Publication date: Available online 5 February 2025
Source: Advances in Space Research
Author(s): P.R. Fagundes, V.G. Pillat, J.B. Habarulema, M.T.A.H. Muella, K. Venkatesh, A.J. de Abreu, C.M. Anoruo, F. Vieira, K.H. Welyargis, E. Agyei-Yeboah, A. Tardelli, G.S. Felix, G.A.S. Picanço
A new publication by researchers from the Department of Earth Sciences at the University of Oxford shows that the relationship between water temperature and the main biological mechanism by which the ocean captures atmospheric carbon dioxide (CO2) is far more complicated than previously thought. The paper is published in the journal Geophysical Research Letters.
A recent study published in npj Climate and Atmospheric Science has shed light on the factors influencing floods in the Kumalak River catchment, China.
Ice made of frozen slush or snow is much weaker than core ice, and researchers from the Universities of Gothenburg and Uppsala have observed that this type of fragile ice has become more common on Swedish lakes over the past 50 years.
Abstract
The predictive learning of total electron content (TEC) spatiotemporal sequences aims to generate future TEC maps by learning from historical data, where both the spatial appearances and temporal variations are crucial for accurate predictions. However, the state-of-the-art TEC map prediction models typically employ sequential stacking of ConvLSTM, ConvGRU, and their variants. These models focus more on modeling temporal variations, and the spatial features extracted from the historical sequence are highly abstracted, resulting in the fine-grained spatial appearances not being adequately memorized or transmitted, leading to fuzzy prediction results during storm time. In this paper, we used PredRNN to propose a storm-time ionospheric TEC spatiotemporal prediction model with multichannel features, named Multichannel PredRNN, which can simultaneously remember the temporal patterns and spatial appearances in input sequence. The temporal memory as well as the spatial memory are updated repeatedly over time, ensuring that both temporal memory and spatiotemporal memory are fully utilized in prediction. According to Dst index, 60 magnetic storm events from 2011 to 2019 were selected as the dataset. We first discussed the impact of feature combinations on predictive performance. The results show that using multichannel feature (TEC + Dst&F10.7), the Multichannel PredRNN and the comparison models ConvGRU and ConvLSTM have the best prediction performance. Then we used the optimal feature combination for prediction. We compared Multichannel PredRNN with IRI-2016, COPG, ConvLSTM and ConvGRU under various conditions, including the entire test magnetic events, periods of quiet and storm, different phases of geomagnetic storms, and the most severe geomagnetic storms. Finally, we compared the performance of different output steps. The experimental results indicate that in all cases, Multichannel PredRNN with dual memory state and zigzag flow is superior to four compared models.
As climate change warms the Arctic, permafrost is thawing, and carbon trapped within the soil is moving into the atmosphere. Permafrost stores twice as much carbon as the atmosphere, but the degree to which this frozen carbon will thaw and accelerate climate change has remained a point of scientific inquiry. Taking widespread on-the-ground permafrost measurements is not logistically feasible in the remote Far North.
The successful development of sustainable georesources for the energy transition is a key challenge for humankind in the 21st century. Hydrogen gas (H2) has great potential to replace current fossil fuels while simultaneously eliminating the associated emission of CO2 and other pollutants.
To achieve its net zero climate target by 2050, Switzerland must press forward with the energy transition—whether in electricity, heating or mobility. The permanent storage of CO2 is another important challenge. In particular, Switzerland must find a permanent solution for emissions that are difficult or impossible to avoid, such as those produced by waste incinerators.
More than half of Brazil's rivers are at risk of reduced flow due to water seeping into underground aquifers. This is the result of an analysis of 17,972 wells throughout the country. Of these, 55.4% had water levels below the surface of the nearest rivers. This difference in hydraulic level creates a gradient that favors the seepage of water from the river into the subsoil, which can turn rivers into water flow losers. The study, conducted by researchers from Brazil and abroad, was published in the journal Nature Communications.
The melting ice from glaciers worldwide is leading to an increased loss of regional freshwater resources. And it is causing global sea levels to rise at ever-greater rates. Since the year 2000, glaciers have been losing 273 billion tons of ice annually, according to estimates by an international research community led by researchers of the University of Zurich.
Gabrielle Kleber and Leonard Magerl, postdoctoral researchers at iC3, have discovered that Arctic glaciers are leaking significant amounts of methane, a potent greenhouse gas.
Eddies are large, rotating currents that contribute to ocean mixing and transport of heat and salt in seawater. Importantly, eddies modify ocean circulation and can influence climate variability by interacting with larger, more dominant ocean currents, or mean flow.
Hurricanes are likely to become more frequent and more intense in both the Atlantic and eastern Pacific oceans over the next decade, a new study has shown, spelling increased danger for coastal communities across North and Central America.
