A document by Jennifer K McWhorter. Click on the document to view its contents.

Subsurface characterization remains a pivotal challenge in geology, hindered by the inherent complexity and nonstationarity of geological processes. Conventional geostatistical methods, relying on two-point statistics and kriging, often fall short in capturing the spatial heterogeneity and transitional dynamics of subsurface materials. This study introduces a novel three-dimensional lithofacies characterization method that incorporates manifold embedding and a transition probability-based spatial estimation technique, significantly deviating from conventional approaches. The proposed method addresses existing limitations by providing a robust framework for capturing the nonstationary nature of geological processes, utilizing data such as pole-to-plane orientations and lithological transitions for structural and juxtapositional information. Through hypothetical scenarios and simulations, the performance of the proposed method is demonstrated, showcasing its capability to model complex geological formations and accurately estimate subsurface characteristics. The study underscores the importance of considering nonstationarity in geological estimations and highlights the potential impact on hydrogeological modeling. Accurate lithological distribution estimation is crucial for reliable groundwater flow and solute transport modeling. This study advances the methodological toolkit for subsurface characterization and paves the way for future research, including empirical validation with real-world data and exploration of the method's applicability in early-stage site characterization, where data scarcity and quality are pressing concerns.

Bi-spectral retrievals of droplet effective radius and cloud optical depth are widely utilized to estimate aerosol cloud interactions (ACI) in warm clouds in the marine boundary layer. Here, we assess the effect of retrieval errors due to the neglect of 3D radiative transfer during the retrieval process on this analysis of ACI. We use an ensemble of stochastically-modelled cloud fields and 3D radiative transfer simulations to study the retrieval errors at a solar zenith angle of 30. Simulated retrieval biases in droplet number concentration (𝑵𝒅) for all three MODIS channels vary systematically from +35% to -80% as cloud heterogeneity increases. Pixel-level errors can be much larger. Commonly utilized subsampling strategies do not reduce the systematic variation in retrieval error. Negative error correlations between optical depth and droplet effective radius produce spuriously negative slopes between the logarithm of liquid water path and 𝑵𝒅 (-1.0 to -0.3). Pixels at the centre of (8 km)2 patches that are not overcast have a relative bias in 𝑵𝒅 of -50%. The relative frequency of these biased pixels varies linearly with the clear fraction in MODIS data and form the basis for a simple parameterization of 𝑵𝒅 bias that varies with cloud fraction. Using this parameterization, synthetic data experiments indicate that estimates of the first indirect effect in the tropical ocean can be overestimated by up to 30%, and the effect of aerosol on cloud fraction is overestimated by 50%, due to neglecting the correlation between retrieval errors and cloud microphysics.

A document by Aparna Sharma. Click on the document to view its contents.

This work presents the characterization of the effective conductivity of additive manufacturing materials used for the fabrication of high-frequency communication devices. Factors such as material type, porosity, roughness, and oxidation reduce this conductivity, leading to increased insertion loss in the manufactured devices. For characterization, an X-band cavity resonator on a rectangular waveguide was designed, and the quality factor was measured for different implementations using various techniques and materials. Conductivity values of up to 17.70 MS/m were obtained for metallized polymeric materials and up to 6.44 MS/m for metallic alloys.

Annual North Atlantic tropical cyclone (TC) counts are frequently modeled as a Poisson process with a state-dependent rate. We provide a lower bound on the forecasting error of this class of models. Remarkably we find this bound is already saturated by a simple linear model that explains roughly 50\% of the annual variance using three climate indices: El Ni\ no/Southern Oscillation (ENSO), average sea surface temperature (SST) in the main development region (MDR) of the North Atlantic and the North Atlantic oscillation (NAO) atmospheric circulation index \cite{Kozar2012}. As expected under the bound, increased model complexity does not help: we demonstrate that allowing for quadratic and interaction terms, or using an Elastic Net to forecast TC counts using global SST maps, produces no detectable increase in skill. We provide evidence that observed TC counts are consistent with a Poisson process, limiting possible improvements in TC modeling by relaxing the Poisson assumption.

