We compute stress drops for earthquakes in Northern Chile recorded between 2007to 2021. By applying two different analysis techniques, (1) the spectral ratio (SR) method and (2) the spectral decomposition (SDC) method, a stress drop map for the subduction zone consisting of 51,510 stress drop values is produced. We build an extended set of empirical Green’s function (EGF) events for the SR method by systematic template matching. Outputs are used to compare with results from the SDC approach, where we apply cell-wise obtained global EGF’s to compensate for the structural heterogeneity of the subduction zone. We find a good consistency of results of the two methods. The increased spatial coverage and quantity of stress drop estimates from the SDC method facilitate a consistent stress drop mapping of the different seismo-tectonic domains. Albeit differences of median stress drops are relatively small, strike-perpendicular depth sections clearly reveal systematic variations, with earthquakes within the upper plate, along the interface and at intermediate depth exhibiting distinct values. In particular, interface seismicity is characterized by the lowest observed median value, whereas upper plate earthquakes show noticeably higher stress drop values. Intermediate depth earthquakes show comparatively high average stress drop and a rather strong depth-dependent in-crease of median stress drop.  Additionally, we observe spatio-temporal variability of stress drops related to the occurrence of the two megathrust earthquakes in the study region. The here presented study is the first coherent large scale 3D stress drop mapping of the Northern Chilean subduction zone. It provides an important component for further detailed analysis of the physics of earthquake ruptures.

The lateral erosion of the salt marshes in Galveston Island wetlands was investigated through field UAV surveys. Using a GNSS receiver on the ground and a GNSS receiver attached to the UAV, highly accurate location information for the UAV images was derived through PPK analysis. Based on the collected UAV images with location information, Structure from Motion (SfM) by GNSS-supported aerial triangulation was conducted to generate orthomosaic images of wetlands. Drone surveys have been conducted seven times over a year, and the boundary erosion (represented by boundary retreat) for each period was calculated. The averaged error was less than 2 cm for the horizontal direction in the final orthomosaic images of wetlands. The average wave power, hypothesized to be a significant indicator of potential wetlands erosion, was calculated by investigating the wave climate of the survey area through a simplified numerical model. The relationship between wave power and erosion rate was then summarized. The average amount of erosion from 2019 DEC to 2020 MAR, where winds from north to south were dominant, was higher than from 2020 MAR to 2020 OCT. Much erosion was observed in a protruding part of the wetland. This is caused by waves coming from many different directions surrounding these parts. The erosion of this protrusion had a significant influence on the average erosion rate of the survey areas.

The Beaufort Sea High is a high-pressure system located in the Beaufort Sea and influences ocean circulation in the western Arctic known as the Beaufort Gyre. Wrangel Island, located in the western Chukchi Sea, typically experiences easterly sea ice motion due to the Beaufort Gyre. We find that under these climatological conditions, moving ice is blocked by the island and piles up on its eastern side, while ice on its western side continues to drift. This results in an ice thickness dipole across the island. A reversal in the sense of the oceanic and atmospheric circulation across the western Arctic results in a dipole with the opposing sign. We find the dipole is present throughout the year and is strongest in January when the ice thickness difference is approximately 1m. During the spring, it is associated with the transient opening of a polynya to the west of the island. The dipole is the result of opposing ice divergence and convergence across the western Arctic and may impact ocean circulation and ecosystems within the Chukchi Sea.