Author(s): Ian M. DesJardin, Christine M. Hartzell, and Jonathan Wrieden
Ion acoustic solitons can be generated by a charged object immersed in an electrostatic quasineutral two-temperature plasma flow. These are often described by the forced Korteweg–de Vries equation. Two-fluid simulations of this scenario are conducted and compared to numerical solutions of the forced…
[Phys. Rev. E 111, 025204] Published Wed Feb 19, 2025
Author(s): Hong Qin, Elijah J. Kolmes, Michael Updike, Nicholas Bohlsen, and Nathaniel J. Fisch
Phase space engineering by rf waves plays important roles in both thermal D-T fusion and nonthermal advanced fuel fusion, but not all phase space manipulation is allowed; certain fundamental limits exist. In addition to Liouville's theorem, which requires the manipulation to be volume preserving, Gr…
[Phys. Rev. E 111, 025205] Published Wed Feb 19, 2025
SummaryIn this paper, we present a catalogue of relocated seismic events in the North Sea spanning 1961 to 2022. Data from all relevant agencies were combined, incorporating all available seismic phase readings, thereby enhancing station coverage. As a result, our updated locations reveal a more clustered and aligned seismicity pattern compared with the original catalogue. Even with our combined dataset, only 157 of the 7,089 relocated events have azimuthal gaps of less than 90 degrees. Additionally, the distances between onshore stations and offshore events are considerable. Both of these factors lead to relatively poorly constrained hypocentres for most events. We therefore evaluate the performance of 1D velocity models routinely used by different North Sea adjacent monitoring agencies for earthquake location estimations in the North Sea. The variations in assessments due to the seismic velocity model used are significantly larger than the uncertainty ellipses calculated in the relocation, demonstrating that arithmetic uncertainties systematically underestimate location uncertainties in this setting. Obtaining a realistic estimate of location uncertainty is however crucial, particularly for distinguishing between natural and induced seismicity. This is fundamental to safe monitoring of the North Sea offshore industries, including geological CO2 storage. To overcome these discrepancies between the uncertainty ellipses and our multiple relocations, we introduce an alternative method that accounts for variability in the 1D velocity models. This approach enhances the reliability of the earthquake catalogue, and provides a more robust assessment of seismic activity in the North Sea.
SummaryElastic full-waveform inversion has recently been utilized to estimate the physical properties of the upper tens of meters of the subsurface, leveraging its capability to exploit the complete information contained in recorded seismograms. However, due to the non-linear and ill-posed nature of the problem, standard approaches typically require an optimal starting model to avoid producing non-physical solutions. Additionally, conventional optimization methods lack a robust uncertainty quantification, which is essential for subsequent informed decision-making.Bayesian inference offers a framework for estimating the posterior probability density function through the application of Bayes’ theorem. Methods based on Markov Chain Monte Carlo processes use multiple sample chains to quantify and characterize the uncertainty of the solution.However, despite their ability to theoretically handle any form of distribution, these methods are computationally expensive, limiting their usage in large-scale problems with computationally expensive forward modelings, as in the case of full-waveform inversion. Variational Inference provides an alternative approach to estimating the posterior distribution through a parametric or non-parametric proposal distribution. Among this class of methods, Stein Variational Gradient Descent stands out for its ability to iteratively refine a set of samples, usually referred to as particles, to approximate the target distribution through an optimization process. However, mode and variance-collapse issues affect this approach when applied to high-dimensional inverse problems.To address these challenges, in this work we propose to utilize an annealed variant of the Stein Variational Gradient Descent algorithm and apply this method to solve the elastic full-waveform inversion of surface waves. We validate our proposed approach with a synthetic test, where the velocity model is characterized by significant lateral and vertical velocity variations. Then, we invert a field dataset from the InterPACIFIC project, proving that our method is robust against cycle-skipping issues and can provide reasonable uncertainty estimations with a limited computational cost.
SummaryProbabilistic forecasts of earthquakes caused by anthropogenic changes in subsurface stresses require seismicity models that link rupture nucleation to stress states in geological faults. The recently introduced time-dependent stress response (TDSR) model is based on an exponential dependence of the time-to-failure on stress and is a generalization of the well-known rate-and-state (RS) seismicity model. Unlike RS, TDSR can directly incorporate estimates of the initial stress distribution on affected faults in the seismogenic zone. For the case of the Groningen gas field in the Netherlands, we utilize detailed field and borehole studies to estimate the initial stress distribution and rock properties of the reservoir faults. Using these initial conditions, we show that TDSR outperforms the Coulomb failure model, which assumes instantaneous failure, as well as the RS model, which relies on simplified pre-stress assumptions. Furthermore, an instantaneous Coulomb failure model cannot explain the effect of seasonal gas production in Groningen on the timing of induced earthquakes, in contrast to the TDSR model, which shows a good agreement between prediction and observation. Pseudo-prospective tests show that the seismic response to the reduced production since 2014 could have been predicted as early as 2010 if the production scenario had been known.
