Many physical processes in the field of rock physics are influenced by the presence of fractures and microcracks. Therefore, intact rock samples are often used for reproducible experimental studies, and cracks are artificially created by various methods. For this, one possibility is the use of thermal treatments. In this work, twelve thermal treatments, differing in the applied maximum temperature and the applied cooling condition (slow versus fast cooling) are experimentally studied for dry Bianco Carrara marble under ambient conditions. Two sizes of cylindrical core samples are investigated to identify a potential size effect. As effective quantities on the core-scale, the bulk volume, the bulk density, and the P- and S-wave velocities, including shear wave splitting, are examined. To obtain a three-dimensional insight into the mechanisms occurring on the micro-scale level, micro X-Ray Computed Tomography (micro-XRCT) imaging is employed. For both cooling conditions, with increasing maximum temperature, the bulk volume increases, and the propagation velocities significantly drop. This behavior is amplified for fast cooling. The bulk volume increase is related to the initiated crack volume as micro-XRCT shows. Based on comprehensive measurements, a logarithmic relationship between the relative bulk volume change and the relative change of the ultrasound velocities can be observed. Although there is a size effect for fast cooling, the relationship found is independent of the sample size. Also the cooling protocol has almost no influence. A model is derived which predicts the relative change of the ultrasound velocities depending on the initiated relative bulk volume change.

Serpentinization ubiquitously affects ultramafic rocks that interact with water, with strong implications for the origin of ancient microbial ecosystems, the chemical budget of the ocean, and the rheology of the oceanic crust. The increase in volume associated with this reaction, and its consequences for reaction progress, have been debated for over a century. Serpentine minerals are ~40% more voluminous than olivine, which suggests that fully serpentinized peridotite should have negative, or at least very low, porosity. Recent studies have proposed that self-propagating, reaction-driven fracturing can facilitate serpentinization reactions, but the nanoscale mechanisms by which fluid is transported through serpentinized fractures in order to react with fresh olivine surfaces remain poorly understood. To address this issue, we studied a sample of serpentinized harzburgite collected during ODP expedition 209 at site 1274, using Focused Ion Beam – Scanning Electron Microscopy (FIB-SEM)-assisted tomography. We specifically targeted the interface between the serpentinized peridotite and the unaltered primary mineralogy. The resultant images illustrate the presence of nanopores within the serpentine alteration products, primarily at the interface with the unreplaced minerals. Importantly, no pores were observed in the serpentine away from the grain boundaries with olivine, suggesting that these nanopores form during the initial stage of reaction, and then disappear with further reaction progress. We argue that the observed nanoporosity is an intrinsic feature of the serpentinization reaction, and that its transient presence during serpentinization is vital for facilitating reaction progress. We suggest that the transient nature of the pores arises from the opposing kinetics and thermodynamics of the replacement reaction. The former promotes the formation of voids that enhance the advective transport of fluids to the reaction front, while the latter drives the reduction of pore space by means of recrystallization of the serpentine aggregates and minimization of the interfacial energy.

Projected changes in climate are likely to affect not only its mean state but also its variability. As such, improving our understanding of the spectrum of climate variability and how different feedbacks in the climate system influence it is of vital importance. We perform a process-based examination of variability with respect to changing orbital insulation, ice coverage, and land/sea distribution during the Last Glacial Maximum and the Holocene. To this end, we adapt a two-dimensional energy balance model [Zhuang, North & Stevens, 2017] to run transient simulations. The model is forced by carbon dioxide and solar insolation changes for the last Glacial cycle. We evaluate the model’s ability to reproduce changes in local to global, seasonal to millennial temperature distributions during the Last Glacial Maximum and the Holocene. We compare the simulated states and the transient evolution to those obtained by comprehensive coupled climate models. Finally, we test the mean-state dependence of temperature variability over a large range of model configurations and discuss implications for future climate.

Streamers are millimeter-diameter cold plasma discharges that can extend, branch, and interact with complex collective dynamics to form meter-scale systems. Such streamer systems can be expected to play an important role in lightning. Small-scale streamer systems may grow to provide sufficient ionization and intensification of field to support initiation of hot plasma channel development. Streamer systems developing ahead of the lightning channel may guide channel extension and help explain the observed step-wise extension process. Rapidly-developing streamer systems may also help explain recent observations of fast positive and negative breakdown. We present simulations of streamer system development based on approximate particle-like treatment of individual streamer behavior but including large-scale system dynamics including interaction, collision/connection, and secondary streamer initiation. Here we focus on the observable effects of such streamer system development, including electrostatic field change and electromagnetic wave emissions with frequencies up to 500 MHz. Preliminary results suggest high-frequency (HF) emissions due to streamer acceleration and interaction in isolated streamer systems are comparable in scale to observations of HF emission from lightning and develop before detectable static field change occurs, but these results depend on simulation parameters.