Terrestrial hot springs have existed throughout Earth’s history and house some of the most ancient evidence of life on our planet. These settings are known for their high habitability and preservation potential, and are extensively studied as analog environments since hot spring deposits are thought to exist on the surface of Mars. Hot spring water commonly precipitates silica that coats microbial life dwelling in the hot spring outflow streams. This process can entomb microorganisms and preserve microbial remains over long timescales and with high morphological fidelity. Here we present research carried out on modern and sub-recent remains of microbial filaments from amorphous (unaltered) silica deposits in Yellowstone National Park. This work suggests that various elements sequestered by hot spring-dwelling organisms during life are preserved in microbial remains and persist over > 10,000 years. We also present findings from microfossils preserved in mid-Paleozoic terrestrial hot spring deposits which also show sequestrations of select elements in microfossil remains, suggesting that certain elements may persist even after several hundred million years and substantial host rock alteration. These elemental concentrations may be indicative of metabolic functioning during life and have application as biosignatures. Recent developments in analytical instrumentation now allow for even extremely low trace elemental abundances to be detected and mapped, regardless of sample complexity. This work is especially relevant to the search for life on Mars, as evidence of impact-induced hydrothermal activity may exist near the rim of Jezero Crater and may be sampled by the Perseverance rover. As a primary objective of the Mars 2020 mission is to search for evidence of past life on Mars, we suggest the application of this analytical technique to be valuable for potential samples returned to Earth by future Mars Sample Return missions. Distribution Statement A. Approved for public release: distribution unlimited.

A regional meteorology–chemistry model was used to assess the effects of passenger car conversion to battery electric vehicles (BEV) on summer O3 concentrations in Kanto (Japan’s most populous region). Four sensitivity experiments were conducted on different on-road and upstream (power plant and gas station) emission conditions. Daytime 8-h maximum O3 decreased by 3 ppb (5%) and 4 ppb (5%) in urban and inland suburbs, respectively. O3 levels decreased even in urban (VOC-limited regions) because exhaust and evaporative VOC emissions from vehicle and gas stations were reduced effectively (especially alkenes from gasoline evaporation; highly reactive in O3 formation). In the suburbs (NOx-limited regions), reduction of exhaust NOx by BEV shifting was significant, but in urban, even only evaporation measures induced almost the same O3 reduction effect as BEV shifting. The additional emissions from thermal power plants due to BEV night charging contributed little to the next day’s daytime O3 on a monthly average basis. However, on some days, pollutants were stored in the upper part of the stable nighttime boundary layer and could affect the surface O3 as the next day’s mixed layer development. Depending on the O3 sensitivity regime (NOx- or VOC-limited), additional NOx plumes from rural (urban) power plants tended to increase (decrease) the next day’s O3. However, the distribution of the regime changes temporally and spatially. The H2O2/HNO3 ratio was discovered to be a clear indicator for distinguishing regime boundaries and was effective in predicting positive or negative O3 sensitivity to the additional emissions from power plants.

The Moho discontinuity plays an important role in crustal growth and evolution. In this study, we delineate the Moho geometry in southern California by jointly using local Moho-reflected waves PmP and teleseismic Moho-converted waves Ps. To well constrain the Moho geometry, we have developed a two-stage process to pick PmP waves and have created a reliable PmP travel time data set with a total of 10,192 picks. We have also extracted 38,648 high-quality P-wave receiver functions (RFs). The Moho depth is initially estimated via the common conversion point (CCP) stacking of RFs and then refined by inverting the PmP travel time data in a community velocity model (CVM-H, version 15.1.1). The newly built Moho geometry is generally consistent with the California Moho Model version 1.0 (CMM-1.0), that is, a shallow Moho beneath the Salton Trough (23 km), a uniformly shallow Moho beneath the Mojave Desert and the Basin and Range (<29 km), and a sliver of deep Moho under the western Peninsular Ranges, the eastern Transverse Ranges, and the western Sierra Nevada (>34 km). However, our Moho model reveals some new features different from the CMM–1.0, such as a deep Moho (∼34 km) beneath the northern end of the central and western Transverse Ranges, consistent with the observation of deep seismicities due to a thick brittle crust there. We also find a gradual transition from the lower crust to the uppermost mantle beneath the western Peninsular Ranges, leading to the rareness of pickable PmP waves as well as weak Moho-converted signals there.

