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.

Quantifying the response of human activities to different COVID-19 measures may serve as a potential way to evaluate the effectiveness of the measures and optimize measures. Recent studies reported that seismic noise reduction caused by less human activities due to COVID-19 lockdown had been observed by seismometers. However, it is difficult for current seismic infrastructure in urban cities to characterize spatiotemporal seismic noise during the post-COVID-19 lockdown because of sparse distribution. Here we show key connections between progressive COVID-19 measures and spatiotemporal seismic noise changes recorded by a distributed acoustic sensing (DAS) array deployed in State College, PA. We first show spatiotemporal seismic noise reduction (up to 90%) corresponding to the reduced human activities in different city blocks during the period of stay-at-home. We also show partial noise recovery corresponding to increased road traffics and machines in Phase Yellow/Green. It is interesting to note that non-recovery seismic noise in 0.01-10 Hz suggests the low level of pedestrian movement in Phase Yellow/Green. Despite of a linear correlation between mobility change and seismic noise change, we emphasize that DAS recordings using city-wide fiber optics could provide a way for quantifying the impact of COVID-19 measures on human activities in city blocks.

Major tropical volcanic eruptions have emitted large quantities of stratospheric sulphate and are potential sources of stratospheric chlorine although this is less well constrained by observations. This study combines model and ice core analysis to investigate past changes in total column ozone. Historic eruptions are a good analogue for future eruptions as stratospheric chlorine levels have been decreasing since the year 2000. We perturb the pre-industrial atmosphere of a chemistry-climate model with high and low emissions of sulphate and chlorine. The sign of the resulting Antarctic ozone change is highly sensitive to the background stratospheric chlorine loading. In the first year, the response is dynamical, with ozone increases over Antarctica. In the high HCl (10 Tg emission) experiment, the injected chlorine is slowly transported to the polar regions with subsequent chemical ozone depletion. These model results are then compared to measurements of the stable nitrogen isotopic ratio, δ15N(NO−3), from a low snow accumulation Antarctic ice core from Dronning Maud Land (recovered in 2016-17). We expect ozone depletion to lead to increased surface ultraviolet (UV) radiation, enhanced air-snow nitrate photo-chemistry and enrichment in δ15N(NO−3) in the ice core. We focus on the possible ozone depletion event that followed the largest volcanic eruption in the past 1000 years, Samalas in 1257. The characteristic sulphate signal from this volcano is present in the ice-core but the variability in the δ15N(NO−3) dominates any signal arising from changes in UV from ozone depletion. Whether Samalas caused ozone depletion over Antarctica remains an open question.

Seasonal and diurnal variations of the cloud are profoundly affected by the large-scale environment and the local topography. In this study, the one-year-long simulation was conducted by a two-dimensional Cloud Resolving Model over the eastern Tibetan Plateau (ETP) and two sub-region of East China (EC), which are the middle and low reaches of the Yangtze River and Pearl River Delta. It found that deep convections (DCCs) can hardly happen in the cold season over the ETP while it can happen all the year around over the EC, and it is about 20-30% thinner in the ETP than in the EC. Most of EC rainfall events (Precipitation Intensity, PI> 2.5 mm hr) relates to DCCs with cloud ice processes during the warm season. Because of the high elevation, the ETP warm-season freezing level is much lower than that of EC, making a favorable condition for cloud ice processes. DCCs are responsible for the diurnal variations of warm-season rainfall in all the regions. Warn-season DCCs have the greatest total cloud water content and frequency in the afternoon over the ETP, resulting in the afternoon peak of rainfall events. Besides the afternoon peak, rainfall events also have a nocturnal peak due to the DCCs over the ETP in spring, summer, and autumn. Strong surface heat fluxes around noon can trigger or promote DCCs in spring, summer, and autumn over the ETP, but only produce cumulus in winter due to the cold and dry environment.

