Tracking the onset of inelastic (permanent) deformation is critical to quantifying the stress experienced by an aquifer system so that the effects of current groundwater extraction practices are put in the context of the sedimentary and geological histories of a region. However, the pre-consolidation stress is rarely known due to the lack of multi-decadal ground-based data. In this paper, we propose a new approach to track the onset and spatial evolution of inelastic deformation based on a 2003-2020 multi-sensor Interferometric Synthetic Aperture Radar time series analysis. Our study reveals that in central Iran, many locations that used to experience elastic (recoverable) deformation just a few years ago, are now deforming inelastically, leading to irreversible lowering of the ground surface and irreversible loss of aquifer storage. Lithologic data reveals that the total thickness of the drained clay layers controls the extent and timing of the observed inelastic deformation, while groundwater data confirms that the multi-decadal lowering of groundwater levels is driving the long-term compaction. These results highlight that we are now at or near a tipping point in time between sustainability and permanent damage to our underground water resources, emphasizing the fact that current decisions have the potential to change the natural resources landscape permanently.

Transient deformations associated with foreshocks activity has been observed before large earthquakes, suggesting the occurrence of a detectable pre-seismic slow slip during the initiation phase. In this respect, a critical issue consists in discriminating the relative contributions from seismic and aseismic fault slip during the preparation phase of large earthquakes. We focus on the April-May 2017 Valparaíso earthquake sequence, which involved a Mw=6.9 earthquake preceded by an intense foreshock activity. To assess the relative contribution of seismic and aseismic slip, we compare surface displacements predicted from foreshock source models to the transient motion measured prior to the mainshock. The comparison between observed and predicted displacements shows that only half of the total displacement can be explained by the contribution of foreshocks. This result suggests the presence of aseismic preslip during an initiation phase preceding the mainshock.

The SEIS instrument package with the three very broad-band and three short period seismic sensors is installed on the surface on Mars as part of NASA’s InSight Discovery mission. When compared to terrestrial installations, SEIS is deployed in a very harsh wind and temperature environment that leads to inevitable degradation for the quality of the recorded data. One ubiquitous artifact in the raw data is an abundance of transient one-sided pulses often accompanied by high-frequency precursors. These pulses, which we term “glitches”, can be modeled as the response of the instrument to a step in acceleration, while the precursors can be modeled as the response to a simultaneous step in displacement. We attribute the glitches primarily to SEIS-internal stress relaxations caused by the large temperature variations to which the instrument is exposed during a Martian day. Only a small fraction of glitches correspond to a motion of the SEIS package as a whole and they are all due to minuscule instrument tilts. In this study, we focus on the analysis of the glitch+precursor phenomenon and present how these signals can be automatically detected and removed from SEIS’ raw data. As glitches affect many standard seismological analysis methods such as receiver functions or spectral decomposition, we anticipate that studies of the Martian seismicity as well as studies of Mars’ internal structure should benefit from deglitched seismic data.

Current lightning predictions are uncertain because they either rely on empirical diagnostic relationships based on the present climate or use coarse-scale climate scenario simulations in which deep convection is parameterized. Previous studies demonstrated that simulations with convection-permitting resolutions (km-scale) improve lightning predictions compared to coarser-grid simulations using convection parameterization for different geographical locations but not over the boreal zone. In this study, lightning simulations with the NASA Unified-Weather Research and Forecasting (NU-WRF) model are evaluated over a 955x540 km2 domain including the Great Slave Lake in Canada for six lightning seasons. The simulations are performed at convection-parameterized (9 km) and convection-permitting (3 km) resolution using the Goddard 4ICE and the Thompson microphysics (MP) schemes. Four lightning indices are evaluated against observations from the Canadian Lightning Detection Network (CLDN), in terms of spatiotemporal frequency distribution, spatial pattern, daily climatology, and an event-based overall skill assessment. Concerning the model configuration, regardless of the spatial resolution, the Thompson scheme is superior to the Goddard 4ICE scheme in predicting the daily climatology but worse in predicting the spatial patterns of lightning occurrence. Several evaluation metrics indicate the benefit of working at a convection-permitting resolution. The relative performance of the different lightning indices depends on the evaluation criteria. Finally, this study demonstrates issues of the models to reproduce the observed spatial pattern of lightning well, which might be related to an insufficient representation of land surface heterogeneity in the study area.