This study examines how greenhouse gas (GHG) production and organic matter (OM) transformations in coastal wetland soils vary with the availability of oxygen and other terminal electron acceptors. We also evaluated how OM and redox-sensitive species varied across different size fractions: particulates (0.45-1μm), fine colloids (0.1-0.45μm), and nano particulates plus truly soluble (<0.1μm; NP+S) during 21-day aerobic and anaerobic slurry incubations. Soils were collected from the center of a freshwater coastal wetland (FW-C) in Lake Erie, the upland-wetland edge of the same wetland (FW-E), and the center of a saline coastal wetland (SW-C) in Washington state. Anaerobic methane production for FW-E soils were 47 and 27,537 times greater than FW-C and SW-C soils, respectively. High particulate Fe2+ and dissolved sulfate concentrations in FW-C and SW-C soils suggest that iron and/or sulfate reduction inhibited methanogenesis. Aerobic CO2 production was highest for both freshwater soils, which had a higher proportion of OM in the NP+S fraction (64±28% and 70±10% for FW-C and FW-E, respectively) and C:N ratios reflective of microbial detritus (1.7±0.2 and 1.4±0.3 for FW-E and FW-C, respectively) compared to SW-C, which had a higher fraction of particulate (58±9%) and fine colloidal (19±7%) OM and C:N ratios reflective of vegetation detritus (11.2 ± 0.5). The variability in GHG production and shifts in OM size fractionation and composition observed across freshwater and saline soils collected within individual and across different sites reinforce the high spatial variability in the processes controlling OM stability, mobility, and bioavailability in coastal wetland soils.

Coupled climate simulations that span several hundred years cannot be run at a high-enough spatial resolution to resolve mesoscale ocean dynamics. These mesoscale dynamics backscatter to macroscales. Recently, several studies have considered Deep Learning to parameterize subgrid forcing within macroscale ocean equations using data from idealized simulations. In this manuscript, we present a stochastic Deep Learning parameterization that is trained on data generated by CM2.6, a high-resolution state-of-the-art coupled climate model with nominal resolution 1/10° . We train a Convolutional Neural Network for the subgrid momentum forcing using macroscale surface velocities from a few selected subdomains. At each location and each time step of the coarse grid, rather than predicting a single number, we predict the mean and standard deviation of a Gaussian probability distribution. This approach requires training our neural network to minimize a negative log-likelihood loss function rather than the Mean Square Error, which has been the standard in applications of Deep Learning to the problem of parameterizations. Each prediction of the mean subgrid forcing can be associated with an uncertainty estimate and can form the basis for a stochastic subgrid parameterization. Offline tests show that our parameterization generalizes well to the global oceans, and a climate with increased CO2 levels, without further training. We test our stochastic parameterization in an idealized shallow water model. The implementation is stable and improves some statistics of the flow. Our work demonstrates the potential of combining Deep Learning tools with a probabilistic approach in parameterizing unresolved ocean dynamics.

The Cabled Observatory Vent Imaging Sonar, otherwise known as COVIS, has acquired several months of plume centerlines and strengths for several vents in the ASHES vent field of Axial Seamount. COVIS was initially installed at ASHES vent field in July 2018 and acquired imaging data throughout July-September 2018 and since July 2019. COVIS uses acoustic imaging to monitor the strength and behavior of the plumes formed above black smokers and diffuse discharge sites in an approximately 40 m by 40 m region which includes Inferno and Mushroom vents. Preliminary observations suggest that the plumes above Inferno are highly variable; sometimes a distinct rising column is seen to expand with height while other times there is little acoustic evidence for a plume at all. Potential explanations range from variable discharge rates to variable discharge salinity driving collapsing plumes to extremes in bottom currents. The obvious simple explanation of bent plumes produced by extreme bottom currents is unsatisfactory as such bent (even horizontal) plumes should be visible in the acoustic imaging data. Initial explorations of the impact of near seawater salinity variations suggest this is a plausible explanation for variations in plume maximum height independent of heat content. However, the paucity of recent or continuous salinity and temperature sampling on Inferno limits the certainty of interpretations suggesting variations in venting. In contrast to the variable plumes, the sulfide mounds of the region (Inferno, Mushroom, Hell and Phoenix) appear as consistent (stable) silhouettes in the acoustic images. On Inferno, we can even see indications of the thin chimneys on top of the mound from which primary venting occurs. Preliminary work is focused on refining the classification of acoustic returns between rock, sulfide, and water to see if we can track the growth (and collapse) of the actively venting chimneys.

