Observations made in Gale Crater by instruments on the MSL Curiosity Rover show that the diurnal amplitude of the surface pressure is increased and the depth of the Convective Boundary Layer (CBL) is decreased relative to other lander locations on flatter regions of Mars (Haberle et al., 2014; Moores et al., 2015). Mesoscale modeling studies of Gale Crater suggest that crater circulations produce these effects. Tyler & Barnes (2013) show that local upslope/downslope flows along the crater rim and Mt. Sharp amplify the diurnal pressure cycle. These same flows are thought to be at least partly responsible for the suppression of the CBL because upward air flow at the rim and in the center (due to Mt. Sharp) forces subsidence over the lowest regions of the crater during the day. Regional flows, largely due to the location of Gale near the dichotomy boundary, may also play a role in shaping the circulation internal to the crater. Whether the behavior of the CBL and the amplified diurnal pressure cycle are phenomena observed in craters morphologically different from Gale (i.e. bowl-shaped, irregular, degraded) is not yet understood. We will explore these questions by characterizing the behavior of these processes as they are shaped by the morphology of craters greater than 100 km in diameter. We use the NASA Ames Mars Global Circulation Model (GCM) that now utilizes the NOAA/GFDL cubed-sphere finite-volume dynamical core to examine ~100 craters of varying size and shape from a database of known Martian craters (Robbins & Hynek, 2014). Run at 7.5 km resolution, the GCM is capable of resolving surface winds, temperature, and pressure inside craters of this size allowing for the analysis of dozens of craters simulated at various seasons and within the context of synoptic and global-scale phenomena.

Akutan Island hosts a volcano considered as one of most active in the Aleutians Arc. Besides the regular eruption activity, Akutan in 1996 experienced a remarkable seismic unrest with earthquakes reaching the magnitude of M 5.3. We build a new tomography model including the 3D distributions of the , and ratio based on arrival time data from more than 4000 local earthquakes. In this model, we reveal a columnar anomaly of high ratio with the top boundary at a depth of 5-6 km below surface, which represents a steady conduit feeding the Akutan volcano. At a depth of ~4 km, the deep conduit is split in two branches, one of which ascend to the summit area and another one directs to a fumarole field at the flank of the volcano. This structure explains distinct geochemical features of emitted gases in these two areas. In the upper part of the velocity model, the highly heterogeneous structures are associated with interactions of shallow magmatic sources, meteoric and magmatic fluids, as well as degassing. Besides the prominent anomaly associated with the shallow magma reservoir beneath the caldera and the active cone, we observe several areas with high ratio, some of which are interpreted as shallow magma storages, and some as zones of meteoric water penetration. We propose a scenario of abrupt fluid ejection from the deep magma conduit that led to the seismic unrest and strong ground deformations in the Akutan area in 1996.

The Stratospheric Aerosol and Gas Experiment operating on the International Space Station (SAGE III/ISS) is an occultation instrument that retrieves aerosol extinction and ozone, water vapor, and other trace gas concentrations. Water vapor in the stratosphere plays a significant role in ozone depletion and global temperature regulation making it an important species to monitor. The new SAGE III/ISS instrument provides high quality, vertically resolved profiles of water vapor throughout the stratosphere and upper troposphere. A discussion is presented on the sensitivity of the SAGE III/ISS instrument to water vapor and provide an early evaluation of the retrieved profiles.

The Thermal And Near infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observing SATellite (GOSAT) was launched in Jan. 2009 to monitor global CO2 and CH4 distribution from space. The wide-spectral-range data by FTS can measure the partial-column density of the lower troposphere using sun-light reflected from the surface and thermal emission from the atmosphere. In addition to nominal global grid-observation, TANSO-FTS has an agile pointing system to target various CH4 point-sources as well as reference points every three days over years, and can capture the entire flux emitted vertically and horizontally from the source. We demonstrated the monitoring capability by using the natural-gas blowout event at Aliso Canyon, CA and tried to estimate the CH4 flux from a dairy farm in Chino, CA with the Weather Research and Forecasting model. The GOSAT footprint, which is much larger than the point-source area, reduces the enhancement of the retrieved column density close to the detection level. As the single-pixel data and acquisition time of 4 s by FTS limits the number of sampling points near the emission source, careful screening with wind speed and direction is required to acquire reference and source dataset for analysis. The largely fluctuated single data requires to be averaged to improve the precision, but the GOSAT data is spatially too sparse. We calculated the local CH4 flux from Chino using the correlation between wind speed and density, but the lack of a proper reference result in large errors. To solve the above-mentioned issues, we manufactured airborne imaging-spectrometer suites comprising two bands to measure CH4 and CO2 at 1.6 μm and O2 at 0.76 μm. They have 4,000 times more data than GOSAT, increase enhancement with higher spatial resolution, and select proper upwind reference with imaging capability. In Feb. 2018, we flew over greater Nagoya with the mixture of possible emission sources such as energy production, waste water, dairy farm, and agriculture. The spectral image of spatial resolution <100 m has clearly detected enhancement from individual local sources of CH4 and CO2. We add one more spectrometer to measure short-lived NO2 to detect plume orientation. Our goal is to individually estimate city-level CH4 flux from different source sectors.