The lapse-rate feedback is the dominant driver of stronger warming in the Arctic than the Antarctic in simulations with increased CO2. While Antarctic surface elevation has been implicated in promoting a weaker Antarctic lapse-rate feedback, the mechanisms in which elevation impacts the lapse-rate feedback are still unclear. Here we suggest that weaker Antarctic warming under CO2 forcing stems from shallower, less intense climatological inversions due to limited atmospheric heat transport above the ice sheet elevation and elevation-induced katabatic winds. In slab ocean model experiments with flattened Antarctic topography, stronger climatological inversions support a stronger lapse-rate feedback and annual-mean Antarctic warming comparable to the Arctic under CO2 doubling. Unlike the Arctic, seasonality in warming over flat Antarctica is mainly driven by a negative shortwave cloud feedback which exclusively dampens summer warming, with a smaller contribution from the winter-enhanced lapse-rate feedback.

Bed particles were tracked using passive integrated transponder (PIT) tags in a wandering reach of the San Juan River, British Columbia, Canada, to assess particle movement around three major bars in the river. In-channel topographic changes were monitored through repeat LiDAR surveys during this period and used in concert with the tracer dataset to assess the relationship between particle displacements and changes in channel morphology, specifically, the development and re-working of bars. This has direct implications for virtual velocity and morphologic based estimates of bedload flux, which rely on accurate estimates of the variability and magnitude of particle path lengths over time. Tracers were deployed in the river at three separate locations in the Fall of 2015, 2016, 2017 and 2018, with recovery surveys conducted during the summer low-flow season the year after tracer deployment and multiple mobilising events. Tracers exhibited path length distributions reflective of both morphologic controls and year to year differences related to the annual flow regime. Annual tracer transport was restricted primarily to less than one riffle-pool-bar unit, even during years with a greater number of peak floods and duration of competent flow . Tracer deposition and burial was focused along bar margins, particularly at or downstream of the bar apex, reflecting the downstream migration and lateral bar accretion observed on Digital Elevation Models (DEMs) of difference. This highlights the fundamental importance of bar development and re-working underpinning bedload transport processes in bar-dominated channels.

Enceladus and possibly Europa spew materials from their internal ocean into their exosphere, some of which are deposited back onto the surface of those Ocean Worlds. This setting provides a unique opportunity to seek traces of past or extant life in ice plume deposits on their surfaces. However, the design of lander missions and surface sampling techniques, and the choice of sampling locations rely heavily on strength expectations. Here we present an experimental investigation of the evolution in strength of ice plume deposit analogs at several temperatures, as well as a model that predicts first-order estimates of the strength of evolved ice plume deposits under geologic timescales relevant to Enceladus and Europa. These results suggest that plume deposits remain weak and poorly consolidated on Enceladus, while they may develop substantial strength (comparable to solid ice) within < 100 My on Europa.

The Earth’s upper atmosphere is a dynamic environment that is continuously affected by space weather from above and atmospheric processes from below. An effective way to observe this interface region is the monitoring of airglow. Since the 1950s, airglow emissions have been systematically measured by ground-based photometers in specific wavelength bands during the nighttime. The availability of the calibrated data from over 30 years of photometric airglow measurements at Abastumani in Georgia (41.75 N, 42.82 E), at wavelengths of 557.7 nm and 630.0 nm, enable us to investigate if a data-driven model based on advanced machine learning techniques can be successfully employed for modeling airglow intensities. A regression task was performed using the time series of space weather indices and thermosphere-ionosphere parameters. We have found that the developed data-driven model has good consistency with the commonly used GLOW airglow model and also captures airglow variations caused by cycles of solar activity and changes of the seasons. This enables us to visualize the green and red airglow variations over a period of three solar cycles with a one-hour time resolution.

Ocean bathymetry exerts a strong control on ice sheet-ocean interactions within Antarctic ice-shelf cavities, where it can limit the access of warm, dense water at depth to the underside of floating ice shelves. However, ocean bathymetry is challenging to measure within or close to ice-shelf cavities. It remains unclear how uncertainty in existing bathymetry datasets affect simulated sub-ice shelf melt rates. Here we infer linear sensitivities of ice shelf melt rates to bathymetric shape with grid-scale detail by means of the adjoint of an ocean general circulation model. Both idealised and realistic-geometry experiments of sub-ice shelf cavities in West Antarctica reveal that bathymetry has a strong impact on melt in localised regions such as topographic obstacles to flow. Moreover, response of melt to bathymetric perturbation is found to be non-monotonic, with deepening leading to either increased or decreased melt depending on location. Our computational approach provides a comprehensive way of identifying regions where refined knowledge of bathymetry is most impactful, and also where bathymetric errors have relatively little effect on modelled ice sheet-ocean interactions.

