The recent development of the TOUGH3 code allows for a faster and more reliable fluid flow simulator. At the same time, new versions of FLAC3D are released periodically, allowing for new features and faster execution. In this paper, we present the first implementation of the coupling between TOUGH3 and FLAC3Dv6/7, maintaining parallel computing capabilities for the coupled fluid flow and geomechanical codes. We compare the newly developed version with analytical solutions and with the previous approach, and provide some performance analysis on different meshes and varying the number of running processors. Finally, we present two case studies related to fault reactivation during CO2 sequestration and nuclear waste disposal. The use of parallel computing allows for meshes with a larger number of elements, and hence more detailed understanding of thermo-hydro-mechanical processes occurring at depth.

Southern Hemisphere (SH) Stratospheric Warming Events (SWEs) are usually associated with a negative phase of the tropospheric Southern Annular Mode (SAM) during the following summer. In contrast, using ensemble climate model simulations we show that the anomalously high ozone concentrations typically occurring during SWEs can force periods of persistent positive tropospheric SAM in austral spring by increasing lower stratospheric static stability and changing troposphere-to-stratosphere wave propagation. Eventually, the tropospheric SAM switches sign to its negative phase in late spring/early summer, but this ‘downward propagation’ of the stratospheric signal does not occur in simulations without seasonal cycle. We find that the downward propagation is forced both dynamically by adiabatic heating and radiatively by increased shortwave absorption by ozone due to the seasonal cycle. Capturing this ozone forcing mechanism in models requires the inclusion of interactive ozone, which has important implications for the predictive skill of current seasonal forecasting systems.

Increased industrialization and pollution necessitate the development of new energy sources while minimizing the impact on the environment. Camelina sativa (L.) Crantz is being investigated as a potential source of novel biofuels, especially as a source of isoparaffin-rich jet fuel. However, currently, there is a paucity of phenotypic and genotypic data for C. sativa. Using a collection of 236 C. sativa genotypes from Eurasia, we assessed the phenotypic diversity of the collection using image data from the Bellwether Foundation Phenotyping Facility at the Donald Danforth Plant Science Center. Traits were measured using PlantCV. Based on phenotypic data, we found that most of the C. sativa accessions fell into 15 groups. There is strong phenotypic variance within each of these groups. Our study provides insight into defining potential C. sativa ideotypes.

Plant architecture is an important contributing factor for enhanced yield production and quality. The architecture traits are analyzed for crop health monitoring and genetic manipulation for generating high yielding varieties. Computer vision methods applied on 3D pointcloud allow more accurate extraction of architecture traits but consume more time and memory compared to 2D images. This study aims to design light weight 3D deep network for Cotton plant part segmentation and derive seven architectural traits of mainstem height, mainstem diameter, branch inclination angle, branch diameter, and number of branches, nodes, and cotton bolls. The pointcloud data is collected using FARO LiDAR scanner. The mainstem, branches and cotton bolls are manually annotated using Open3D. The preprocessing steps of denoising, normalization and down sampling are applied. 3D Deep network is designed to sample 1024, 512 and 256 points where neighborhood aggregation is performed at radius levels of 1cm, 5cm, and 30cm respectively. Features for remaining points are interpolated. The features from each radius level are concatenated and passed to multi-layer perceptron for pointwise classification. Results indicate that mean IoU and accuracy of 84% and 94% are achieved respectively. A 6.5 times speedup in inference time and 2.4 times reduction in memory consumption compared to Pointnet++ is gained. After applying postprocessing on part segments, an R square value of more than 0.8 and mean absolute percentage error of less than 11% are achieved on all derived architecture traits. The trait extraction results indicate potential utility of this process in plant physiology and breeding programs.

Plant roots exhibit distinct architectural organization and overall shape. Current concepts to quantify architectural variation assume a homogeneous phenotype for a given genotype. However, this assumption neglects the observable variation in root architecture for two reasons: (i) sampling strategies are designed to capture architectural variation only for the most common phenotype, and (ii) traits are often measured locally within a root system and ignore the architectural organization. Here, we introduce a new concept: the phenotypic spectrum of crop roots to quantify architectural variation as the number of architecture types for one genotype in a specific environment. We use the shape descriptor DS-curve to characterize the whole root system architecture. Using DS curves as a core, we developed a computing pipeline that combines Kmeans++ clustering, outlier filtering and the Fréchet distance as a similarity metric to classify types of root architectures. Subsequently, we applied this pipeline to analyze a field dataset including three common bean (Phaseolus vulgaris) genotypes DOR364 (n=797), L88_57 (n=1772), and SEQ7 (n=768) under non-limiting and water-stressed conditions in 2015 and 2016. We found DOR364 showed five different root architecture types across environments, while L88_57 and SEQ7 showed four. The total variation within classified root architecture types of DOR364, L88_57, and SEQ reduced by 58.59%, 50.19% and 53.01%, compared to the variation of the complete data sets. DOR364 had stable fractions of root architecture types across environments. In contrast, L88_57 and SEQ7 showed more variation in their fractions. There was no significant biomass difference among root architecture types for all studied genotypes within each environment. As such, we hypothesize that the phenotypic spectrum might buffer the impact of environmental stresses as an acclimatization strategy by changing the composition of root architecture types at the population level.

