The different olivine fabrics in ultramafic rocks have been widely used to discuss past tectonic settings, given that the olivine fabrics vary with pressure, temperature and water content. However, there are no researches that whether and how the olivine fabrics transform at different metamorphic stages in a natural rock during the process of deep subduction and exhumation. Yinggelisayi garnet lherzolites from South Altyn have experienced deep continental subduction and exhumation. The garnet lherzolites contain well-preserved residual protolith minerals, and near-peak (M1), granulite-facies retrograde (M2), and amphibolite-facies retrograde (M3) metamorphic mineral assemblages. Olivine grains in M1 formed at P–T conditions of 2.52–3.08 GPa, 1095–1136°C and low water contents (183–213 ppm H/Si), and showed [010] axes sub-normal to the foliation and [001] axes subparallel to the lineation, which is characteristic of B-type fabric ((010)[001]). Olivine grains in M2 formed at P–T conditions of 1.31–1.80 GPa, 851–893°C and also low water contents (93–139 ppm H/Si), and exhibited [010] axes sub-normal to the foliation and [100] axes subparallel to the lineation, which is characteristic of A-type fabric ((010)[100]). These observations suggest that olivine fabrics in HP-UHP metamorphosed ultramafic rocks are different in the near-peak and retrograde metamorphic stages, and also that the olivine fabrics can be transformed during deep continental subduction and exhumation. Therefore, the dispersed or no clear olivine fabric probably caused by multi-stage deformation and metamorphism, and the distinct olivine fabrics can be used as a clue to identify geological processes and better understand metamorphism and deformation during subduction and exhumation.

We conducted field work in South San Francisco Bay to examine cohesive sediment flocculation dynamics in a shallow, wave- and current-driven estuarine environment. Drawing on data collected using a suite of acoustic and optical instrumentation over three distinct seasons, we found that the factors driving floc size variability differed substantially when comparing locally-sourced sediment (i.e., through wave-driven resuspension) to suspended sediment advected from upstream. Statistical analysis of our extensive field data revealed additional seasonal variability in these trends, with wave stress promoting floc breakup during the summer and winter months, and biological processes encouraging floc growth during the spring productive period. Combining these data with fractal dimension estimates, we found that seasonally-varying floc composition can lead to differences in floc settling velocity by a factor of approximately two to five for a given floc size. Finally, by analyzing co-located turbulence and sediment flux measurements from the bottom boundary layer, we present evidence that the relationship between floc size and the inverse turbulent Schmidt number varies with floc structure. These results can be used to inform sediment transport modeling parameterizations in estuarine environments.

Mechanistic representations of biogeochemical processes in ecosystem models are rapidly advancing, requiring advancements in model evaluation approaches. Here we quantify multiple aspects of model functional performance to evaluate improved process representations in ecosystem models. We compare semi-empirical stomatal models with hydraulic constraints against more mechanistic representations of stomatal and hydraulic functioning at a semi-arid pine site using a suite of metrics and analytical tools. We find that models generally perform similarly under unstressed conditions, but performance diverges under atmospheric and soil drought. The more empirical models better capture synergistic information flows between soil water potential and vapor pressure deficit to transpiration, while the more mechanistic models are overly deterministic. Additionally, both multilayer canopy and big-leaf models were unable to capture the magnitude of canopy temperature divergence from air temperature. Lastly, modeled stable carbon isotope fractionation differed under canopy water stress which illustrates the value of carbon isotopes in helping to characterize ecosystem function and elucidate differences attributable to model structure. This study demonstrates the value of merging underutilized observational data streams with emerging analytical tools to characterize ecosystem function and discriminate among model process representations.

In this study, we apply the three-dimensional Stockwell Transform (3DST) to a novel dataset, namely airglow imager data from Rothera (68S, 68W). We use this approach to investigate small-scale high-frequency gravity waves (GWs) in the hydroxyl (OH) airglow layer, at a height $\sim$87 km in the mesosphere and lower thermosphere (MLT). MLT GWs are often underrepresented in models, being parameterised due to their small scale size and as such, the significant quantities of momentum and energy transferred by these small waves are missed. Better quantification of these waves is thus needed to support future model development. We find that the 3DST can identify waves and extract wave properties and their locations. Horizontal wavelengths are observed ranging from 10 to 40 km and vertical wavelengths of 15 to 40 km, with wave periods of 5 to 9 minutes, peaking at 7.5 minutes. These values are consistent with previous studies. Group speeds are found to be non-zero and large, implying that these GWs travel horizontally and fast. This case study demonstrates that the 3DST can be applied to airglow imager data and can successfully extract GW parameters. This is an important step in automating GW analysis in airglow.