The ionosphere profoundly impacts our daily lives, with ionograms serving as essential tools for assessing its state. However, due to the ionosphere’s unstable nature, these ionograms are often complex, highly variable, and plagued by various forms of noise and signal interference. While existing methods for ionogram analysis have focused on technical and model innovations, they have largely ignored the importance of improving the quality of the ionograms themselves. The basic principle ”Quality in, quality out” aptly describes this situation: the quality of the input ionograms fundamentally limits the best performance of any analysis. By enhancing data quality, we can boost the potential of analytic methods. To this end, we define ionogram reconstruction as a process that includes denoising, enhancing weak signals, and connecting interrupted signals. We present a low-cost and high-quality ionogram reconstruction pipeline, meticulously tailored in its training strategies, model architecture, and loss functions to align with the characteristics of ionograms. Key innovations include the use of semi-supervised learning to reduce manual labeling costs, the invention of the Tanh Valve and External Residual Connection mechanisms to speed up model convergence, and the creation of a loss function with a tracking mechanism to shift model focus. Experimental results confirm that our pipeline is exceptionally well-suited for ionogram reconstruction, with every design choice proving indispensable.

Global Ionosphere Thermosphere Model (GITM) results have been compared with measurements from Global-scale Observations of the Limb and Disk (GOLD) and Sounding of the Atmosphere using Broadband Emission Radiometry (SABER). For the first time, GOLD-derived exospheric temperature and column-integrated O/N$_2$ ratio measurements have been used to validate GITM model results. We examine two geomagnetic storm events for which we drive GITM with space weather conditions to understand how well the model reproduces the thermospheric responses to geomagnetic activity. In this paper, a recently developed auroral model, the Feature Tracking of Aurora (FTA) model, has been employed to calculate auroral electron precipitation in GITM (GITM w/FTA), and results are compared with the OVATION prime (OP) driven GITM model (GITM w/OP). GITM w/FTA simulated temperature, neutral density, and nitric oxide (NO) density are generally higher compared to the GITM w/OP model. During the geomagnetic storm, the GITM model and GOLD-derived exospheric temperature agree between $\sim$$0^{\circ}$ to $5^{\circ}$N latitude in the equatorial region. GOLD measurements show strong O/N$_2$ peaks and two distinct equatorial zones during the geomagnetic storm period, which is also observed in our model results. During geomagnetic storm conditions, there is an upward shift in the peak altitude profile of NO emission by up to $\approx$10 km. The NO cooling peaks estimated by GITM models are $\sim$20 km lower than SABER observations during geomagnetic storms. The SABER-derived NO emission is better reproduced by the GITM w/FTA model than by the GITM w/OP model during geomagnetic storms at high latitudes.

Human populations are increasingly impacted by climate change, especially in the Arctic, where Polar Amplification is accelerating climate impacts to more than four times the global average. The complex, interconnected risks that result from climate change need to be measured, tracked and prioritized, to characterize highly impacted areas. We reviewed a cross section of published vulnerability indices developed to characterize change in the Alaskan Arctic, including ecosystem dynamics like permafrost thaw, water availability, animal habitat and migration, and anthropogenic risks from marine & aviation infrastructure, and oil spill impacts. Further, we evaluated the spatiotemporal relationship of vulnerability indices within the context of industrial development in the Arctic, including shipping, mining, and energy extraction. While this cross section offers a diversity of analysis, the accessibility of the indices to Arctic communities needs improvement. Increasing accessibility of these tools will allow further evaluation of these interconnected vulnerabilities, and understanding feasibility and limitations.