The Apollo 16 sample 66095, named “rusty rock”, is uniquely enriched in volatile and moderately volatile elements. The impact melt breccia is characterized by the abundant occurrence of Fe-rich sulfide and chloride alteration phases, including FeS, ZnS and FeCl. These phases have previously been interpreted to be the result of fumarolic alteration of the breccia. Here we present the results of two different experimental approaches, which aim to constrain the temperature conditions and the process under which the “rusty rock” alteration formed. The first experimental set-up assumes that the metals Zn, Cu and Fe were introduced into the rock by a C-O-S-Cl gas phase, and that the Fe-rich sulfides and chlorides were deposited from this gas phase. This “gas deposition” experiment suggests that the alteration assemblage formed over the temperature range of 538-638 °C. The second experimental set-up simulates a scenario, where Fe metal particles in the lunar rock react with a Zn-C-O-S-Cl gas phase at six different temperatures between 396 °C and 1005 °C. This latter “metal reaction” experiment resulted in the formation of sulfide and chloride coatings on the Fe metal chips. The “rusty rock” alteration phases FeCl and (Zn,Fe)S were abundantly present in the coating of the Fe metal chip reacted at 580 °C. Both experiments lead to results which are in agreement, providing a temperature of 580 ± 50 °C for the fumarolic alteration on the Moon, as observed in the Apollo 16 “rusty rock”.

Explosions and earthquakes are effectively discriminated by P/S amplitude ratios for moderate magnitude events (M≥4) observed at regional to teleseismic distances (≥200 km). It is less clear if P/S ratios are effective explosion discriminants for lower magnitudes observed at shorter distances. We report new tests of P/S discrimination using a dense seismic array in a continental volcanic arc setting near Mount St. Helens, with 23 single-fired borehole explosions (ML 0.9-2.3) and 406 earthquakes (ML 1-3.3). The array provides up to 95 three-component broadband seismographs and most source-receiver distances are <120 km. Additional insight is provided by ~3,000 vertical component geophone recordings of each explosion. Potential controls on local distance P/S ratios are investigated, including: frequency range, distance, magnitude, source depth, number of seismographs, and site effects. A frequency band of about 10-18 Hz performs better than lower or narrower bands because explosion-induced S-wave amplitudes diminish relative to P for higher frequencies. Source depth and magnitude exhibited weak influences on P/S ratios. Site responses for earthquakes and explosions are correlated with each other and with shallow crustal Vp and Vs from travel-time tomography. Overall, the results indicate high potential for local distance P/S explosion discrimination in a continental volcanic arc setting, with ≥98% true positives and ≤6.3% false positives when using the array median from ≥16 stations. Performance is reduced for smaller arrays, especially those with ≤4 stations, thereby emphasizing the importance of array data for discrimination of low magnitude explosions.

Twenty-two sites, subjected to an IZZI-modified Thellier-Thellier experiment and strict selection criteria, recover a paleomagnetic axial dipole moment (PADM) of 62.24$\pm$ 30.6 ZAm$^2$ in Northern Israel over the Pleistocene (0.012 - 2.58 Ma). Pleistocene data from comparable studies from Antarctica, Iceland, and Hawaii, re-analyzed using the same criteria and age range, show that the Northern Israeli data are on average slightly higher than those from Iceland (PADM = 53.8 $\pm$ 23 ZAm$^2$, n = 51 sites) and even higher than the Antarctica average %\cite{asefaw21} (PADM = 40.3 $\pm$ 17.3 ZAm$^2$, n = 42 sites). Also, the data from the Hawaiian drill core, HSDP2, spanning the last half million years (PADM = 76.7 $\pm$ 21.3 ZAm$^2$, n = 59 sites) are higher than those from Northern Israel. These results, when compared to Pleistocene results filtered from the PINT database (www.pintdb.org) suggest that data from the Northern hemisphere mid-latitudes are on average higher than those from the southern hemisphere and than those from latitudes higher than 60$^{\circ}$N. The weaker intensities found at high (northern and southern) latitudes therefore, cannot be attributed to inadequate spatio-temporal sampling of a time-varying dipole moment or low quality data. The high fields in mid-latitude Northern hemisphere could result from long-lived non-axial dipole terms in the geomagnetic field with episodes of high field intensities occurring at different times in different longitudes. This hypothesis is supported by an asymmetry predicted from the Holocene, 100 kyr, and five million year time-averaged geomagnetic field models.

