The composition of volcanic ash, which is a source of primary description data in volcanological study, is important information for estimating the eruption styles and sequences. However, its description under a microscope by human operation has difficulties in classification thresholds and time and effort-consumptions. This study demonstrates an accurate and rapid description of volcanic ash samples that consist of thousands of grains. We analyzed nine tephra samples (two magmatic (dry) and seven phreatomagmatic (wet)), which were produced in the 1983 A.D. fissure eruption event at Miyakejima volcano, Japan. Our dataset, which is consists of multivariate shape and transparency parameters, was rapidly obtained using an automated grain analyzer. In this study, we applied a two-step cluster analysis to objectively and quantitatively define grain type and classify samples. To define grain types, we referred to the statistically appropriate number of clusters of whole-ash grains in our samples. For our samples, the appropriate number of clusters for grain type was five. Each grain type is characterized by parameters and has different proportions among our samples. In wet tephra samples, grains that were categorized as transparent and highly irregularly shaped types were relatively abundant. Those grains can be considered as vesicular sideromelane grains, which are often found in products of phreatomagmatic eruptions. Such a standardized description of volcanic ash based on statistically determined grain type will contribute to initial descriptions before subsequent detailed analysis.

Field kits for testing the level of a toxicant in the environment are inherently less accurate than a laboratory instrument. Using a specific example, we argue here that kit measurements still have a key role to play when the spatial distribution of a toxicant is very heterogeneous. The context is provided by the groundwater arsenic problem in Bangladesh. We combine here two data sets, a blanket survey of 6595 wells over a 25 km2 based on laboratory measurements and 900 paired kit and laboratory measurements from the same area. We explore different hypothetical mitigation scenarios based on actual data that rely on households with a high-arsenic well switching to a nearby low-arsenic well. We show that the decline in average exposure to arsenic from relying on kit rather than laboratory data is modest in relation to the logistical and financial challenge of delivering exclusively laboratory data. Our analysis indicates that the 50 ug/L threshold used in Bangladesh to distinguish safe and unsafe wells, rather than the WHO guideline of 10 ug/L, is close to optimal in terms of average exposure reduction. We also show, however, that providing kit data at the maximum possible resolution rather than merely classifying wells as unsafe or safe would be even better. These findings are relevant as the government of Bangladesh is about to launch a new blanket testing campaign of millions of wells using field kits.

Despite the importance of turbidity currents in environmental and resource geology, their flow conditions and mechanisms are not well understood. This study proposes a novel method for the inverse analysis of turbidity currents using a deep learning neural network (DNN) to better explore the properties of turbidity currents. The aim of this study is to verify the DNN inverse method using numerical and flume experiment datasets. Numerical datasets of turbidites were generated with a forward model. Then, the DNN model was trained to find the functional relationship between flow conditions and turbidites by processing the numerical datasets. The performance of the trained DNN model was evaluated with 2000 numerical test datasets and 5 experiment datasets. Inverse analysis results on numerical test datasets indicated that flow conditions can be reconstructed from depositional characteristics of turbidites. For experimental turbidites, spatial distributions of grain size and thickness were consistent with the sample values. Concerning hydraulic conditions, flow depth H, layer-averaged velocity U, and flow duration Td were reconstructed with a certain level of deviation. Greater discrepancies between the measured and reconstructed values of flow concentration were observed relative to the former three parameters (H, U, Td), which may be attributed to difficulties in measuring the flow concentration during experiments. The precision of the reconstructions for experimental datasets was estimated using Jackknife resampling. Although the DNN model did not provide perfect reconstruction, it proved to be a significant advance for the inverse analysis of turbidity currents.

The ocean plays a critical role in modulating climate change by sequestering CO2 from the atmosphere. Quantifying the CO2 flux across the air-sea interface requires time-dependent maps of surface ocean partial pressure of CO2 (pCO2), which can be estimated using global ocean biogeochemical models (GOBMs) and observational-based data products. GOBMs are internally consistent, mechanistic representations of the ocean circulation and carbon cycle, and have long been the standard for making spatio-temporally resolved estimates of air-sea CO2 fluxes. However, there are concerns about the fidelity of GOBM flux estimates. Observation-based products have the strength of being data-based, but the underlying data are sparse and require significant extrapolation to create global full-coverage flux estimates. The Lamont Doherty Earth Observatory-Hybrid Physics Data (LDEO-HPD) pCO2 product is a new approach to estimating the temporal evolution of surface ocean pCO2 and air-sea CO2 exchange. LDEO-HPD uses machine learning to merge high-quality observations with state-of-the-art GOBMs. We train an eXtreme Gradient Boosting (XGB) algorithm to learn a non-linear relationship between model-data mismatch and observed predictors. GOBM fields are then corrected with the predicted model-data misfit to estimate real-world pCO2 for 1982-2018. A benefit of this approach is that model-data misfit has reduced temporal skewness compared to the observed pCO2 that is the target variable for other machine-learning based reconstructions. This supports a robust reconstruction by LDEO-HPD that is in better agreement with independent observations than other estimates. LDEO-HPD global ocean uptake of CO2 is in agreement with other products and the Global Carbon Budget 2020.