GL plays a significant role in shaping research studies and academic endeavors, particularly in analyzing crucial outcomes related to contemporary issues. Its application is especially prevalent in the social sciences, particularly in policy-making, where it is essential to consider existing policies, their outcomes, and expert recommendations. The success of these outcomes is paramount in achieving optimal results and informing policy development in decision-making processes. The utilization of GL is increasingly gaining recognition across various areas of academia, demonstrating its notable impact. Grey literature's influence is evident in its application across diverse academic spheres. When conducting research, the primary objective is to utilize the most up-to-date information that supports the argument for fundamental research. The advancement of literacy, predominantly in education, enables individuals to engage with written materials, resulting in the emergence of GL across various mediums, including newspapers and pamphlets. These sources provide insights into different aspects of the relevant discipline. For example, government publications on news and policies, such as the impact of war on the economy, have become integral to academia and general knowledge accessible to the public today. The Vietnam War gave rise to numerous issues that significantly influenced societal perspectives. This period saw the emergence of new trends, including the formation of associations, the expansion of peace treaties between nations, and the establishment of agreements that facilitated more effective problem-solving approaches. Information disseminated by organizations and documented in peace contracts played a crucial role in shaping people's understanding and contributing to the development of robust outcomes. Introduction The idea of GL is an emerging way of sharing information that is reputable and useful in various aspects of life. GL entails all information produced outside of the traditional publishing and distribution channels classical to the academic sphere. All information published or provided to the public but has not undergone peer review constitutes grey literature. For instance, all reports, policy literature, newsletters, government documents, written speeches and urban plans are produced and accepted as facts by all people. The same information can be used in research as indeed in different academic purposes. For instance, population statistics released periodically by governments of different countries are used mainly by researchers as sources of reputable information when undertaking any given research in different fields that requires the use of demographic figures. The impacts of GL in world science are gain traction since the majority of information provided by GL is usually useful in making policy and tends to be based on expert opinions informed by the academic and existing body of research. For many years GL was considered non-scientific due to the amorphous nature of the approach utilized by authors of reports.

Pialassa Baiona is a shallow temperate coastal lagoon influenced by a variety of factors, including regional climate change and local anthropogenic disturbances. To better understand how these factors influenced modern organic carbon (OC) sources and accumulation, we measured OC as well as stable carbon isotopes (d13C) in 210Pb-dated sediments within a vegetated saltmarsh habitat and a human impacted habitat. Relative Sea Level (RSL) at the nearby tide gauge station data and four different Sea Surface Temperature (SST) data sets were analyzed starting from 1900 to assess the potential effect of sea ingression and warming on the coastal lagoon sedimentary process. The source contribution calculated from the MixSIAR Bayesian model revealed a mixed sedimentary organic matter (OM) composition dominated by increasing marine-derived OM after the 1950s, parallel with decreasing autochthonous saltmarsh vegetation (Juncus spp.) in the saltmarsh habitat and riverine-estuarine-derived OM in the impacted habitat. RSL rise in the area (8.7±0.5 mm yr−1 in the period 1900-2014) has been mainly driven by the land subsidence, especially during the central decades of the last century, enhancing the sea ingression in the lagoon. Annual SST anomalies present, starting from the eighties, a continuous warming tendency from 0.034±0.01 to 0.044±0.009°C yr-1. No direct effect on sedimentary properties was detected; however, RSL influenced OM sediment properties, although this effect was different between the two habitats.

