We cluster a global data base of 3529 M>5.5 earthquakes in 1995-2018 based on a dynamic time warping dissimilarity of their source time functions (STFs). The clustering exhibits different degrees of STF shape complexity and suggests an association between STF complexity and earthquake source parameters. Thrust events are in large proportion with simple STF shapes and at all depths. In contrast, earthquakes with complex STF shapes tend to be located at shallow depth in complicated tectonic regions with preferentially strike slip mechanism and relatively longer duration. With 2D dynamic modeling of earthquake ruptures on heterogeneous pre-stress and linear slip-weakening friction, we find a systematic variation of the simulated STF complexity with frictional properties. Comparison between the observed and synthetic clustering distributions provides useful constraints on elements of the frictional properties. In particular, the characteristic slip-weakening distance could be constrained to be generally short (< 0.1 m) and depth dependent.

Since 2004, following prolonged stratospheric sudden warming (SSW) events, it has been observed that the stratopause disappeared and reformed at a higher altitude, forming an elevated stratopause (ES). The relative roles of atmospheric waves in the mechanism of ES formation are still not fully understood. We performed a hindcast of the 2018/19 SSW event using a gravity-wave (GW) permitting general circulation model that resolves the mesosphere and lower thermosphere (MLT) and analyzed dynamical phenomena throughout the entire middle atmosphere. An ES formed after the major warming on 1 January 2019. There was a marked temperature maximum in the polar upper mesosphere around 28 December 2018 prior to the disappearance of the descending stratopause associated with the SSW. This temperature structure is referred to as a mesospheric inversion layer (MIL). We show that adiabatic heating from the residual circulation driven by GW forcing (GWF) causes barotropic and/or baroclinic instability before the MIL formation, causing in situ generation of planetary waves (PWs). These PWs propagate into the MLT and exert negative (westward) forcing, which contributes to the MIL formation. Both GWF and PW forcing (PWF) above the recovered eastward jet play crucial roles in ES formation. The altitude of the recovered eastward jet, which regulates GWF and PWF height, is likely affected by the MIL structure. Simple vertical propagation from the lower atmosphere is insufficient to explain the presence of the GWs observed in this event.

The damage potential of a hurricane is widely considered to depend more strongly on an integrated measure of the hurricane wind field, such as Integrated Kinetic Energy (IKE), than a point-based wind measure, such as maximum sustained wind speed (Vmax). Recent work has demonstrated that minimum sea level pressure (MSLP) is also an integrated measure of the wind field. This study investigates how well historical continental US hurricane damage is predicted by MSLP compared to both Vmax and IKE for continental United States hurricane landfalls for the period 1988–2020. We first show for the entire North Atlantic basin that MSLP is much better correlated with IKE (rrank = 0.50) than Vmax (rrank = 0.26). We then show that continental US hurricane normalized damage is better predicted by MSLP (rrank = 0.81) than either Vmax (rrank = 0.65) or IKE (rrank = 0.68). For Georgia to Maine hurricane landfalls specifically, MSLP and IKE show similar levels of skill at predicting damage, whereas Vmax provides effectively no predictive power. Conclusions for IKE extend to power dissipation as well, as the two quantities are highly correlated because wind radii closely follow a Modified Rankine vortex. The physical relationship of MSLP to IKE and power dissipation is discussed. In addition to better representing damage, MSLP is also much easier to measure via aircraft or surface observations than either Vmax or IKE, and it is already routinely estimated operationally. We conclude that MSLP is an ideal metric for characterizing hurricane damage risk.

