We study vertical ground displacement time-series from GNSS stations in the Po river basin to measure deformation signals associated with the severe drought occurring since 2021 and estimate the spatial distribution of water loss. The detection of vertical ground displacement trend changes allows us to extract a spatially correlated signal that follows the Po level trend changes and SPEI-12 drought index. GNSS stations in the basin mostly underwent uplift, up to 7 mm, since 2021, which corresponds to ~80 Gtons of water loss. Compared to GLDAS and GRACE, the GNSS results show a similar temporal evolution of water content, but a different spatial distribution. Our study indicates that using dense GNSS networks is an effective way to monitor long-term changes in water storage even in small water basins and serve as a reliable indicator of drought severity.
We considered various non-uniformities such as branch faults, rotation of stress field directions, and changes in tectonic environments to simulate the dynamic rupture process of the 6th February 2023 Mw 7.8 Kahramanmaraş earthquake in SE Türkiye. We utilized near-fault waveform data, GNSS static displacements, and surface rupture to constrain the dynamic model. The results indicate that the high initial stress accumulated in the seismic gap leads to the successful triggering of the East Anatolian Fault (EAF) and the supershear rupture in the northeast segment. Due to the complexity of fault geometry, the rupture speed along the southeastern segment of the EAF varied repeatedly between supershear and subshear, which contributed to the unexpectedly strong ground motion. Furthermore, the triggering of the EAF reminds us to be aware of the risk of seismic gaps on major faults being triggered by secondary faults, which is crucial to prevent significant disasters.
East Asia (China, Japan, Koreas and Mongolia) has been the world’s economic engine over at least the past two decades, exhibiting a rapid increase in fossil fuel emissions of greenhouse gases (GHGs) and has expressed the recent ambition to achieve climate neutrality by mid-century. However, the GHG balance of its terrestrial ecosystems remains poorly constrained. Here, we present a synthesis of the three most important long-lived greenhouse gases (CO2, CH4 and N2O) budgets over East Asia during the decades of 2000s and 2010s, following a dual constraint bottom-up and top-down approach. We estimate that terrestrial ecosystems in East Asia is close to neutrality of GHGs, with a magnitude of between 196.9 ± 527.0 Tg CO2eq yr-1 (the top-down approach) and -20.8 ± 205.5 Tg CO2eq yr-1 (the bottom-up approach) during 2000-2019. This net GHG emission includes a large land CO2 sink (-1251.3 ± 456.9 Tg CO2 yr-1 based on the top-down approach and -1356.1 ± 155.6 Tg CO2 yr-1 based on the bottom-up approach), which is being fully offset by biogenic CH4 and N2O emissions, predominantly coming from the agricultural sector. Emerging data sources and modelling capacities have helped achieve agreement between the top-down and bottom-up approaches to within 20% for all three GHGs, but sizeable uncertainties remain in several flux terms. For example, the reported CO2 flux from land use and land cover change varies from a net source of more than 300 Tg CO2 yr-1 to a net sink of ~-700 Tg CO2 yr-1.
Though one of the most common methods for reproducing plants, propagation through seeds is often not feasible because of a consistently low germination percentage. Instead, this project aims to study the viability of using Phaseolus vulgaris (known as the common bean) as a model organism in antibiotic resistance studies. Specifically, this project seeks to investigate the effectiveness of external antibiotics in promoting the growth and differentiation of common bean callus growth. The single experimental group encapsulates bean callus growth medium with added cefotaxime, streptomycin, and kanamycin which are grown in standard growth medium with the addition of these antibiotics. The control group compares green bean callus growth in standard medium. To further evaluate the morphological differences between various mediums, a measure of the dry mass of the callus along with the study of its mitotic index was used to determine the effectiveness of each antibiotic reagent in improving the growth of the callus. Ultimately, the results refute the original hypothesis which predicted that all four antibiotics would have a positive benefit on growth and regeneration of the callus tissue. Rather, when measuring callus health, only kanamycin had a significant effect (Mann-Whitney U = 3.5, p-value = 0.0385) on the growth factors of the callus tissue through its high mitotic index. Future research may apply these findings to focus on computational aspects in studying the effect kanamycin has on somatic embryogenesis via callus growth in an effort to inhibit bacterial growth which reduces the chance of infection in the callus. By utilizing kanamycin resistance as a selectable marker, researchers can easily identify and select transformed plants taken up by foreign DNA and further simplify the study of genetically modified plant species, which has significant implications for the future of agricultural production.