We develop a data assimilation scheme with the Icosahedral Non-hydrostatic Earth System Model (ICON-ESM) for operational decadal and seasonal climate predictions at the German weather service. For this purpose, we implement an Ensemble Kalman Filter to the ocean component as a first step towards a weakly coupled data assimilation. We performed an assimilation experiment over the period 1960-2014. This ocean-only assimilation experiment serves to initialize 10-year long retrospective predictions (hindcasts) started each year on 1 November. On multi-annual time scales, we find predictability of sea surface temperature and salinity as well as oceanic heat and salt contents especially in the North Atlantic. The mean Atlantic Meridional Overturning Circulation is realistic and the variability is stable during the assimilation. On seasonal time scales, we find high predictive skill in the tropics with highest values in variables related to the El Niño/Southern Oscillation phenomenon. In the Arctic, the hindcasts correctly represent the decreasing sea ice trend in winter and, to a lesser degree, also in summer, although sea ice concentration is generally much too low in both hemispheres in summer. However, compared to other prediction systems, prediction skill is relatively low in regions apart from the tropical Pacific due to the missing atmospheric assimilation. In addition, we expect a better fine-tuning of the sea ice and the oceanic circulation in the Southern Ocean in ICON-ESM to improve the predictive skill. In general, we demonstrate that our data assimilation method is successfully initializing the oceanic component of the climate system.

Water scarcity becomes more severe and there will need to be a concerted effort to ensure that water supply remains secure. To mitigate the danger of water scarcity using an energy efficient method for water desalination is crucial. This review objectively compares the most practical thermal and reverse osmosis desalination technologies and the latest energy recovery opportunities for each process. This analysis reveals that thermal and reverse osmosis desalination should not be considered as a competitor since (mostly) the feed water quality and the final permeate water standards are the main parameters that impose choosing either of the most appropriate desalination method or hybridization of both approaches. Desalination of seawater using improved thermal desalination in which pre-heated helium gas instead of air is used to increase evaporation efficiency up to 3 times, is a promising technique that could make this sub-boiling thermal desalination approach the future of thermal desalination. The effect of Renewable energy resources, BCE, multiple stage units and optimizing liquid height in the dehumidifier were particularly evaluated in the HDH process which could perfectly handle the sub-boiling thermal desalination approach. Results of this comprehensive review can aid decision-makers by manifesting the main developments in desalination techniques to a reasonable degree of accuracy.

Ionospheric perturbations induced by tsunamis and earthquakes can be used for tsunami early warning and remote sensing of earthquakes, provided the perturbations are characterized properly to distinguish them from the ones caused by other sources. The ionospheric perturbations are increasingly being obtained from Global Positioning System (GPS) based Total Electron Content (TEC) measurements sampled at uniform time intervals. However, the sampling is not uniform in space. The nonuniform spatial sampling along the GPS satellite tracks introduces aliasing if it is not accounted while computing the ionospheric perturbations. All the methods hitherto used to detect the co-seismic and tsunamigenic ionospheric perturbations did not account the nonuniform spatial sampling while computing these perturbations. In addition, the residual approach used to obtain the perturbations by detrending the TEC time series using high-order polynomial fit introduces artifacts. These aliasing and artifacts corrupt amplitude, Signal-to-Noise Ratio (SNR), phase, and frequency of ionospheric perturbations which are vital to distinguish the perturbations induced by tsunamis and earthquakes from the rest. We show that Spatio-Periodic Leveling Algorithm (SPLA) successfully removes such aliasing and artifacts. The efficiency of SPLA in removing the aliases and artifacts is validated under two simulated scenarios, and using GPS observations carried out during two natural disasters – the 2004 Indian Ocean tsunami and the 2015 Nepal-Gorkha earthquake. We, further, studied the severity of aliasing and artifacts on co-seismic and tsunamigenic perturbations by analyzing its characteristics employing SNR, spatiotemporal, and wavelet analyses. The results reveal that removal of aliasing and artifacts using SPLA i) increases the SNR up to ~149% compared to the residual method and ~39% compared to the differential method, ii) distinctly resolves signals from sharp static variations, and iii) detects 50% more co-seismic ionospheric perturbations and 25% more tsunami-induced ionospheric perturbations in the two events studied. Cross-correlation of the perturbation time series obtained using the residual method and SPLA reveals that aliasing and artifacts shift the time of occurrence by -7.64 minutes to +4.21 minutes. Further, the results show that the SPLA efficiently detects the ionospheric perturbations at low elevation angles, thereby removes the need of applying elevation cut-off and increases the area of ionospheric exploration of a GPS receiver.