This evaluates the potential for a newly proposed non-linear subsurface flux equation to improve the performance of the hydrological Hillslope Link Model (HLM). The equation contains parameters that are functionally related to the hillslope steepness and the presence of tile drainage. As a result, the equation allows a better representation of hydrograph recession curves, hydrograph timing, and total runoff volume. The authors explore the new parameterization’s potential by comparing a set of diagnostic and prognostic setups in HLM. In the diagnostic approach, they configure 12 different scenarios with spatially uniform parameters over the state of Iowa. In the prognostic case, they use information from topographical maps and known locations of tile drainage to distribute parameter values. To assess performance improvements, they compare simulation results to streamflow observations during a 17-year period (2002–2018) at 140 U.S. Geological Survey (USGS) gauging stations. The operational setup of the HLM model used at the Iowa Flood Center (IFC) serves as a benchmark to quantify overall model improvement. In particular, the new equation provides better representation of recession curves and the total streamflow volumes. However, when comparing the diagnostic and prognostic setups, the authors find discrepancies in the spatial distribution of hillslope scale parameters. The results suggest that more work is required when using maps of physical attributes to parameterize hydrological models. The findings also demonstrate that the diagnostic approach is a useful strategy to evaluate models and assess changes in their formulations.

A sequential inversion methodology for combining geophysical data types of different resolutions is developed and applied to monitoring of large-scale CO2 injection. The methodology is a two-step approach within the Bayesian framework where lower resolution data are inverted first, and subsequently used in the generation of the prior model for inversion of the higher resolution data. For the application of CO2 monitoring, the first step is done with either controlled-source electromagnetic (CSEM) or gravimetric data, while the second step is done with seismic amplitude-versus-offset (AVO) data. The Bayesian inverse problems are solved by sampling the posterior probability distributions using either the ensemble Kalman filter or ensemble smoother with multiple data assimilation. A carefully designed parameterization is used to represent the unknown geophysical parameters: electric conductivity, density, and seismic velocity. The parameterization is well suited for identification of CO2 plume location and variation of geophysical parameters within the regions corresponding to inside and outside of the plume. The inversion methodology is applied to a synthetic monitoring test case where geophysical data are made from fluid-flow simulation of large-scale CO2 sequestration in the Skade formation in the North Sea. The numerical experiments show that seismic AVO inversion results are improved with the sequential inversion methodology using prior information from either CSEM or gravimetric inversion.

Interferometric Synthetic Aperture Radar (InSAR) phase unwrapping error is a major limiting factor on the InSAR-derived tectonic deformation velocity. This is particularly the case when atmospheric turbulence, large deformation gradient and strong phase noise exist. To address limitations of existing phase unwrapping error correction methods which are not supported for multi-looked InSAR data, here we present a new algorithm that integrates decorrelation phase correction, triplet phase closure (TPC) test and integer linear programming (ILP) to overcome this limit. The rationale behind is that we mitigate the phase inconsistence using decorrelation correction and then detect the phase unwrapping error magnitude using TPC. Next we borrow the ILP from Compressed Sensing that converts the phase unwrapping error correction to a sparse signal recovery problem. We demonstrated the validity of our method by using synthetic data and 5-years Sentinel-1 real data covering the Central San Andreas Fault creep section, where exists obvious tectonic deformation, strong atmospheric disturbance and decorrelated scatterers, and the inverted long-term creep model constrained by InSAR velocity after correction shows a lower uncertainty than that constrained by the uncorrected one.

