

Iron hydrides are a potentially dominant component of the metallic cores of planets, primarily because of hydrogen’s ubiquity in the universe and affinity with iron. Using ab initio molecular dynamics, we examine iron hydrides with 0.1, 0.33, 0.5, and 0.6 mole fraction hydrogen up to 100 GPa between 3000-5000 K to describe how hydrogen content affects the melt structure, hydrogen speciation, equation of state, atomic diffusivity, and melt viscosity. We find that the addition of hydrogen decreases the average Fe-Fe coordination number and lengthens Fe-Fe bonds, while Fe-H coordination number increases. The pair distribution function of hydrogen at low pressure indicates the presence of molecular hydrogen. By tracking chemical speciation, we show that the amount of molecular hydrogen increases and the number of iron in Hx≥1Fey≥0 clusters decreases as hydrogen concentration increases. We parameterize a pressure, volume, temperature, and composition equation of state and show that the molar volume and Grüneisen parameter of the melts decrease while the compressibility and thermal expansivity increase as a function of hydrogen concentration. We find that hydrogen acts as a lubricant in the melts as the iron and hydrogen become more diffusive and the melts become more inviscid as hydrogen concentration increases. We estimate 2.7 wt% hydrogen in the Martian core and 0.49-1.1 wt% hydrogen in Earth’s outer core based on comparisons to seismic models, with the assumption that the cores are pure liquid iron-hydrogen alloy, and we compare the small exoplanet population with mass-radius curves of iron hydride planets.

We train random and boosted forests, two machine learning architectures based on regression trees, to emulate a physics-based parameterization of atmospheric gravity wave momentum transport. We compare the forests to a neural network benchmark, evaluating both offline errors and online performance when coupled to an atmospheric model under the present day climate and in 800 and 1200 ppm CO2 global warming scenarios. Offline, the boosted forest exhibits similar skill to the neural network, while the random forest scores significantly lower. Both forest models couple stably to the atmospheric model, and control climate integrations with the boosted forest exhibit lower biases than those with the neural network. Integrations with all three data-driven emulators successfully capture the Quasi-Biennial Oscillation (QBO) and sudden stratospheric warmings, key modes of stratospheric variability, with the boosted forest more accurate than the random forest in replicating their statistics across our range of carbon dioxide perturbations. The boosted forest and neural network capture the sign of the QBO period response to increased CO2, though both struggle with the magnitude of this response under the more extreme 1200 ppm scenario. To investigate the connection between performance in the control climate and the ability to generalize, we use techniques from interpretable machine learning to understand how the data-driven methods use physical information. We leverage this understanding to develop a retraining procedure that improves the coupled performance of the boosted forest in the control climate and under the 800 ppm CO2 scenario.

The Atlantic Meridional Overturning Circulation (AMOC) plays a crucial role in the global climate system. Various studies report both ongoing and projected reductions in AMOC strength, with important implications for climate and society. While Stratospheric Aerosol Injection (SAI) has been proposed to mitigate some impacts of a warming climate, model simulations disagree whether it could also be successful in ameliorating the projected AMOC decline. Using SAI sensitivity simulations with the Community Earth System Model, we demonstrate that whether SAI could restore AMOC depends on the details of SAI realization, particularly its latitude(s). Specifically, Northern-hemispheric SAI initially impacts upper-ocean densities in the North Atlantic through changes in surface heat flux and temperature, ultimately preventing AMOC decline. On the other hand, Southern-hemispheric SAI does not substantially impact AMOC strength even though global mean cooling is achieved. We show that different processes play different roles in determining the AMOC response between the initial (~10-15 years) and longer timescales, with the former dominated by the direct SAI effect and the latter influenced by feedbacks from AMOC adjustments. These processes may also offset each other, leading to a relatively stable evolution of AMOC under each SAI realization and a small, yet substantially different, subset of potential AMOC responses. Overall, our results demonstrate the potential for SAI to help avoid climatic tipping points, but also highlight the need to understand the dependence of the outcomes on the specific SAI realization as well as for a better process-based understanding of the many factors influencing such outcomes.

Wildfires have become a major ecological issue impacting humans and the environment alike. To prevent wildfires from starting in the first place, better spatial prediction tools are needed to assess wildfire risk between locations to allow better specialization of firefighting agencies in performing preventative measures. Current methods of predicting spatial wildfire risk rely on satellite imagery, which is significantly limited in terms of spatial and temporal resolution. The use of unmanned aerial vehicles (UAVs) for assessing wildfire risk has significant advantages over satellites due to their much higher spatial and temporal resolution. This project sought to develop a novel deep learning model to predict wildfire risk through aerial imaging specialized for deployment on UAVs. Convolutional neural network architectures were investigated due to their ability to extract implicit spatial features from data. Google Earth Engine was used to assemble a geospatial dataset containing patches of vegetation burnability indices each mapped to a wildfire risk index based on historical fire prevalence. The results for the model show extremely high performance across all metrics evaluated. From this proof of concept research, UAV-based prediction offers a promising prospective solution to the drawbacks of satellite imaging in remote sensing of wildfire risk.

A document by Sara S. Fouad. Click on the document to view its contents.

An increasing number of legacy petroleum wells are reported to suffer integrity failure, releasing methane (CH4) into the subsurface and atmosphere. Subsurface released methane is reactively transported towards ground surface with a portion converted to carbon dioxide by soil microbes. Currently, the extent to which fugitive CH4 oxidation occurs, including microbial taxa responsible and controlling parameters are poorly understood. Here, we examined fugitive CH4 leakage at a legacy well in the Montney region of British Columbia, Canada and find up to 90% is oxidised at rates as high as 230 g of CH4 /m2 of soils/day during summer. Meanwhile, a profound difference in microbiome between soils at the wellhead and background was observed, while modelling suggests that prevailing seasonal temperature will moderate CH4 oxidation extent. Overall, we find that filtration of fugitive CH4 through natural soils can significantly reduce emissions of CH4 and mitigate climate impacts from such sources

A document by Joanne L Wood. Click on the document to view its contents.

