Carbon sequestration in geologic formations is a promising strategy that enables long-term storage of CO2. Continental flood basalts are an attractive storage option, primarily due to their reactivity. In 2009 a Basalt Pilot study was initiated in the Columbia River Basalt where ~1000 MT of CO2 was injected ~850 m into the subsurface. Two years later, 50 sidewall cores were collected from the injection zone and characterized for evidence of CO2 mineralization. This effort summarizes those key findings associated with seven sidewall cores

Studies have implicated the importance of longwave (LW) cloud-radiative forcing (CRF) in facilitating or accelerating the upscale development of tropical moist convection. While different cloud types are known to have distinct CRF, their individual roles in driving upscale development through radiative feedback is largely unexplored. Here we examine the hypothesis that CRF from stratiform regions has the greatest positive effect on upscale development of tropical convection. We do so through numerical model experiments using convection-permitting ensemble WRF (Weather Research and Forecasting) simulations of tropical cyclone formation. Using a new column-by-column cloud classification scheme, we identify the contributions of five cloud types (shallow, congestus, and deep convective; and stratiform and anvil clouds). We examine their relative impacts on longwave radiation moist static energy (MSE) variance feedback and test the removal of this forcing in additional mechanism-denial simulations. Our results indicate the importance stratiform and anvil regions in accelerating convective upscale development.

Storms can have a direct impact on sea ice, but whether their effect is seen weeks to months later has received little attention. The immediate and longer term impacts of an idealized open water wind storm are investigated with a one-dimensional coupled ice-ocean model. Storms with different momentum, duration and date of occurrence are tested. During the storm, the mechanical forcing causes a deepening of the mixed layer, leading to an increase in mixed layer heat content, despite a decrease in mixed layer temperature. This results in a delay in sea ice formation that ranges between a few hours to weeks compared to the control run, depending on the storm characteristics. Throughout the freezing period, the storm-induced thick mixed layer experiences little variability, preventing warm water entrainment at the base of the mixed layer. This leads to faster sea ice growth compared to the control run, resulting in sea ice thickness differences of a few millimeters to around 10 cm before the melting onset. These results are stronger for runs with higher momentum storms which cause greater mixed layer deepening. Storms occurring in early August, when the ocean surface heat flux is positive, also amplify the results by forcing a greater increase in mixed layer heat content. The impacts of the storms are sensitive to the initial stratification, and amplified for a highly stratified ocean. We suggest that localized storms could significantly influence the seasonal dynamics of the mixed layer and consequently impact sea ice conditions.

We perform a statistical study of 3-s ultra-low frequency (ULF) waves using MMS observations in the Earth’s foreshock region. The average phase velocity in the plasma rest frame is determined to be anti-sunward, and the intrinsic polarization is right-handed. We further examine the linear instability conditions based on drift-bi-Maxwellian distribution functions according to observed plasma conditions. The resulting instability is a solution to the common dispersion equation of the ion/ion right-hand non-resonant and left-hand resonant instabilities. The predicted wave propagation is also predominantly anti-sunward. The cyclotron resonant conditions of the solar wind and backstreaming beam ions are evaluated, and we find that in some cases, the anti-sunward propagating waves can be resonant with beam ions, which was overlooked in previous studies. The result suggests that the dispersion equation provides the 3-s ULF waves a fundamental explanation that unifies a rich variety of resonant conditions. In the later stage, the 3s ULF waves could further develop into Short Large Amplitude Magnetic Structures, contributing to the turbulence in the foreshock region.

Widely employed to model collisionless plasma phenomena occurring naturally in Earth’s magnetic environment, throughout the heliosphere, and in laboratory fusion devices, the Vlasov equation self-consistently describes the fundamental kinetic dynamics of plasma particles as they are accelerated through phase space via electric and magnetic forces. The Fast Plasma Investigation (FPI) onboard NASA’s Magnetospheric Multiscale (MMS) four-spacecraft mission sufficiently resolves the seven spatial, temporal, and velocity-space dimensions of phase space needed to directly observe terms in the Vlasov equation, as recently demonstrated by Shuster et al. [2021] in the context of electron-scale current layers at the reconnecting magnetopause. These results motivate novel exploration of the types of distinct kinetic signatures in ∂fe/∂t, v⋅∇fe, and (F/me)⋅∇vfe which are associated with the magnetic reconnection process, where F = −e(E + v×B) represents the Lorentz force on an electron, and fe specifies the electron phase space density. We apply this approach to characterize the structure of the velocity-space gradient terms in the electron Vlasov equation measured by MMS. Discussion of the uncertainties which arise when computing the velocity-space gradients of the FPI phase space densities is presented, along with initial validation of the (F/me)⋅∇vfe measurements by comparison to the ∂fe/∂t and v⋅∇fe terms. Successful measurement of the force term (F/me)⋅∇vfe in the Vlasov equation suggests a new technique for inferring spatial gradients from single spacecraft measurements which may be applied to improve the spatial resolution of the electron pressure divergence ∇⋅Pe necessary to understand the microphysics of the electron diffusion region of magnetic reconnection. Reference: Shuster, J. R., et al. (2021), Structures in the terms of the Vlasov equation observed at Earth’s magnetosphere, Nature Physics, doi:10.1038/s41567-021-01280-6.