The variability of streams in the atmosphere and the ocean, as shown in a number of studies, affects the change in the speed of the Earth’s rotation. However, it can cause a reverse reaction—a change in the Coriolis force; as a result of this, atmospheric and oceanic streams can have some variability. In the following work, a hypothesis is presented and considered: it suggests that a change in the volume of Atlantic water inflow into the Barents Sea is related to the change in the Earth’s rotation speed. The paper presents a methodology for determining representative values of the temperature and salinity of seawater that describe the largest possible volume of the sea, as well as a methodology for calculating the content of Atlantic, river and melt water for the period of 100 years. The change of these parameters, and the length of day values, demonstrates the presence of both linear trends and cyclical fluctuations with a period of about 80 years. As a result, it was shown that a decrease in the Earth’s rotation speed with a linear trend somewhat decreases the observed intensity of the processes of global climate change in the Arctic region (an increase in temperature and salinity). Due to the summation of positive anomalies, both a linear trend and a quasi-80-year cycle, the modern period is characterized by abnormally high values of water temperature, the growth of which has not stopped and will possibly reach its maximum between 2025 and 2030.

We present the Malawi Active Fault Database (MAFD), a geospatial database of 114 active fault traces in Malawi, and in neighboring Tanzania and Mozambique. The MAFD has been developed from a multidisciplinary dataset: high resolution digital elevation models, field observations, aeromagnetic and gravity data, and seismic reflection surveys from offshore Lake Malawi. Active faults longer than 50 km are found throughout Malawi, where seismic risk is increasing due to its rapidly growing population and its seismically vulnerable building stock. The MAFD also provides an opportunity to investigate the population of normal faults in an incipient continental rift. We find that the null hypothesis that the distribution of fault lengths in the MAFD is described by a power law cannot be rejected. Furthermore, a power-law distribution of faults in Malawi is consistent with its thick seismogenic crust (~35 km), and low (<8%) regional extensional strain that is predominantly (50-75%) accommodated across relatively long hard-linked border faults. Cumulatively, the data and inferences drawn from the MAFD highlight the importance of integrating onshore and offshore geological and geophysical data to develop active fault databases along the East African Rift and similar continental settings, both to understand the regional seismic hazard and tectonic evolution.

Hydrofluorocarbons (HFCs) have been increasingly replacing chlorofluorocarbons and hydrochlorofluorocarbons. Although their ozone-depleting potential is negligible, as potent greenhouse gases they indirectly influence stratospheric ozone recovery. Measurements and model projections must continue to evaluate HFC limitation measures and assess the long-term impact of HFCs on the atmospheric radiation budget and stratospheric ozone. In this study, we present multi-member ensemble simulations designed to estimate the impact of HFCs on stratospheric temperature, ozone and circulation changes at the end of the century. We compared simulations with and without HFCs for two three-dimensional chemistry-climate models that use the same chemistry module but different physical schemes. At low and mid-latitudes, temperature and ozone responses were comparable for both models and in general agreement with previous studies. HFCs induced a marked temperature increase up to 5 hPa and vertically alternating positive and negative ozone anomalies. We explained this pattern by competing effects of vertical motion (low and middle stratosphere) and temperature (upper stratosphere) anomalies. At northern high latitudes, there were strong discrepancies with previous studies and between the models themselves, attributed to differences in ozone anomalies caused by wave activity during winter. Quantitatively, we found a net positive, but small, HFC impact on total ozone amounts. Largest anomalies were less than 1% in the winter polar stratosphere. Our results indicate that increasing HFC amounts will likely have a limited impact on stratospheric ozone recovery within this century, with large uncertainty in the polar regions.

Induced polarization (IP) is increasingly applied for hydrological, environmental and agricultural purposes. Interpretation of IP data is based on understanding the relationship between the IP signature and the porous media property of interest. Mechanistic models on the IP phenomenon rely on the Poisson-Nernst-Plank equations, where diffusion and electromigration fluxes are the driving forces of charge transport and are directly related to IP. However, to our knowledge, the impact of advection flux on IP was not investigated experimentally and was not considered in any IP model. In this work, we measured the spectral IP (SIP) signature of porous media under varying flow conditions, in addition to developing and solving a model for SIP signature of porous media, which takes flow into consideration. The experimental and the model results demonstrate that as bulk velocity increases, polarization and relaxation time decrease. Using a numerical model, we established that fluid flow near the particle deforms the electrical double layer (EDL) structure, accounting for the observed reduction in polarization. We found a qualitative agreement between the model and the measurements. Still, the model overestimates the impact of flow rate on SIP signature, which we explain in terms of the flow boundary conditions. Overall, our results demonstrate the sensitivity of the SIP signature to fluid flow, highlighting the need to consider fluid velocity in the interpretation of the SIP signature of porous media, and opening an exciting new direction for noninvasive measurements of fluid flow at the EDL scale.