We report on the annual variation of quiet-time suprathermal ion composition and spectral properties for C-Fe using Advanced Composition Explorer (ACE)/Ultra-Low Energy Ion Spectrometer (ULEIS) over the energy range 0.3 MeV/nuc to 1.28 MeV/nuc from 1998 through 2020. This extends the work of Desai et al. (2006) and Dayeh et al. (2009, 2017) to cover Solar Cycle 23’s rising phase through Solar Cycle 24’s declining phase. With 5 additional years of data, we show that the number of quiet-time hours strongly anti-correlates with the Sunspot Number (SSN) at better than the -0.9 level. We also show (1) a clear ordering of the cross correlation between abundance (normalized to O) and SSN as a function of solar wind M/Q; (2) the slope of X/O’s abundance as a function of Fe/C decreases with increasing M/Q; and (3) discuss the trend of annual spectral indicies with respect to Oxygen’s spectral index as a function of solar cycle and M/Q. The contrast between our abundance and spectral index results suggests that the source from which suprathermal ions are drawn or accelerated varies with solar activity and is tied to each element’s chemistry, but he acceleration mechanism that governs the spectral shape does not.

Despite significant developments in global modeling, the determination of ionospheric conductance in the auroral region remains a challenge in the space science community. With advances in adiabatic kinetic theory and numerical couplings between global magnetohydrodynamic models and ring current models, the dynamic prediction of individual sources of auroral conductance have improved significantly. However, the individual impact of these sources on the total conductance and ionospheric electrodynamics remains understudied. In this study, we have investigated individual contributions from four types of auroral precipitation - electron & ion diffuse, monoenergetic & Alfven wave-driven - on ionospheric electrodynamics using a novel modeling setup. The setup encompasses recent developments within the University of Michigan’s Space Weather Modeling Framework (SWMF), specifically through the use of the MAGNetosphere - Ionosphere - Thermosphere auroral precipitation model and dynamic two-way coupling with the Global Ionosphere-Thermosphere Model. This modeling setup replaces the empirical idealizations traditionally used to estimate conductance in SWMF, with a physics-based approach capable of resolving 3-D high-resolution mesoscale features in the ionosphere-thermosphere system. Using this setup, we have simulated an idealized case of southward Bz 5nT & the April 5-7 “Galaxy15” Event. Contributions from each source of precipitation are compared against the OVATION Prime Model, while auroral patterns and hemispheric power during Galaxy15 are compared against observations from DMSP SSUSI and the AE-based FTA model. Additionally, comparison of field aligned currents (FACs) and potential patterns are also conducted against AMPERE, SuperDARN & AMIE estimations. Progressively applying conductance sources, we find that diffuse contributions from ions and electrons provide ~75% of the total energy flux and Hall conductance in the auroral region. Despite this, we find that Region 2 FACs increase by ~11% & cross-polar potential reduces by ~8.5% with the addition of monoenergetic and broadband sources, compared to <1% change in potential for diffuse additions to the conductance. Results also indicate a dominant impact of ring current on the strength and morphology of the precipitation pattern.