To assist plant scientists, geneticists, and growers to understand crop-environment interactions, plant phenotyping is a powerful tool for improving crop cultivars and developing decision support systems in farm management. Recent trends use LiDAR to capture three-dimensional (3D) information from plants to analyze traits vital to plant growth and development. However, current terrestrial-based 3D analysis methodologies are time and labor intensive and can be a bottleneck when large agricultural fields need to be analyzed. Robotic technologies can be used to accelerate the field-based measurements of relevant plant features and optimize the high-throughput phenotyping process. In this paper, we present a robotic system with a 3D LiDAR and a data processing pipeline for efficient, high-throughput field phenotyping of cotton crops. The robotic system consists of a Husky robotic platform equipped with a FARO Focus 3D laser scanner. The components of the system are integrated under the ROS framework to ensure interoperability and data integrity and availability at any given time. The data processing pipeline involves the data collection, registration, and analysis tasks for measuring crop traits at the plot level—canopy height, volume, and light interception—and estimating yield. This work demonstrates a crop phenotyping platform that leverages two off-the-shelf equipment for the quantitative assessment of cotton plant traits in the field. This methodology can be extended to other agricultural crops contributing to the advancement of plant phenomics.

Fire regimes are influenced by both exogenous drivers (e.g., increases in atmospheric CO2; and climate change) and endogenous drivers (e.g., vegetation and soil/litter moisture), which constrain fuel loads and fuel aridity. Herein, we identified how exogenous and endogenous drivers can interact to affect fuels and fire regimes in a semiarid watershed in the inland northwestern U.S. throughout the 21st century. We used a coupled ecohydrologic and fire regime model to examine how climate change and CO2 scenarios influence fire regimes over space and time. In this semiarid watershed we found that, in the mid-21st century (2040s), the CO2 fertilization effect on vegetation productivity outstripped the effects of climate change-induced fuel decreases, resulting in greater fuel loading and, thus, a net increase in fire size and burn probability; however, by the late-21st century (2070s), climatic warming dominated over CO2 fertilization, thus reducing fuel loading and fire activity. We also found that, under future climate change scenarios, fire regimes will shift progressively from being flammability to fuel-limited, and we identified a metric to quantify this shift: the ratio of the change in fuel loading to the change in its aridity. The threshold value for which this metric indicates a flammability versus fuel-limited regime differed between grasses and woody species but remained stationary over time. Our results suggest that identifying these thresholds in other systems requires narrowing uncertainty in exogenous drivers, such as future precipitation patterns and CO2 effects on vegetation.

Ground-based microwave radiometry is a common tool to estimate profiles of the atmosphere. With a high temporal resolution radiometers have became an alternative to atmospheric sounding like radiosondes. However remote sensing radiometry requires the use of inversion algorithms, where methods like linear-, quadratic-regression or Artificial Neural Network are commonly used. The present study implements a Bayesian inversion technique as alternative to the state-of-the-art retrieval algorithms provided by the radiometer’s manufacturer firmware. The Bayesian inversion provides advantages over other established methods, namely: the use of a-priori suited for the specific climatology under observation, the estimation of the most likely profile along with its uncertainty obtained from the posteriori distribution, and the feasibility to add synergistic observations from other instruments to increase retrieval capabilities. To estimate the uncertainties resulting from the Bayesian and firmware retrieval algorithms, synthetic radiometer data have been created by means of radiative transfer simulations using radiosonde profiles as descriptor of atmospheric states. These synthetic data mimics the instrument’s firmware binary files letting the radiometer to perform retrievals as real measurements. By analyzing the differences from retrieval results relative to the known true profile we assess uncertainty metrics to characterize the algorithms. It has been found that Bayesian inversion reproduces more accurately the profile vertical structure as compared to the firmware, specially for humidity profiles. Absolute errors have been strongly reduced mainly at the lower atmosphere. The study concludes that Bayesian inversion for ground-based atmospheric profiling produces results resembling observations by radiosondes when a suitable a-priori distribution is used.