The shape of earthquake source spectra, traditionally fit by physics-based models, contains important parameters to constrain rupture dimension, duration, and geometry. Here we apply machine learning (ML) to derive single-variable and double-variable data-driven models of source spectra from 3675 Mw>5.5 global earthquakes, assuming that the Fourier transform of source time functions well represent earthquake source spectra below 1 Hz. The single-variable ML model, in the same degree of freedom as the Brune model, improves the goodness of fit by 8.5%. Specifically, the ML model fits the data without systematic bias, whereas the Brune model tends to underestimate at intermediate frequencies and overestimate at high frequencies. The latter discrepancy cannot be modelled by increasing the fall-off exponent in the Brune-type or the Boatwright-type models. The double-variable ML model is compared to existing double-corner-frequency models and is found to capture the second-order features such as the subtle curvature differences around the corner. Our results demonstrate that unsupervised machine learning can extract hidden global characteristics of high-dimensional data and provide observational evidence to amend existing physical models.

Health and environmental hazards related to high pollutant concentrations have become a serious issue from the perspectives of public policy and human health. The objective of this research is to improve the estimation of grid-wise PM2.5, a criteria pollutant, by reducing systematic bias in estimating PM2.5 empirically from speciation provided by MERRA-2 using a ML approach. We present a unique application of machine learning (ML) for estimating hourly PM2.5 concentrations at grid points of Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). The model was trained using various meteorological parameters and aerosol species simulated by MERRA-2 and ground measurements from Environmental Protection Agency (EPA) air quality system (AQS) stations. monitors. The ML approach significantly improved performance and reduced mean bias in the 0-10 µg m-3 range. We also used the Random Forest ML model for each EPA region using one year of collocated datasets. The resulting ML models for each EPA region were validated and the aggregate data set has a Pearson correlation of 0.88 (RMSE = 4.8 µg m-3) and 0.82 (RMSE = 5.8 µg m-3) for training and testing, respectively. The correlation (and RMSE) increased to 0.89 (4.0), 0.95 (1.6), 0.94 (1.1) for daily, monthly, and yearly average comparisons. The results from initial implementation of the ML model for global region are encouraging but require more research and development to overcome challenges associated with data gaps in many parts of the world.

A sequence of three strong (M W 7.2, 6.4, 6.6) earthquakes struck the Pamir of Central Asia in 2015–2017. With a local seismic network, we recorded the succession of the fore-, main-, and aftershock sequences at local distances with good azimuthal coverage. We located 11,784 seismic events and determined 33 earthquake moment tensors. The seismicity delineates the tectonic structures of the Pamir in unprecedented detail, i.e., the thrusts that absorb shortening along the Pamir’s thrust front, and the strike-slip and normal faults that dissect the Pamir Plateau into a westward extruding block and a northward advancing block. Ruptures on the kinematically dissimilar faults were activated subsequently from the initial M W 7.2 Sarez event at times and distances that follow a diffusion equation. All mainshock areas but the initial one exhibited foreshock activity, which was not modulated by the occurrence of the earlier earthquakes. Modeling of the static Coulomb stress changes indicates that aftershock triggering occurred over distances of ≤90 km on favorably oriented faults. The third event in the sequence, the M W 6.6 Muji earthquake, ruptured despite its repeated stabilization through stress transfer in the order of -10 kPa. To explain the accumulation of M W > 6 earthquakes, we reason that the initial mainshock may have increased nearby fault permeability, and facilitated fluid migration into the mature fault zones, eventually triggering the later large earthquakes.