Accurate flood inundation modelling using a complex high-resolution hydrodynamic (high-fidelity) model can be very computationally demanding. To address this issue, efficient approximation methods (surrogate models) have been developed. Despite recent developments, there remain significant challenges in using surrogate methods for modelling the dynamical behaviour of flood inundation in an efficient manner. Most methods focus on estimating the maximum flood extent due to the high spatial-temporal dimensionality of the data. This study presents a hybrid surrogate model, consisting of a low-resolution hydrodynamic (low-fidelity) and a Sparse Gaussian Process (Sparse GP) model, to capture the dynamic evolution of the flood extent. The low-fidelity model is computationally efficient but has reduced accuracy compared to a high-fidelity model. To account for the reduced accuracy, a Sparse GP model is used to correct the low-fidelity modelling results. To address the challenges posed by the high dimensionality of the data from the low- and high-fidelity models, Empirical Orthogonal Functions (EOF) analysis is applied to reduce the spatial-temporal data into a few key features. This enables training of the Sparse GP model to predict high-fidelity flood data from low-fidelity flood data, so that the hybrid surrogate model can accurately simulate the dynamic flood extent without using a high-fidelity model. The hybrid surrogate model is validated on the flat and complex Chowilla floodplain in Australia. The hybrid model was found to improve the results significantly compared to just using the low-fidelity model and incurred only 39% of the computational cost of a high-fidelity model.

Geomagnetically induced currents (GICs) at middle latitudes have received increased attention after reported power-grid disruptions due to geomagnetic disturbances. However, quantifying the risk to the electric power grid at middle latitudes is difficult without understanding how the GIC sensors respond to geomagnetic activity on a daily basis. Therefore, in this study the question “Do measured GICs have distinguishable and quantifiable long- and short-period characteristics?” is addressed. The study focuses on the long-term variability of measured GIC, and establishes the extent to which the variability relates to quiet-time geomagnetic activity. GIC quiet-day curves (QDCs) are computed from measured data for each GIC node, covering all four seasons, and then compared with the seasonal variability of Thermosphere-Ionosphere- Electrodynamics General Circulation Model (TIE-GCM)-simulated neutral wind and height-integrated current density. The results show strong evidence that the middle-latitude nodes routinely respond to the tidal-driven Sq variation, with a local time and seasonal dependence on the the direction of the ionospheric currents, which is specific to each node. The strong dependence of GICs on the Sq currents demonstrates that the GIC QDCs may be employed as a robust baseline from which to quantify the significance of GICs during geomagnetically active times and to isolate those variations to study independently. The QDC-based significance score computed in this study provides power utilities with a node-specific measure of the geomagnetic significance of a given GIC observation. Finally, this study shows that the power grid acts as a giant sensor that may detect ionospheric current systems.

Chemical and biological composition of surface materials and physical structure and arrangement of those materials determine the intrinsic spectral reflectance of Earth’s land surface at the plot scale. As measured by a spaceborne or airborne sensor, the apparent reflectance depends on the intrinsic reflectance, the surface texture, the contribution and attenuation by the atmosphere, and the topography. Compensation or correction for the topographic effect requires information in digital elevation models (DEMs). Available DEMs with global coverage at ~30 m spatial resolution are derived from interferometric radar and stereo-photogrammetry. Locally or regionally, airborne lidar altimetry, airborne interferometric radar, or stereo-photogrammetry from airborne or fine-resolution satellite imagery produces DEMs with finer spatial resolutions. Characterization of the quality of DEMs typically expresses the root-mean-square (RMS) error of the elevation, but the accuracy of remote sensing retrievals is acutely sensitive to uncertainties in the topographic properties that affect the illumination geometry. The essential variables are the cosine of the local illumination angle and the shadows cast by neighboring terrain. We show that calculations with globally available DEMs underrepresent shadows and consistently underestimate the values of the cosine of illumination angle; the RMS error increases with solar zenith angle and in more rugged terrain. Analyzing imagery of Earth’s mountains from current and future missions requires addressing the uncertainty introduced by errors in DEMs on algorithms that estimate surface properties from retrievals of the apparent spectral reflectance. Intriguing potential improvements lie in novel methods to gain information about topography from the imagery itself.