Landslide susceptibility models are fundamental components of landslide risk management strategies. These models typically assume that landslide occurrence is time-independent, even though processes including earthquake preconditioning and landslide path dependency transiently impact landslide occurrence. Understanding the temporal characteristics of landslide occurrence remains limited by a lack of systematic investigation into how landslide distributions vary through time, and how this impacts landslide susceptibility. Here, we apply Kolmogorov-Smirnoff and Chi-2 statistics to a 30-year inventory of monsoon-triggered landslides from Nepal to systematically quantify how landslide spatial distributions vary through time in ‘normal’ years and years impacted by extreme events. We then develop Binary Logistic Regression (BLR) susceptibility models for 12 years in our inventory with > 400 landslides and use Area Under Receiver Operator Curve (AUROC) validation to assess how well these models can hindcast landslide occurrence in other years. Landslide distributions are found to vary through time, particularly in years impacted by storms (1993 and 2002), earthquakes (2015) and floods (2017). Notably, Gorkha earthquake landscape preconditioning shifted 2015 monsoon-triggered landslides to higher slopes, reliefs and excess topographies. These variations significantly impact BLR susceptibility modelling, with models trained on extreme years unable to consistently hindcast landslide occurrence in other years. However, developing BLR models using increasingly long historical inventories shows that susceptibility models developed using > 6 - 8 years of landslide data provide consistently good hindcasting accuracy. Overall, our results challenge time-independent assumptions of landslide susceptibility approaches, highlighting the need for time-dependent modelling techniques or historical inventories for landslide susceptibility modelling.

Beaches are highly variable environments and respond to changes in wave forcing, themselves modulated by climate variability. Here, we analyse three high-quality beach profile datasets to robustly investigate, for the first time, the link between shoreline change, wave forcing and climate variability along the Atlantic coast of Europe. Winter wave conditions are strongly associated with North Atlantic Oscillation (NAO) and Western Europe Pressure Anomaly (WEPA), with WEPA explaining 50–80% of the winter wave power variability. Shoreline variability during winter is also strongly linked to NAO and WEPA, with WEPA explaining 25% of the winter shoreline variability. Winter wave conditions and associated shoreline variability are both unrelated to El Nino Southern Oscillation (ENSO). In addition to the atmospherically-forced beach morphological response, shoreline change also depends strongly on the antecedent conditions as evidenced by significant correlations between summer/winter shoreline response and the shoreline position at the start of each season.

A document by Terence John O'Kane. Click on the document to view its contents.

Aquatic vegetation modifies hydrodynamics, turbulence structure, sediment transport, and ecological processes in marine ecosystems. Recent turbulence models for vegetated flows have focused on open channel unidirectional flows. However, the unsteadiness and turbulent structure of oscillatory flows often prevent the direct application of such models in wave-dominated environments. We investigate Turbulent Kinetic Energy (TKE) connected to the flow structure in oscillatory flows through aquatic vegetation. Using an oscillatory tunnel, we test vegetation densities up to $\phi=0.10$ with wave periods between 2.1-5.3 s and wave amplitudes between 2-10 cm. Our measurements show a nonlinear relation between the TKE inside the canopy and vegetation density due to the change from the stem- to canopy-scale dominated regime. We observe that $ah\geq 0.8$ marks a threshold for this transition: a reduction of wake TKE inside the canopy and an increase of shear TKE at the top of the canopy. This transition is characterized by increasing frequency and intensity of sweeps and ejections near the bed and at the canopy top. We developed a two-equation predictor for TKE at the top of the canopy using the “short-cut” TKE transfer first proposed by \citeA{finnigan2000turbulence} where canopy-scale eddies convert TKE into stem-scale eddies via the work against vegetation drag. For near-bed TKE, we adapt \citeA{tanino2008lateral}’s model to predict the maximum TKE values on oscillatory flows. These two predictors provide easy-to-use tools suitable for wave-dominated environments to accurately estimate TKE levels inside the canopy for estimating sediment transport rates and mass exchange across the canopy.