A novel model called PyIRI was recently developed. It offers a fully Python alternative to the widely used FORTRAN International Reference Ionosphere (IRI) model to construct the ionospheric electron density for the entire day and on the entire global grid in one computation, which has a significantly lower computational overhead. PyIRI introduced a novel approach to the computation of the global and diurnal functions and their matrix multiplication with Consultative Committee on International Radio (CCIR) coefficients, that enabled this global approach for the density specification. Since the IRI-based Real-Time Assimilative Model (IRTAM) produces coefficients in a similar format as CCIR coefficients, the PyIRI software was extended to be able to work with IRTAM coefficients using the same computationally efficient approach. This technical note describes the PyIRTAM software and shows examples of how to use it. PyIRTAM is 21 times more efficient than the classical IRTAM. PyIRTAM tool is made publicly available.

Among the great diversity of atmospheric circulation patterns observed throughout the solar system, polar vortices stand out as a nearly ubiquitous planetary-scale phenomenon. In recent years there have been significant advances in the observation of planetary polar vortices, culminating in the fascinating discovery of Jupiter's polar vortex clusters during the Juno mission. Alongside these observational advances has been a major effort to understand polar vortex dynamics using theory, idealised and comprehensive numerical models, and laboratory experiments. Here we review our current knowledge of planetary polar vortices, highlighting both the diversity of their structures, as well as fundamental dynamical similarities. We propose a new convention of vortex classification, which adequately captures all those observed in our solar system, and demonstrates the key role of polar vortices in the global circulation, transport, and climate of all planets. We discuss where knowledge gaps remain, and the observational, experimental, and theoretical advances needed to address them. In particular, as the diversity of both solar system and exoplanetary data increases exponentially, there is now a unique opportunity to unify our understanding of polar vortices under a single dynamical framework.

Improved understanding of the impact of crystal mush rheology on the response of magma chambers to magmatic events is critical for better understanding crustal igneous systems with abundant crystals. In this study, we extend an earlier model by (Liao et al, 2018) which considers the mechanical response of a magma chamber with poroelastic crystal mush, by including poroviscoelastic rheology of crystal mush. We find that the coexistence of the two mechanisms of poroelastic diffusion and viscoelastic relaxation causes the magma chamber to react to a magma injection event with more complex time-dependent behaviors. Specifically, we find that the system’s short-term evolution is dominated by the poroelastic diffusion process, while its long-term evolution is dominated by the viscoelastic relaxation process. We identify two post-injection timescales that represent these two stages and examine their relation to the material properties of the system. We find that better constraints on the poroelastic diffusion time are more important for the potential interpretation of surface deformation using the model. We also find that the combination of the two mechanisms causes magma transport to reverse direction in the system, which would successively expose crystals to magma with different chemical compositions.

We utilise Principal Component Analysis to identify and quantify the primary electric potential morphologies during geomagnetic storms. Ordering data from the Super Dual Auroral Radar Network (SuperDARN) by geomagnetic storm phase, we are able to discern changes that occur in association with the development of the storm phases. Along with information on the size of the patterns, the first 6 eigenvectors provide over ~80% of the variability in the morphology, providing us with a robust analysis tool to quantify the main changes in the patterns. Studying the first 6 eigenvectors and their eigenvalues with respect to storm phase shows that the primary changes in the morphologies with respect to storm phase are the convection potential enhancing and the dayside throat rotating from pointing towards the early afternoon sector to being more sunward aligned during the main phase of the storm. We find that the ionospheric electric potential increases through the main phase and then decreases after the end of the main phase is reached. The dayside convection throat points towards the afternoon sector before the main phase and then as the potential increases throughout the main phase, the dayside throat rotates towards magnetic noon. Furthermore, we find that a two cell convection pattern is dominant throughout and that the dusk cell is overall stronger than the dawn cell.