Estuaries in the northern California current system (NCCS) experience seasonally reversing wind stress which is expected to impact the origin and properties of inflowing ocean water. Wind stress has been shown to affect the source of estuarine inflow by driving alongshelf currents. However, the effects of vertical transport by wind-driven Ekman dynamics and other shelf and slope currents on inflow have yet to be explored. Variations in inflow to two NCCS estuarine systems, the Salish Sea and the Columbia River estuary, were studied using particle tracking in a hydrodynamic model. Particles were released in a grid extending two degrees of latitude north and south of each estuary every two weeks of 2017 and tracked for sixty days. Inflow was identified as particles that crossed the estuary mouths. Wind stress was compared with initial horizontal and vertical positions and physical properties of shelf inflow particles. Inflow to the Salish Sea came from Vancouver Island and Washington slope water upwelled through canyons during upwelling-favorable wind stress, and from Washington slope water or Columbia River plume water during downwelling-favorable wind stress. Inflow to the Columbia River estuary came from Washington shelf bottom water during upwelling-favorable wind stress and Oregon shelf surface water during downwelling-favorable wind stress. For both estuaries, upwelling-favorable wind stress direction was significantly correlated with a denser and deeper shelf inflow source north of the estuary mouth. These results may help predict the source and properties of inflow to estuaries in other regions with known wind or shelf current patterns.

 Wide convective cell depth responds most to available instability, while narrow cell depth responds most to mid-level relative humidity.  Entrainment-driven reduction of buoyancy decreases as updraft width increases, causing disparate narrow and wide cell depth responses.  A convection-permitting simulation with 3-km horizontal grid spacing generally reproduces observed relationships.

Based on over 160,000 quality-controlled measurements of surface ocean carbon dioxide fugacity from 2000 to 2020, and employing machine learning methods, a satellite-based assessment model for sea-air carbon dioxide fugacity (fCO2) has been developed, aiming to reveal global changes in sea-air carbon dioxide fugacity over the past 20 years. Examining factors affecting fCO2, this study encompasses satellite data coordinates, basic seawater parameters such as salinity ,temperature, wind speed, seawater acidity and alkalinity, seawater velocity, surface geostrophic sea water velocity, surface partial pressure of carbon dioxide in sea water, surface downward mass flux of carbon dioxide expressed as carbon, as well as concentrations of dissolved inorganic carbon, phosphate, nitrate concentration, thickness of the marine mixed layer, seawater total alkalinity, silicate influencing seawater solubility, chlorophyll concentration indicating biological activity, and dissolved oxygen concentration. A comparative analysis was conducted on various machine learning methods, including XGBoost, Random Forest, Light Gradient Booster, Feedforward Neural Network, Convolutional Neural Network, and Backpropagation Neural Network. XGBoost machine learning algorithm was chosen for model construction based on the best performance. The results of independent field validation indicate that the model has a low root mean square error (RMES=18.08μatm) and mean absolute percentage error (MAPE=1.1%) and R-squared (R2=0.91). Finally, the global distribution of sea-air carbon dioxide fugacity at a resolution of 0.25°×0.25° from 2000 to 2020 has been reconstructed. The carbon dioxide fugacity in the global oceans has shown a slow upward trend, over the past 20 years, the carbon dioxide fugacity in global oceans has increased by 6.7%.

The impacts of El Niño-Southern Oscillation (ENSO) on salinity and alkalinity in an equatorial coral reef lagoon (Kanton) are investigated using water samples collected in 1973, 2012, 2015, and 2018. A simple advective-diffusive model is developed to aid in the interpretation of the sparse observations and make estimates of net ecosystem calcification (NEC) rates. Salinity and alkalinity variations in Kanton lagoon are primarily driven by ENSO variations in precipitation. During non-El Niño years (1973, 2012, 2018), salinity increases from the ocean (35.5 psu) to the back of the lagoon (38 psu) because evaporation exceeds precipitation and water resides in the back of the lagoon for ~180 days. At the onset of the 2015-16 El Niño the back of the lagoon is only ~1 psu saltier than the ocean because precipitation had begun to exceed evaporation. The model suggests that during El Niños, when precipitation exceeds evaporation, the back of the lagoon is less salty than the ocean (30 – 32 psu). Alkalinity variations in the lagoon are primarily due to dilution or concentration driven by the ENSO variations in precipitation and NEC that causes an alkalinity deficit of ~250 micromoles/kg in the back of the lagoon. NEC rates in the early stages of the 2015 – 2016 El Niño were ~10% lower (4.7 mmol/day) than in the non-El Niño years (5.2 – 5. 5 mmol/day). The NEC rates and coral cover indicate that Kanton Lagoon has recovered from the complete loss of coral cover during the 2002-03 El Niño.