This paper analyses magnetosphere-ionosphere (MI) coupling from a perspective that is independent of inertial reference frame, explicitly acknowledging the role of the principle of relativity in MI coupling. For the first time in the context of MI coupling, we discuss the literature on the low-velocity limit of the theory of special relativity applied to electrodynamics. In many MI coupling theories, a particular low-velocity limit applies, known as the “magnetic limit”. Two important consequences of the magnetic limit are: 1) Maxwell’s equations cannot contain a displacement current and be consistent with the magnetic limit and 2) the magnetic field is not modified by currents created by charge densities in motion, thus charge density is approximately zero. We show how reference frame-independent descriptions of MI coupling require that ion-neutral relative velocities and ion-neutral collisions are key drivers of the physics. Electric fields, on the other hand, depend on reference frame, and can be zero in an appropriate frame. Currents are independent of reference frame and will flow when the electric field is close to zero. Starting with the same momentum equations that are typically used to derive Ohm’s law, we derive an equation that relates the perpendicular current to collisions between ions and neutrals, and electrons and neutrals, without reference to electric fields. Ignoring the relative motion between ions and neutrals will result in errors exceeding 100% for estimates of high latitude Joule heating during significant geomagnetic storms when ion-neutral velocity differences are largest near the initiation of large-scale ion convection.

We investigate the applicability of deep learning methods for reconstructing daily weather data. Inspired by video inpainting, we propose a novel method, WeRec3D, which utilizes a three-dimensional convolutional neural network. Our approach was developed iteratively by evaluating six modeling improvement techniques. The resulting method reduces the validation error to 48% compared to the baseline. Additionally, we demonstrate the impact of the spatial distribution of observations on reconstruction accuracy and propose a potential integration with the analogue resampling method. WeRec3D is trained and validated in a self-supervised manner using ERA5’s surface temperature and pressure data over Europe. On a hold-out set from 1950 to 1954, the validation results in an MAE of 1.11 °C and 199 Pa. As a case study, we reconstruct the 1807 heat wave and validate it using a leave-one-out method in space. Compared to the original data, the reconstructed time series exhibit a correlation of at least 0.91, with a maximum normalized RMSE and standard deviation delta of 0.58 and 0.51 respectively. To the best of our knowledge, this is the first study to investigate weather reconstruction using deep learning algorithms, proposing video inpainting as a novel approach for reconstructing missing weather information.

The precipitation of magnesium oxide (MgO) from the Earth’s core has been proposed as a potential energy source to power the geodynamo prior to the inner core solidification. Yet, the stable phase and exact amount of MgO exsolution remain elusive. Here we utilize an iterative learning scheme to develop a unified deep learning interatomic potential for the Mg-Fe-O system valid over a wide pressure-temperature range. This potential enables direct, large-scale simulations of MgO exsolution processes at the Earth’s core-mantle boundary. Our results suggest that Mg exsolves in the form of crystalline Fe-poor ferropericlase as opposed to a liquid MgO component presumed previously. The solubility of Mg in the core is limited, and the present-day core is nearly Mg-free. The resulting exsolution rate is small yet nonnegligible, suggesting that MgO exsolution can provide a potentially important energy source, although it alone may be difficult to drive an early geodynamo.

Why do some Martian dust storms, in some Mars years, expand to reach planet-encircling status, while the majority do not? In what ways do the largest regional events differ from those that become global? Comparisons of observations from these two categories of events may help answer these questions. The dust storm season of 2018, which included a global-scale dust event, was preceded by five successive dust storm seasons in which only regional-scale events were observed. The recent record thus presents an opportunity for making quantitative comparisons between regional-scale and global-scale dust events. Observations by the Mars Climate Sounder, on board the Mars Reconnaissance Orbiter spacecraft, provide unique 4D information on temperatures and aerosol loading of the Mars atmosphere, to altitudes of >80 km. Available MCS observations span the past eight Mars years. We have previously employed MCS observations to characterize the evolution in latitude, longitude, and altitude of atmospheric dust clouds during the initiation phase of the 2018 global event. Other atmospheric fields provide complementary information. For instance, observed changes with time in atmospheric ‘dynamical heating’ also help characterize the response of the Mars atmosphere to added dust loading. In this process, the atmosphere in regions far removed from the locations where dust is lifted may be warmed by adiabatic compression within the descending branches of Hadley-like meridional circulation cells. We will present and interpret MCS observations of these and other phenomena for selected large regional-scale dust events of Mars Years 29-33 (from 2009 through 2017), and draw comparisons with observations obtained during the 2018 global event. We will additionally explore the implications of the results within the context of current hypotheses for the triggering of the largest dust storms on sub-seasonal time scales.