Every river and its bed has a flourishing life thriving environment. Search of life in these region will help us to find out if really life existed in Mars. Commonly life could be of any form based on the surrounding environment. Artificial Intelligence could help us to find what kind of organisms could have existed based on the minerals found in the Jezero Crater site. In this research we deep learning with the list of elements found at site and conclude to what kind of creature sediments to search for. This could make our search easier to look for sediments. The basic idea of this research to bring out what to search for based on the environment in and around the Jezero Crater.

Gravity waves (GW) carry energy and momentum from troposphere to the middle atmosphere and have a strong influence on the circulation there. Global atmospheric models cannot fully resolve GWs, and therefore rely on highly simplified GW parametrizations that, among other limitations, account for vertical wave propagation only and neglect refraction. This is a major source of uncertainty in models, and leads to well-known problems, such as late break-up of polar vortex due to the “missing” GW drag around 60°S. To investigate these phenomena, GW observations over Southern Andes were performed during SouthTRAC aircraft campaign. This paper presents measurements from a SouthTRAC flight on 21~September 2019, including 3-D tomographic temperature data of the infrared limb imager GLORIA (8-15 km altitude) and temperature profiles of the ALIMA lidar (20-80 km altitude). GLORIA observations revealed multiple overlapping waves of different wavelengths. 3-D wave vectors were determined from the GLORIA data and used to initialise a GW ray-tracer. The ray-traced GW parameters were compared with ALIMA observations, showing good agreement between the instruments and direct evidence of oblique (partly meridional) GW propagation. ALIMA data analysis confirmed that most waves at 25-40 km altitudes were indeed orographic GWs, including waves seemingly upstream of the Andes. We directly observed horizontal GW refraction, which has not been achieved before SouthTRAC. Refraction and oblique propagation caused significant meridional transport of horizontal momentum as well as horizontal momentum exchange between waves and the background flow all along the wave paths, not just in wave excitation and breaking regions.

Advances in machine learning (ML) techniques and computational capacity have yielded state-of-the-art methodologies for processing, sorting, and analyzing large seismic data sets. In this work, we consider an application of ML for automatically identifying dominant types of impulsive seismicity contained in observations from a 34-station broadband seismic array deployed on the Ross Ice Shelf (RIS), Antarctica from 2014 to 2017. The RIS seismic data contain signals and noise generated by many glaciological processes that are useful for monitoring the integrity and dynamics of ice shelves. Deep clustering was employed to efficiently investigate these signals. Deep clustering automatically groups signals into hypothetical classes without the need for manual labeling, allowing for comparison of their signal characteristics and spatial and temporal distribution with potential source mechanisms. The method uses spectrograms as input and encodes their salient features into a lower-dimensional latent representation using an autoencoder, a type of deep neural network. For comparison, two clustering methods are applied to the latent data: a Gaussian mixture model (GMM) and deep embedded clustering (DEC). Eight classes of dominant seismic signals were identified and compared with environmental data such as temperature, wind speed, tides, and sea ice concentration. The greatest seismicity levels occurred at the RIS front during the 2016 El Niño summer, and near grounding zones near the front throughout the deployment. We demonstrate the spatial and temporal association of certain classes of seismicity with seasonal changes at the RIS front, and with tidally driven seismicity at Roosevelt Island.

Results of laboratory experimental study of the hydraulic fracture initiation, its propagation and closing, re-fracturing, the fluid pore pressure variations and related acoustic emission (AE) are considered. The experimental setup differs from common equipment designed for testing cores or cubic samples; it is designed for work with samples of disk form with diameter 430 mm and height 66 mm, which allows us to measure the pore pressure distributions along the sample boundary together with passive and active acoustic emission measurements (Fig.1). The experiments were conducted with artificial porous saturated samples made from gypsum-cement mixture in agreement with simulation criteria. The samples were created by filling the pressure chamber with the gypsum-cement-water mixture. After drying, the samples were saturated by gypsum water solution and loaded by three-axial stresses. To produce the fracture, mineral oil was injected with the constant rate 0.3 sm3/sec through the cased borehole. A set of experiments was conducted, in which the main stress axis orientation was changed after the first hydraulic fracture creation. Registration of the change in the amplitude of the waves passing through the fracture allowed to determine the fracture formations, its filling with fluid, the increase in the fracture opening as the injection continued, and the fracture closing after the injection was stopped. It was found that the influence of the fluid pore pressure diffusion from the point of injection is of great significance: the fracturing pressure was several megapascals higher than it should be expected. The fracture closing pressure also occurred to be higher in comparison with the case, when the pressure diffusion is neglected. It was obtained, that the AE source positions are closely related with the fracture position. It is remarkable, that we did not registered any AE outside the fracture. Comparison that fact with the results of the pore pressure measurements allows to suggest, that the pressure increase due to the viscous fluid injection is not enough to induce AE pulses at some distance from the borehole and the fracture. When the injection fluid has the same viscosity as the fluid saturating the sample, the pressure increase is sufficient to induce acoustic emission corresponding to the pore pressure increase.