Climate modulates the incidence of mosquito-borne diseases, in part due to climatic impacts on the suitability of vector breeding habitats. While the existence of a mechanistic link between climate and habitat suitability is clear—the aquatic early life stages of mosquitoes are impacted by climate-driven variability in water level and temperature—what is less well-defined is the sensitivity of these habitats to climate variability, which can be dependent on myriad factors such as the physical properties of the habitats as well as the timescale of interest.In this work we focus on the habitats of Aedes aegypti and Aedes albopictus, the urban-adapted vectors of dengue that primarily breed in artificial containers (e.g., water tanks, flower pots, discarded tires). We investigate the climate sensitivity of these habitats using the energy balance container model WHATCH’EM (NCAR). WHATCH’EM simulates the (hourly) temporal evolution of water height and temperature within a container habitat based on user-specified parameters (e.g., container dimensions, shading, thermal conductivity) and climate inputs (e.g., timeseries of air temperature, relative humidity, rainfall). Here we discuss our implementation of this model, using WHATCH’EM to (a) understand model sensitivity within a parameter space informed by existing entomological surveillance data for Sri Lanka, and (b) test habitat sensitivity to climate variability due to the Madden–Julian Oscillation (MJO), the quasiperiodic atmospheric disturbance that primarily drives subseasonal variability in the tropics. By doing so we will assess the extent to which the habitats of dengue vectors show MJO-associated subseasonal climate sensitivities.
Seismic interrogation of the upper mantle from the base of the crust to the top of the mantle transition zone has revealed discontinuities that are variable in space, depth, lateral extent, amplitude, and lack a unified explanation for their origin. Improved constraints on the detectability and properties of mantle discontinuities can be obtained with P-to-S receiver function (Ps-RF) where energy scatters from P to S as seismic waves propagate across discontinuities of interest. However, due to the interference of crustal multiples, uppermost mantle discontinuities are more commonly imaged with lower resolution S-to-P receiver function (Sp-RF). In this study, a new method called CRISP-RF (Clean Receiver-function Imaging using SParse Radon Filters) is proposed, which incorporates ideas from compressive sensing and model-based image reconstruction. The central idea involves applying a sparse Radon transform to effectively decompose the Ps-RF into its underlying wavefield contributions, i.e., direct conversions, multiples, and noise, based on the phase moveout and coherence. A masking filter is then designed and applied to create a multiple-free and denoised Ps-RF. We demonstrate, using synthetic experiment, that our implementation of the Radon transform using a sparsity-promoting regularization outperforms the conventional least-squares methods and can effectively isolate direct Ps conversions. We further apply the CRISP-RF workflow on real data, including single station data on cratons, common-conversion-point (CCP) stack at continental margins, and seismic data from ocean islands. The application of CRISP-RF to global datasets will advance our understanding of the enigmatic origins of the upper mantle discontinuities like the ubiquitous Mid-Lithospheric Discontinuity (MLD) and the elusive X-discontinuity.
We implement a damage parametrization in the standard viscous-plastic sea ice model to disentangle its effect from model physics (visco-elastic or elasto-brittle vs. visco-plastic) on its ability to reproduce observed scaling laws of deformation. To this end, we compare scaling properties and multifractality of simulated divergence and shear strain rate (as proposed in SIREx1), with those derived from the RADARSAT Geophysical Processor System (RGPS). Results show that including a damage parametrization in the standard viscous-plastic model increases the spatial, but decreases temporal localization of simulated Linear Kinematic Features, and brings all spatial deformation rate statistics in line with observations from RGPS without the need to increase the mechanical shear strength of sea ice as recently proposed for lower resolution viscous-plastic sea ice models. In fact, including damage an healing timescale of $t_h=30\>$days and an increased mechanical strength unveil multifractal behavior that does not fit the theory. Therefore, a damage parametrization is a powerful tuning knob affecting the deformation statistics.
Inversions of interseismic geodetic surface velocities often cannot uniquely resolve the three-dimensional slip-rate distribution along closely spaced faults. Microseismic focal mechanisms reveal stress information at depth and may provide additional constraints for inversions that estimate slip rates. Here, we present a new inverse approach that utilizes both surface velocities and subsurface stressing-rate tensors to constrain interseismic slip rates and activity of closely spaced faults. We assess the ability of the inverse approach to recover slip rate distributions from stressing-rate tensors and surface velocities generated by two forward models: 1) a single strike-slip fault model and 2) a complex southern San Andreas fault system (SAFS) model. The single fault model inversions reveal that a sparse array of regularly spaced stressing-rate tensors can recover the forward model slip distribution better than surface velocity inversions alone. Because focal mechanism inversions currently provide normalized deviatoric stress tensors, we perform inversions for slip rate using full, deviatoric or normalized deviatoric forward-model-generated stressing-rate tensors to assess the impact of removing stress magnitude from the constraining data. All the inversions, except for those that use normalized deviatoric stressing-rate tensors, recover the forward model slip-rate distribution well, even for the SAFS model. Jointly inverting stressing rate and velocity data best recovers the forward model slip-rate distribution and may improve estimates of interseismic deep slip rates in regions of complex faulting, such as the southern SAFS; however, successful inversions of crustal data will require methods to estimate stressing-rate magnitudes.