Magnetic reconnection has been commonly reported between the solar wind IMF and the magnetic field of Earth and other planets. This article is the first report of a reconnection event in the tail region of Ganymede, the only known moon with an intrinsic magnetic field. We show strong evidence of Juno passing very close to an X-line at Ganymede's tail. We report the observation of distinctive electron Bernstein mode waves with unique characteristics particular to a reconnection site. We detect a clear reversal of a magnetic field component. Electron densities and pitch angle distributions also support the findings. Finally, from the time sequence of the observations by the different instruments on Juno, we reconstruct a likely trajectory of Juno around the reconnection site.

A document by Rebecca E Skinner. Click on the document to view its contents.

Coastal hypoxia often develops in stratified estuaries, where river plume expansion and bottom salinity intrusion coexist. However, how surface plume fronts alter bottom hypoxia is not straightforward. In situ observations in the Pearl River Estuary showed that bottom hypoxia can be alleviated by frontal subductions. We observed a bottom-attached subductionevent during the passage of an active front, driven by surface convergence with weak mixing. This subduction effectively transports surface dissolved oxygen (DO)-rich waters to the bottom, increasing bottom DO concentrations. While this downward transport is often suppressed by a mid-depth barrier layer in the three-layer stratified flow, it can move along the sloping isopycnal during the slack tide. Our findings reveal a mechanism that frontal processes can mitigate hypoxia via the subduction of surface DO-rich waters along tilted isopycnals during the slack tide, thereby helping improve the numerical modelling accuracy of the extent and degree of coastal hypoxia.

Hydrodynamic disconnectivity between surface water and groundwater is common in arid environments. It is also prone to affect shallow streams in wetter climate. Sediment layers with low permeability, owing to clogging, for instance, reduce hydrodynamic connectivity. The resistance of this clogging layer, results in unsaturated infiltration, which is characterized by the non-linear Richards equation. Either due to lack of field information, parsimony regarding computational resources or mere misunderstanding of the system, infiltration is often assumed saturated or drastically simplified in hydrogeological models. Here we show the existence of three simple generic asymptotic solutions to the unsaturated problem of vertical steady-state infiltration through a clogged profile, which we associate to three regimes: one dominated by the clogging layer, one by the underlying sediments and one depending on both layers. We also argue that infiltration rate roughly grows linearly with ponding depth. These observations motivate a refined definition of clogging potentially helpful in model selection as well as two novel approximate infiltration formulas. Our refined generic analytic framework is useful to better understand and formalize infiltration.

Deep long-period earthquakes (DLPs) are often detected near volcanoes from the crust down to the upper mantle. Exhibiting coincidence with some volcanic eruptions, DLPs are recognized as potential precursors to volcanic activities yet their detection remains challenging. Meanwhile, their relation to volcanic activities and specific source mechanisms remains uncertain. In this study, we first classify earthquakes into volcano-tectonic (VTs) and long-period (LPs) earthquakes at 10 Alaskan volcanoes by frequency index (FI). The consistent differences in FI distribution between LPs and VTs across volcanoes suggest it can be leveraged for automatic classification. Presence of both VTs and DLPs in some regions suggests frequency content differences mainly arise from source effect. We then analyze 12 years of continuous waveforms by template matching and detect 20,979 DLPs. No repeating DLPs are identified below the depth of 10 km except ~1% of DLPs at Spurr Volcano, indicating source processes of DLPs at Alaskan volcanoes are primarily non-repetitive. Detections of mantle DLPs and positive correlations between DLPs and volume change rate in the magma reservoir support the involvement of magmatic activities in some DLP sources, though there is limited correlation in DLP depth distribution with magmatic water content and kinematic parameters of plate motion. In addition, only ~12% of DLP bursts occur simultaneously with VT bursts, suggesting most DLP rate anomalies are not directly linked to shallow volcanic unrests. Finally, since on average only ~26% of DLP rate anomalies coincide with eruption episodes, it is still challenging to foretell eruptions uniquely based on DLP occurrences.