The core tools of science (data, software, and computers) are undergoing a rapid and historic evolution, changing what questions scientists ask and how they find answers. Earth science data are being transformed into new formats optimized for cloud storage that enable rapid analysis of multi-petabyte datasets. Datasets are moving from archive centers to vast cloud data storage, adjacent to massive server farms. Open source cloud-based data science platforms, accessed through a web-browser window, are enabling advanced, collaborative, interdisciplinary science to be performed wherever scientists can connect to the internet. Specialized software and hardware for machine learning and artificial intelligence (AI/ML) are being integrated into data science platforms, making them more accessible to average scientists. Increasing amounts of data and computational power in the cloud are unlocking new approaches for data-driven discovery. For the first time, it is truly feasible for scientists to bring their analysis to data in the cloud without specialized cloud computing knowledge. This shift in paradigm has the potential to lower the threshold for entry, expand the science community, and increase opportunities for collaboration while promoting scientific innovation, transparency, and reproducibility. Yet, we have all witnessed promising new tools which seem harmless and beneficial at the outset become damaging or limiting. What do we need to consider as this new way of doing science is evolving?

For nearly a century, the functional response curves, which describe how predation rates vary with prey density, have been a mainstay of ecological modelling. While originally derived to describe terrestrial interactions, they have been adopted to characterize aquatic systems in marine biogeochemical, size-spectrum, and population models. However, marine ecological modellers disagree over the qualitative shape of the curve (e.g. Type II vs. III), whether its parameters should be mechanistically or empirically defined (e.g. disk vs. Michaelis-Menten scheme), and the most representative value of those parameters. As a case study, we focus on marine biogeochemical models, providing a comprehensive theoretical, empirical, and numerical road-map for interpreting, formulating, and parameterizing the functional response when used to prescribe zooplankton specific grazing rates on a single prey source. After providing a detailed derivation of each of the canonical functional response types explicitly for aquatic systems, we review the literature describing their parameterization. Empirical estimates of each parameter vary by over three orders of magnitude across 10 orders of magnitude in zooplankton size. However, the strength and direction of the allometric relationship between each parameter and size differs depending on the range of sizes being considered. In models, which must represent the mean state of different functional groups, size spectra or in many cases the entire ocean’s zooplankton population, the range of parameter values is smaller, but still varies by two to three orders of magnitude. Next, we conduct a suite of 0-D NPZ simulations to isolate the sensitivity of phytoplankton population size and stability to the grazing formulation. We find that the disk parameterizations scheme is much less sensitive to it parameterization than the Michaelis-Menten scheme, and quantify the range of parameters over which the Type II response, long known to have destabilizing properties, introduces dynamic instabilities. Finally, we use a simple theoretical model to show how the mean apparent functional response, averaged across sufficient sub-grid scale heterogeneity diverges from the local response. Collectively, we recommend using a type II disk response for models with smaller scales and finer resolutions but suggest that a type III Michaelis-Menten response may do a better job of capturing the complexity of all processes being averaged across in larger scale and coarser resolution modal, not just local consumption and capture rates. While we focus specifically on the grazing formulation in marine biogeochemical models, we believe these recommendations are robust across a much broader range of ecosystem models.

Tight oil and gas reservoirs have attracted an increasing amount of attentions and have become one of the focus of research field in recent years. Tight sandstones have complex pore structures and narrow pores and throats with pore sizes varying from nanometers to micrometers, and studying flow mechanisms in tight sandstones is of great importance to tight oil/gas reservoir development. Reconstructing digital rock, which can comprehensively represent the petrophysical properties of tight sandstone, is key to simulating the fluid flow in micro/nanopores. This paper proposes a new method of reconstructing 3D digital rock from CT images of tight sandstones based on a deep convolutional generative adversative network (DCGAN), and 3D convolution in the generator and discriminator are adopted to realize reconstruction from 1D data to a 3D digital rock model. The model adopts pore area, volume, spatial distribution and connectivity, Fréchet inception distance score to evaluate the proposed model. Studies show that when the training effect is slightly poor, the generated digital rock model will exhibit noise, which can be reduced by postprocessing; when the training effect is good, DCGAN can accurately reconstruct the 3D digital rock model of tight sandstones, and the reconstructed digital rock is very consistent with the pore size, geometric structure, and connectivity of natural tight sandstones. When multiple 3D tight sandstone CT images are used for training, the DCGAN can learn the pore structure characteristics of entire tight sandstone bodies, which have strong heterogeneous, and the porosity distribution obtained from the generated digital rock is similar to that of the original tight sandstone.

