CO2-foams show significant potential for improving mobility control in CO2 Enhanced Oil Recovery (EOR) processes and for the geological carbon sequestration. The addition of nanoparticles (NPs), such as Fe3O4, has been shown to enhance the stability of these foams, especially when used in conjunction with surfactants. Apart from their role in stabilization, Fe3O4 NPs possess distinctive magnetic properties, making them useful in various applications. In this study, we explore the novel application of these nanoparticles, initially intended for stabilizing CO2-foams, as tracers for monitoring the migration of subsurface CO2 plumes. This monitoring is envisioned through the detection of small quantities of these particles within subsurface fluid environments. To facilitate this, we use an Induction Heating (IH) technique, involving exposing a solution with small amounts of Fe3O4 NPs to a high-frequency alternating magnetic field, then measuring the resulting temperature changes using an infrared camera variations. The results indicate a direct correlation between the NP concentration and the observed temperature increase in the solution.

A document by Zacharie Gousseau. Click on the document to view its contents.

Observational investigations of Earth's bow shock have highlighted distinct variations in turbulence characteristics when comparing fluctuations in the shock transition with those in the upstream and downstream plasma regions. To gain a more focused understanding in each of these areas, we have examined a range of local 2D and 3D hybrid simulations, using kinetic ions and fluid electrons. Each simulation has been chosen to cover a range of shock geometries, from quasi-parallel to quasi-perpendicular and high to low Mach number. In-situ observations, such as those from the Magnetospheric Multiscale (MMS) mission, are often unable to fully disentangle spatial and temporal effects. This is particularly evident in the shock transition and the magnetosheath, where, for example, whistler waves may have speeds comparable to the bulk flow and thus locally violate Taylor’s hypothesis for kinetic-scale fluctuations. Simulations overcome these limitations, enabling us to model the evolution of turbulence in the shock transition and further downstream. We characterize the turbulent fluctuations using the following three methods: Firstly, we examine the magnetic spectral indices spanning the inertial range and extending into the ion range as they change across the shock. Secondly, we investigate intermittency by means of the scale-dependent kurtosis. Lastly, we quantify the correlation lengths as measured across the shock, offering insights into the physical dimensions of fluctuations at scales smaller than the shock width. We will discuss the application of these measures to simulations in understanding the kinetic-scale behaviour of turbulence at Earth's bow shock.

A document by John Louie. Click on the document to view its contents.

In the era of information overload, how can research institutions eeectively manage and utilize the wealth of data at their disposal? State-of-the-art Large Language Models (LLMs) could hold the key. These models have the potential to revolutionize work processes at research institutions, aiding tasks such as ideation, summarizing text, generating code, and question answering. These applications could signiicant-ly enhance scientiic research and knowledge discovery in earth and space science, aid decision making, and enable better alignment of challenges with the right experts. We conducted a survey of current thrusts and use cases from diverse user groups across the Jet Propulsion Laboratory (JPL), using a comprehensive approach that combines quantitative and qualitative research methods. Focusing on both available capabilities from industry providers and custom-built solutions, we'll share strategic recommendations for harnessing LLM capabilities and discuss their implications for governance workkows. We also explored how LLMs can enhance knowledge management and discovery by making complex scientiic information more accessible and easier to analyze. By leveraging the unique capabilities of LLMs, JPL and other research institutions can accelerate scientiic discovery and technology development in earth and space science .

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

Basal melting of ice shelves is fundamental to Antarctic Ice Sheet mass loss, yet direct observations are sparse. We present the first melt record (2017 to 2021) from a phase-sensitive radar at Fimbulisen, East Antarctica, one of the fastest flowing ice shelves in Dronning Maud Land. The observed long-term mean ablation below the central part of the ice shelf was 1.0 ±0.4 m yr–1, marked by substantial sub-weekly variability ranging from 0.3 to 3.8 m yr–1. 36-h filtered fluctuations in basal melt exhibit a close alignment with ocean velocity, revealing shear-driven turbulent heat transfer as the predominant driver of melt variability at sub-weekly to monthly timescale. Seasonally, basal melt rates are highest in the austral summer, when ocean temperature is higher. Our observed in-situ melt rates show threefold lower amplitudes and a 3-month delay in seasonality compared to satellite-derived melt rates, however, the long-term multi-year mean is of similar magnitude (1.0 m yr–1 vs 0.8 m yr–1). Our detailed ice–ocean observations provide essential validation data for remote sensing and numerical models aiming to measure and project ice-shelf response to ocean forcing. In-situ measurements and continued monitoring are crucial for accurately assessing and modelling future basal melt rates, as well as understanding the complex dynamics driving ice-shelf stability and sea-level change.

