While Hg in sediments is increasingly used as a proxy for deep-time volcanic activity, the behaviour of Hg in OM-rich sediments as they undergo thermal maturation is not well understood. In this study, we evaluate the effects of thermal maturation on sedimentary Hg contents and, thereby, the impact of thermal maturity on the use of the Hg/TOC proxy for large igneous province (LIP) volcanism. We investigate three cores (marine organic matter) with different levels of thermal maturity in lowermost Toarcian sediments (Posidonienschiefer) from the Lower Saxony Basin in Germany. We present Hg content, bulk organic geochemistry, and total sulfur in three cores with different levels of thermal maturity. The comparison of Hg data between the three cores indicates that Hg content in the mature/overmature sediments have increased > 2-fold compared to Hg in the immature deposits. Although difficult to confirm with the present data, we speculate that redistribution within the sedimentary sequence caused by the mobility and volatility of the element under relatively high temperatures may have contributed to Hg enrichment in distinct stratigraphic levels of the mature cores. Regardless of the exact mechanism, elevated Hg content together with organic-carbon loss by thermal maturation exaggerate the value of Hg/TOC in mature sediments, suggesting that thermal effects have to be considered when using TOC-normalised Hg as a proxy for far-field volcanic activity.
Volcanic seismicity provides essential insights into the behavior of an active volcano across multiple time scales. However, to understand how magma moves as an eruption evolves, better knowledge of the geometry and physical properties of the magma plumbing system is required. In this study, using full-wave ambient noise tomography, we image the 3-D crustal shear-wave velocity structure below GreatSitkin Volcano in the central Aleutian Arc. The new velocity model reveals two low-velocity anomalies, which correlate with the migration of volcanic seismicity. With a partial melt of up to about 30%, these low-velocity anomalies are characterized as mushy magma reservoirs. We propose a six-stage eruption cycle to explain the migration of seismicity and the alternating eruption of two reservoirs with different recharging histories. The findings in this study have broad implications for the dynamics of magma plumbing systems and the structural control of eruption behaviors.
The total meridional heat transport (MHT) is relatively stable across different climates. Nevertheless, the strength of individual processes contributing to the total transport are not stable. Here we investigate the MHT and its main components especially in the atmosphere, in five coupled climate model simulations from the Deep-Time Model Intercomparison Project (DeepMIP). These simulations target the Early Eocene Climatic Optimum (EECO), a geological time period with high CO2 concentrations, analogous to the upper range of end-of-century CO2 projections. Preindustrial and early Eocene simulations at a range of CO2 levels (1x, 3x and 6x preindustrial values) are used to quantify the MHT changes in response to both CO2 and non-CO2 related forcings. We found that atmospheric poleward heat transport increases with CO2, while the effect of non-CO2 boundary conditions (e.g., paleogeography, land ice, vegetation) is causing more poleward atmospheric heat transport on the Northern and less on the Southern Hemisphere. The changes in paleogeography increase the heat transport via transient eddies at the mid-latitudes in the Eocene. The Hadley cells have an asymmetric response to both the CO2 and non-CO2 constraints. The poleward latent heat transport of monsoon systems increases with rising CO2 concentrations, but this effect is offset by the Eocene topography. Our results show that the changes in the monsoon systems’ latent heat transport is a robust feature of CO2 warming, which is in line with the currently observed precipitation increase of present day monsoon systems.