High-resolution underwater glider data collected in the Gulf of Oman (2015-16), combined with reanalysis datasets, describe the spatial and temporal variability of the mixed layer during winter and spring. We assess the effect of surface forcing and submesoscale processes on upper ocean buoyancy and their effects on mixed layer stratification. Episodic strong and dry wind events from the northwest (Shamals) drive rapid latent heat loss events which lead to intraseasonal deepening of the mixed layer. Comparatively, the prevailing southeasterly winds in the region are more humid, and do not lead to significant heat loss, thereby reducing intraseasonal upper ocean variability in stratification. We use this unique dataset to investigate the presence and strength of submesoscale flows, particularly in winter, during deep mixed layers. These submesoscale instabilities act mainly to restratify the upper ocean during winter through mixed layer eddies. The timing of the spring restratification differs by three weeks between 2015 and 2016 and matches the sign change of the net heat flux entering the ocean and the presence of restratifying submesoscale fluxes. These findings describe key high temporal and spatial resolution drivers of upper ocean variability, with downstream effects on phytoplankton bloom dynamics and ventilation of the oxygen minimum zone.

Earthquake focal mechanism data provide information about the stress state at the origin of these earthquakes. The inversion methods that are commonly used to infer the stress tensor from focal mechanisms have varying complexity but always rely on a number of assumptions. We present an iterative method built upon a classic linear stress tensor inversion that allows to relax the assumption on shear stress magnitudes while preserving the computational simplicity of the linear problem. Every iteration of our method computes the least-squares solution of the problem, which makes the method fast enough to estimate the inverted parameter errors with non-parametric resampling methods such as bootstrapping. Following previous studies, this method removes the fault plane ambiguity in focal mechanism data by selecting the nodal plane that best satisfies the Mohr-Coulomb failure criterion. We first test the performance and the robustness to noise of the proposed method on synthetic data sets, and then apply it to data from Southern California and from the Geysers geothermal field. We focus the study on investigating the consequences of relaxing the assumption of constant shear stress magnitudes. Our variable shear method successfully generalizes its constant shear counterpart: it is able to perform similarly when the constant shear assumption is a good approximation and provides more accurate results when it is not. We provide the Python package ILSI to implement the proposed method.

On average, two tropical storms or cyclones enter the Mozambique Channel between the African mainland and Madagascar each year. Their impact can be devastating. The tropical cyclone Idai, which hit land in Mozambique in 2019, was one of the deadliest storms on record in the Southern Hemisphere. Previous studies have found that the tracks and strengths of tropical storms and cyclones are difficult to predict more than a few days ahead. An extension of this forecast horizon would be crucial for enabling authorities to take precautionary actions. Here, the ability of a state-of-the-art ensemble prediction model to predict Idai and 38 other tropical systems is assessed. It is found that the minimum sea level pressure (SLP) associated with Idai was only skilfully predicted at lead times up to three days. When considering all the systems, less than a quarter of the ensemble members predicted lower minimum SLP than the MERRA-2 reanalysis at lead times of five days and longer. However, several variables are found to be useful precursors of tropical storms and cyclones, and some of these are skilfully predicted at long lead times. In particular, area-averaged anomalies of geopotential height and specific humidity at 500 hPa and SLP one week before the passage of storms are significantly correlated with minimum SLP anomalies during the storms. As these precursor variables are skilfully predicted by the model at lead times of 10–12 days, it should be possible to include their forecasted values in hybrid statistical–dynamical prediction systems at lead times beyond a few days. An additional interesting finding is that warm sea surface temperature anomalies and weak vertical wind shear, which are generally considered to be favourable for tropical storms and cyclones, do not qualify as precursors of the systems investigated here.

The magnetohydrodynamics with embedded particle-in-cell (MHD-EPIC) model has been successfully applied to global magnetospheric simulations in recent years. However, the PIC region was restricted to be one or more static boxes, which is not always sufficient to cover the whole physical structure of interest efficiently. The FLexible Exascale Kinetic Simulator (FLEKS), which is a new PIC code and allows a dynamic PIC region of any shape, is designed to break this restriction. FLEKS is usually used as the PIC component of the MHD with adaptively embedded particle-in-cell (MHD-AEPIC) model. FLEKS supports dynamically activating or deactivating cells to fit the regions of interest during a simulation. An adaptive time-stepping scheme is also introduced to improve the accuracy and efficiency of a long simulation. The particle number per cell may increase or decrease significantly and lead to load imbalance and large statistical noise in the cells with fewer particles. A particle splitting scheme and a particle merging algorithm are designed to limit the change of the particle number and hence improve the accuracy of the simulation as well as load balancing. Both particle splitting and particle merging conserve the total mass, momentum, and energy. FLEKS also contains a test-particle module to enable tracking particle trajectories due to the time-dependent electromagnetic field that is obtained from a global simulation.