Does river topography have stage thresholds for maintaining fluvial landforms, and if so how can they be quantified? Geomorphic covariance structure analysis offers a novel, systematic framework for evaluating nested topographic patterns in river corridors. In this study, a threshold in mountain river stage was hypothesized to exist; above this stage landform structure is organized to be freely self-maintaining via flow convergence routing morphodynamics. A 13.2 km segment of the canyon-confined Yuba River, California, was studied using 2944 cross-sections. Geomorphic covariance structure analysis was carried out on a meter-resolution topographic model to test the hypothesis. A critical stage threshold governing flow convergence routing morphodynamics was evident in several metrics. Below this threshold, narrow/high “nozzle” and wide/low “oversized” landforms that are out-of-phase with flow convergence routing morphodynamics dominated (excluding “normal channel”), while above it wide/high “wide bar” and narrow/low “constricted pool” landforms consistent with the flow convergence mechanism were dominant. Three-level nesting of co-located base-bankfull-flood stage landforms was dictated by canyon confinement, with nozzle-nozzle-nozzle nesting as the top permutation, excluding normal channel. In conclusion, this study demonstrates a significantly different and highly effective approach to finding process-based fluvial thresholds that can complement pre-existing methods, such as estimating incipient sediment motion, to get at more powerful dynamics controlling fluvial landforms structure.

Soil property and litter quality are two key factors that control soil organic matter decomposition. Under climate change, it remains unclear how the changes of soil microbial community and litter quality affect soil organic carbon decomposition, although significant changes of these two factors have been reported intensively. This limits our ability to model the dynamics of terrestrial soil carbon in a changing climate. Using a long-term Free Air CO2 Enrichment facility equipped with warming, we investigated the effect of soil property and litter quality change on the decomposition rate of soil organic matter. Results showed that significant change of litter quality was observed under elevated CO2 and warming. Elevated CO2 decreased the concentration of N of rice and wheat straw, while warming decreased the concentration of N and K in wheat straw. However, these changes in plant litter quality did not lead to a shift in soil organic matter decomposition. The legacy effect of long-term elevated CO2 and warming on soil properties dominated the decomposition rate of soil organic matter. Elevated CO2 suppressed soil organic matter decomposition mainly by increasing phosphorous availability and lowering soil C/N, fungi/bacteria ratio, and N-acetyl-glucosaminidase activity; while warming or elevated CO2 plus warming had no effect on soil organic matter decomposition. Our results demonstrated that the change of soil properties other than litter quality control the decomposition of soil organic carbon; and soil property change should be taken into consideration in model developing when predicting terrestrial soil carbon dynamics under elevated atmospheric CO2 and warming.

Volcanic flood basalt eruptions have covered 1000s of km2 with basalt deposits up to kilometers thick. The massive size and extended duration result in enormous releases of climactically-relevant gases such as SO2 and CO2. However, it is still unknown precisely how flood basalt eruptions influence climate via eruption rates and cadence, height of the volcanic plumes, and relative degassing abundance of species like SO2. Once eruptions occur, the complex interplay of photochemistry, greenhouse gas warming, changes to the atmospheric circulation, and aerosol-cloud interactions can only be properly simulated with a comprehensive global climate model (GCM). We created an eruption scenario for the Goddard Chemistry Climate Model (GEOSCCM) that emits SO2 in the near-surface atmosphere constantly and four times per year an explosive eruption that emits much more SO2 in the upper troposphere/lower stratosphere. The eruption lasts for 4 years and emits 30 Gt of SO2 total. This corresponds to ~1/10th of what may have been emitted during the Wapshilla Ridge eruption phase of the Columbia River flood basalt eruption 15-17 Ma. We use a pre-industrial atmosphere and otherwise modern initial and boundary conditions. The massive flux of SO2 into the atmosphere is quickly converted to H2SO4 aerosols. Global area-weighted mean visible band sulfate aerosol optical depth reaches 220 near the end of the eruption, comparable to cumulonimbus clouds. This reduces the surface shortwave radiative flux by 85% and top-of-atmosphere outgoing longwave flux by 70%. Contrary to our expectations, we find that the climate warms during and immediately following the eruption after a very brief initial cooling. Global mean surface temperature peaks 3-4 years after the eruption ends with a +6 K anomaly relative to a baseline simulation without the eruption. Post-eruption regional temperatures, particularly near-equatorial continental areas, see drastic rises of summertime temperatures with monthly mean temperatures equaling or exceeding 40°C. These temperature responses are radiative- and circulation-driven. The eruption warms and raises the tropical tropopause, allowing a massive flux of water vapor into the stratosphere. Stratospheric water vapor, usually ~3 parts per million reaches 1-2 parts per thousand.