The appearance of alluvial channel networks is similar to that of trees and their roots what have self-similarity. In the process of a fluvial evolution, alluvial channels are inner-shaping, adjust to achieve a stable equilibrium state, and form some channel patterns that establish balance relationships between the parameters of the local turbulence field of the channel flow and the supplies of the water discharge, sediment transport rate and sediment composition in a river basin. Therefore, the characteristic dimension lΩ of the representing eddy of the turbulence field of the channel flow and the dynamic characteristic length ld of a catchment are supposed to respectively represent the local dynamic factor and the dynamic factor stream along. The geometric dimension of the cross section of a channel is deduced. And then, it is clarified that the self-similarity equation of cross section morphology has similarity relationship conjugated with the self-similarity equation of the widths of a channel network. One of the hydraulic geometry relationship equations reveals that the nominal dimension lΩ and the gravitational acceleration g have the same effects on shaping channel patterns in the fluvial processes. Finally, the governing equations for channels in regime are derived. It is considered that the regime situation can be regarded approximately to that of the bankfull water level of channels, and exponential relationship equations of channels in regime are given systematically. All the results of the study are consistent with the actual statistical data of the channels of Songhua River Basin.

The incapability of processing flow velocities under low tracer density conditions is one of the limitations of the traditional Large-Scale Particle Image Velocimetry (LSPIV). This study developed a new LSPIV algorithm, Time Frequency Analysis (TiFA), to overcome such a limitation, enhance computational efficiency, and improve the accuracy of derived velocities. TiFA investigates the temporal joint distribution pattern of two velocity components at each location. By assuming that the valid velocities follow a quasi-normal distribution in the velocity time series, TiFA can quickly and accurately separate the valid velocities from background noise and outliers. The performance of TiFA was evaluated by comparing with other algorithms including Traditional LSPIV, Ensemble Correlation (EC), Large-Scale Particle Tracking Velocimetry (LSPTV), and Seeding Density Index (SDI) in an experimental hydraulic model and two field cases. TiFA showed the highest overall accuracy and lowest computation cost in data analysis, especially under low tracer density conditions. In addition, TiFA showed its unique ability of automatically filtering out velocity data from low-quality zones such as no-tracer zones and surface glare zones. TiFA also showed its potential in processing turbulent flow. In summary, the newly-developed algorithm, TiFA, has demonstrated strong capability and competence in various flow and tracer scenarios, making it a valuable candidate for future applications.

The representation of easterly waves (EWs) over the east Pacific Ocean (PEWs) in Coupled Model Intercomparison Project phase 6 (CMIP6) simulations is examined. Models are assessed based on their ability of reproducing PEW-related precipitation and its evolution. The leading patterns reveal a large spread in the representation of PEW structure and amplitude. A comparison of the models with the most realistic PEWs with those with unrealistically weak PEW skill showed that the more accurate simulations more effectively capture the mean state and EW thermodynamic structures over the northeast Pacific. Particularly, good models exhibit realistic PEW precipitation, weaker meridional mean state humidity gradients, larger mean state precipitation over the Panama Bight, and realistic convective moisture adjustment timescales. These results underscore the importance of accurately representing the sensitivity of PEW convection to anomalous moisture for the realistic representation of the waves.

Bifurcations in river-dominated deltas are the main actors in the routing of water and sediments throughout the fluvial network. In spite of previous acknowledgments of their importance, we still lack a comprehensive framework on how bifurcation geometry affects the water and sediment partitioning. To investigate this issue, we firstly combine previously calibrated 2D hydrodynamic simulations on the Wax Lake Delta with a Lagrangian particle-tracking model, quantifying the partitioning of water and sediments with different buoyancy at five bifurcations and their correlations with differences in channel width, branching angle and inlet bed elevation between the downstream branches. We compare the sediment partitioning at bifurcations with available field data to validate our methodology. We then employ the same modeling tools on a simplified geometry, whose geometrical and hydraulic features resemble those of the bifurcations in the Wax Lake Delta. Model results show that the branching angle does not affect the partitioning of water and sediments. The combined effect of asymmetries in the channel width and inlet bed elevation is captured by a simple linear formula that accurately estimates the partitioning of water at bifurcations returned by the 2D calibrated hydrodynamic simulations. Our results also highlight the key role played by transverse gradients in the bathymetry of the upstream channel in determining the partitioning of sediments, suggesting that deeper portions of the cross-section of the upstream channel can cause a proportionately larger fraction of sediments with larger Rouse number to be routed towards the corresponding bifurcate.

Detection of induced polarisation (IP) effects in airborne electromagnetic (AEM) measurements does not yet have an established methodology. This contribution develops a Bayesian approach to the IP-detectability problem using decoupled transdimensional layered models, and applies an approach novel to geophysics whereby transdimensional proposals are used within the embarrassingly parallelisable and robust static Sequential Monte Carlo (SMC) class of algorithms for the simultaneous inference of parameters and models. Henceforth referring to this algorithm as Reversible Jump Sequential Monte Carlo (RJSMC), the statistical methodological contributions to the algorithm account for adaptivity considerations for multiple models and proposal types, especially surrounding particle impoverishment in unlikely models. Methodological contributions to solid Earth geophysics include the decoupled model approach and proposal of a statistic that use posterior model odds for IP detectability. A case study is included investigating detectability of IP effects in AEM data at a broad scale.