We use the surface temperature response to Phobos transits as observed by a radiometer on board of the InSight lander to constrain the thermal properties of the uppermost layer of regolith. Modeled transit lightcurves validated by solar panel current measurements are used to modify the boundary conditions of a 1D heat conduction model. We test several model parameter sets, varying the thickness and thermal conductivity of the top layer to explore the range of parameters that match the observed temperature response within its uncertainty both during the eclipse as well as the full diurnal cycle. The measurements indicate a thermal inertia of 103+48-24 Jm-2K-1s-1/2 in the uppermost layer of 0.2 to 4 mm, significantly smaller than the thermal inertia of 200 Jm-2K-1s-1/2 derived from the diurnal temperature curve. This could be explained by larger particles, higher density, or a very small amount of cementation in the lower layers.

The traditional method of measuring the lettuce height is a manual measurement with instruments, which is greatly affected by human error.At present, researchers have proposed to use color cameras to obtain RGB images of lettuce, and to obtain the height of lettuce from the images. However, these tasks usually require camera calibration or a reference object with a known height, which is somewhat restrictive. Considering that deep neural networks have a powerful ability to feature extraction and expression, without camera calibration and reference objects, we try to use four networks of image recognition to explore the effect of deep learning on abstracting the lettuce height from RGB images. On the test set, including 80 images and height from 0.9 cm to 7.5 cm, we achieve a good result with a mean absolute error of 1.22 mm.

Measurements and models of global geophysical parameters such as potential fields, seismic velocity models and dynamic / residual topography are well represented as 2D coloured / contoured maps. However, as teaching aids and for outreach, they offer little impact. Modern 3D-printing techniques help to visualize these and other concepts that are difficult to grasp, such as the intangible structures in the deep Earth. We developed a simple method for portraying scalar fields by 3D printing modified globes of surface topography, representing the parameter of interest as additional, exaggerated topography. This is particularly effective for long-wavelength (>500 km) fields. The workflow uses only open source and free-to-use software, and the resulting models print easily and effectively on a cheap (<300 GBP, 400USD) desktop 3D-printer. We have printed 3D representations of different scalar fields, including models of the surface topography of rocky planets, which can be used in outreach events. These objects are powerful to explain the importance of plate tectonics in shaping a planet. The workflow was extended to 3D scalar fields by analogy to Russian nesting dolls, where the audience can remove shallower layers to see how structures change with depth. We applied this to global seismic tomography models resulting in prototypes of “seismic matryoshkas” (see Figure). The tactile nature of these objects ensures that anyone, including the visually impaired, can explore the structures present deep within our planet

Numerical geodynamo simulations capture several features of the spatial and temporal geomagnetic field variability on historical and Holocene timescales. However, a recent analysis questioned the ability of these numerical models to comply with long-term palaeomagnetic field behaviour. Analysing a suite of 50 geodynamo models, we present here the first numerical simulations known to reproduce the salient aspects of the palaeosecular variation and time-averaged field behaviour since 10 Ma. We find that the simulated field characteristics covary with the relative dipole field strength at the core-mantle boundary (dipolarity). Only models dominantly driven by compositional convection, with an Ekman number (ratio of viscous to Coriolis forces) lower than $10^{-3}$ and a dipolarity in the range $0.34-0.56$ can capture the observed palaeomagnetic field behaviour. This dipolarity range agrees well with state-of-the-art statistical field models and represent a testable prediction for next generation global palaeomagnetic field model reconstructions.

Strong subauroral plasma flows were observed in the dawnside ionosphere during the 20 November 2003 super geomagnetic storm. They are identified as dawnside subauroral polarization streams (SAPS) in which plasma drift direction is eastward and opposite to the westward SAPS typically found in the dusk sector. Both dawnside and duskside SAPS are driven by the enhanced meridional electric field in the low latitude portion of Region-2 field-aligned currents (FACs) in the subauroral region where ionospheric conductance is relatively low. However, dawnside eastward SAPS were only observed in the main and recovery phases while duskside westward SAPS were found much earlier before the sudden storm commencement. Simulations with the Multiscale Atmosphere-Geospace Environment (MAGE) model demonstrate that the eastward SAPS are associated with dawnside ring current build-up. Unlike the duskside where ring current build-up and SAPS formation can occur under moderate driving conditions, strong magnetospheric convection is required for plasmasheet ions to overcome their energy-dependent drifts to effectively build up the dawnside ring current and upward Region-2 FACs. We further used test particle simulations to show the characteristic drift pattern of energetic protons under strong convection conditions and how they are related to the dawnside SAPS occurrence. This study demonstrates the connection between the level of solar wind driving condition and a rare ionospheric structure, eastward SAPS on the dawnside, which only occur under strong convection typically associated with intense or super storms. Dawnside SAPS are suggested as a unique feature of major geomagnetic storms.