Recent advancements in proximal remote sensing have increased the spatial and temporal resolution of data collection, as well as the availability of these technologies for applications to precision agriculture. These sensors have allowed the collection of new and large quantities of data, which have been used to successfully determine phenotypes and parametrize crop growth models. So far, these data streams have been mostly used separately, though they contain unique structural, spatial, and spectral information. Thus, this research aims to integrate these disparate data sources to improve estimations of agronomically important crop traits. In this study, we examine two high-throughput and relatively inexpensive remote platforms: unoccupied ground vehicles (UGV) and unoccupied aerial vehicles (UAV). Data were collected on maize hybrids from the Genomes to Fields initiative over 5 years, from 2018 to 2022, in Aurora, NY. We used ground rovers to collect lidar scans, which were converted to point clouds, to construct the three-dimensional sub-canopy architecture of maize plants. Multispectral sensors, covering red, green, blue, red-edge, and near infrared (NIR) were deployed on a UAV platform to characterize maize canopies. Machine learning methods, including autoencoders, will be used to extract latent phenotypes from the lidar point clouds and multispectral images. Ultimately, these will be used to predict manually measured traits, such as yield, in order to compare the prediction accuracies of models using these measurements separately and jointly.
Seismological data can provide timely information for slope failure hazard assessments, among which rockfall waveform identification is challenging for its high waveform variations across different events and stations. A rockfall waveform does not have typical body waves as earthquakes do, so researchers have made enormous efforts to explore characteristic function parameters for automatic rockfall waveform detection. With recent advances in deep learning, algorithms can learn to automatically map the input data to target functions. We develop RockNet via multitask and transfer learning; the network consists of a single-station detection model and an association model. The former discriminates rockfall and earthquake waveforms. The latter determines the local occurrences of rockfall and earthquake events by assembling the single-station detection model representations with multiple station recordings. RockNet achieves macro F1 scores of 0.990 and 0.981 in terms of discriminating earthquakes and rockfalls from other events with the single-station detection and association models, respectively.
The current narrative of artificial upwelling (AU) is to use ocean pipes to pump nutrient rich deep water to the ocean surface, thereby stimulating the biological carbon pump. This simplistic concept of AU does not take the response of the solubility pump or the CO2 emission scenario into account. Using global ocean-atmosphere model experiments and several idealized model tracers we show that the effectiveness of almost globally applied AU from the year 2020 to 2100 to draw down CO2 from the atmosphere is strongly dependent on the CO2 emission scenario and ranges from 1.01 Pg C / year under RCP 8.5 to 0.32 Pg C / year under RCP 2.6. The solubility pump becomes equally effective compared to the biological carbon pump under the highest emission scenario (RCP 8.5), but responds with CO2 outgassing under low CO2emission scenarios.
3D scans of real world objects are often represented by point clouds, creating XYZ-coordinates of individual scan points. However, unlike point clouds that are generated from CAD data, points generated from a real world scene lack information about their local context, making segmentation of the structural information contained in the data difficult. Using neural networks (e.g. PointNet) has shown promising results. However, this approach is not well suited for scans of large areas of similar objects, like e.g. a wheat field, because of limitations of the input vector size of the neural network. In addition, point clouds are often unordered, further complicating processing. Since point clouds of biological objects often contain recurring features, we propose to subdivide the point cloud into locally neighboring subsets with a fixed number of points. The collection of subsets can then be used to train neural networks. This approach preserves the original resolution of the point cloud while offering simple data augmentation concepts like creating a number of different subset collections from the same ground truth. There are several advantages to this approach, like significantly simplifying the training phase, because a single, large annotated scan can be sufficient for training, utilizing the similarity of the instances of a plant in the field.