A document by Yusuke Kuwahara. Click on the document to view its contents.

Ground magnetic observatories measure the Earth’s magnetic field and its coupling with the solar wind responsible for ionospheric and magnetospheric current systems. Predicting effects of solar- and atmospheric-driven disturbances is a crucial task. Using data from the magnetic observatory Chambon-la-Forêt at mid-latitude, we investigate the capability of our developed deep artificial neural networks in the modeling of the contributions above 24 hours and the daily variations. Two neural networks were built with the long short-term memory architecture with multiple layers. Using the data from 1995 onwards, the neural networks were trained with physical parameters indicative of solar variabilities and geographical daily and seasonal variations. By excluding the secular variation owing to the change of the Earth’s intrinsic magnetic field, we demonstrate that our approach can model the observed signals with overall good agreements for both a solar-quiet period in 2009 and a solar-active period in 2012. Particularly, using the walk forward training, we updated our models with new data leading up to the test year. The implication of this work is twofold. First, our approach can be adapted for near real-time prediction of intensity of solar and atmospheric disturbances. Second, the neural networks can be used to model the quiet variations when excluding the solar variabilities with important applications in the calculation of magnetic activity indices. This work is a proof-of-concept that deep neural networks can be used to model solar- and atmospheric-driven perturbations modulated by daily and seasonal variations as recorded at a ground magnetic observatory.

Breaking atmospheric gravity waves in the tropical stratosphere are essential in driving the roughly two year oscillation of zonal winds in this region known as the Quasi-Biennial Oscillation (QBO). As Global Climate Models (GCM)s are not typically able to directly resolve the spectrum of waves required to drive the QBO, parameterizations are necessary. Such parameterizations often require knowledge of poorly constrained physical parameters. In the case of the spectral gravity parameterization used in this work, these parameters are the total equatorial gravity wave stress and the half width of phase speed distribution. Radiosonde observations are used to obtain the period and amplitude of the QBO, which are compared against values obtained from a GCM. We utilize two established calibration techniques to obtain estimates of the range of plausible parameter values: History Matching & Ensemble Kalman Inversion (EKI). History Matching is found to reduce the size of the initial range of plausible parameters by a factor of 98%, requiring only 60 model integrations. EKI cannot natively provide any uncertainty quantification but is able to produce a single best estimate of the calibrated values in 25 integrations. When directly comparing the approaches using the Calibrate, Emulate, Sample method to produce a posterior estimate from EKI, History Matching produces more compact posteriors with fewer model integrations at lower ensemble sizes compared to EKI; however, these differences become less apparent at higher ensemble sizes.

We investigate the tidal dissipation in Io’s hypothetical fluid magma ocean using a new approach based on the solution of the 3D Navier-Stokes equations. Our results indicate that Io may have experienced a period of intense tidal heating (104 TW) accompanied by excessive volcanism in the equatorial region, leading to catastrophic resurfacing of the pre-existing terrain. Tidal heating in Io’s magma ocean does not correlate with the distribution of hot spots, and is maximum for an ocean thickness of about 1 km and a viscosity of less than 104 Pa s. Due to the Coriolis effect, the k2 Love number can depend on the harmonic order. We show that the analysis of k2 may not reveal the presence of a fluid magma ocean if the ocean thickness is less than 2 km. If the fluid layer is thicker than 2 km, k20 ≈ k22/2 ≈ 0.7.

Benthic δ18O stacks are the benchmarks by which paleoceanographic data are stratigraphically aligned and compared. However, a recent study found that between 1.8-1.9 million years ago (Ma) several Ceara Rise records differed substantially from the widely used LR04 global stack. Here, we use new Bayesian stacking software to construct regional stacks and demonstrate a geographical divergence in benthic δ18O features from 1.8-1.9 Ma. The pattern of isotopic stage features observed in the Ceara Rise is widespread throughout the Atlantic and differs notably from Pacific records. We propose that this regional difference in isotopic stages may be the result of relatively strong precession forcing and weaker obliquity forcing between 1.8-1.9 Ma. In accordance with the Antiphase Hypothesis, our results highlight a period of apparent sensitivity to regional precession forcing that is masked during most of the 41-kyr world due to the amplitude modulation of obliquity forcing.

