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Abstract

We propose the lag-1 autocorrelation of daily precipitation as a simple diagnostic of tropical precipitation variability in climate models. This metric generally has a relatively uniform distribution of positive values across the tropics. However, selected land regions are characterized by exceptionally low autocorrelation values. Low values correspond to the dominance of high frequency variance in precipitation, and specifically of high frequency convectively coupled equatorial waves. Consistent with previous work, we show that CMIP6 climate models overestimate the autocorrelation. Global kilometer-scale models capture the observed autocorrelation when deep convection is explicitly simulated. When a deep convection parameterization is used, though, the autocorrelation increases over land and ocean, suggesting that land surface-atmosphere interactions are not responsible for the changes in autocorrelation. Furthermore, the metric also tracks the accuracy of the representation of the relative importance of high frequency and low frequency convectively coupled equatorial waves in the models.

Abstract

The globally averaged diurnal temperature range (DTR) has shrunk since the mid-20th century, and climate models project further shrinking. Observations indicate a slowdown or reversal of this trend in recent decades. Here, we show that DTR has a minimum for average temperatures close to 0°C. Observed DTR shrinks strongly at colder temperature, where warming shifts the average temperature toward the DTR minimum, and expands at warmer temperature, where warming shifts the average temperature away from the DTR minimum. Most, but not all climate models reproduce the minimum DTR close to average temperatures of 0°C and a stronger DTR shrinking at colder temperature. In models that reproduce the DTR minimum, DTR shrinking slows down significantly in recent decades. Models project that the global-mean DTR will shrink over the 21st century, and models with a DTR minimum close to 0°C project slower shrinking than other models.

Abstract

Wet and cold biases on the Tibetan Plateau (TP) commonly exist in global and regional climate simulations. Previous studies have explored the possible causes of wet and cold biases and contributed to reducing these biases. However, the connection between wet and cold biases remains insufficiently addressed. Our research indicates that the TP wet bias converts into positive snow amount bias not continually but efficiently and concentratedly, under the control of snow phenology in different regions. Furthermore, the complex relationship between snow amount, snow coverage and surface albedo restricts the transformation of snow amount to surface albedo bias, and thus to cold bias. Our research highlights the spatio-temporally divergent transformation of wet to cold bias on the TP during snow season, providing a novel perspective to understand the intrinsic connection between wet and cold biases and improve climate simulations on the TP.

Abstract

Microbial acetate metabolism is an important part of marine carbon cycling. We present a comprehensive study to constrain microbial acetate metabolism and its regulation in surface seawater of the northwest Pacific Ocean. We found that acetate oxidation (rate constant k: 0.016–0.506 day−1) accounted for 77.6%–99.4% of the total microbial acetate uptake, suggesting that acetate was predominantly used as a microbial energy source. Acetate also served as a significant biomass carbon source, as reflected by the elevated contribution of acetate assimilation to bacterial carbon production. Acetate turnover was largely influenced by water mass mixing and nutrient conditions. Atmospheric deposition was a source of acetate in surface water and this process can also impact the microbial acetate uptake. Microbial utilization of acetate could account for up to 25.9% of the bacterial carbon demand, suggesting the significant role of acetate metabolism in microbial carbon cycling in the open ocean.

No abstract is available for this article.

Author(s): E. L. Gaggioli, Laura C. Estrada, and Oscar P. Bruno

We consider boundary-layer structures that arise in connection with the transport of neutral particles (e.g., photons or neutrons) through a participating medium. Such boundary-layer structures were previously identified by the authors in certain particular cases [Phys. Rev. E 104, L032801 (2021)]. …

[Phys. Rev. E 110, 025306] Published Wed Aug 28, 2024

Imperial College London research has found limits to how quickly we can scale up technology to store gigatonnes of carbon dioxide under Earth's surface.

