Paleomagnetic studies of meteorites constrain the evolution of magnetic fields in the early solar system. These studies rely on the identification of magnetic minerals that can retain stable magnetizations over ≳4.5 billion years (Ga). The ferromagnetic mineral tetrataenite (γ’-Fe0.5Ni0.5) is found in iron, stony-iron and chondrite meteorite groups. Nanoscale intergrowths of magnetostatically-interacting tetrataenite have been shown to carry records of paleomagnetic fields. Tetrataenite can also occur as isolated, non-interacting grains in many meteorite groups. Here we study non-interacting tetrataenite to establish the grain size range over which it can retain magnetization that is stable over solar system history. We present the results of analytical calculations and micromagnetic modelling of isolated tetrataenite grains with various sizes and geometries. We find that tetrataenite forms a stable single domain state for grain lengths between ~10 and 160 nm dependent on its axial ratio. It also possesses a magnetization resistant to viscous remagnetization over the lifetime of the solar system at 293 K. At smaller grain sizes, tetrataenite is superparamagnetic while at larger grain sizes, tetrataenite’s lowest energy state is a lamellar two-domain state that is stable over Ga-scale timescales. Unlike many other ferromagnetic minerals, tetrataenite does not form a single-vortex domain state due to its large uniaxial anisotropy. Our results show that both single domain and two-domain tetrataenite carry extremely stable magnetization and therefore are promising for paleomagnetic studies.

The ocean stores the bulk of excess anthropogenic heat in the Earth system. The ocean heat uptake efficiency (OHUE) – the flux of heat into the ocean per degree of global warming – is therefore a key factor in how much warming will occur in the coming decades. In climate models, OHUE is well-characterised, tending to decrease on centennial timescales; in contrast, OHUE is not well-constrained from Earth observations. Here OHUE and its rate of change are diagnosed from global temperature and ocean heat content records. OHUE increased from $0.57\pm0.06$W/m$^2$K to $0.7\pm0.02$W/m$^2$K over the past five decades. This increase is attributed to steepening heat content gradients in the ocean, and corresponds to $\sim$4 years’ delay until temperature targets such as 1.5$^\circ$C or 2$^\circ$C are exceeded.

Estimating the radiated energy of small-to-moderate (Mw < 5) events remains challenging because their waveforms are strongly distorted during wave propagation. Even when near-source records are available, seismic waves pass through the shallow crust with strong attenuation; consequently, high-frequency energy may be significantly dissipated. Here, we evaluated the degree of energy dissipation in the shallow crust by estimating the depth-dependent attenuation (Q-1) by modeling near-source (< 12 km) waveform data in northern Ibaraki Prefecture, Japan. High-quality waveforms recorded by a downhole sensor confined by granite with high seismic velocity helped to investigate this issue. We first estimated the moment tensors for M1–4 events and computed their synthetic waveforms, assuming a tentative one-dimensional -model. We then modified the -model in the 5–20 Hz range such that the frequency components of the synthetic and observed waveforms of small events (Mw < 1.7) matched. The results show that the Q-value is 55 at depths of < 4 km and shows no obvious frequency dependence. Using the derived -model, we estimated the moment-scaled energy (eR) of 3,884 events with Mw 2.0–4.5. The median eR is 3.6×10-5 , similar to the values reported for Mw >6 events, with no obvious Mw dependence. If we use an empirically derived Q-model (~350), the median eR becomes a one-order underestimation (3.1×10-6). These results indicate the importance of accurately assuming the Q-value in the shallow crust for energy estimation of small events, even when near-source high-quality waveforms are available.

Global cloud resolving models (GCRMs) are a new type of general circulation model that explicitly calculates the growth of cloud systems with fine spatial resolutions and more than 10 GCRMs have been developed at present. This chapter of the monograph reviews cloud microphysics schemes used in GCRMs with introductions to the recent progress and researches with GCRMs. Especially, research progress using a pioneer of GCRMs, Nonhydrostatic ICosahedral Atmospheric Model (NICAM), is focused. Since GCRMs deal with climatology and meteorology, it is a challenging issue to establish cloud microphysics schemes for GCRMs. A brief history of the development of cloud microphysics schemes and cloud-radiation coupling in NICAM is described. In addition, current progress in analytical techniques using satellite simulators is described. The combined use of multi-optical sensors enables us to constrain uncertain processes in cloud microphysics without artificial tuning. As a result, cloud microphysics schemes used in the NICAM naturally represent cloud systems, and hence, the radiative budget is well balanced with little optimization. Finally, a new satellite and a ground validation campaign are introduced for future work.