Studying diffusion of hydrogen in nominally anhydrous minerals (NAMs), like clinopyroxene, at low temperatures is a challenging task due to experimental and analytical difficulties. We applied a combination of hydrogen implantation to produce concentration gradients in natural diopside crystals with Nuclear Resonance Reaction Analysis (NRRA) measurements of nanoscale diffusion profiles. Thereby, we were able to conduct experiments at temperatures between 195 – 400 °C. Obtained diffusion rates show a consistent Arrhenius relation Ｄн = 5.47 (± 13.98) ·10⁻⁸ · exp (-115.64 (±11.5) kJ mol⁻¹/RT) m²s⁻¹. Notably, our results lie well within the range of extrapolations from high temperature experiments (≥ 600 °C) of previous studies. This implies that fast diffusion of hydrogen (compared to other elements) extends to low temperatures. We used these results in a non-isothermal diffusion model that simulates the ascent of crystals (0.5, 1.0, and 2.0 mm) along two representative geotherms (oceanic and continental) from 600 to 100 °C, to assess potential re-equilibration of H contents in clinopyroxene at low temperatures. Our model highlights the need to carefully consider boundary conditions, which are a function of P-T-𝘧O₂, that control the concentration gradient at the crystal’s rim. The results from this model allow an assessment when re-equilibration in dependence of crystal size and cooling rate must be considered. Fast ascent (e.g., kimberlitic melt) preserves initial hydrogen contents even in 0.5 mm size clinopyroxene crystals. However, dwelling at low temperatures (e.g., 300 °C) for several thousands of years (e.g., serpentinization) leads to extensive re-equilibration in 2 mm crystals.

The terrestrial magnetosphere is perpetually exposed to, and significantly deformed by the Interplanetary Magnetic Field (IMF) in the solar wind. This deformation is typically detected at discrete locations by space- and ground-based observations. Earth’s aurora, on the other hand, is a globally distributed phenomenon that may be used to elucidate magnetospheric deformations caused by IMF variations, as well as plasma supply from the deformed magnetotail to the high-latitude atmosphere. We report the utilization of an auroral form known as the transpolar arc (TPA) to diagnose the plasma dynamics of the globally deformed magnetosphere. Nine TPAs examined in this study have two types of a newly identified morphology, which are designated as “J”- and “L”-shaped TPAs from their shapes, and are shown to have antisymmetric morphologies in the Northern and Southern Hemispheres, depending on the IMF polarity. The TPA-associated ionospheric current profiles suggest that electric currents flowing along the magnetic field lines (Field-Aligned Currents: FACs), connecting the magnetotail and the ionosphere, may be related to the “J”- and “L”-shaped TPA formations. The FACs can be generated by velocity shear between fast plasma flows associated with nightside magnetic reconnection and slower background magnetotail plasma flows. Complex large-scale TPA FAC structures, previously unravelled by an Magnetohydrodynamic (MHD) simulation, cannot be elucidated by our observations. However, our interpretation of TPA features in a global context facilitates the usage of TPA as a diagnostic tool to effectively remote-sense globally deformed terrestrial and planetary magnetospheric processes in response to the IMF and solar wind plasma conditions.

The boundary between the solar wind (SW) and the Earths magnetosphere, the magnetopause (MP), is highly dynamic. Its location and shape depend on SW dynamic pressure and interplanetary magnetic field (IMF) orientation. We use a 3D kinetic Particle-In-Cell code (IAPIC) to simulate an event observed by THEMIS spacecraft on July 16, 2007. We investigate the impact of radial (θBx=0◦) and non-radial (θBx=50◦) IMF on the shape and size of Earths MP for a dipole tilt of 31◦ using maximum density gradient and pressure balance methods. Using the Shue model as a reference (MP at 10.3 RE), we find that for non-radial IMF the MP expands by 1.4 and 1.7RE along the the Sun-Earth (OX) and tilted magnetic equatorial (Tilt) axes, respectively, and it expands by 0.5 and 1.6RE for radial IMF along the same respective axes. When the effect of backstreaming ions is removed from the bulk flow, the expansion ranges are 1.0 and 1.3RE and 0.2, and 1.2RE, respectively. It is found that the percentage of backstreaming to bulk flow ions are 16.5% and 20% for radial and non-radial IMF. We also show that when the backstreaming ions are not identified, up to 40% of the observed expansion that is due to backstreaming particles can be inadvertently attributed to a change in the SW upstream properties. Finally, we quantified the temperature anisotropy in the magnetosheath, and observe a strong dawn-dusk asymmetry in the MP location, being more extended on the duskside than on the dawnside.