Earth System Models’ complex land components simulate a patchwork of increases and decreases in surface water availability when driven by projected future climate changes. Yet, commonly-used simple theories for surface water availability, such as the Aridity Index (P/E0) and Palmer Drought Severity Index (PDSI), obtain severe, globally dominant drying when driven by those same climate changes, leading to disagreement among published studies. In this work, we use a common modeling framework to show that ESM simulated runoff-ratio and soil-moisture responses become much more consistent with the P/E0 and PDSI responses when several previously known factors that the latter do not account for are cut out of the simulations. This reconciles the disagreement and makes the full ESM responses more understandable. For ESM runoff ratio, the most important factor causing the more positive global response compared to P/E0 is the concentration of precipitation in time with greenhouse warming. For ESM soil moisture, the most important factor causing the more positive global response compared to PDSI is the effect of increasing carbon dioxide on plant physiology, which also drives most of the spatial variation in the runoff ratio enhancement. The effect of increasing vapor-pressure deficit on plant physiology is a key secondary factor for both. Future work will assess the utility of both the ESMs and the simple indices for understanding observed, historical trends.

Our understanding of geomagnetic field intensity prior to the era of direct instrumental measurements rely on paleointensity analysis of rocks and archaeological materials that serve as magnetic recorders. Only in rare cases absolute paleointensity datasets are continuous over millennial timescales, provide sub-centennial resolution, and are directly dated using radiocarbon. As a result, fundamental properties of the geomagnetic field, such as its maximum intensity and maximum rate of change have remained a subject of lively discussion. Here, we place firm constraints on these two quantities using Bayesian modelling of well-dated archaeomagnetic intensity data from the Levant and Upper Mesopotamia. We report new data from 23 groups of pottery collected from 18 consecutive radiocarbon-dated archaeological strata from Tel Megiddo, Israel. In the Near East, the period between 1700-550 BCE is now represented by 87 groups of archaeological artifacts, 57 of which dated using radiocarbon and/or direct association to clear historically-dated events, providing an unprecedented sub-century resolution. Moreover, stratigraphic relation between samples collected from multi-layered sited enable further refinement of the archaeomagnetic ages. The Bayesian curve shows four geomagnetic spikes between 1050 and 600 BCE, with virtual axial dipole moment (VADM) reaching values of 155-162 ZAm2 – much higher than any prediction from geomagnetic field models. Rates of change associated with the four spikes are ~0.35-0.55 μT/year (~0.7-1.1 ZAm2/year) – at least twice the maximum rate inferred from direct observations spanning the past 190 years. Moreover, the increase from 1750 BCE to the first spike depicts the Holocene largest change in field intensity.

Reaction with iodide (I) at the sea surface is an important sink for atmospheric ozone, and causes sea-air emission of reactive iodine which in turn drives further ozone destruction. To incorporate this process into chemical transport models, improved understanding of the factors controlling marine iodine speciation, and especially sea-surface iodide concentrations, is needed. The oxidation of I to iodate (IO) is the main sink for oceanic I, but the mechanism for this remains unknown. We demonstrate for the first time that marine nitrifying bacteria mediate I oxidation to IO. A significant increase in IO concentrations compared to media-only controls was observed in cultures of the ammonia-oxidising bacteria sp(Nm51) and (Nc10) supplied with 9-10 mM I, indicating I oxidation to IO. Cell-normalised production rates were 15.69 (±4.71) fmol IO cell d for sp., and 11.96 (±6.96) fmol IO cell d for , and molar ratios of iodate-to-nitrite production were 9.2±4.1 and 1.88±0.91 respectively Preliminary experiments on nitrite-oxidising bacteria showed no evidence of ItoIO oxidation. If the link between ammonia and I oxidation observed here is representative, our ocean iodine cycling model predicts that decreases in marine nitrification under ocean acidification could lead to significantly higher sea surface I. A global sensitivity analysis suggests a 0.13 nM increase in sea surface I concentrations per percentage decrease in nitrification rate. In turn, this could result in increased O deposition to the sea surface and sea-air iodine emissions, with implications for atmospheric chemistry and air quality.

