Department of Earth and Planetary Science

Abstract Nanoconfined fluids, particularly water, govern subsurface geochemistry, yet the molecular-level mechanisms by which mineral surfaces dictate confined water structure and mobility remain poorly resolved. Here, we combine solution- and solid-state proton (1H) NMR spectroscopy, NMR relaxometry, modulated-gradient spin–echo (MGSE) NMR diffusometry, infrared spectroscopy, and molecular dynamics simulations to demonstrate that surface hydroxyls drive the structural and dynamic differentiation of water in imogolite nanotubes. In saturated suspensions, we observe the coexistence of distinct water 1H environments, each exhibiting significantly reduced mobility, which we attribute to strong interactions with the imogolite surfaces. As more mobile water is removed, long-range water diffusivity in the fibrous solid samples slows to 1.6 × 10–10 m2·s–1 while local fluctuations increase to 3.4 × 10–8 m2·s–1, indicating a significant increase in molecular restriction through surface interactions. Together, our experiments reveal three coexisting populations with varied structures and mobilities, all distinct from liquid bulk water. We resolve a persistent solid-like interfacial layer strongly bound to surface hydroxyls, characterized by an unusually short T2 (<1 ms), and a very close effective 1H–1H distance of ∼1.55 Å between water and the inner-surface silanol group. An inner-core population occupying the inner cavity exhibits an intermediate dynamic regime with restricted axial diffusion, while outer-surface water associated with aluminum hydroxyls retains relatively higher mobility. These results experimentally substantiate a hierarchy of water populations in imogolite and show how surface chemistry dictates the structures and dynamics of confined water.

Abstract Fractured rocks play a crucial role in myriad natural and engineered systems, including Earth's critical zone, oil/gas/geothermal reservoirs, and geological CO 2 /H 2 /waste storage systems. While complex electrical conductivity is extensively used to estimate the pore and grain sizes of conventional porous rocks and soils, it is rarely used to predict the aperture size of fractured rocks and this remains poorly understood. Here, integrating theory, simulations, and experiments, we show that under external fields, fractured rocks follow the fracture‐and‐matrix coupling to make the bulk complex conductivity non‐linear with respect to water conductivity. We find that the relaxation time and quadrature conductivity for porous media do not apply to fractured rocks, but, instead, reasonably accurate predictions of aperture size can be made based on the true formation factor. This study unravels the fundamental mechanism governing conduction and polarization of fractured rocks and paves the way for the non‐invasive investigation of global fractured rocks. Plain Language Summary In fractured rocks, most water flow and solute transport occur in fractures and hence depend on the aperture of those fractures. While the complex electrical conductivity is widely used to infer the pore and grain size of porous rocks and soils, equivalent methods for fractured rocks are limited by theoretical and experimental challenges. Specifically, the applicability of Archie's law that relates electrical conductivity merely to water conductivity remains debated in this context, and experimental measurements often face difficulties due to the small phases in impedance from the dominant conduction in fractures. To bridge this knowledge gap, we systematically investigate the complex conductivity of fractured rocks by integrating theory, simulations, and experiments. For the first time, we demonstrate that the underlying conduction and polarization mechanism of fractured rocks to be the fracture‐and‐matrix coupling. We then employ a non‐linear model to account for this coupling to obtain the true formation factor, and finally predict the mean aperture size with the cementation exponent and the fracture shape. The methodology used in this study may be extended to large scales to facilitate the quantitative non‐invasive investigation of fractured rocks in various natural and engineered systems. Key Points Electrical conduction and polarization of fractured rocks are affected by the coupling between the fracture and matrix Fracture‐and‐matrix coupling leads to non‐linear complex conductivity, which are more pronounced for low‐salinity or high‐polarizable matrix Reasonably accurate aperture size predictions can be made based on the true formation factor, cementation exponent, and fracture shape