Abstract

In this paper, we demonstrate the applicability of the data-driven solar energetic particle (SEP) model, SOlar-wind with FIeld-lines and Energetic-particles (SOFIE), to simulate the acceleration and transport processes of SEPs and make forecast of the energetic proton flux at energies ≥10 MeV that will be observed near 1 AU. The SOFIE model is built upon the Space Weather Modeling Framework developed at the University of Michigan. In SOFIE, the background solar wind plasma in the solar corona and interplanetary space is calculated by the Stream-Aligned Aflvén Wave Solar-atmosphere Model(-Realtime) driven by the near-real-time hourly updated Global Oscillation Network Group solar magnetograms. In the background solar wind, coronal mass ejections (CMEs) are launched by placing an force-imbalanced magnetic flux rope on top of the parent active region, using the Eruptive Event Generator using Gibson-Low model. The acceleration and transport processes are modeled by the Multiple-Field-Line Advection Model for Particle Acceleration. In this work, nine SEP events (Solar Heliospheric and INterplanetary Environment challenge/campaign events) are modeled. The three modules in SOFIE are validated and evaluated by comparing with observations, including the steady-state background solar wind properties, the white-light image of the CMEs, and the flux of solar energetic protons, at energies of ≥10 MeV.

Abstract

In this study, the thermospheric mass density (TMD) features observed by the CHAllenging Minisatellite Payload between 2002 and 2010 were extracted using deep learning (DL) technology; the TMD features were then mapped and modeled with the Interplanetary environment information (IEI), solar radiation, and geomagnetic indices. The DL model was used to simulate the TMD features during Day of Year (DOY) 222–241 in 2014, a period that experienced complex solar-terrestrial environmental variations. We explore the TMD features under different solar-terrestrial environmental conditions and discuss the effects of various inputs by comparing the DL simulation results with satellite observations from Gravity Recovery and Climate Experiment-A and Swarm-A, as well as the simulation results from Jacchia-Bowman 2008, Naval Research Laboratory Mass Spectrometer Incoherent Scatter radar model 2.1, and Drag Temperature Model 2013. These results show that the DL model can better capture the TMD features after adding IEI. Part of these TMD features, including the high-latitude TMD enhancement during the space hurricane event (DOY 232, 2014) and global TMD variations under complex solar-terrestrial environmental disturbances (DOY 222–225, 2014), cannot be well described by the geomagnetic indices. The DL model indicates that the east-west component of the interplanetary magnetic field (IMF By) has a great impact on TMD variations, and its modulation is different from the typical energy injection process during storms. Our results emphasize the crucial influence of IEI on TMD under both geomagnetic disturbances and quiet conditions.

Abstract

The Earth's ionosphere undergoes regular intra-annual variations (IAVs) characterized by two peaks and troughs around the equinoxes and solstices. This phenomenon is crucial for analyzing the ionospheric response to geomagnetic storms. This study presents a comprehensive analysis of the IAVs contributed by diurnal and semidiurnal migrating tides (DW1 and SW2) using Global Ionospheric Maps (GIMs) data from 2017 to 2021. Through data stacking techniques, the seasonal variability and splitting phenomenon of DW1 and SW2 across different latitudes are examined. The findings indicate that the splitting of these tides can be attributed to their quasi-periodic variations, predominantly composed of annual oscillation (AO) and semiannual oscillation (SAO). The combination of DW1, SW2, and their side-band harmonics results in beats with annual and semiannual periodicities, enabling the restoration of the seasonal variations in DW1 and SW2. The ionospheric day-to-day variations were reconstructed by superimposing DW1 and SW2, and their IAVs were evaluated using the envelope method. Comparison with IAVs driven by Earth's orbital geometry reveals that tide-driven IAVs are more significant, and both exhibit solar activity dependence. The results advance the understanding of ionospheric variability, emphasizing the critical role of tidal contributions.