With radar sounders, coherent backscattering simulations from global planetary DEMs typically display a deficit in diffuse clutter, which is mainly due to the implicit assumption that roughness at scales below the resolution of the DEM is absent. Indeed, while polynomial approximations of the phase evolution across the facet allow for fast and mathematically rigorous simulators, the coarse resolution of these planetary DEMs leads to a potentially significant portion of the backscattering response being neglected. In this paper, we derive the analytical phase response of a rough rectangular facet characterised by Gaussian roughness and a Gaussian isotropic correlation function under the linear phase approximation. Formulae for the coherent and incoherent power scattered by such an object are obtained for arbitrary bistatic scattering angles. Validation is done both in isolation and after inclusion in different Stratton-Chu simulators. In order to illustrate the different uses of such a formulation, we reproduce two lunar radargrams acquired by the LRS instrument with a Stratton-Chu simulator incorporating the proposed rough facet phase integral, and we show that the original radargrams are significantly better-reproduced than with state-of-the-art methods, at a similar computational cost. We also show how the rough facet integral formulation can be used in isolation to better characterise subglacial water bodies on Earth.

Physiological dynamics at plant level are essential but also challenging for precision agriculture applications linked to plant phenotyping. In this study, we explore not only the spatial dynamics of corn in field conditions but also their temporal analysis via skeleton reconstruction of individual plants as a shape descriptor. For this purpose, an optimized approach for high-throughput was developed by point cloud data derived from UAS imagery. The curve-skeleton extraction is calculated based on a constrained Laplacian smoothing algorithm. The experimental setup was performed at the Indiana Corn and Soybean Innovation Center at the Agronomy Center for Research and Education (ACRE) in West Lafayette, Indiana, USA. On July 27th and August 3rd of 2021, two flights were performed over a trial with more than 200 maize plants using a custom designed UAS platform with a Sony Alpha ILCE-7R photogrammetric sensor. RGB images were processed by a standard photogrammetric pipeline by Structure from Motion (SfM) to get a scaled 3D point cloud of the individual corn. Filtering techniques and labeling algorithms were joined together to reconstruct a robust and accurate skeleton of individual maize. Therefore, significant traits such as number, length, growth angle and elongation rate of leaves and stem can be easily extracted. Height variations computed from the skeleton at the two dates show a coefficient of correlation with on-field measurements better than 92%. Our experimental outcomes demonstrate the UAS-data’s ability to provide practical information to efficiently select phenotypes in plant breeding programs.

We quantify the shape of hooked hairs which is a newly observed phenotype of epidermal cell extensions [1] in the common bean genotype L88-57 (Phaseolus vulgaris). The hooked hairs emerge below-ground before the root hairs and have a distinct ‘hooking’ morphology. We generated a dataset capturing their full distribution under the microscope within 3-5 days of germination. We quantify their shape by a novel computational pipeline that can automatically phenotype morphology. Our phenotyping pipeline quantifies traits like length, curvature, perimeter, area, and ‘hooking.’ Our objective is to quantify their response to nutrient stress to determine the function of hooked hairs in common bean during early development. We used the pipeline for analyzing our dataset of hydroponically grown beans and observed statistically significant responses compared to the control for length, curvature, perimeter, and area to nitrogen (p<0.001**) and phosphorus (p<0.001**) stress treatments. The calculation of ‘hooking’ for our dataset is still ongoing. We are simultaneously developing a landmark-free method for the two-dimensional shape analysis of our dataset and believe that our phenotyping efforts will enable the high-throughput analysis of morphological root hair traits for any plant species.