To prevent serious problems occurring in abandoned mines such as ground subsidence, it is commonly carried out to fill cavities with some materials. During or after the cavity-filling process, we need to monitor distribution of filling materials in abandoned mines. Various geophysical methods, such as microgravity, electrical resistivity, ground penetrating radar, and seismic methods, have been used to describe abandoned mines themselves or to monitor distribution of filling materials. Microgravity, electrical resistivity, ground penetrating radar, and microseismic methods can be used to detect cavities, but may have some limitation in monitoring material distributions. In this study, we apply the seismic reflection method to image distribution of filling materials in near-surface abandoned mines. As the imaging methods, we use full waveform inversion and reverse time migration. In addition, we apply seismic interferometry to obtain better results. The full waveform inversion and reverse time migration methods are applied to four models which can mainly appear in abandoned mines. Through numerical examples, we investigate feasibility of seismic reflection method for describing filling material distribution in abandoned mine. Acknowledgements This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2017R1A2B4002031) and by the project funded by the Ministry of Oceans and Fisheries, Korea (D11603317H480000112).

Improved imaging of the spatio-temporal growth of fault slip is crucial for understanding the driving mechanisms of earthquakes and faulting. This is especially critical to properly evaluate the evolution of seismic swarms and earthquake precursory phenomena. Fault slip inversion is an ill-posed problem and hence regularisation is required to obtain stable and interpretable solutions. An analysis of compiled finite fault slip models shows that slip distributions can be approximated with a generic elliptical shape, particularly well for M≤7.5 events. Therefore, we introduce a new physically-informed regularisation to constrain the spatial pattern of slip distribution. Our approach adapts a crack model derived from mechanical laboratory experiments and allows for complex slipping patterns by stacking multiple cracks. The new inversion method successfully recovered different simulated time-dependent patterns of slip propagation, i.e., crack-like and pulse-like ruptures, directly using wrapped satellite radar interferometry (InSAR) phase observations. We find that the new method reduces model parameter space, and favours simpler interpretable spatio-temporal fault slip distributions. We apply the proposed method to the 2011 March-September normal-faulting seismic swarm at Hawthorne (Nevada, USA), by computing ENVISAT and RADARSAT-2 interferograms to estimate the spatio-temporal evolution of fault slip distribution. The results show that (1) aseismic slip might play a significant role during the initial stage, and (2) this shallow seismic swarm had slip rates consistent with those of slow earthquake processes. The proposed method will be useful in retrieving time-dependent fault slip evolution and is expected to be widely applicable to studying fault mechanics, particularly in slow earthquakes.

Idealized models can reveal insights into Earth’s climate system by reducing its complexities. However, their potential is undermined by the scarcity of fully coupled idealized models with components comparable to contemporary, comprehensive Earth System Models. To fill this gap, we compare and contrast the climates of two idealized planets which build on the Simpler Models initiative of the Community Earth System Model (CESM). Using the fully coupled CESM, the Aqua configuration is ocean-covered except for two polar land caps, and the Ridge configuration has an additional pole-to-pole grid-cell-wide continent. Contrary to most sea surface temperature profiles assumed for atmosphere-only aquaplanet experiments with the thermal maximum on the equator, the coupled Aqua configuration is characterized by a global cold belt of wind-driven equatorial upwelling, analogous to the eastern Pacific cold tongue. The presence of the meridional boundary on Ridge introduces zonal asymmetry in thermal and circulation features, similar to the contrast between western and eastern Pacific. This zonal asymmetry leads to a distinct climate state from Aqua, cooled by ~2{degree sign}C via the radiative feedback of clouds and water vapor. The meridional boundary of Ridge is also crucial for producing a more Earth-like climate state compared to Aqua, including features of atmospheric and ocean circulation, the seasonal cycle of the Intertropical Convergence Zone, and the meridional heat transport. The mean climates of these two basic configurations provide a baseline for exploring other idealized ocean geometries, and their application for investigating various features and scale interactions in the coupled climate system.