Reproducibility and replicability in analyzing data is one of the main requirements for the advancement of scientific fields that rely heavily on computational data analysis, such as atmospheric science. However, there are very few research activities that field in Indonesia that emphasize the principle of transparency of codes and data in the dissemination of the results. This issue is a major challenge for the Indonesian scientific community to verify the output of research activities from their peers. One common obstacle to the reproducibility of data-driven research is the portability issue of the computing environment used to reproduce the results. Therefore, in this article, we would like to offer a solution through Debian-based dockerized Jupyter Notebook that have been installed with several Python libraries that are often used in atmospheric science research. Through this containerized computing environment, we expect to overcome the portability and dependency constraints that often faced by atmospheric scientists and also to encourage the growth of research ecosystem in Indonesia through an open and replicable environment.

The observing system design of multi-disciplinary field measurements involves a variety of considerations on logistics, safety, and science objectives. Typically, this is done based on investigator intuition and designs of prior field measurements. However, there is potential for considerable increase in efficiency, safety, and scientific success by integrating numerical simulations in the design process. Here, we present a novel approach to observing system simulation experiments that aids surface-atmosphere synthesis at the interface of meso- and microscale meteorology. We used this approach to optimize the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19). During pre-field simulation experiments, we considered the placement of 20 eddy-covariance flux towers, operations for 72 hours of low-altitude flux aircraft measurements, and integration of various remote sensing data products. High-resolution Large Eddy Simulations generated a super-sample of virtual ground, airborne, and satellite observations to explore two specific design hypotheses. We then analyzed these virtual observations through Environmental Response Functions to yield an optimal aircraft flight strategy for augmenting a stratified random flux tower network in combination with satellite retrievals. We demonstrate how this novel approach doubled CHEESEHEAD19’s ability to explore energy balance closure and spatial patterning science objectives while substantially simplifying logistics. Owing to its extensibility, the approach lends itself to optimize observing system designs also for natural climate solutions, emission inventory validation, urban air quality, industry leak detection and multi-species applications, among other use cases.

Here we test the precursory enhancement in ionospheric total electron content (TEC) which has been reported by Heki (2011) and numerous Global Navigation Satellite System (GNSS) TEC observational studies before the 2011 Mw9.0 Tohoku-Oki and many great earthquakes. We verify the frequency of this TEC enhancement via analysis of a two-month vertical TEC (VTEC) time series that includes the Tohoku-Oki Earthquake using the procedure, based on Akaike’s information criterion, and threshold of Heki and Enomoto (2015). The averaged occurrence rate of the TEC enhancement is much larger than that reported by Heki and Enomoto (2015) when all of the visible GPS satellites at a given station are taken into account. We cannot rule out the possibility that the pre-seismic VTEC changes before the great earthquakes that were reported by Heki and Enomoto (2015) are not precursors but just a product of chance. We also analyze the spatial distribution of the pre-seismic TEC enhancement and co-seismic TEC depletion for the Tohoku-Oki Earthquake with the data after reducing inter-trace biases. We observe significant post-seismic depletion that lasted at least 2 h after the earthquake and extended at least 500 km around the center of the large-slip area. This means that evaluation of the enhancements using reference curves which was adopted by Heki 2011 and even by the recent papers (e.g. He and Heki 2016, 2017, 2018) is in danger of mistaking a large and long-lasting post-seismic TEC depletion for a pre-seismic enhancement.

Water storage plays an important role in mitigating heat and flooding in urban areas. Assessment of the water storage capacity of cities remains challenging due to the inherent heterogeneity of the urban surface. Traditionally, effective storage has been estimated from runoff. Here, we present a novel approach to estimate effective water storage capacity from recession rates of observed evaporation during precipitation-free periods. We test this approach for cities at neighborhood scale with eddy-covariance based latent heat flux observations from fourteen contrasting sites with different local climate zones, vegetation cover and characteristics, and climates. Based on analysis of 583 drydowns, we find storage capacities to vary between 1.3-28.4 mm, corresponding to e-folding timescales of 1.8-20.1 days. This makes the storage capacity at least one order of magnitude smaller than the observed values for natural ecosystems, reflecting an evaporation regime characterised by extreme water limitation.