The quasi-biennial oscillation (QBO), a ubiquitous feature of the zonal mean zonal winds in the equatorial lower stratosphere, is forced by selective dissipation of atmospheric waves that range in periods from days to hours. However, QBO circulations in numerical models tend to be weak compared with observations, probably because of limited vertical resolution that cannot adequately resolve gravity waves and the height range over which they dissipate. Observations are required to help quantify wave effects. The passage of a superpressure balloon (SPB) near a radiosonde launch site in the equatorial Western Pacific during the transition from the eastward to westward phase of the QBO at 20 km permits a coordinated study of the intrinsic frequencies and vertical structures of two inertia-gravity wave packets with periods near 1-day and 3 days, respectively. Both waves have large horizontal wavelengths of about 970 and 5500 km. The complementary nature of the observations provided information on their momentum fluxes and the evolution of the waves in the vertical. The near 1-day westward propagating wave has a critical level near 20 km, while the eastward propagating 3-day wave is able to propagate through to heights near 30 km before dissipation. Estimates of the forcing provided by the momentum flux convergence, taking into account the duration and scale of the forcing, suggests zonal force of about 0.3-0.4 m/s/day for the 1-day wave and about 0.4-0.6 m/s/day for the 3-day wave, which acts for several days.

Managing river temperature in highly urbanized stream systems is critical for maintaining aquatic ecosystems and associated beneficial uses. Elevated river temperatures arise from warm surface inflows from impervious areas, channelization, the absence of riparian forests, and the lack of groundwater-surface water interactions. In the current work, we utilize a mechanistic river temperature model, i-Tree Cool River, to evaluate the cooling impacts of alternative ecological restoration scenarios: a) shading effects of tree planting in riparian areas and b) alternative streambed materials. The model was calibrated and validated on a 4.2 km reach of the Compton Creek in the Los Angeles (LA) River watershed, California, for low and high flow periods. The Arroyo Chub and Stickleback were considered the target species for analyzing thermal habitat suitability. River temperature simulations showed that like the ambient air temperature. The thermal response of the river in high flow periods was a function of upstream river temperature , where in low flow periods river water temperature was most affected by the tested restoration scenarios. Tree planting in the riparian corridor decreased the median thermal metrics: Max Weekly Max, Max Weekly Average, and Min Weekly Min Temperatures by an average of 3 ℃ (13%) to 20.4 ℃, 19.7 ℃, and 17.8 ℃, respectively. Using limecrete as an alternative bed material to the current concrete bottom decreased the median thermal metrics by an average of 0.9 ℃ (4%) to 22.7 ℃, 22 ℃, and 19 ℃, respectively. Combining the two scenarios decreased the river temperature metrics by an average of 4 ℃ (18%) to 18.2 ℃. Besides riparian vegetation, altering bed material is an impactful option in case of groundwater contamination and if channelized urban corridors lack the substrate to support vegetation. The use of ecological restoration scenarios resulted in summertime temperatures were within the documented spawning temperature thresholds and therefore temperature would not be a limiting factor in the potential reintroduction of the Arroyo Chub and Stickleback to Compton Creek. This tributary could be considered as a potential refuge and improved fish habitat in the LA basin during low flow periods.

Diurnal tidal currents are the dominate contributors to diapycnal mixing in many regions along the pathways for warm Atlantic Water (AW) circulating within the Arctic Ocean along the continental slope. This mixing diffuses AW heat and salt into the cooler and fresher surroundings, including the upper ocean where ocean heat fluxes play a role in the stability of the ice pack. The strongest diurnal currents are associated with topographically-trapped vorticity waves, which are sensitive to stratification and mean flow. In models, these waves are also sensitive to choices for forcing and geometry. Sensitivity to background conditions implies that tidal currents and mixing will change as the Arctic evolves towards a new climate state. Here, as a first step towards understanding how diurnal tidal currents might change in a future Arctic Ocean, we describe results from a suite of high-resolution (dx=2 km) 2-D and 3-D models for Arctic diurnal tides, focusing on their currents at locations along the AW pathways. We first demonstrate that accurate representation of barotropic diurnal tides requires forcing with both open boundary conditions and the direct potential tide. Next, we use 3-D models with realistic, ocean background stratification and mean flow to describe the annual cycle of depth-averaged diurnal tidal currents. Finally, we investigate the baroclinic structure of diurnally forced waves including the generation of harmonics (semidiurnal and higher) that can contribute to mixing within the water column. Our results show that tides should be explicitly included in ocean and coupled predictive models for the Arctic to represent the feedbacks between tidal energetics and ocean mean state via mixing.