Poor understanding of aerosol-cloud interactions and cloud feedback processes in the Arctic climate system limit our ability to constrain future climate in the region and one important knowledge gap is the source of particles upon which cloud droplets and ice crystals have formed. If representative cloud-water can be obtained from Arctic clouds, it’s chemical composition can be analysed to infer the sources of particles present within it. However, the balloon-borne active cloud water sampling systems required to obtain such samples have not previously been feasible due to their weight and the challenging environmental conditions. Here we present a miniaturised cloud-water sampler for balloon-borne collection of cloud water which was deployed during the Microbiology-Ocean-Cloud-Coupling in the High Arctic (MOCCHA) campaign in August and September 2018 along with the deployment protocol required to obtain representative samples in the pristine conditions encountered. We present the chemical composition of the samples obtained as well as the ice-nucleating activity of the samples and discuss the implications of our results on aerosol-cloud interactions in the high Arctic.

Observed increases in wildfire activity across the contiguous United States, which have occurred amid a warming climate and expanding residential footprint within flammable landscapes, illustrate the urgency of understanding near-future changes in fire regimes. Here, we use a statistical model including future projections of both human population distribution and atmospheric conditions from climate models to predict the number, size, and cumulative area burned by wildfires. We find an overall increase in both the number of fires (+56%) and total burned area (+60%) during 2020-2060 relative to a 1984-2019 baseline, as well as ubiquitous increases in area burned (+63%) by the largest fires. Additionally, we predict the emergence of observationally unprecedented fire frequency in eastern U.S. locations where wildfire was rare historically (+71%), and unprecedented increases in the size of the largest fires in the Western U.S. where fires were historically common—underscoring the need to prepare for more frequent and severe fire even in communities unaccustomed to them.

To address climate change, social issues need to be integrated in geoscience-related STEM research and curriculum. However, social issues are often ignored in STEM fields, even though STEM practices are usually implicitly rooted in inequitable values and practices. A previous study showed that some students do not see the relevance of diversity, equity, and inclusion (DEI) activities when they are presented as separate from technical content, and some students argued that social content was irrelevant or inappropriate in a technical STEM course. Some engineering faculty have identified the lack of curriculum as a barrier to integrating the socially embedded nature of STEM into their introductory courses. Therefore, we developed a case that addresses social issues such as race, class, and gender in relationship to the social and environmental characteristics of New Orleans and its built environment. The case shows how marginalized people, who have been historically and systemic discriminated against, suffered the greatest losses under the impacts of climate-change driven Hurricane Katrina. The case uses student-centered learning strategies, is applicable for use in a range of courses, including many geoscience courses, and will be publicly available. In Fall 2019 the case will be taught in two first year undergraduate civil and environmental engineering courses (n ≈120) at a R1 university. Coursework from the case will be analyzed using Qualitative Content Analysis, which distills responses into categories and themes that characterize both commonalities and differences. While we do not yet have results, results will be presented at AGU. Our results will provide insight into the effectiveness of our case study as an intervention as well as challenges and successes in implementation, which may help others implement this or other similar activities. More broadly, our intervention demonstrates one way of integrating content more commonly found in interdisciplinary courses into technical, disciplinary-focuses courses. Our study will provide insight into how students respond when DEI and technical content are interconnected, helping further our ability to prepare students in a range of geoscience-related courses to respond to the challenges of climate change through an equity and justice-oriented lens.

Depressions in magnetic field strength, commonly referred to as magnetic holes, are observed ubiquitously in space plasmas. Sub-proton-scale magnetic holes with spatial scales smaller than or on the order of $\rho_p$, are likely supported by electron currents vortices, rotating perpendicular to the ambient magnetic field. While there are numerous accounts of sub-proton-scale magnetic holes within the Earth’s magnetosphere, there are no reported observations in other space plasma environments. We present the first evidence of sub-proton-scale magnetic holes in the Venusian magnetosheath. During Parker Solar Probe’s first Venus Gravity Assist, the spacecraft crossed the planet’s bow shock and subsequently observed the Venusian magnetosheath. The FIELDS instrument suite onboard the spacecraft achieved magnetic and electric field measurements of magnetic hole structures. The electric field associated with magnetic depressions are consistent with electron current vortices with amplitudes on the order of 1 $\mu$A/m$^2$.