Abstract

This paper investigates time-lag effects of ionospheric response to two severe geomagnetic storms (Kp = 8) on the degradation of kinematic precise point positioning (PPP) solutions, utilizing over 5500 Global Navigation Satellite Systems (GNSS) stations distributed worldwide. Focusing on these two severe geomagnetic storms that occurred during solar cycle 24, the study employs an open-source positioning software package, namely RTKLIB, to derive the PPP solutions. The findings reveal significant variations in time lags across different magnetic latitudes. These variations are driven by ionospheric responses to a southward interplanetary magnetic field and subsequent decreases in the SMY-H index during the 2015 St. Patrick's Day Storm and the 2017 September 7–8 Storm. Specifically, at high latitudes, PPP degradation primarily manifests during the main phase of the storm, resulting in delays spanning from several minutes to 1–2 hr after the sudden onset of the storm. In contrast, mid- and low latitudes exhibit a wider range of delays extending up to tens of hours. Notably, rapid positioning degradation is observed predominantly at the magnetic local time noon and midnight sectors. The study discusses these time lag effects concerning the intensity of various ionospheric disturbances triggered by the interactions among the solar wind, magnetosphere, and ionosphere during geomagnetic storms. The insights obtained from this research have the potential to be integrated into physics-based and machine-learning models to enhance forecasting capabilities of space weather impacts.

No abstract is available for this article.

Abstract

In this study, we investigate the impacts of the 17 March 2013 strong geomagnetic storm on the wholly coupled Solar Wind-Magnetosphere-Ionosphere-Thermosphere system-of-systems. Obtained from multipoint observations, our new results show (1) the solar-wind Alfven waves propagating antisunward in the sheath region and (2) oscillating solar wind interplanetary magnetic field (IMF) and electric (E) field (IEF EY) that powered (3) rigorous dayside and nightside flux transfer events (FTEs) when (4) the nightside-reconnection-related short circuiting led to fast-time Subauroral Ion Drifts (SAID) and Subauroral Polarization Streams (SAPS) E field development across the inner-magnetosphere plasmapause where the solar-wind Alfven waves (4) transitioned into kinetic Alfven waves (5) fueling the hot zone. Also, the antisunward solar-wind Alfven waves (6) drove enhanced large-scale region-1 field-aligned currents creating (7) undershielding conditions (8) allowing the dawn-to-dusk convection E field's earthward penetration, and (9) generated increased solar-wind kinetic energy, which became deposited (10) to the ionosphere increasing the ionospheric electron temperature (by the downward flowing suprathermal electron fluxes) and (11) to the thermosphere oscillating the neutral winds and increasing the neutral temperature, and finally leading to (12) the development of bright stable auroral red (SAR) arcs in (13) the enhanced SAID/SAPS flow channels (FCs) developed during FTEs, (14) demonstrated with FC-2 and FC-3 events, in the enhanced polar convection that (15) the Rice Convection Model could reproduce. Finally, we conclude the antisunward-propagating large-amplitude solar-wind Alfven waves' ultimate significant role in creating the favorable conditions for the various phenomena documented with the new observational results (1–14).

No abstract is available for this article.

Abstract

The low frictional strength of smectite minerals, such as montmorillonite, is thought to play a critical role in controlling the rheology and the stability of clay-rich faults. In this study, we perform molecular dynamics simulations on a model clay system. Clay platelets are simplified as oblate ellipsoids interacting via the Gay-Berne potential. We study the rheology and structural development during shear in this model system, which is sheared at constant strain rates for 10 strains after compression and equilibrium. We find that the system exhibits velocity-strengthening behavior over a range of normal stresses from 1.68 to 56.18 MPa and a range of strain rates from 6.93 × 105 to 6.93 × 108/s. The relationship between shear stress and strain rate follows the Herschel-Bulkley model. Shear localization is observed at lower strain rates despite the velocity-strengthening friction, while homogeneous shear is realized at higher strain rates. The structure change due to shear is analyzed from various aspects: the porosity, particle orientation, velocity profile, and the parallel radial distribution function. We find that particle rearrangement and compaction dominate at the early stage of shear when the shear stress increases. The shear band starts to form in the later stage as the shear stress decreases and relaxes to a steady-state value. The structural development at low strain rates is similar to previous experimental observations. The stacking structure is reduced during shear and restores logarithmically with time in the rest period.