We investigate the rare events of sudden appearances of relativistic electrons (>700 keV), which are normally confined to the Van Allen belts, in the slot region. The frequency of occurrence of these events are on average 1-2 per year. To cope with the scarcity of events, in this study we examine 21 years of trapped relativistic electron fluxes available from the POES and MetOp Space Environment Monitor (SEM‐2). Our statistical analysis show that these events can occur even during moderate geomagnetic activity. Occurrence of these events correlates with high speed solar winds or ICMEs depending on the phase of the solar cycle. Most importantly, we show that ULF wave activity plays a significant role in causing these events and the events could be predicted in 75% of the cases.

A halo Coronal Mass Ejection can have a devastating impact on Earth by causing damage to satellites and electrical transmission line facilities and disrupting radio transmissions. To predict the orientation of the magnetic field (and therefore the occurrence of a geomagnetic storm) associated with an occurring CME, filaments’ sign of magnetic helicity can be used. This would allow us to predict a geomagnetic storm. With the deluge of image data produced by ground-based and space-borne observatories and the unprecedented success of computer vision algorithms in detecting and classifying objects (events) on images, identification of filaments’ chirality appears to be a well-fitted problem in this domain. To be more specific, Deep Learning algorithms with a Convolutional Neural Network (CNN) backbone are made to attack this very type of problem. The only challenge is that these supervised algorithms are data-hungry; their large number of model parameters demand millions of labeled instances to learn. Datasets of filaments with manually identified chirality, however, are costly to be built. This scarcity exists primarily because of the tedious task of data annotation, especially that identification of filaments’ chirality requires domain expertise. In response, we created a pipeline for the augmentation of filaments based on the existing and labeled instances. This Python toolkit provides a resource of unlimited augmented (new) filaments with labeled magnetic helicity signs. Using an existing dataset of H-alpha based manually-labeled filaments as input seeds, collected from August 2000 to 2016 from the big bear solar observatory (BBSO) full-disk solar images, we augment new filament instances by passing labeled filaments through a pipeline of chirality-preserving transformation functions. This augmentation engine is fully compatible with PyTorch, a popular library for deep learning and generates the data based on users requirement.

Magnetic sounding is a powerful tool to explore the interior of planetary bodies through the electrical conductivity structure. The electrical conductivity structure of the lunar mantle has previously been derived from surface magnetic field measurements as part of the Apollo 12 mission and concurrent magnetometer data acquired from orbit through the Explorer 35 satellite. Here, we derive the first global conductivity structure using only satellite magnetometer data collected by the recent Lunar Prospector and Kaguya Selene satellite missions. We show that the field in the geomagnetic tail exhibits a simple geometrical structure and can be well described by a single spherical harmonic of degree and order one. Employing this information about the inducing field geometry and assuming a potential representation of the field in the geomagnetic tail, we derive a frequency-dependent transfer function and invert it for a global one-dimensional (1-D) electrical conductivity profile. Our global transfer function shows striking similarity with the local one obtained from joint analysis of Apollo 12 and Explorer 35 magnetometer data. This indicates the lack of local variations at the Apollo 12 landing site compared to the globally-averaged upper to mid-mantle electrical conductivity structure.

While more hydrological data is being generated than ever before, the power of modelling this collected information is not fully realized unless it is of high quality, especially considering hydrological data from sensor networks, which is often errant due to the possibility of malfunction or non-conducive environmental conditions. Fluctuations or errors are difficult to predict, identify, and interpret. Manual models of quality assurance are not designed for managing datasets with continuous timeseries or spatially extensive coverage, resulting in time- consuming models that rely on humanmade decision making and lack statistical inference. This research hypothesizes that the stochasticity of rainfall and deterministic properties of flow can be used in concert to create a more characteristic quality assurance model for high-resolution environmental data. An automated implementation of this model is presented herein with the application of two use-cases, which maintains statistical integrity and circumvents biases and potential for user error of manual frameworks.