OCO-2, launched in 2014, uses reflected solar spectra and other retrieved geophysical variables to estimate (“retrieve”) the column averaged dry air mole fraction of CO2, termed XCO2. A critical issue in satellite estimates of trace greenhouse gasses and remote sensing at large is the error distribution of an estimated target variable which arises from instrument artifacts as well as the under-determined nature of the retrieval of the quantities of interest. A large portion of the error is often incurred during inference from measurement of retrieved physical variables. These residual errors are typically corrected using ground truth observations of the target variable or some other truth proxy. Previous studies used multilinear regression to model the error distribution with a few covariates from the retrieved state vector, sometimes termed “features.” This presentation will cover the bias correction of XCO2 error attributed to retrieved covariates with a novel approach utilizing explainable Machine Learning methods (XAI) on simulated sounding retrievals from GeoCarb. Utilization of non-linear models (Zhou, Grassotti 2020) or models that can capture non-linearity implicitly (Lorente et al. 2021) have been shown to improve on linear methods in operation. Our approach uses a gradient boosted decision tree ensemble method, XGBoost, that captures non-linear relations between input features and the target variable. XGBoost also incorporates regularization to prevent overfitting, while also remaining resilient to noise and large outliers – a feature missing from other ensemble DT methods. Decision Tree based models provide inherent feature importance that allows for high interpretability. We also approach post training analysis with model agnostic, explainable methods (XAI). XAI methods allow for rigorous insight into the causes of a model’s decision (Gilpin et al. 2018). By applying these techniques, we will demonstrate our approach provides reduced residual errors relative to the operational method as well as yielding an uncertainty estimate in bias corrected XCO2, which is currently not treated separately from the posterior uncertainty estimate derived from the retrieval algorithm.

The assessment of planktonic organisms is a prevailing task in marine ecology and oceanography as planktons form the principal food source for consumers at higher trophic levels. Reliable estimates on the production at the lowermost trophic levels are thus an integral part for the management of marine ecosystems. Traditional plankton sampling and analysis are limited in their spatial and temporal context of the organisms’ environment, which are often critical clues to a biologist for its habitation. In addition, ship-based sampling as described leads to a high level of uncertainty in the estimation, since point measurements that are intermittent in space and time are used (Reid et al 2003; Lermusiaux 2006; Vannier 2018). A disruptive change in approach to tackle this problem is currently taking place, enabled by the use of autonomous robots (Henthorn et al 2006) and augmented by visual sensing for real-time analysis (Ohman et al 2019; AILARON 2019). Approaches providing taxonomy estimates from time-series image analysis (Sosik and Olson 2008) or via computer simulations (Roberts and Jaffe 2007), with the recent advances in deep learning, enabled by the computational power of multicore CPUs and GPUs, made possible processing and classification of large datasets while learning higher level representations. Enhanced traditional machine learning methods are driven by multiple kernel learning, where general features are combined with robust features and new types from multiple views are defined in order to generate multiple classifiers (Py et al 2016; Dai et al 2016; Lee et al 2016; Moniruzzaman et al 2017). In this paper, we present recent DL methods for microscopic organisms’ identification and classification. A proposed DL architecture (cf. figure 2) reported an accuracy of 95% as opposed to (90% - 93% cf. table 2) achieved by the state-of-the-art networks: ZooplanktoNet, VGGNet, AlexNet, ResNet, and GoogleNet, while training over a labeled dataset of extracted objects from images of plankton organisms captured in-situ (cf. table 1). COAPNet is embedded into a light-weight autonomous vehicle (LAUV) for real-time in-situ plankton taxa identification and classification. The LAUV in turn utilizes the feedback from the image analysis to constantly update a probability density map that further enables an adaptive sampling process.