Abstract

Electromagnetic (EM) imaging aims to produce large-scale, high-resolution soil conductivity maps that provide essential information for Earth subsurface exploration. To rigorously generate EM subsurface models, one must address both the forward problem and the inverse problem. From these subsurface resistivity maps, also referred to as volumes of resistivity distribution, it is possible to extract useful information (lithology, temperature, porosity, permeability, among others) to improve our knowledge about geo-resources on which modern society depends (e.g., energy, groundwater, and raw materials, among others). However, this ability to detect electrical resistivity contrasts also makes EM imaging techniques sensitive to metallic structures whose EM footprint often exceeds their diminutive stature compared to surrounding materials. Depending on target applications, this behavior can be advantageous or disadvantageous. In this work, we review EM modeling and inverse solutions in the presence of metallic structures, emphasizing how these structures affect EM data acquisition and interpretation. By addressing the challenges posed by metallic structures, our aim is to enhance the accuracy and reliability of subsurface EM characterization, ultimately leading to improved management of geo-resources and environmental monitoring. Here, we consider the latter through the lens of a triple helix approach: physics behind metallic structures in EM modeling and imaging, development of computational tools (conventional strategies and artificial intelligence schemes), and configurations and applications. The literature review shows that, despite recent scientific advancements, EM imaging techniques are still being developed, as are software-based data processing and interpretation tools. Such progress must address geological complexities and metallic casing measurements integrity in increasing detail setups. We hope this review will provide inspiration for researchers to study the fascinating EM problem, as well as establishing a robust technological ecosystem to those interested in studying EM fields affected by metallic artifacts.

SummaryThe Baihetan Reservoir, the second largest in the world, is located at the intersection of multiple large active fault zones on the eastern boundary of the Sichuan-Yunnan rhombic block. After impoundment on April 6, 2021, many earthquakes occurred around the reservoir area submerged by water. The largest ML 4.7 earthquake in the reservoir area occurred after the water level reached its highest point. But the seismogenic structures and mechanisms of earthquakes in the reservoir area are still unclear. Based on dense array data from the reservoir area, this paper uses the experimental site sub-model (CSES) of USTC-Pickers, transfer learned with “DiTing” dataset of China to obtain a high-precision earthquake catalog that is twice as large as that the manual catalog. This study show that earthquakes in the reservoir region primarily occur on secondary faults of pre-existing ones, characterized by a prominent feature of high dip angles trending northwest to southeast. Combined with the spatiotemperal migration characteristics of earthquakes and the relationship between earthquakes and water levels, we infer that most earthquakes are rapid response type and may be induced by rapid increase in elastic stress. Only the spatiotemporal distribution image of the ML 3.2 earthquakes sequence in the dam site-Toudaogou section conforms to the law of pore pressure diffusion, and belongs to the fast response type, which may be induced by the poroelasiticity coupling mechanism. The ML 3.0 earthquake swarm with deep depths in the Heishui River section belongs to the delayed response type and may be induced by the poroelasiticity coupling mechanism.

SummaryThe recent developments in array-based surface-wave tomography have made it possible to directly measure apparent phase velocities through wavefront tracking. While directionally dependent measurements have been used to infer intrinsic $2\psi $ azimuthal anisotropy (with a 180° periodicity), a few studies have also demonstrated strong but spurious $1\psi $ azimuthal anisotropy (360° periodicity) near major structure boundaries particularly for long period surface waves. In such observations, Rayleigh waves propagating in the direction perpendicular to the boundary from the slow to the fast side persistently show a higher apparent velocity compared to waves propagating in the opposite direction. In this study, we conduct numerical and theoretical investigations to explore the effect of scattering on the apparent Rayleigh-wave phase velocity measurement. Using two-dimensional spectral-element numerical wavefield simulations, we first reproduce the observation that waves propagating in opposite directions show different apparent phase velocities when passing through a major velocity contrast. Based on mode coupling theory and the locked mode approximation, we then investigate the effect of the scattered fundamental-mode Rayleigh wave and body waves interfering with the incident Rayleigh wave separately. We show that scattered fundamental-mode Rayleigh waves, while dominating the scattered wavefield, mostly cause short wavelength apparent phase velocity variations that could only be studied if the station spacing is less than about one tenth of the surface wave wavelength. Scattered body waves, on the other hand, cause longer wavelength velocity variations that correspond to the existing real data observations. Because of the sensitivity of the $1\psi $ apparent anisotropy to velocity contrasts, incorporating such measurements in surface wave tomography could improve the resolution and sharpen the structural boundaries of the inverted model.