Tsunami deposits provide information for estimating the magnitude and flow conditions of paleotsunamis, and inverse models have potential for reconstructing hydraulic conditions of tsunamis from their deposits. The majority of the previously proposed models are based on oversimplified assumptions and possess some limitations. We present a new inverse model based on the FITTNUSS model, which incorporates nonuniform and unsteady transport of suspended sediment and turbulent mixing. The present model uses a deep neural network (DNN) for the inversion method. In this method, forward model calculations are repeated for random initial flow conditions (e.g., maximum inundation length, flow velocity, maximum flow depth and sediment concentration) to produce artificial training data sets of depositional characteristics such as thickness and grain size distribution. The DNN was then trained to establish a general inverse model based on artificial data sets derived from the forward model. Tests conducted using independent artificial data sets indicated that this trained DNN can reconstruct the original flow conditions from the characteristics of the deposits. Finally, the model was applied to a data set of 2011 Tohoku-Oki tsunami deposits. The predicted results of flow conditions were verified by the observational records at Sendai plain. Jackknife resampling was applied to estimate the precision of the result. The estimated results of the flow velocity and maximum flow depth were approximately 5.4\pm0.140 m/s and 4.11\pm0.152 m, respectively after the uncertainty analysis. The DNN shows promise for reconstruction of tsunami characteristics from its deposits, which would help in estimating the hydraulic conditions of paleotsunamis.

Exhumed high-pressure/low-temperature (HP/LT) metamorphic rocks provide insights into deep (~20-70 km) subduction interface dynamics. On Syros Island (Cyclades, Greece), the Cycladic Blueschist Unit (CBU) preserves blueschist-to-eclogite facies oceanic- and continental-affinity rocks that record the structural and thermal evolution associated with Eocene subduction. Despite decades of research on Syros, the pressure-temperature-deformation history (P-T-D), and timing of subduction and exhumation, are matters of ongoing discussion. Here we show that the CBU on Syros comprises three coherent tectonic slices, and each one underwent subduction, underplating, and syn-subduction return flow along similar P-T trajectories, but at progressively younger times. Subduction and return flow are distinguished by stretching lineations and ductile fold axis orientations: top-to-the-S (prograde-to-peak subduction), top-to-the-NE (blueschist facies exhumation), and then E-W coaxial stretching (greenschist facies exhumation). Amphibole chemical zonations record cooling during decompression, indicating return flow along the top of a cold subducting slab. New multi-mineral Rb-Sr isochrons and compiled metamorphic geochronology demonstrate that three nappes record distinct stages of peak subduction (53 Ma, ~50 Ma (?), and 47 Ma) that young with structural depth. Retrograde blueschist and greenschist facies fabrics span ~50-40 Ma and~43-20 Ma, respectively, and also young with structural depth. The datasets support a revised tectonic framework for the CBU, involving subduction of structurally distinct nappes and simultaneous return flow of previously accreted tectonic slices in the subduction channel shear zone. Distributed, ductile, dominantly coaxial return flow in an Eocene-Oligocene subduction channel proceeded at rates of ~1.5-5 mm/yr, and accommodated ~80% of the total exhumation of this HP/LT complex.

It’s very difficult to understand the mechanism producing solar magnetic fields, as it mingled with various activities, it also hindered by gaseous model of the sun; an alternative view is suggested based on characteristics of electrons exhibited in electric current; in 1820 Ørsted discovered both the relation between electricity and magnesium and the Circular Magnetic Field (CMF) produced by electric current, later discovered its produced by electrons in motion; thus the bulky rotation of charged particles (electrons, protons and ions) in tornado mode, produced intense CMF, designated as Plasma Pillar Intense Magnetic Field (PPIMF) with magnitude exceeds millions Tesla; and since EUV images in F-A, illustrates subsurface intense Magnetic Lines of Force (MLF), it also shows activities of Solar Flare (SF), both are suggested as due to PPIMF, which accounted for most solar activities, the Active Region (AR) as in F-B suggested to represent the PPIMF, where AR near surface are in circle, while AR at deep depth in squares; at deep depths the influence of PPIMF on photosphere during quiet sun resulted in pairs of negative and positive magnetic fields represented by magnetogram in F-C; during active sun, PPIMF raise nearer photosphere, it’s negative and positive fields interacted with the photosphere’s state, resulted in pairs of sunspots in F-D, look like iron filings, but formed by plasma, their shapes determined by proximity to PPIMF; as charged particles gyrate around the pillar, any increase in field’s intensity reduced radius of gyration, hence the adjacent distances between ions, thus at critical distance Solar Flare (SF) is triggered producing great energy, radiations and plasma including heavy ions; this knowledge will unlock dynamics of the sun, it’s internal structures and related mechanisms, it will help attained the alternative renewable energy, avert negative consequences of climate change, improve prediction of solar activity and space weather among others.