Oceanic emissions of nitrous oxide (N2O) account for roughly one-third of all natural sources to the atmosphere. Hot-spots of N2O outgassing occur over oxygen minimum zones (OMZs), where the presence of steep oxygen gradients surrounding anoxic waters leads to enhanced N2O production from both nitrification and denitrification. However, the relative contributions from these pathways to N2O production and outgassing in these regions remains poorly constrained, in part due to shared intermediary nitrogen tracers, and the tight coupling of denitrification sources and sinks. To shed light on this problem, we embed a new, mechanistic model of the OMZ nitrogen cycle within a three-dimensional eddy-resolving physical-biogeochemical model of the ETSP, tracking contributions from remote advection, atmospheric exchange, and local nitrification and denitrification. Our results indicate that net N2O production from denitrification is approximately one order of magnitude greater than nitrification within the ETSP OMZ. However, only ~30% of denitrification-derived N2O production ultimately outgasses to the atmosphere in this region (contributing ~34% of the air-sea N2O flux on an annual basis), while the remaining is exported out of the domain. Instead, remotely-produced N2O advected into the OMZ region accounts for roughly half (~56%) of the total N2O outgassing, with smaller contributions from nitrification (~7%). Our results suggests that, together with enhanced production by denitrification, upwelling of remotely-derived N2O (likely produced via nitrification in the oxygenated ocean) contributes the most to N2O outgassing over the ETSP OMZ.
Image based high throughput plant phenotyping is a powerful tool to capture and quantify diverse plant traits. The available commercial platforms are often cost-prohibitive. This study describes the development of a low cost, automated plant phenotyping platform, which can acquire images, transfer data, segment the images, extract the traits and perform data analysis using low-cost microcomputers, cameras and IoT irrigation system. Quantifiable plant traits (e.g., shape, area, height, color) were extracted from the plant images using an in-house pipeline developed in R language. An experiment of water stress (waterlogging and drought) on Mentha arvensis (Menthol mint) crop (cv. CIM-Kosi) was conducted to demonstrate image traits being used as a proxy for plant response to water stress. It was found that the effect of drought stress on plant height and number of secondary branches could be correlated to color traits of plant canopy images. Also, the effect of waterlogging stress on chlorophyll and flavonoid content could be related to the shape traits of plant canopy images and effect on waterlogging on plant height and canopy width could be associated with color and texture traits. The imaging platforms could successfully demonstrate a viable low-cost solution for incorporating high-throughput plant phenotyping in various plant stress related research applications.
WWLLN (World Wide Lightning Location Network) data on global lightning are used to investigate the increase of total lightning strokes at Arctic latitudes. We focus on the summertime data from June, July and August, which average >200,000 strokes each year above 65o North latitude, for each of the years from 2010 – 2020. The influence of WWLLN network detection efficiency increases is minimized by normalizing to the total global strokes for each northern summer. The ratio of strokes occurring above 65o increases with latitude, showing that the Arctic is becoming much more influenced by lightning. We compare the increasing fraction of strokes with the global temperature anomaly for those months, and find that the fraction of strokes above 65o to total global strokes for these months increases linearly with the temperature anomaly and grows by a factor of 3 as the anomaly increases from 0.65 to 0.95 degrees C.
Through machine learning and remote sensing, a high-end model with a finer resolution for groundwater recharge has been developed for the region of South-East Asia. The groundwater recharge coefficient can be found by the application of Random Forest regression followed by the implication of the water budget method to calculate the Groundwater Recharge values. Climatic factors such as precipitation and actual evapotranspiration to map Groundwater Recharge has been framed with a sophisticated machine learning method to be considered as a scale predicting model. A comprehensive visualization of the dataset has been done; the accuracy of the model is noted through random forest regression. Thus, the model can be used for various regions of the dataset specifically for the area where there is a lack of reach for data. It can be successfully used to form a sophisticated end-to-end ML model. Keywords: Machine Learning, Remote Sensing, Groundwater Recharge, Climate science.