Culverwell et al. (2023) described a new one-dimensional variational (1D-Var) retrieval approach for ionospheric GNSS radio occultation (GNSS-RO) measurements. The approach maps a one-dimensional ionospheric electron density profile, modeled with multiple ”Vary-Chap’ layers, to bending angle space. This paper improves the computational performance of the the 1D-Var retrieval using an improved background model and validates the approach by comparing with the COSMIC-2 profile retrievals, based on an Abel Transform inversion, and co-located (within 200 km) ionosonde observations using all suitable data from 2020. A three or four layer Vary-Chap in the 1D-Var retrieval shows improved performance compared to COSMIC-2 retrievals in terms of percentage error for the F2 peak parameters (NmF2 and hmF2). Furthermore, skill in retrieval (compared to COSMIC-2 profiles) throughout the bottomside (~90 km to 300 km) has been demonstrated. With a single Vary-Chap layer the performance is similar, but this improves by approximately 40% when using four-layers.
A new one-dimensional variational (1D-Var) retrieval method for ionospheric GNSS ra- dio occultation (GNSS-RO) measurements is described. The forward model implicit in the retrieval calculates the bending angles produced by a one-dimensional ionospheric electron density profile, modeled with multiple “Vary-Chap” layers. It is demonstrated that gradient based minimisation techniques can be applied to this retrieval problem. The use of ionospheric bending angles is discussed. This approach circumvents the need for Differential Code Bias (DCB) estimates when using the measurements. This new, general retrieval method is applicable to both standard GNSS-RO retrieval problems, and the truncated geometry of EUMETSAT’s Metop Second Generation (Metop-SG), which will provide GNSS-RO measurements up to about 600 km above the surface. The climatological a priori information used in the 1D-Var is effectively a starting point for the 1D-Var minimisation, rather than a strong constraint on the final solution. In this paper the approach has been tested with 143 COSMIC-1 measurements. We find that the method converges in 135 of the cases, but around 25 of those have high “cost at convergence” values. In the companion paper (Elvidge et al., 2023), a full statistical analysis of the method, using over 10,000 COSMIC-2 measurements, has been made.
Reconstruction of complete seismic data is a crucial step in seismic data processing, which has seen the application of various convolutional neural networks (CNNs). These CNNs typically establish a direct mapping function between input and output data. In contrast, diffusion models which learn the feature distribution of the data, have shown promise in enhancing the accuracy and generalization capabilities of predictions by capturing the distribution of output data. However, diffusion models lack constraints based on input data. In order to use the diffusion model for seismic data interpolation, our study introduces conditional constraints to control the interpolation results of diffusion models based on input data. Furthermore, we improving the sampling process of the diffusion model to ensure higher consistency between the interpolation results and the existing data. Experimental results conducted on synthetic and field datasets demonstrate that our method outperforms existing methods in terms of achieving more accurate interpolation results.
Interactions between whistler mode chorus waves and electrons are a dominant mechanism for particle acceleration and loss in the outer radiation belt. One form of this loss is electron microburst precipitation: a sub-second intense burst of electrons. Despite previous investigations, details regarding the microburst-chorus scattering mechanism—such as dominant resonance harmonic—are largely unconstrained. One way to observationally probe this is via the time-of-flight energy dispersion. If a single cyclotron resonance is dominant, then higher energy electrons will resonate at higher magnetic latitudes: sometimes resulting in an inverse time-of-flight dispersion with lower-energy electrons leading. Here we present a clear example of this phenomena, observed by a FIREBIRD-II CubeSat on 27 August 2015, that shows good agreement with the Miyoshi-Saito time-of-flight model. When constrained by this observation, the Miyoshi-Saito model predicts that a relatively narrowband chorus wave with a ~0.2 of the equatorial electron gyrofrequency scattered the microburst.