In climate reconstructions by data assimilation, the sensitivities to both proxies and prior estimates need to be taken into account because models are uncertain and proxies are limited spatiotemporally. This study examines these sensitivities using multiple climate model simulations and different combinations of proxies (corals, ice cores, and tree-ring cellulose). Experiments were conducted based on an offline data assimilation approach. These experiments show annual variations in the global distribution of surface air temperature and precipitation range from 850 to 2000. The results indicate that standard deviations of surface air temperature and precipitation amount during the entire period differ by up to 50% due to prior estimates. Experiments with different types of proxies show that the El Niño-like distribution of positive anomalies in the central to eastern tropical Pacific can be reproduced adequately in experiments with corals, but not in experiments without corals. The correlation coefficient of the NINO.3 index from 1971 to 2000 between experiments with corals and the Japanese 55-year Reanalysis (JRA-55) were 0.79 at maximum, while the correlation coefficient between experiments without corals and JRA-55 were 0.20 at maximum.

Supervised deep learning models have become a popular choice for seismic phase arrival detection. However, they don’t always perform well on out-of-distribution data and require large training sets to aid generalization and prevent overfitting. This can present issues when using these models in new monitoring settings. In this work, we develop a deep learning model for automating phase arrival detection at Nabro volcano using a limited amount of training data (2498 event waveforms recorded over 35 days) through a process known as transfer learning. We use the feature extraction layers of an existing, extensively-trained seismic phase picking model to form the base of a new all-convolutional model, which we call U-GPD. We demonstrate that transfer learning reduces overfitting and model error relative to training the same model from scratch, particularly for small training sets (e.g., 500 waveforms). The new U-GPD model achieves greater classification accuracy and smaller arrival time residuals than off-the-shelf applications of two existing, extensively-trained baseline models for a test set of 800 event and noise waveforms from Nabro volcano. When applied to 14 months of continuous Nabro data, the new U-GPD model detects 31,387 events with at least four P-wave arrivals and one S-wave arrival, which is more than the original base model (26,808 events) and our existing manual catalogue (2,926 events), with smaller location errors. The new model is also more efficient when applied as a sliding window, processing 14 months of data from 7 stations in less than 4 hours on a single GPU.

The 15 January 2022 explosion of the Hunga Tonga-Hunga Ha’apai (HT-HH) volcano generated an extreme, quasi-instantaneous perturbation of the atmosphere. As part of its adjustment following the eruption, a rich spectrum of waves radiated away from HT-HH and achieved worldwide propagation. Among numerous platforms monitoring the event, two long-duration stratospheric balloons flying over the tropical Pacific provided unique observations of Lamb and infrasonic wave arrivals, detecting three revolutions of the Lamb wave and five of infrasound waves. Combined with ground measurements from the infrasound network of the International Monitoring System, such observations bring precious insights into the eruption process (chronology and altitude of energy release), and highlight previously unobserved long-range propagation of infrasound modes triggered by the eruption and their dispersion patterns. A comparison between ground- and balloon-based measurements emphasizes generally larger signal-to-noise ratios onboard the balloons and further demonstrates their potential for infrasound studies.

The spatiotemporal patterns of precipitation are critical for understanding the underlying mechanism of many hydrological and climate phenomena. Over the last decade, applications of the complex network theory as a data-driven technique has contributed significantly to study the intricate relationship between many variable in a compact way. In our work, we conduct a study to compare an extreme precipitation pattern in Ganga River Basin, by constructing the networks using two nonlinear methods - event synchronization (ES) and edit distance (ED). Event synchronization has been frequently used to measure the synchronicity between the climate extremes like extreme precipitation by calculating the number of synchronized events between two events like time series. Edit distance measures the similarity/dissimilarity between the events by reducing the number of operations required to convert one segment to another, that consider the events’ occurrence and amplitude. Here, we compare the extreme precipitation patterns obtained from both network construction methods based on different network’s characteristics. We used degree to understand network topology and identify important nodes in the networks. We also attempted to quantify the impact of precipitation seasonality and topography on extreme events. The study outcomes suggested that the degree is decreased in the southwest to the northwest direction and the timing of peak precipitation influences it. We also found an inverse relationship between elevation and timing of peak precipitation exists and the lower elevation greatly influences the connectivity of the stations. The study highlights that Edit distance better captures the network’s topology without getting affected by artificial boundaries.