Abstract Mesoscale convective systems (MCS) and low‐pressure systems (LPS) are both strongly associated with precipitation across the regions where they occur, particularly within global monsoon systems; however, their co‐occurrence and its relationship to precipitation have not been systematically examined. Here, we use LPS and MCS trackers to detect compound MCS and LPS events in five monsoon regions and assess the association of this co‐occurrence with anomalies of winds, precipitation, and other atmospheric variables. Additionally, we investigate the spatial distribution of precipitating MCS and LPS events. Our results show that most (∼60%) MCS and LPS co‐occurrences are located in the lower latitudes, where they contribute up to 40% of annual precipitation. We find that compound events generally produce more extreme precipitation than MCS‐only or LPS‐only events. Furthermore, our assessment of the synoptic and mesoscale composites reveals that the underlying dynamics of compound events exhibit anomalously positive convective available potential energy and an anomalously low‐pressure within the location of the event. In terms of the synoptic environment of the features, the compound MCS and LPS events are associated with inverted troughs in three out of five monsoon locations assessed. Plain Language Summary This study aims to examine the relationship between low‐pressure systems and mesoscale convective systems and their combined influence on precipitation in near‐equatorial regions. Our results show that these systems could lead to ∼40% of the precipitation in the regions where they occur and further lead to extreme precipitation during their co‐occurrence. Our results provide a systematic approach to assessing the weather during single events and co‐occurrence. Key Points 60% Mesoscale convective systems (MCS) and low‐pressure systems (LPS) co‐occurrences are located in the lower latitudes, and contribute up to 40% of annual precipitation Compound events generally produce more extreme precipitation than MCS‐only or LPS‐only events Compound MCS and LPS events could be associated with inverted troughs

Abstract The Cretaceous–Paleogene (K-Pg) extinction caused significant environmental upheaval, including the disappearance of non-avian dinosaurs and widespread disruption of ecosystems. The Hell Creek and Fort Union formations in the upper Great Plains of North America preserve a record of environmental change across the K-Pg boundary (KPB). Sedimentological studies suggest that the Hell Creek Formation represents a well-drained floodplain, with increased standing water leading to the formation of ponds and peat bogs close to the KPB. This shift has been attributed to the disruption of river systems by short-term extinction-driven flooding in the Western Interior. To test this, we analyze ~2900 paleocurrent measurements from elongate concretions mapped over ~5000 km2 in the Hell Creek area of eastern Montana, USA. These measurements, combined with previously published chronostratigraphic constraints, allow us to trace river system evolution over time. Paleocurrents shift from a uniform NW-SE direction to a chaotic and later bimodal pattern ~400 k.y. after the KPB, but no major shifts occur at the KPB itself. Additionally, we develop a method to identify meandering rivers based on paleocurrent distributions and use it to assess river planform changes. Environmental crises have been suggested to cause transitions from meandering to braided rivers, but we find no evidence of such a change. This suggests that the K-Pg extinction did not significantly disrupt regional drainage or river systems in the Hell Creek area. The results demonstrate the potential of remote sensing for reconstructing paleoriver dynamics and suggest that some river systems may be stable across mass extinction events.

Abstract The existing heat index is complicated and slow. Furthermore, its complexity has obfuscated both mathematical inconsistencies (double values) and physical inconsistencies (supersaturation). This paper presents a simplified heat index that resolves these issues. The new approach results in small changes to the heat index for air temperatures below 300 K but leaves the heat index unchanged for air temperatures above 300 K, where it is most commonly used. This simplification enhances interpretability, and a refactored algorithm accelerates the computation of the heat index by orders of magnitude. The optimized implementation is freely available in C++, R, and Python. In this article, we also clarify a long-standing ambiguity regarding “compensable” and “uncompensable” heat stress. Historically, uncompensable denoted conditions leading to fatal core temperatures. Recent studies, however, have applied the term to any inflection followed by a rise in core temperature. Using the heat index model, we show that such an observation does not necessarily imply lethality because heat loss increases with core temperature and can yield a stable, nonfatal equilibrium. To avoid ambiguity, we therefore replace compensable/uncompensable with normothermic, hyperthermic, and lethal categories based on the predicted steady-state core temperature. Significance Statement Despite the wide use of the heat index in meteorology and climate science, the thermoregulatory model underlying the heat index is not widely known or understood, likely due to its complexity. In this article, we simplify the model at low temperatures (<300 K) and present a graphical method for calculating the heat index, making its structure more intuitive. Additionally, we provide an implementation of the heat index in a low-level programming language, enabling efficient computation of the heat index for various applications.