Air-sea exchange of carbon dioxide (CO$_2$) in the Southern Ocean plays an important role in the global carbon budget. Previous studies have suggested that flow around topographic features of the Southern Ocean enhances the upward supply of carbon from the deep to the surface, influencing air-sea CO$_2$ exchange. Here, we investigate the role of seafloor topography on the transport of carbon and associated air-sea CO$_2$ flux in an idealized channel model. We find elevated CO$_2$ outgassing downstream of a seafloor ridge, driven by anomalous advection of dissolved inorganic carbon. Argo-like Lagrangian particles in our channel model sample heterogeneously in the vicinity of the seafloor ridge, which could impact float-based estimates of CO$_2$ flux.
A pyroclastic density current (PDC) is characterized by its strong stratification of particle concentration; it consists of upper dilute and lower dense currents, which control the dynamics and deposits of PDCs, respectively. To explain the relationship between the dynamics and deposits for magmatic and phreatomagmatic eruptions in a unified way, we have developed a two-layer PDC model considering thermal energy conservation for mixing of magma, external water, and air. The results show that the run-out distance of dilute currents increases with the mass fraction of external water at the source (wmw) owing to the suppression of thermal expansion of entrained air. For wmw~0.07–0.38, the dense current is absent owing to the decrease in particle concentration in the dilute current, resulting in the direct formation of the deposits from the dilute current in the entire area. These results capture the diverse features of natural PDCs in magmatic and phreatomagmatic eruptions.
Turbulence in stable boundary layers is typically unsteady and intermittent. The study implements a stochastic modelling approach to represent unsteady mixing possibly associated with intermittency of turbulence and with unresolved fluid motions such as dirty waves or drainage flows. The stochastic parameterization is introduced by randomizing the mixing lengthscale used in a Reynolds average Navier-Stokes (RANS) model with turbulent kinetic energy closure, resulting in a stochastic unsteady RANS model. The randomization alters the turbulent momentum diffusion and accounts for sporadic events of possibly unknown origin that cause unsteady mixing. The paper shows how the proposed stochastic parameterization can be integrated into a RANS model used in weather-forecasting and its impact is analyzed using neutrally and stably stratified idealized numerical case studies. The simulations show that the framework can successfully model intermittent mixing in stably stratified conditions, and does not alter the representation of neutrally stratified conditions. It could thus present a way forward for dealing with the complexities of unsteady flows in numerical weather prediction or climate models.
Seismic time series provide crucial information for monitoring the state of a volcano with discrete event catalogs describing impulsive seismic activity and hand-designed features describing more emergent signals (e.g. real-time seismic amplitude measurement for volcanic tremor signals). However, the emergent and long-term seismo-volcanic activity such as volcanic tremors are a complex and non-stationary phenomena that might contain more information than current methods can retrieve. In the present study, we consider the whole seismic time series as a valuable source of information by retrieving data-driven continuous features with an independent component analysis (ICA) and seismogram atlases with Uniform Manifold Approximation and Projection (UMAP). The data of interest are year-long seismic time series recorded at individual stations near the Klyuchevskoy Volcanic Group (Kamchatka, Russia). The features extracted from data recorded close to the active volcano depict a succession of short-lived patterns in the time series, indicating continuously changing signal characteristics. Additionally, the seismogram atlas reveals that, especially during periods of volcanic activation, the signal evolves continuously with some occasional sudden changes, resulting in new patterns throughout the recording time. The features and seismogram atlases reveal unique characteristics of the continuous seismograms recorded close to the volcano and related to its activity, suggesting that the complete seismic time series contains subtle but interesting information not captured by conventional methods. The seismogram atlases open new avenues to perceive large seismic time series visually and to connect the signal changes to physical processes.
The specifics of the simulated injection choices in the case of Stratospheric Aerosol Injections (SAI) are part of the crucial context necessary for meaningfully discussing the impacts that a deployment of SAI would have on the planet. One of the main choices is the desired amount of cooling that the injections are aiming to achieve. Previous SAI simulations have usually either simulated a fixed amount of injection, resulting in a fixed amount of warming being offset, or have specified one target temperature, so that the amount of cooling is only dependent on the underlying trajectory of greenhouse gases. Here, we use three sets of SAI simulations achieving different amounts of global mean surface cooling while following a middle-of-the-road greenhouse gas emission trajectory: one SAI scenario maintains temperatures at 1.5ºC above preindustrial levels (PI), and two other scenarios which achieve additional cooling to 1.0ºC and 0.5ºC above PI. We demonstrate that various surface impacts scale proportionally with respect to the amount of cooling, such as global mean precipitation changes, changes to the Atlantic Meridional Overturning Circulation (AMOC) and to the Walker Cell. We also highlight the importance of the choice of the baseline period when comparing the SAI responses to one another and to the greenhouse gas emission pathway. This analysis leads to policy-relevant discussions around the concept of a reference period altogether, and to what constitutes a relevant, or significant, change produced by SAI.