Abstract

Global navigation satellite system reflectometry (GNSS-R) has emerged as a pivotal remote sensing (RS) technology, widely utilized for retrieving crucial oceanic parameters such as wind speed, sea surface height, and sea ice detection. However, the retrieval of ocean swell height remains an underexplored area within this domain. The complexity of constructing multivariate regression models for swell height retrieval poses a significant challenge, particularly in contrast to existing empirical models. For this purpose, this article proposes a novel deep learning (DL) hybrid model, namely Multi-scale Conv-BiLSTM, which combines multi-scale convolution and bidirectional long short-term memory (BiLSTM) networks for the first time to retrieve ocean swell height using spaceborne GNSS-R data. This innovative hybrid model comprises three fundamental modules: a multi-scale feature extraction module, a feature relationship inference module based on BiLSTM network, and a manual extraction of multiple feature parameters module encompassing GNSS-R variable and auxiliary variable. Specifically, the multi-scale feature extraction module leverages deep convolutional neural network (DCNN) to extract spatial features surrounding the specular reflection point (SP) from the two-dimensional matrix of the bistatic radar scattering cross-section (BRCS) image and effective scattering area. Subsequently, the feature relationship inference module employs the BiLSTM network to engage in the inference process between feature relationships. This module excels in considering critical information associated with temporal characteristics, effectively capturing preceding and subsequent information. Validation was conducted using ERA5 and WaveWatch III (WW3) data by comparingthe proposed Multi-scale Conv-BiLSTM model against seven traditional machine learning (ML) models, including support vector machine (SVM), decision tree (DTR), light gradient boosting machine (Lightgbm), eXtreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), gradient boosting decision tree (GBDT) and DCNN. The results show that when ERA5 is used as reference data, the proposed Multi-scale Conv-BiLSTM model achieves a reduction in root mean square error (RMSE) by 23.67%, 28.63%, 9.77%, 8.91%, 28.50%, 16.46%, and 15.05% compared to the SVM, DTR, Lightgbm, XGBoost, AdaBoost, GBDT, and DCNN models, respectively. When WW3 is used as reference data, the proposed Multi-scale Conv-BiLSTM model exhibits an improvement in RMSE by 35.99%, 36.62%, 25.49%, 24.74%, 41.37%, 30.82%, and 29.61% compared to the SVM, DTR, Lightgbm, XGBoost, AdaBoost, GBDT, and DCNN models, respectively.

Abstract

Real-time precise orbits of low earth orbit (LEO) satellites are becoming indispensable with the rapid development of real-time Earth observation application and LEO enhanced precise point positioning. Currently, GNSS-based precise orbit determination is a widely used method for LEO onboard navigation. However, the real-time LEO satellite orbits are usually obtained by ambiguity-float solution even when the GNSS augmentation corrections are considered. In this study, we perform LEO ambiguity-fixed multi-GNSS real-time precise orbit determination (RTPOD) based on square root information filter. One month of onboard GPS + Galileo observations from Sentinel-6A and real-time products of the Centre National d’Etudes Spatiales (CNES) are used to investigate the contribution of integer ambiguity resolution (IAR). The benefit of dual-system combination on LEO RTPOD is firstly evaluated. The combination of GPS and Galileo dual-system contributes to more visible GNSS satellites and better observation geometry for LEO RTPOD, which results in an evident accuracy improvement of 19% over GPS and a convergence time reduction of 43% and 41% compared to the GPS and Galileo solutions respectively. Considering the short arc of onboard GNSS observations and imperfections in GNSS real-time products, we propose a strict IAR quality control method to avoid fixing the ambiguity to the wrong values. The results indicate that the IAR quality control method we used can effectively reduce the wrong fixing risk and increase the robustness of the IAR solution. Using onboard GPS and Galileo observations, the 3D orbit accuracy of the ambiguity-fixed solution is significantly improved from 5.17 to 3.61cm, by 30%, compared to the ambiguity-float solution. Furthermore, the application of IAR also achieves a faster convergence to the centimeter-level orbit.