Cr-bearing clay minerals are products of hydrothermal alteration and fluid-rock interactions of ultramafic rocks that form serpentine minerals. Cr is typically observed to substitute for Al in serpentine minerals, but the crystal chemistry and environmental constraints on this substitution are unknown. Here, we synthesized endmember and Cr-substituted amesite, a typical Al-serpentine mineral, via the hydrothermal method. We found that the phase purity highly depends on the pH of the hydrothermal solution, which should be controlled at ~12.7 to avoid the formation of impurity phases. Additionally, amesite can incorporate Cr at a concentration equivalent to ~39.5% substitution of Al. The Cr-free and Cr-substituted amesite are highly defective and contain multiple polytypes, including 6R₂, 2M₁, and possibly 2H₂. However, the relative proportions of these polytypes do not change with increasing chromium substitution.

The capacity of some plants to access water and nutrients at depths greater than one meter is a critical functional trait that confers resistance to drought and impacts both belowground and shallow soil processes. Here, we report water and strontium isotopic data from an alpine meadow transect showing the correlation between water and nutrient acquisition depths. The isotopic compositions of Sr (⁸⁷Sr/⁸⁶Sr ratio) and water in rock and soil, and in plant leaf tissues, reveal that deeper-rooted plants acquire a higher proportion of water, Sr, and cation nutrients that are derived from the saprolite, a zone of silicate weathering, than shallow-rooted grass. A three-decade dendrochemical record reveals that reductions of wet precipitation drive deep-rooted plants to acquire cation nutrients from deeper saprolite or bedrock regions. Thus, the depth of cation nutrient acquisition by deep-rooted plant species at this site is tightly coupled with, and likely determined by, water availability in soil, saprolite, and bedrock. The enhanced uptake of cations as well as water from deeper saprolite zones could impact the rate of bedrock weathering and watershed chemistry during drought.

Dry granular intrusion and extraction occur commonly for off-road vehicles, foundation work, and plant uprooting. Although many models exist to describe such scenarios, reduced-order models such as granular resistive force theory (RFT), offer a good balance between accuracy and computational cost. RFT efficiently approximates the resistive force experienced by an intruder moving through a granular medium as a function of the intruder's velocity direction, geometry, and material parameters. However, due to the explicitly velocity-dependent nature of its formulation, it fails at modeling static conditions, the force build-up that occurs before flow, and stagnant points on moving intruders. We propose elastic RFT to remedy these shortcomings. Elastic RFT intrinsically models both granular elasticity and flow adjacent to intruders by splitting intruder motion into separate parts corresponding to elastic and plastic granular deformation. This allows force to build up elastically before flow and lets the plastic part of motion maintain RFT flow rules. We present the details of the underlying kinematic and constitutive assumptions for elastic RFT. Moreover, we also propose a procedure to couple RFT or elastic RFT to deformable intruders, which we demonstrate in applications of elastic RFT to sample plant uprooting problems treating the roots as nonlinear inextensible beams. We highlight potential application areas for elastic RFT coupled to deformable objects, including simulation of deformable wheel locomotion.