Radiatively-driven convection is a physical process that occurs in freshwater below the temperature of maximum density wherein volumetric heating of surface waters by solar radiation creates a diurnal, spatially distributed, destabilizing buoyancy flux that drives penetrative convection. While this process has typically been studied under ice-covered conditions, it can also occur in open water during springtime warming leading up to overturn, and in such systems, it may serve as the dominant process driving mixing of nutrients and biota. Despite the ecological significance and unique physical dynamics of radiatively-driven convection, little is understood regarding the spatial heterogeneity and three-dimensional structure of the process. The addition of wind shear also modifies radiatively-driven convection dynamics in open water conditions, yet observations have not yet been used to quantify the relative scales and importance of these separate forcings in driving mixing and turbulence. This study examines data collected with a buoyancy-driven autonomous underwater vehicle (aka glider) during a period of active radiatively-driven convection and low surface wind shear in early springtime in Lake Superior. Conductivity, temperature and depth (CTD) measurements reveal distinct convective plumes of anomalously warm downwelling water with width scales on the order of 100 m and temperature anomalies of ~0.1 °C. Shear and temperature microstructure measurements indicate turbulence kinetic energy (TKE) dissipation rates exceeding 10-8 W/kg, orders of magnitude greater than laterally adjacent waters. This is the first known observation of lateral variability in TKE dissipation rates during radiatively-driven convection. Spatially and temporally averaged TKE budgets illustrate buildup, vertical transport, and dissipation of TKE, while the ~3 hr lag between buoyancy forcing and dissipation is consistent with the Deardorff convective timescale. These observations demonstrate that radiatively-driven convection can dominate vertical mixing dynamics even in deep, open water systems.

Mixed-phase clouds (MPCs), which consist of both supercooled cloud droplets and ice crystals, play an important role in the Earth’s radiative energy budget and hydrological cycle. In particular, the fraction of ice crystals in MPCs determines their radiative effects, precipitation formation and lifetime. In order for ice crystals to form in MPCs, ice-nucleating particles (INPs) are required. However, a large-scale relationship between INPs and ice initiation in clouds has yet to be observed. By analyzing satellite observations of the typical transition temperature (T*) where MPCs become more frequent than liquid clouds, we constrain the importance of INPs in MPC formation. We find that over the Arctic and Southern Ocean, snow and sea ice cover significantly reduces T*. This indicates that the availability of INPs is essential in controlling cloud phase evolution and that local sources of INPs in the high-latitudes play a key role in the formation of MPCs.

Key points: • A new divergence method is developed to estimate methane emissions based on satellite observations, requiring no a priori emissions. • The applicability of this method in identifying and quantifying sources is proven by a GEOS-Chem simulation with known a priori emissions. • The estimated emissions over Texas (United States) based on TROPOMI observations are evaluated and are found to be robust. Abstract We present a new divergence method to estimated methane (CH 4) emissions from satellite observed mean mixing ratio of methane (XCH 4) by deriving the regional enhancement of XCH 4 in the Planetary Boundary Layer (PBL). The applicability is proven by comparing the estimated emissions with its a priori emission inventory from a 3-month GEOS-Chem simulation. When applied to TROPOspheric Monitoring Instrument (TROPOMI) observations, sources from well-known oil/gas production areas, livestock farms and wetlands in Texas become clearly visible in the emission maps. The calculated yearly averaged total CH 4 emission over the Permian Basin is 3.06 [2.82, 3.78] Tg a-1 for 2019, which is consistent with previous studies and double that of EDGAR v4.3.2 for 2012. Sensitivity tests on PBL heights, on the derived regional background and on wind speeds suggest our divergence method is quite robust. It is also a fast and simple method to estimate the CH 4 emissions globally. Plain Language Summary Methane (CH 4) is an important greenhouse gas in the atmosphere and plays a crucial role in the global climate change. It kept increasing over the last decades. About 70% of CH 4 comes from human activities like oil/gas productions or livestock farms. The recently launched TROPOspheric Monitoring Instrument (TROPOMI) provides an opportunity to estimate the emissions of CH 4 on a regional scale. This work presents a new method to fastly derive CH 4 emissions at a fairly high spatial resolution without a priori knowledge of sources.

The Southern Annular Mode (SAM) is the leading mode of atmospheric variability in the extratropical Southern Hemisphere, and its variations affect westerly winds, regional storm tracks, midlatitude wildfire activity, Antarctic and Southern Ocean dynamics, and surface mass balance. The SAM is therefore of high importance to both ecosystems and societies across the Southern Hemisphere. The behavior of the SAM has been extensively studied during the instrumental era, but there is substantially less confidence and considerable disagreement in its decadal to centennial-scale variability over the Common Era. Studying these longer time scales requires millennial-length reconstructions, but the sparsity of multi-century proxy records in the Southern Hemisphere has hindered the production of such reconstructions. Consequently, variability and trends in the SAM remain uncertain through most of the Common Era. Here, we use paleoclimate data assimilation to reconstruct the austral summer (DJF) SAM index (SAMI) over the entire Common Era. Our method integrates the South American Drought Atlas, Australia-New Zealand Drought Atlas, and the PAGES2k temperature-sensitive proxy network with a multi-model ensemble of last millennium GCM simulations using an offline ensemble Kalman Filter with a stationary prior. We use a novel nested variance adjustment to correct for the effect of changing proxy availability through time. Our reconstruction is not calibrated to the observed SAMI, yet exhibits a correlation coefficient greater than 0.6 over the instrumental era. Using superposed-epoch and wavelet analyses, we find the reconstruction exhibits minimal response to volcanic and solar forcings and is instead dominated by internal climate variability until the late 20th century. Our data assimilation framework also facilitates the use of optimal-sensor analysis, which we use to identify key proxy sites at different time periods in the reconstruction. Prior to 1400 CE, the reconstruction is strongly influenced by two tree-ring records (Mt. Read, Tasmania and Oroko, New Zealand) and two ice-cores (WDC05A and Plateau Remote). Finally, we examine the coherence of our results against existing reconstructions and compare reconstructed 20th century trends with the instrumental record.