Clouds over tropical oceans are an important factor in Earth’s response to increased greenhouse gas concentrations, but their representation in climate models is challenging due to the small-scale nature of the involved convective processes. We perform two 4-year-long simulations at kilometer-resolution (3.3 km horizontal grid spacing) with the limited-area model COSMO over the tropical Atlantic on a 9000x7000 km2 domain: A control simulation under current climate conditions driven by the ERA5 reanalysis, and a climate change scenario simulation using the Pseudo-Global Warming (PGW) approach. We compare these results to the changes projected in the CMIP6 scenario ensemble. We find a good representation of the annual cycle of albedo, in particular for trade-wind clouds, even compared to the ERA5 reanalysis. Also, the vertical structure and annual cycle of the marine intertropical convergence zone (ITCZ) is accurately simulated, and the simulation does not suffer from the double ITCZ problem commonly present in global climate models (GCMs). The ITCZ responds to warming through a vertical extension and intensification primarily at high levels, as well as a slight southward extension of the annual mean ITCZ, while the narrowing typically seen in GCMs is not visible.

We present improvements in the modeling of the vertical wavenumber spectrum of the internal gravity wave continuum in high-resolution regional ocean simulations. We focus on model sensitivities to mixing parameters and comparisons to McLane moored profiler observations in a Pacific region near the Hawaiian Ridge, which features strong semidiurnal tidal beams. In these simulations, the modeled continuum exhibits high sensitivity to the background mixing components of the K-Profile Parameterization (KPP) vertical mixing scheme. Without the KPP background mixing, stronger vertical gradients in velocity are sustained in the simulations and the modeled kinetic energy and shear spectral slopes are significantly closer to the observations. The improved representation of internal wave dynamics in these simulations makes them suitable for improving ocean mixing estimates and for the interpretation of satellite missions such as the Surface Water and Ocean Topography (SWOT) mission.

We examine the wave coupling efficiency of solar wind to magnetospheric fluctuations in the ULF frequency range using an advanced full-wave simulation code, Petra-M. Earth’s magnetic field is tilted to the ecliptic plane; thus, compressional wave sources can be incident over a wide range of magnetic latitudes. When compressional waves are incident at a low latitude, very little wave power can reach the inner magnetosphere. On the other hand, waves incident from a high latitude source can propagate efficiently into the inner magnetosphere and reach the ground near the cusp region. The mode-conversion and polarization reversal at the crossover plays a critical role in compressional wave propagation. The mode-converted linearly polarized electromagnetic ion cyclotron waves also occur at the Alfvén and ion-ion hybrid resonances. Therefore, the results suggest that solar wind compression can drive the linearly polarized EMIC waves, and the wave occurrence can have seasonal and diurnal dependence.

The 13 km SHiELD (System for High-resolution prediction on Earth-to-Local Domains) global model that is under development at the Geophysical Fluid Dynamics Laboratory (GFDL) and runs in near real time, produced outstanding tropical cyclone track forecasts during the 2021 Atlantic hurricane season, compared to both the upgraded National Weather Service Global Forecast System (GFSv16), the Hurricane Weather Research and Forecasting (HWRF) model and the European Centre for Medium-Range Weather Forecast Integrated Forecasting System (IFS). SHiELD’s average track forecast errors were 10% and 15% less than the GFSv16 and HWRF, respectively, for the 3-5 day forecast lead times. SHiELD’s track forecast skill was comparable to the National Hurricane Center’s official forecast at several forecast lead times, and approached 70% skill relative to the Climatology and Persistence Model (CLIPER) at 3 and 4 days. Similar improvements were found in the western Pacific basin in 2021, with improvements also seen in the eastern Pacific at days 4 and 5. Improved performance was also found in the 2019 Atlantic hurricane season, with neutral performance in 2020, when SHiELD was run retrospectively from the GFSv16 initial conditions. Distribution of the spatial errors and biases showed that in both the 2021 Atlantic hurricane season and the previous two years, the largest track forecast errors from both SHiELD and GFSv16 occurred in the subtropical eastern Atlantic, associated with a distinct northeast bias. Analysis indicated that some of the excessive north bias in the GFSv16 is associated with lower geopotential height fields compared to those in SHiELD.