The in-situ magnetospheric exploration of the four large planets of our solar system had started with Pioneer 10’s flyby of Jupiter in Dec. 1973. The second collection of field, particle and radio data of the gas giant was carried out by Pioneer 11 in Dec. 1974, before this spacecraft made its closest approach to Saturn in Sep. 1979. Around the same period, Voyager 1 (2) flew by Jupiter in Mar. (Jul.) 1979 then Saturn in Nov. (Aug.) 1980 (1981). As of today, only Voyager 2 visited the magnetospheres of Uranus (Jan. 1986) and Neptune (Aug. 1989). Galileo had remained the only spacecraft to orbit an outer planet for several years (1995 - 2003) until the arrival of Juno at Jupiter in 2016. Between 2004 and 2017, the Cassini mission had provided a wealth of in-situ data pertinent to the study of magnetospheric particles at Saturn. In this paper, we present our current understanding of the processes that shape the spatial distributions of energetic electrons trapped in the magnetospheres of Jupiter (L < 6), Saturn (L < 15) and Uranus (L < 15) obtained by combining multi-instrument analyses of data from past missions (Pioneer, Voyager, Galileo, Cassini) and computational models of charged particle fluxes. To determine what controls the energy and spatial distributions throughout the different magnetospheres, we compute the time evolution of particle distributions with the help of a diffusion theory particle transport code that solves the governing 3-D Fokker-Planck equation. Particle, field and wave datasets are either used to provide model constraints, assist in modeling physical processes, or validate our simulation results. We first emphasize our latest results regarding the relative (or coupled) role of mechanisms at Saturn, including the radial transport and interactions of electrons with Saturn’s dust/neutral/plasma environments and waves, as well as particle sources from high-latitudes, interchange injections, and outer magnetospheric region. The lessons learned from our modeling of electron distributions at Saturn are used to identify the processes that may be missing in our modeling of Jupiter’s energetic electron environment or those in need to be implemented using new modeling concepts. Our first physics-based modeling of electron populations at Uranus is also assessed with our data-model comparison approach.

Bangladesh, part of Indo-Gangetic-Brahmaputra Plain, is frequently affected by floods and droughts. As the economy of Bangladesh is still agriculture based, effective measurement of soil moisture will not only strengthen the irrigation management but also improve the hydrological modelling and drought prediction. But only nine agro-meteorological stations of Bangladesh measure the soil moisture four times a month which creates a vacuum to scientifically manage her water resources. SMAP (Soil Moisture Active Passive) satellite of NASA provides an unprecedented opportunity for full scale measurement of soil moisture over this region. Field measurements of soil moisture from April 2015 were used to assess the effectiveness of the SMAP’s measurement over the North West Region of Bangladesh which suffers from frequent dry spells. Initially the Root mean squared error (RMSE) between the SMAP and observed soil moisture were found to vary between 12.28 to 16.72% for the available stations. The results showed a bias in SMAP data and it was significantly reduced using bias correction. Later multiple linear regression, based on supplementary climate data in addition to SMAP observations, was applied to obtain an improved estimate of soil moisture and the RMSE were reduced to 1.19 to 3.18%. Lastly, different machine learning techniques (i.e. ANN, SVR, XGBoost etc.) were used to reduce the bias further. This study demonstrates a promising potential of using the SMAP data in soil moisture estimation over Bangladesh for its effective water resources management.