The rupture process of earthquakes at intermediate depth (~70-300 km) have been rarely illuminated by a joint analysis of geodetic and seismic observations, hindering our understanding on their dynamic rupture mechanisms. Here we present detailed rupture process of the 2019 Mw8.0 Peru earthquake at the depth of 122 km with a holistic approach reconciling InSAR and broadband seismological waveform data. The joint inversion of InSAR observations and teleseismic body waves results in a finite rupture model that extends ~200 km along strike, with unilateral rupture towards north that lasted for ~60 s. There are four major asperities in the finite fault model which are well corresponding to position and timing of the sources in back-projection (BP) and multiple points source (MPS) results. The largest asperity, which occurred ~40 s after the rupture initiation, was featured with longer and smoother risetime, and radiated much weaker high-frequency seismic waves compared to other smaller asperities. This distinct frequency-dependent rupture requires a strong dynamic weakening mechanism, likely thermal pressurization of pore free water rather than thermal runaway. Our frequency content analysis could be generalized to study other earthquakes including those deeper than 300 km.

Due to its substantial role on the Earth’s biogeochemical cycles and human health, nitrogen is recognized as one of the major water quality indicators of Sustainable Development Goal 6.3.2. Quantifying these potential impacts in large spatial scales still appears to be a grand challenge because of the high computational demand required by the distributed physically based global models and their intensive data requirements for calibration and validation. The former prevents a comprehensive analysis of the full spectrum of the model behavior under different conditions, and the latter impinges on the reliability of model-based inference. To tackle this problem, we developed a data-driven model using a spatio-temporal Random Forest algorithm to predict levels of nitrogen at 0.5-degree spatial resolution from 1992 to 2010 across the world. Several variables representing livestock, climate, hydrology, topography, etc. have been selected as predictors. The response variable of interest was nitrate–nitrite, which is responsible for the high risk of infant methemoglobinemia. Our results indicate that changes in the nitrogen concentration is mainly driven by cattle and sheep population, fertilizer application, precipitation, and temperature variability, implying livestock population, climate change, and anthropogenic forces can be important risk factors for global water quality deterioration. Furthermore, using the predicted levels of nitrogen, we characterized large-scale water quality patterns, and thus identified a few major ‘hot spots’ of water quality. The proposed model can also help assess potential impacts of future scenarios (e.g., livestock production or land use change) on global water quality conditions for better development of effective policy strategies.

While hydraulic fracturing is a widely employed process, the underlying fracturing processes are not clearly understood. Scaled laboratory hydraulic fracturing experiments with seismic monitoring can help with better understanding of the relationship between the generated hydraulic fracture network and the induced micro-seismicity while taking into account the effect of different HF parameters (injection fluid type and rate, stress conditions). In this study, hydraulic fracturing experiments were performed on true-triaxially loaded Barre granite cubes, with real-time micro-seismic monitoring, to identify and characterize the stimulation processes associated with the viscosity and toughness dominated hydraulic fracturing propagation regimes. Water and gear oil were used as the fracturing fluids. Moment tensor inversion technique was employed to determine the fracture mechanisms (tensile, shear, or mixed-mode). Viscosity propagation regime experiments involved higher breakdown pressures and larger injection fluid volumes relative to toughness propagation regime experiments. The micro-seismicity from toughness propagation regime experiments resulted in relatively larger b-value (2.35 compared to 1.62), indicating dominance of small magnitude events. Overall, tensile fractures were dominant in both propagation regimes (ranging from 52% to 58%), which can be attributed to the very low permeability of the granite rock. These results indicate that even for a relatively impermeable rock, theoretical assumptions of mode-I tensile fracturing and the scaling analysis may only be applicable to the near borehole region and as the fracture propagates away from the borehole, the fracturing pattern varies depending on the locally encountered conditions.

The standard Polar Cap (PC) indices, PCN (North) based on magnetic data from Qaanaaq in Greenland and PCS (South) based on data from Vostok in Antarctica, have been submitted from the Arctic and Antarctic Research Institute (AARI) in St. Petersburg, Russia, the Danish Meteorological Institute (DMI), and the Danish Space Research Institute (DTU Space) in different versions. In order to consolidate PCS indices based on Vostok data or replace poor or missing index data, derivation procedures have been developed to generate alternative PCS index values based on data from Dome Concordia (Dome-C) magnetic observations from epoch 2009-2020 of solar cycle 24. The reference levels and calibration parameters needed for calculations of Dome-C-based PCS values in post-event and real-time versions are defined and explained in the present work. Assessment of the new PCS index has shown its unprecedented high relevance. Part of the methods used here such as the quiet reference level construction and the correlation and regression procedures used for calculations of scaling parameters deviate from corresponding features considered inadequate of the IAGA-endorsed PC index derivation methods