The increasing use of the seasonally frozen and permafrost regions for civil engineering constructions and the effects of global warming on these regions have stimulated research on the behaviors of frozen soils. In the present study, the frost heave characteristics of a coarse-grained soil with volcanic nature was experimentally investigated. A large soil tank model was established in laboratory for this purpose. The effects of temperature boundary, external water supply, and water transfer type on the frost heave characteristics of the volcanic soil were studied, through a series of frost heave tests. The particle image velocimetry (PIV) technique was used to quantify the full field deformation of the soil specimen. The results suggest that temperature gradient inside the soil specimen is the driving force for the migration of pore water and vapor. The largest increment in water content generally agrees well with the frost penetration depth. The contribution of vapor to the frost heave of the Komaoka soil specimen is typically small. The applied seeding method, selected subset size, image-object space calibration, and calculation processes ensured accurate PIV results. Discussions regarding the presented experimental investigation and the employment of PIV technique for quantifying frozen soil deformation are summarized. These findings and discussions can provide valuable insights into the frost heave behavior of the studied soil in particular, as well as promote the application of PIV for frozen soil engineering.

Height Above Nearest Drainage (HAND), a drainage normalizing terrain index, is a means able of producing flood inundation maps (FIMs) from the National Water Model (NWM) at large scales and high resolutions using reach-averaged synthetic rating curves. We highlight here that HAND is limited to producing inundation only when sourced from its nearest drainage line, thus lacks the ability to source inundation from multiple fluvial sources. A version of HAND, known as Generalized Mainstems (GMS), is proposed that discretizes a target stream network into segments of unit Horton-Strahler stream order known as level paths (LP). The FIMs associated with each independent LP are then mosaiced together, effectively turning the stream network into discrete groups of homogeneous unit stream order by removing the influence of neighboring tributaries. Improvement in mapping skill is observed by significantly reducing false negatives at river junctions when the inundation extents are compared to FIMs from that of benchmarks. A more marginal reduction in the false alarm rate is also observed due to a shift introduced in the stage-discharge relationship by increasing the size of the catchments. We observe that the improvement of this method applied at 4-5% of the entire stream network to 100% of the network is about the same magnitude improvement as going from no drainage order reduction to 4-5% of the network. This novel contribution is framed in a new open-source implementation that utilizes the latest combination of hydro-conditioning techniques to enforce drainage and counter limitations in the input data.

The relative roles of parameters governing relative permeability, a crucial property for two-phase fluid flows, are incompletely known. To characterize the influence of viscosity ratio (M) and capillary number (Ca), we calculated relative permeabilities of nonwetting fluids (knw) and wetting fluids (kw) in a 3D model of Berea sandstone under steady-state condition using the lattice Boltzmann method. We show that knw increases and kw decreases as M increases due to the lubricating effect, locally occurred pore-filling behavior, and instability at fluid interfaces. We also show that knw decreases markedly at low Ca (log Ca < −1.25), whereas kw undergoes negligible change with changing Ca. An M–Ca–knw correlation diagram, displaying the simultaneous effects of M and Ca, shows that they cause knw to vary by an order of magnitude. The color map produced is useful to provide accurate estimates of knw in reservoir-scale simulations and to help identify the optimum properties of the immiscible fluids to be used in a geologic reservoir.

The flow of organic matter (OM) along rivers and its retention within floodplains are fundamental to the function of aquatic and riparian ecosystems and are significant components of terrestrial carbon storage and budgets. Carbon storage and ecosystem processing of OM largely depends upon hydrogeomorphic characteristics of streams and valleys. To examine the role of channel complexity on carbon dynamics in mountain streams, we (1) quantify organic carbon (OC) storage in sediment and wood along 24 forested stream reaches in the Rocky Mountains of CO, U.S.A., (2) employ six years of logjam surveys and examine related morphological factors that regulate sediment and carbon storage, and (3) utilize fluorescence spectroscopy to examine how the composition of OM in surface water and floodplain soil leachates is influenced by valley and channel morphology. We find that lower-gradient stream reaches in unconfined valley segments at high elevations store more OC per area than higher-gradient reaches in more confined valleys, and those at lower elevations. We find that limited storage of fine sediment and increased mineralization of OC in multithread channel reaches decrease storage per area compared to simpler single-thread channel reaches. Results suggest that the positive feedbacks between channel complexity and persistent channel-spanning logjams that force multiple channels to flow across valley bottoms limit the aggradation of floodplain fine sediment, and promote hotspots for the transformation of OM. These multithread hotspots likely increase ecosystem productivity and ecosystem services by filtering dissolved organic carbon with potential to decrease contaminants associated with organic matter from surface water.