To accelerate plant breeding genetic gain, spatial heterogeneity must be considered. Previously, design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments. This study proposes a two-stage approach for improving agronomic trait genomic prediction (GP) using high-throughput phenotyping (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index (NDVI) is measured using a multi-spectral MicaSense camera and ImageBreed. The first stage separates additive genetic effects from local environmental effects (LEE) present in the NDVI throughout the growing season. Considered NDVI LEE (NLEE) are spatial effects from univariate/multivariate two-dimensional splines (2DSpl) and separable autoregressive (AR1) models, as well as permanent environment (PE) effects from random regression models (RR). The second stage leverages the NLEE within genomic best linear unbiased prediction (GBLUP) in two distinct implementations, either modelling an empirical plot-to-plot covariance (L) for random effects or modelling fixed effects (FE). Testing on Genomes-to-Fields (G2F) hybrid maize (Zea mays) field experiments in 2017, 2019, and 2020 for grain yield (GY), grain moisture (GM), and ear height (EH) improves heritability and model fit equally-or-greater than spatial corrections; however, genotypic effect estimation across replicates is not significantly improved. Electrical conductance (EC), elevation, and curvature from a 2019 soil survey significantly improve GP model fit, but less than NLEE. Soil EC and curvature are most correlated to univariate 2DSpl NLEE. Defining L significantly improves genomic heritability and model fit more than setting FE, and RR NLEE can most significantly improve GP for GY and GM.

Pervasive cirrus clouds in the upper troposphere and tropical tropopause layer (TTL) influence the climate by altering the top-of-atmosphere radiation balance and stratospheric water vapor budget. These cirrus are often associated with deep convection, which global climate models must parameterize and struggle to accurately simulate. By comparing high-resolution global storm-resolving models from the Dynamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) intercomparison that explicitly simulate deep convection to satellite observations, we assess how well these models simulate deep convection, convectively generated cirrus, and deep convective injection of water into the TTL over representative tropical land and ocean regions. The DYAMOND models simulate deep convective precipitation, organization, and cloud structure fairly well over land and ocean regions, but with clear intermodel differences. All models produce frequent overshooting convection whose strongest updrafts humidify the TTL and are its main source of frozen water. Inter-model differences in cloud properties and convective injection exceed differences between land and ocean regions in each model. We argue that global storm-resolving models can better represent tropical cirrus and deep convection in present and future climates than coarser-resolution climate models. To realize this potential, they must use available observations to perfect their ice microphysics and dynamical flow solvers.

Interferometric synthetic aperture radar (InSAR) measures surface deformation from repeated passes of satellites or aircraft and has become an important tool to study geophysical phenomena such as earthquakes. However, InSAR data analysis is challenging due to atmospheric water vapor that can mimic the effects of Earth deformation and thus lead to wrong interpretations. We present preliminary results on how to differentiate between tropospheric effects and surface deformation from earthquakes using a convolutional neural network. As earthquake training sets are sparse, our approach leverages transfer learning techniques for tropospheric patterns from areas where deformations are known to be mostly absent over short time periods, and classifies specific areas of interferograms to reflect regions that are dominated by deformation or tropospheric noise. The applicability of the training set to a new area may depend on the similarity of the two climates. Examples of tropospheric delays are shown from interferograms constructed from Sentinel-1 data over part of southern California with short temporal baselines, and examples of deformation are taken from interferograms generated using Okada models. Our classifier is tested on data from the 2018 Oaxaca earthquake in Mexico from Sentinel-1. This work is a step towards using neural networks for a fine-granular tile-based validation of interferograms and automatically removing unwanted effects from InSAR signals, as well as towards enhancing the agility of disaster response programs. The open source code is available in the PyInSAR package on GitHub under the MIT license. We acknowledge support from NASA AIST80NSSC17K0125 (PI Pankratius) and NSF ACI1442997 (PI Pankratius).

Stable water isotopes in inland Antarctic ice cores are powerful paleoclimate proxies; however, their relationship with dynamical atmospheric circulations remains controversial. Using a water isotope climate model (MIROC5-iso), we assessed the influence of the Last Glacial Maximum (LGM; ~21,000 years ago) sea surface temperatures (SST) and sea ice (SIC) on Antarctic precipitation isotopes (δ18Op) through atmospheric circulation. The results revealed that the synoptic circulation mostly maintained southward moisture transport, reaching inland Antarctica. The steepened meridional SST gradient in the mid-latitudes increased δ18Op in inland Antarctica by enhancing the baroclinic instability and synoptic moisture transport. In contrast, enhanced SIC reduced the atmospheric humidity around Antarctica and lowered δ18Op through extensive surface cooling and transport from the ocean. These findings elucidate the isotopic proxies and enable us to constrain the southern hemisphere atmospheric circulation, including the westerlies, using ice cores during past climates, including the LGM.