The investigation into the physical mechanisms and behavior of geoelectric fields as potential precursors of earthquakes is a crucial yet challenging task in seismo-electromagnetics. Assuming the electrokinetic effect as a mechanism, we propose a novel numerical modeling algorithm based on the framework of the LAC GRTM to explore the behavior of geoelectric fields in stratified porous media during earthquake preparation. This algorithm incorporates two innovative aspects: (1) employing the Jordan decomposition to address the degeneracy problem encountered in quasi-stationary scenarios, and (2) utilizing digital linear filters for Hankel transforms to conduct the wavenumber integration. The accuracy of the algorithm in computing static displacements in an extremely low porosity half-space model is verified through comparison with analytical solutions. Numerical results from a half-space model demonstrate a strong consistency between the vertical electric field and filtration displacement. Notably, the maximum amplitude of the vertical electric field can reach approximately 2878.8 mV/km, which is detectable by receivers located at considerable distances from the epicenter. In addition, three distinct types of pre-earthquake electric fields are identified: (1) the localized electric field induced by slow P waves, (2) the interface electric responses, and (3) the direct converted electric field from the source. Results from a multi-layer porous model show the significant influence of the water table on the amplitude of the vertical electric field. This can be attributed to the influence of water saturation and permeability on the coefficient that combines the vertical electric field and the vertical filtration displacement. We also analyze the temporal evolution of different fields during earthquake preparation, which demonstrates a robust correlation between the temporal characteristics of pore pressure and that of the vertical electric field. This indicates that the vertical electric field offers an effective means to detect pore pressure evolution during earthquake preparation. Its sensitivity to the strike, rake, and dip angles of potential faults indicates its potential for determining fault geometry related to an impending earthquake. Furthermore, fluctuations in displacements enable a rough estimation of variations in stress and confining pressure. These observations underscore the advantage of integrating geoelectric precursor data with continuous displacement observations to enhance our understanding of earthquake preparation mechanisms.

• Frictional ruptures initiate via a characteristic nucleation process that triggers dynamic rupture essentially • Nucleation replaces the concept of a ‘static friction coefficient’ • The nucleation process possesses unique characteristic general properties • Nucleation details depend on local topography

Damage zones are important to the rupture dynamics, evolution and fluid coupling of earthquakes. However, information about the damage zone at depth is limited. It is unclear if damage zones increase or decrease in intensity with depth. Here we use marine 3-D seismic surveys and modern fault detection methods to address the depth-dependent structure of damage zones. We use two overlapping legacy industry seismic volumes collected offshore of Los Angeles span approximately 20 km of the Palos Verdes strike-slip fault. The data here allows visibility of the damage zone in the sedimentary formations to 2,200 meters depth, which is comparable to the constraints provided by SAFOD and other studies. Using both interpreted mapped primary fault strands and seismic attributes to identify subsidiary faults, we map and quantify spatial variations in damage zone size and intensity. The damage zone consists of subsidiary faults, or linked discontinuities in the seismics selected within assigned ranges of geometries to the primary strands. Damage was identified using a variation of the seismic attribute semblance, or multi-trace similarity. This method allows interrogation of damage zone in response to changes sedimentary lithology and fault geometry. Subsidiary faults delineate the damage zone to approximately 1 km in width and fracture density decays with distance from the primary fault strands for all sedimentary lithologies in the study area. The damage zone narrows with depth, but fracture density increases because the intensity of fracturing more than compensates for the decreased width. In the thickest formation we find that fracture density increases as Z1.8, where Z is depth in meters. These results are then compared to resolution changes with depth. The damage intensity increase and localization potentially provides a strong constraint for efforts to determine an appropriate rheology for producing damage zones and studying their effects.