This study examines causes of the double silica maximum in the deep interior Northeast Pacific Basin using a stochastic Lagrangian tracer model based on steady-state advective fields and diapycnal diffusion established by a hydrographic inverse method that conserves potential vorticity and salinity. Lateral diffusion, unresolved by the inverse model, is adjusted for overall agreement with radiocarbon distribution. The double silica maximum in vertical profiles arises from an eastern-intensified single-maximum in the North Pacific Deep Water along the northern domain boundary (originating in the western Pacific), and a strong subarctic bottom source supplying silica to Upper Circumpolar Deep Water density surfaces that successively intersect the seafloor over a broad area east of 150°W, associated geostrophically with southward flow. The existence of the double silica maximum requires weak diapycnal transport in the deep interior, with broader implications for the conceptual picture of meridional overturning circulation in the North Pacific.

Areas of lakes that support emergent aquatic vegetation emit disproportionately more methane than open water but are under-represented in upscaled estimates of lake greenhouse gas emissions. These shallow areas are typically less than ~1.5 m deep and can be estimated through synthetic aperture radar (SAR) mapping. To assess the importance of lake emergent vegetation (LEV) zones to landscape-scale methane emissions, we combine airborne SAR mapping with field measurements of vegetated and open-water methane flux. First, we use Uninhabited Aerial Vehicle SAR (UAVSAR) data from the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) to map LEV in 4,572 lakes across four Arctic-boreal study areas and find it comprises ~16% of lake area, exceeding previous estimates, and exhibiting strong regional differences (averaging 59 [50–68]%, 22 [20-25]%, 1.0 [0.8-1.2]%, and 7.0 [5.0-12]% of lake areas in the Peace-Athabasca Delta, Yukon Flats, and northern and southern Canadian Shield, respectively). Next, we account for these vegetated areas through a simple upscaling exercise using paired methane fluxes from regions of open water and LEV. After excluding vegetated areas that could be accounted for as wetlands, we find that inclusion of LEV increases overall lake emissions by 21 [18-25]% relative to estimates that do not differentiate lake zones. While LEV zones are proportionately greater in small lakes, this relationship is weak and varies regionally, underscoring the need for methane-relevant remote sensing measurements of lake zones and a consistent criterion for distinguishing wetlands. Finally, Arctic-boreal lake methane upscaling estimates can be improved with more measurements from all lake zones.

The North Atlantic ocean is key to climate through its role in heat transport and storage. Climate models suggest that the circulation is weakening but the physical drivers of this change are poorly constrained. Here, the root mechanisms are revealed with the explicitly transparent machine learning method Tracking global Heating with Ocean Regimes (THOR). Addressing the fundamental question of the existence of dynamical coherent regions, THOR identifies these and their link to distinct currents and mechanisms such as the formation regions of deep water masses, and the location of the Gulf Stream and North Atlantic Current. Beyond a black box approach, THOR is engineered to elucidate its source of predictive skill rooted in physical understanding. A labeled dataset is engineered using an explicitly interpretable equation transform and k-means application to model data, allowing theoretical inference. A multilayer perceptron is then trained, explaining its skill using a combination of layerwise relevance propagation and theory. With abrupt CO2 quadrupling, the circulation weakens due to a shift in deep water formation regions, a northward shift of the Gulf stream and an eastwards shift in the North Atlantic Current. If CO2 is increased 1% yearly, similar but weaker patterns emerge influenced by natural variability. THOR is scalable and applicable to a range of models using only the ocean depth, dynamic sea level and wind stress, and could accelerate the analysis and dissemination of climate model data. THOR constitutes a step towards trustworthy machine learning called for within oceanography and beyond.

Anthropogenic factors such as climate change, harsh agricultural practices, and mining have contributed to increases in soil salinization and heavy metal contamination. Highly saline environments drastically lower yield for crop species and elevated levels of toxic metals like cadmium are carcinogenic in the environment. Some plants that have evolved in high-salinity habitats or in soils with heavy metals could be used to remediate contaminated soils. Halophytes are plants with various adaptations that allow them to survive and reproduce in saline conditions. General mechanisms for salt tolerance/uptake in halophytes are hypothesized to help deal with other stresses like heavy metals. Plants with these traits could be utilized to extract salt and heavy metals from affected soils in a process called phytoremediation. We plan to develop Sea Rocket (Cakile maritima) in the Mustard family as a model system to understand mechanisms of salt (NaCl) and cadmium uptake and tolerance, as it has been shown to accumulate both. As part of this study, we will hydroponically grow C. maritima in different stress treatments using salt and cadmium. As the plants uptake the pollutants, the conductivity of the solution will change. We will develop an automated pipeline to track these changes in real-time using conductivity sensors. In addition, we will sample root and leaf tissues at various time points to measure salt and cadmium uptake using ICP-OES elemental analysis. This data will provide insights into salt and cadmium uptake/tolerance and paves a path toward efficient and viable solutions improving phytoremediation approaches.