Due to their limited resolution, numerical ocean models need to be interpreted as representing filtered or averaged equations. How to interpret models in terms of formally averaged equations, however, is not always clear, particularly in the case of hybrid or generalized vertical coordinate models. We derive the averaged hydrostatic Boussinesq equations in generalized vertical coordinates for an arbitrary thickness weighted-average. We then consider various special cases and discuss the extent to which the averaged equations are consistent with existing model formulations. As previously discussed, the momentum equations in existing depth-coordinate models are best interpreted as representing Eulerian averages (i.e., averages taken at fixed depth), while the tracer equations can be interpreted as either Eulerian or thickness-weighted isopycnal averages. Instead we find that no averaging is fully consistent with existing formulations of the parameterizations in semi-Lagrangian discretizations of generalized vertical coordinate ocean models. Perhaps the most natural interpretation of generalized vertical coordinate models is to assume that the average follows the model’s coordinate surfaces. However, the existing model formulations are generally not consistent with coordinate-following averages, which would require “coordinate-aware” parameterizations that can account for the changing nature of the eddy terms as the coordinate changes. Alternatively, the model variables can be interpreted as representing either Eulerian or (thickness-weighted) isopycnal averages, independent of the model coordinate that is being used for the numerical discretization. Existing parameterizations in generalized vertical coordinate models, however, are usually not fully consistent with either of these interpretations. We discuss what changes are needed to achieve consistency.

Airborne electromagnetic (AEM) data can be inverted to recover models of the electrical resistivity of the subsurface; these, in turn, can be transformed to obtain models of sediment type. AEM data were acquired in Butte and Glenn Counties, California, U.S.A. to improve the understanding of the aquifer system. Around 800 line-kilometers of high-quality data were acquired, imaging to a depth of ~300 m. We developed a workflow designed to obtain, from the AEM data, information about the large-scale structure and heterogeneity of the aquifer system to better understand the vertical connectivity. Using six different forms of inversion and posterior sampling of the recovered resistivity models, we produced 6006 resistivity models. These models were transformed to models of sediment type and estimates of percentage of sand/gravel. Exploring the model space, containing the resistivity models and the derived models, allowed us to delineate the large-scale structure of the aquifer system in a way that captures and communicates the uncertainty in the identified sediment type. The uncertainty increased, as expected, with depth, but also served to indicate, as areas of high uncertainty in sediment type, the location of both large-scale and small-scale interfaces between sediment type. A plan view map of the integrated percentage of sand/gravel, when compared to existing hydrographs, revealed the extent of lateral changes in vertical connectivity within the aquifer system throughout the study area.

Co-located pressure and velocity observations in 10-15m depth are used to estimate the relative contribution of bound and free infragravity (IG) wave energy to the IG wave field. Shoreward and seaward going IG waves are analyzed separately. At the Southern California sites, shoreward propagating IG waves are dominated by free waves, with the bound wave energy fraction <30% for moderate energy incident sea-swell and <10% for low energy incident sea-swell. Only the 5% of records with energetic long swell show primarily bound waves. Consistent with bound IG wave theory, the energy scales as the square (frequency integrated) sea-swell energy, with a higher correlation with swell than sea energy. Seaward and shoreward free IG energy is strongly tidally modulated. The ratio of free seaward to shoreward propagating IG energy suggests between 50-100% of the energy radiated offshore is trapped on the shelf seaward of 10-15m and redirected shoreward. Remote sources of IG energy are small. The observed linear dependency of free seaward and shoreward IG energy on local sea-swell wave energy and tide are parameterized with good skill (R2 ~ 0.90). Free (random phase) and bound (phase-coupled) IG waves are included in numerically simulated timeseries for shoreward IG waves that are used to initialize (~ 10m depth) the numerical nonlinear wave transformation SWASH. On the low slope study beach, wave runup is only weakly influenced by free shoreward propagating waves observed at the offshore boundary (foreshore slope = 0.02).