Atmospheric gravity waves (GWs) span a broad range of length scales. As a result, the un-resolved and under-resolved GWs have to be represented using a sub-grid scale (SGS) parameterization in general circulation models (GCMs). In recent years, machine learning (ML) techniques have emerged as novel methods for SGS modeling of climate processes. In the widely-used approach of supervised (offline) learning, the true representation of the SGS terms have to be properly extracted from high-fidelity data (e.g., GW-resolving simulations). However, this is a non-trivial task, and the quality of the ML-based parameterization significantly hinges on the quality of these SGS terms. Here, we compare three methods to extract 3D GW fluxes and the resulting drag (GWD) from high-resolution simulations: Helmholtz decomposition, and spatial filtering to compute the Reynolds stress and the full SGS stress. In addition to previous studies that focused only on vertical fluxes by GWs, we also quantify the SGS GWD due to lateral momentum fluxes. We build and utilize a library of tropical high-resolution ($\Delta x =3~km$) simulations using weather research and forecasting model (WRF). Results show that the SGS lateral momentum fluxes could have a significant contribution to the total GWD. Moreover, when estimating GWD due to lateral effects, interactions between the SGS and the resolved large-scale flow need to be considered. The sensitivity of the results to different filter type and length scale (dependent on GCM resolution) is also explored to inform the scale-awareness in the development of data-driven parameterizations.
Standard climate projections represent future volcanic eruptions by a constant forcing inferred from 1850-2014 volcanic forcing. Using the latest ice-core and satellite records to design stochastic eruption scenarios, we show that there is a 95% probability that explosive eruptions could emit more sulfur dioxide (SO2) into the stratosphere over 2015-2100 than current standard climate projections (i.e., ScenarioMIP). Our simulations using the UK Earth System Model with interactive stratospheric aerosols show that for a median future eruption scenario, the 2015-2100 average global-mean stratospheric aerosol optical depth (SAOD) is double that used in ScenarioMIP, with small-magnitude eruptions (< 3 Tg of SO2) contributing 50% to SAOD perturbations. We show that volcanic effects on large-scale climate indicators, including global surface temperature, sea level and sea ice extent, are underestimated in ScenarioMIP because current climate projections do not fully account for the recurrent frequency of volcanic eruptions of different magnitudes.
An intense earthquake swarm is occurring in the crust of the northeastern Noto Peninsula, Japan. Fluid movement related to volcanic activity is often involved in earthquake swarms in the crust, but the last volcanic activity in this area occurred in the middle Miocene (15.6 Ma), and no volcanic activity has occurred since then. In this study, we investigated the cause of this earthquake swarm using spatiotemporal variation of earthquake hypocenters and seismic reflectors. Hypocenter relocation revealed that earthquakes moved from deep to shallow areas via many planes, similar to earthquake swarms in volcanic regions. The strongest M5.4 earthquake initiated near the migration front of the hypocenters. Moreover, it ruptured the seismic gap between the two different clusters. The initiation of this earthquake swarm occurred at a locally deep depth (z = ~17 km), and we found a distinctive S-wave reflector, suggesting a fluid source in the immediate vicinity. The local hypocenter distribution revealed a characteristic ring-like structure similar to the ring dike that forms just above the magma reservoir and is associated with caldera collapse and/or magma intrusion. These observations suggest that the current seismic activity was impacted by fluids related to ancient or present hidden magmatic activity, although no volcanic activity was reported. Significant crustal deformation was observed during this earthquake swarm, which may also be related to fluid movement and contribute to earthquake occurrences. A seismic gap zone in the center of the swarm region may represent an area with aseismic deformation.
Freshwater ecosystems provide vital services, yet are facing increasing risks from global change. In particular, lake thermal dynamics have been altered around the world as a result of climate change, necessitating a predictive understanding of how climate will continue to alter lakes in the future as well as the associated uncertainty in these predictions. Numerous sources of uncertainty affect projections of future lake conditions but few are quantified, limiting the use of lake modeling projections as management tools. To quantify and evaluate the effects of two potentially important sources of uncertainty, lake model selection uncertainty and climate model selection uncertainty, we developed ensemble projections of lake thermal dynamics for a dimictic lake in New Hampshire, USA (Lake Sunapee). Our ensemble projections used four different climate models as inputs to five vertical one-dimensional (1-D) hydrodynamic lake models under three different climate change scenarios to simulate thermal metrics from 2006 to 2099. We found that almost all the lake thermal metrics modeled (surface water temperature, bottom water temperature, Schmidt stability, stratification duration, and ice cover, but not thermocline depth) are projected to change over the next century. Importantly, we found that the dominant source of uncertainty varied among the thermal metrics, as thermal metrics associated with the surface waters (surface water temperature, total ice duration) were driven primarily by climate model selection uncertainty, while metrics associated with deeper depths (bottom water temperature, stratification duration) were dominated by lake model selection uncertainty. Consequently, our results indicate that researchers generating projections of lake bottom water metrics should prioritize including multiple lake models for best capturing projection uncertainty, while those focusing on lake surface metrics should prioritize including multiple climate models. Overall, our ensemble modeling study reveals important information on how climate change will affect lake thermal properties, and also provides some of the first analyses on how climate model selection uncertainty and lake model selection uncertainty interact to affect projections of future lake dynamics.