Generating data has become cheaper and easier, but alone is not sufficient to answer biological questions – data must be analyzed and interpreted. However, many algorithms can create or exacerbate biases (e.g., facial-recognition, ancestry, and disease risk). This necessitates incorporating diverse perspectives to confront both the moral and technical “big data challenges”. To move to a future where this is possible, it is necessary for researchers to develop skills in data management, processing, and analytics. Specifically, the field of plant phenotyping has moved from time consuming hand measurements to the use and development of high-throughput phenotyping. These systems require data-enabled/fluent users, yet academic programs in biology do not provide sufficient data science training. Here we present the Bioinformatics in Plant Science (BIPS) program at the University of Missouri (MU) as a model for training the next generation of data-enabled/fluent scientists. BIPS aims to mentor undergraduate students to build foundational skills in plant biology, research, and computational science. Our program pairs biology and computer science students to address biological questions through computational methods, with many focusing on plant phenotyping methods. The students learn to tackle problems using multidisciplinary approaches, alongside learning how to work in teams while building science communication skills (e.g., professional conferences, research forums, presenting to lawmakers). Through peer learning, BIPS students can understand and incorporate diverse perspectives from both the biological and computational side to address one of NSF’s 10 big ideas: harnessing the data revolution.

Tiny tomato pollen has an outsized role in reproduction, providing essential cellular and genetic material for fertilization and fruit generation. Unfortunately, high temperatures reduce pollination efficiency, harm fruit set and size, and ultimately diminish yield. This project attempts to answer basic questions about pollen growth and function during normal and heat-stressed conditions. Pollen from ~200 genetically diverse tomato and wild relative accessions will be observed as it grows at various temperatures. High-throughput microscopy will be paired with computer vision to phenotype the thousands of image sequences generated by this experiment. By combining pollen growth phenotypes with genome sequence data for all accessions, we plan to identify relevant genomic regions to target for functional description and crop improvement.

The Atmospheric River (AR) Tracking Method Intercomparison Project (ARTMIP) is a community effort to systematically assess how the uncertainties from AR detectors (ARDTs) impact our scientific understanding of ARs. This study describes the ARTMIP Tier 2 experimental design and initial results using the Coupled Model Intercomparison Project (CMIP) Phases 5 and 6 multi-model ensembles. We show that AR statistics from a given ARDT in CMIP5/6 historical simulations compare remarkably well with the MERRA-2 reanalysis. In CMIP5/6 future simulations, most ARDTs project a global increase in AR frequency, counts, and sizes, especially along the western coastlines of the Pacific and Atlantic oceans. We find that the choice of ARDT is the dominant contributor to the uncertainty in projected AR frequency when compared with model choice. These results imply that new projects investigating future changes in ARs should explicitly consider ARDT uncertainty as a core part of the experimental design.

The Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC) took place in January-February 2020. It was designed to understand the relationship between shallow convection and the large-scale environment in the trade-wind regime. Lagrangian large eddy simulations, following the trajectory of a boundary-layer airmass, can reproduce a transition of trade cumulus organization from “sugar” to “flower” clouds with cold pools, observed on February 2-3. The simulations were driven with reanalysis large-scale meteorology and ATOMIC in-situ aerosol data. During the transition, large-scale upward motion deepens the cloud layer. The total water path and optical depth increase, especially in the moist regions where flowers aggregate. This is due to mesoscale circulation that renders a net convergence of total water in the already moist and cloudy regions, strengthening the organization. An additional simulation shows that stronger large-scale upward motion reinforces the mesoscale circulation and accelerates the organization process by strengthening the cloud-layer mesoscale buoyant turbulence kinetic energy production.