If a kick is not detected and circulated properly out of the wellbore with heavier mud weight, it leads to blowouts. In this case, reservoir fluids gush out of the well uncontrollably without restriction leading to loss of control. This may lead to fractures initiating in the post-blowout capping stages, just below the casing shoe, propagating upwards creating a channel through which reservoir fluids can flow to the ocean floor. Being able to model these fracture failures will help understand wellbore integrity problems from loss of control situations and predict the possibility of broaching preventing many ecological disasters like the 1969 Santa Barbara oil spill from Union Oil’s A-21 well. The hypothesis tested is that fracture initiation from a wellbore in a loss of control situation can be predicted through analysis of the near-wellbore stress field, with knowledge of the in-situ stress state and the properties of the formation and the borehole assembly. A 3D numerical model is employed to assess whether a fracture will initiate. This is done by considering the stressfield at the casing shoe; the point most vulnerable to tensile fracture failure downhole. In-situ stress state, wellbore pressure, casing shoe depth and the casing, cement, and formation’s mechanical properties are independent variables that are shown to control fracture initiation; the dependent variable. A reservoir model is used to predict pressure build-up during capping procedures. A case study on Gulf of Mexico is presented with input wellbore pressure data generated using a worst case discharge model. Wellbore pressure drop during uncontrolled discharge from a well can cause casing collapse failures and subsequently pressure build-up in the post-blowout capping stage, may initiate fractures which can lead fluid leakage to the surface either through the cement or the interfaces with the casing and the formation. The region of the in-situ stress states where fracture initiation will occur is shown in dimensionless plots. This is useful for drilling and wellbore integrity teams. When targeting highly-pressured formations as in deepwater, wellbore architecture must be made with considerations of the wellbore pressures generated from loss of control situations like blowouts. Research reported in this publication was supported by an Early-Career Research Fellowship from the Gulf Research Program of the National Academies of Sciences, Engineering, and Medicine. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Gulf Research Program of the National Academies of Sciences, Engineering, and Medicine.

‘Every Earthquake a Precursor According to Scale’ (EEPAS) is a model to forecast earthquakes within the coming months, years and decades, depending on magnitude. EEPAS performs well for seismically active regions including New Zealand (NZ) and has been formally evaluated in Collaboratory for the Study of Earthquake Predictability (CSEP) centres in NZ and California, USA. It has been used for practical forecasting in NZ for nearly a decade. An EEPAS forecast is formed by accumulating the contributions from past earthquakes to the expectation of future earthquakes. It uses the precursory scale increase (Ψ) phenomenon along with three predictive spatial, temporal and magnitude scaling relations. For a particular mainshock, Ψ is identified as a prior sharp increase in the occurrence of minor earthquakes. Each identification is represented by a value of precursor magnitude MP, precursor time TP and precursory area AP. An algorithm to automatically identify Ψ was developed and applied to real and synthetic earthquake catalogs. Multiple identifications of Ψ were obtained for most mainshocks. A trade-off between AP and TP was observed among such multiple identifications. Here, we examine the implications of the trade-off for the EEPAS temporal and spatial scaling parameters aT and σA. The EEPAS parameters were initially fitted to the NZ earthquake catalog from 1986-2006. The EEPAS parameters are now refitted with a sequence of fixed values for aT and then for σA. The range of fixed values constrain the respective temporal and spatial scales to vary by a factor of a hundred. Results confirm the existence of a similar space-time trade-off in EEPAS as in Ψ, with large aT values being associated with small σA values and vice versa. We conclude that the space-time trade-off is an intrinsic feature of precursory seismicity. This exists independently of other influences, such as the local strain rate, that may contribute to scatter in the predictive scaling relations. Mixing EEPAS models with parameters along the trade-off line should improve forecasting.

The thermohaline intrusion of the warm and saline Atlantic Water (AW) into the Arctic Ocean, referred to as “Arctic Atlantification”, has significant implications and feedback to the dynamics and thermodynamics of the Arctic Ocean. The AW enters the Arctic Ocean through two gateways: Fram Strait and the Barents Sea Opening (BSO). The relative strength of these two AW branches dominates the oceanic heat contribution to the Arctic Ocean. In conjunction with the measurements in key hydrographic sections, numerical ocean modelling provides us with a useful tool to characterize and corroborate the temporal and spatial variability of the AW branches. Simulations are conducted using the regional configuration Arctic and North Hemispheric Atlantic (ANHA) of the ocean/sea-ice model NEMO running at 1/4° and 1/12° resolution. Online passive tracers from the model configuration are used to trace the pathways of the AW inflow in the Arctic Ocean. With the AW becoming more important to the dynamics of the Arctic Ocean, this study aims to examine its variability, transformation, impacts, and ultimately track how it evolves. While the heat in the Fram Strait Branch Water (FSBW) dissipates in a slower process through the mixing with the ambient cold water below sea surface, the vast majority of the heat loss of the Barents Sea Branch Water (BSBW) takes place in Barents Sea due to the sea surface cooling, leading to Cold Atlantic Water (CAW) production during 2013 and 2014. The CAW pulses result in a significant heat content reduction in the eastern Arctic.