Volcanogenic tsunami and wave hazard remains less understood than that of other tsunami sources. Volcanoes can generate waves in a multitude of ways, including subaqueous explosions. Recent events, including a highly explosive eruption at Hunga Tonga-Hunga Ha’apai and subsequent tsunami in January 2022, have reinforced the necessity to explore and quantify volcanic tsunami sources. We utilise a non-hydrostatic multilayer numerical method to simulate 20 scenarios of sublacustrine explosive eruptions under Lake Taupō, New Zealand, across five locations and four eruption sizes. Waves propagate around the entire lake within 15 minutes, and there is a minimum explosive size required to generate significant waves (positive amplitudes incident on foreshore of >1 m) from the impulsive displacement of water from the eruption itself. This corresponds to a mass eruption rate of 5.8x10^7 kg s^-1, or VEI 5 equivalent. Inundation is mapped across five built areas and becomes significant near shore when considering only the two largest sizes, above VEI 5, which preferentially impact areas of low-gradient run-up. In addition, novel hydrographic output is produced showing the impact of incident waves on the Waikato river inlet draining the lake, and is potentially useful for future structural impact analysis. Waves generated from these explosive source types are highly dispersive, resulting in hazard rapidly diminishing with distance from the source. With improved computational efficiency, a probabilistic study could be formulated and other, potentially more significant, volcanic source mechanisms should be investigated.

The anelastic theory of effective buoyancy has been generalized to include the effects of momentum flux convergence. Mediated by the nonlocal perturbation pressure, the dynamics tends to average over details of the forcing, yielding acceleration robust to small-scale variations of the flow. Here we demonstrate in a large-eddy simulation (LES) with a 100-m horizontal grid spacing that including the anelastic nonlocal dynamics can help capture the mean evolution of convection without fully resolving the fine-scale coherent turbulent structures embedded in the flow. Instances of convection in the LES are identified. For these, the buoyancy and dynamic contributions to the vertical momentum tendency are separately diagnosed. The diagnoses show that buoyancy is the leading effect in the vertical acceleration while strongly interacting with the vertical momentum flux convergence. In comparison, the influence of the horizontal momentum flux convergence on the vertical motion are substantially weaker. The sensitivity resulting from averaging over fine-scale features are quantified. For deep-convective cases, these contributions at the cloud scale ($\sim8$ km) exhibit a robustness—as measured in a root-mean-square sense—to horizontally smoothing out turbulent features of scales $\lesssim3$ km. As expected, such scales depend on the size of the convective element of interest, while dynamic contributions tend to be more susceptible to horizontal smoothing than does the buoyancy contribution. By verifying a key attribute of the pressure-mediated dynamics in an LES, results here lend support to simplifying the representation of moist convection under the anelastic nonlocal framework for global climate models and storm-resolving simulations.

Recent advances in the quality and availability of lidar permit high spatial resolution in digital elevation models (DEMs). However, large-scale lidar acquisitions may be flown during high tides, storm events, and irregular tidal regimes leading to temporal change differences, but ultimately the uneven penetration through dense vegetation impacts the reality of ground surface positions. For the low-gradient coastal land margin of the northern Gulf of Mexico, even a small elevation bias (on the order of 0.1 m) can adversely affect surface hydrodynamic model accuracy. Therefore, ground-truthing with a vertical elevation adjustment is essential for robust biogeophysical modeling. This study assessed measurement errors of lidar-derived DEM datasets (1 m DEMs), developed in 2021. The DEM was evaluated for distinct coastal wetlands, especially coastal marshes of Louisiana, Mississippi, and Alabama. Error analysis was conducted using Real-Time Kinematic (RTK) GPS to assess how well the lidar-derived elevations represent the actual marsh surface (ground surface). The performance of the temporally and spatially distinct lidar-derived elevation datasets was evaluated over 7,000 elevation points measured between 2011-2021. We also examined the relationship between measurement errors and vegetation characteristics (marsh type, height, and percent cover). This presentation will demonstrate our ongoing efforts to assess the high-resolution lidar-derived elevations in coastal wetlands and discuss the measurement errors.