Abstract The rich history of observing system simulation experiments (OSSEs) does not yet include a well-established framework for using climate models. The need for a climate OSSE is triggered by the need to quantify the value of a particular measurement for reducing the uncertainty in climate predictions, which differ from numerical weather predictions in that they depend on future atmospheric composition rather than the current state of the weather. However, both weather and climate modeling communities share a need for motivating major observing system investments. Here, we outline a new framework for climate OSSEs that leverages the use of machine learning to calibrate climate model physics against existing satellite data. We demonstrate its application using NASA’s GISS-E3 model to objectively quantify the value of potential future improvements in spaceborne measurements of Earth’s planetary boundary layer. A mature climate OSSE framework should be able to quantitatively compare the ability of proposed observing system architectures to answer a climate-related question, thus offering added value throughout the mission design process, which is subject to increasingly rapid advances in instrument and satellite technology. Technical considerations include selection of observational benchmarks and climate projection metrics, approaches to pinpoint the sources of model physics uncertainty that dominate uncertainty in projections, and the use of instrument simulators. Community and policy-making considerations include the potential to interface with an established culture of model intercomparison projects and a growing need to economically assess the value-driven efficiency of social spending on Earth observations. Significance Statement When planning a new satellite mission, it is important to first make sure that the new measurements will meet the science and end user goals of the broader community. While there are now well-established ways to quantify observation benefits for weather prediction, there is no such similar established framework for determining satellite measurement benefits for climate prediction. This article describes a new way to determine in advance whether new observations can reduce uncertainty in climate model projections.

Abstract Atmospheric rivers (ARs) significantly impact the Arctic climate system by enhancing atmospheric heat and moisture transport and altering the local energy budget. Developing AR detection tools (ARDTs) is critical yet challenging. This study evaluates 12 ARDTs in the Arctic to assess their performance in representing atmospheric heat (represented by moist static energy) and moisture transport, as well as surface downward longwave radiation (LWD) and precipitation impacts, spanning 2000 to 2019 using ERA5 reanalysis. We find that AR occurrence frequency in the Arctic varies widely, from less than 1% to over 13%, depending on the ARDT. This variability leads to differences in contributions to poleward atmospheric heat (<1%–33%) and moisture (<1%–49%) transport. The highest AR frequency, and corresponding contributions to atmospheric heat and moisture transport, occurs over the Atlantic sector during non‐summer seasons for most ARDTs. This region aligns with the primary poleward moisture pathway and the end of climatological mid‐latitude storm tracks, highlighting strong connections between Arctic ARs and mid‐latitude cyclones. ARs induce significant LWD anomalies, largest in winter, smallest in summer, and also substantially contribute to the seasonal precipitation. Global ARDTs detect fewer ARs with larger anomalies (>100 W m −2 in higher Arctic), but contribute <1% to seasonal climatological LWD and precipitation. In contrast, polar‐specific ARDTs detect higher AR occurrences and account for up to 16% of seasonal LWD and 41% precipitation. This suggests that algorithms emphasizing extreme events with large anomalies do not necessarily indicate a large climate radiative and precipitation impact. Plain Language Summary Atmospheric rivers (ARs) are long narrow filaments of intense water vapor transport in the lower atmosphere that play a significant role in the Arctic climate. They bring heat and moisture into the region, influencing the energy balance and potentially accelerating sea ice loss and Arctic warming. However, detecting ARs in the Arctic is challenging because most AR detection tools (ARDTs) are designed for global or mid‐latitudes, with few tailored for polar regions. This study evaluates 12 ARDTs to assess their ability to identify Arctic ARs and their contributions to heat, moisture transport, surface radiation, and precipitation impacts. Results show a wide range of AR detection frequencies, from less than 1% to over 13%, depending on the ARDT used. This variability significantly affects estimates of how much heat and moisture ARs transport into the Arctic. ARDTs designed/adapted for the Arctic detect more frequent ARs and show they contribute meaningfully to seasonal surface radiation and precipitation impacts. In contrast, global ARDTs, which focus on extreme moisture events in mid‐latitudes, detect fewer ARs with larger radiation and precipitation anomalies but have reduced cumulative climate effects. These results emphasize the limitations of using global ARDTs in the Arctic context and should be avoided. Key Points AR detection tools (ARDTs) vary widely, leading to differences in Arctic AR frequency and heat and moisture transport impacts Polar ARDTs detect more ARs, contributing up to 33% heat, 49% moisture transport, 16% surface longwave radiation, and 41% precipitation Global ARDTs focus on extreme events with large anomalies but contribute as low as 1% to heat, moisture, radiative and precipitation impacts