The factors controlling earthquake swarm duration are remain unclear, especially in the long-living ones. A severe earthquake swarm struck the tip of the Noto peninsula, Japan. Ten M > 4.0 earthquakes occurred, and the sequence has continued more than four years. We investigated the spatiotemporal characteristics of the swarm using relocated hypocenters to elucidate the factors causing this long duration. The swarm consists of four seismic clusters-northern, northeastern, western, and southern-the latter of which began first. Diffusive hypocenter migrations were observed in the western, northern, and northeastern clusters with moderate to low diffusivities, implying a low-permeability environment. Rapid diffusive migration associated with intermittent seismicity deep within the southern cluster suggests the presence of a highly pressurized fluid supply. We conclude that the nature of this fluid supply combined with intermittent seismicity from the southern cluster and a low-permeability environment are the key causes of this long-living swarm.
Following laboratory experiments and friction theory, slow slip events and seismicity rate accelerations observed before mainshock are often interpreted as evidence of a nucleation phase. However, such precursory observations still remain scarce and are associated with different time and length scales, raising doubts about their actual preparatory nature. We study the 2017 Valparaiso Mw= 6.9 earthquake, which was preceded by aseismic slip accompanied by an intense seismicity both suspected to reflect its nucleation phase. We complement previous observations, which have focused only on precursory activity, with a continuous investigation of seismic and aseismic processes from the foreshock sequence to the post-mainshock phase. By building a high-resolution seismicity catalog and searching for anomalous seismicity rate increases compared to aftershock triggering models, we highlight an over-productive seismicity starting within the foreshock sequence and persisting several days after the mainshock. Using repeating earthquakes and high-rate GPS observations, we highlight a transient aseismic perturbation starting just before the first foreshock and extending continuously after the mainshock. The estimated slip rate is lightly impacted by large magnitude earthquakes and does not accelerate towards the mainshock. Therefore, the unusual seismic and aseismic activity observed during the 2017 Valparaiso sequence might be interpreted as the result of a slow slip event starting before the mainshock and extending beyond it. Rather than pointing to a possible nucleation phase of the 2017 Valparaiso mainshock, the identified slow slip event acts as an aseismic loading of nearby faults, increasing the seismic activity, and thus the likelihood of a large rupture.
A detailed 3-D tomographic model of the whole mantle beneath the circum-Arctic region is obtained by applying an updated global tomography method to a large amount of P-wave arrival time data. Our model clearly shows the subducted Izanagi and Farallon slabs penetrating into the lower mantle beneath Eurasia and North America, respectively. In the region from Canada to Greenland, a giant stagnant slab lying below the 660-km discontinuity is revealed. Because this slab has a texture that seems to be due to subducted oceanic ridges, the slab might be composed of the Izanagi, Farallon, Kula and Vancouver slabs that had subducted during ~80−20 Ma. During that period, a complex rift system represented by division between Canada and Greenland was developed. The oceanic ridge subduction and hot upwelling in the big mantle wedge above the stagnant slab caused a tensional stress field, which might have induced these complex tectonic events.