A document by Yusuf A. Bhatti. Click on the document to view its contents.

www.PosterPresentations.co m The American Southwest is experiencing increased aridity and wildfire incidence, triggering conversion of some frequent-fire forests to non-forest. These dynamics are well-established in ponderosa pine forests, but we know far less about Madrean pine-oak forests in the Sky Islands of Mexico and USA. We have documented scarce pine regeneration and vigorous post-fire oak resprouting in these forests over 27 yrs. We investigated pine regeneration patterns in long-term plots during severe drought, 10 yrs after the Horseshoe 2 Megafire in the Chiricahua Mountains, AZ-a follow-up to a 5-yr assessment. Our goals were to (1) document changes in pine regeneration and (2) develop remote-sensing tools to identify pine refugia across landscapes. For (2), we tested whether two remotely-sensed predictors-Landsat NDVI & ECOSTRESS evapotranspiration-provided predictive power beyond indices of fire severity and topographic moisture. INTRODUCTION The reliability of projections and restoration under intensifying drought and wildfire depends on a fine-grained understanding of refugia for at-risk tree populations. Fire Severity: Landsat differenced Normalized Burn Ratio (dNBR; 30-m resolution): • NBR = (NIR-SWIR) / (NIR + SWIR), dNBR = Pre-fire NBR-Post-fire NBR Topography: • elevation • topo relative moisture index (TRMI) = aspect + position + % slope + surface shape Landsat Normalized Difference Vegetation Index (NDVI; 30-m resolution): • vegetation greenness: NDVI = (NIR-R) / (NIR+ R) ECOSTRESS evapotranspiration (70-m resolution): • land surface temperature + other inputs à Priestly-Taylor algorithm à ET • Conversion of pine-oak forest to oak shrublands continued 6-10 yrs post-fire. Few pine recruits were found in a matrix of dense, oak sprouts, especially after severe fire (FIG 1) • Fewer large pine seedlings in 2021 (a dry season of record aridity) than 2016 • P. leiophylla post-fire resprouts continue to survive and, unlike seedlings, are beginning to overtop the oak resprout canopy (FIG 2) CONCLUSIONS • Nearly three decades of conversion of pine-oak forest to oak shrublands after high-severity wildfire. • Post-fire resprouting, unusual in pines, may be a lifeline for P. leiophylla. • Remotely-sensed Landsat NDVI, combined with topography and fire severity, do a good job of predicting the locations of pine refugia. • ECOSTRESS ET does not help, likely due to larger, less stationary pixels than NDVI • Field data and models suggest P. engelmannii is more drought sensitive and at risk to climate change and wildfires than P. leiophylla. REFERENCES ACKNOWLEDGEMENTS

A document by Alaguvalliappan Thiagarajan. Click on the document to view its contents.

A document by Matthew G Heberger. Click on the document to view its contents.

A document by Collin Bode. Click on the document to view its contents.

Hands-on activities and geospatial technologies have been shown to support students’ learning of basic geography and geoscience concepts (i.e. topographic map reading and 3D visualization of landscapes) while providing a fun factor to improve student engagement. The objective of this research was to determine if and to what extent students’ learning of these concepts can be improved through incorporation of hands-on play with Play-Doh® and LEGO® and geospatial technologies, specifically drones. The project team conducted six months of biweekly, collaborative instruction at Comstock Middle School in southwest Michigan incorporating in-class activities such as drawing mental maps, creating contour lines of model landscapes in water boxes, constructing 3D landscapes from 2D topographic maps, building and operating small drones, observing drone flight and learning about geospatial data creation from drone-collected aerial images, and more. Pre- and post-test comparisons revealed a 14% increase in overall student scores, which is a statistically significant difference and improvement in student performance. Our instructional approach improved students’ knowledge of topographic mapping and landscape concepts. Beyond providing a broad overview of our pedagogical approach as well as specific curricula and lesson plans, we further outline challenges and limitations.