The lateral size of sea ice floes is receiving increasing attention as an important variable for the polar climate system. We have developed a model for prognostic evolution of the floe size distribution, which emerges due to five key physical processes: new ice formation, welding of floes in freezing conditions, lateral growth and melt, and fracture of floes by ocean surface waves. As a result of the model’s foundation in the governing physics, free parameters occurring in the equations can be directly constrained by observations. Initial model experiments provided insight into the relative importance of various processes, showing floe freezing processes were particularly important for simulation of the floe size distribution. Previously, physical descriptions of lateral growth and welding together of floes had not been constrained by observations. This motivated an analysis of images obtained by drifting wave buoys (SWIFTs) deployed in the autumn Arctic Ocean to quantify these processes in-situ for the first time. We separated floe area growth due to welding from that due to lateral expansion, and compared observations to our physical descriptions of the individual processes. We also found a strong limitation on floe sizes imposed by the wave field. These results have been used to inform new physical descriptions of processes important for the sea ice floe size distribution.

Understanding the formation of the solar system can provide a simplified look at the universe at large. This is because we have a lot of evidence about the formation of our solar system, and because the universe is homogeneous on a large scale. In this paper, we propose a new way for investigating the formation of the Earth and other Solar System objects. Our approach offers insights into details of the formation of the multiple layers within Earth, the existence of water and oil, the variation in mass distribution within Earth, and the origin of mountains, erratic boulders, and moons. According to our proposed approach, Roche Radius can explain the origin of moons, rings and mountains on planets. We have listed and use critical conditions that are required to form celestial objects.

This study evaluated the performance of the Taiwan Earth System Model version 1 (TaiESM1) in simulating the observed climate variability in the historical simulation of the Coupled Model Intercomparison phase 6 (CMIP6). TaiESM1 was developed on the basis of the Community Earth System Model version 1.2.2, with the inclusion of several new physical schemes and improvements in the atmosphere model. The new additions include an improved triggering function in the cumulus convection scheme, a revised distribution-based formula in the cloud fraction scheme, a new aerosol scheme, and a unique scheme for three-dimensional surface absorption of shortwave radiation that accounts for the influence of complex terrains. In contrast to the majority of model evaluation processes, which focus mainly on the climatological mean, this evaluation focuses on climate variability parameters, including the diurnal rainfall cycle, precipitation extremes, synoptic eddy activity, intraseasonal fluctuation, monsoon evolution, and interannual and multidecadal atmospheric and oceanic teleconnection patterns. A series of intercomparisons between the simulations of TaiESM1 and CMIP6 models and observations indicate that TaiESM1, a participating model in CMIP6, can realistically simulate the observed climate variability at various time scales and performs better than the other CMIP6 models in terms of many key climate features.

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

A seafloor-cable seismic and tsunami observation system is ideal for marine geophysical observation because the data can be obtained in real-time. We have developed a new compact seafloor cable seismic and tsunami observation system using Information and Communication Technology (ICT) since 2005. Our new system secures reliability by using Transmission Control Protocol/Internet Protocol technology and provides observational flexibility via observation nodes with a software-based system using up-to-date electronics technology. These features contribute to cost reduction and production sustainability. The system was installed on the Pacific Ocean floor off Sanriku, northeast Japan, in September 2015, in the source area of the 2011 Tohoku-oki earthquake. Our purpose is to better monitor seismic activity and to observe tsunami activity through spatially dense observation. The obtained high quality seismic and pressure data are stored continuously by the new system.

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

Remote-sensed flood monitoring is rapidly advanced by the growing abundance of satellite data. This study presents the progress in building an operational system that harnesses the power of spatially high-resolution (HR), multi-source remote sensing data for event-based flood extent and depth mapping. By integrating pioneering extent retrieval-Radar Produced Inundation Diary (RAPID), Self-supervised Waterbody Detection (SWD), and depth retrieval processors, Global-LOCAL Solvers Integration Algorithm (GLOCAL), and the Emulated Flood Recession Algorithm (EFRA)-our proposed framework marks a significant leap in flood monitoring capabilities. The upgraded RAPID addresses the complexities of Synthetic Aperture Radar (SAR) flood mapping in diverse environments, including snow-covered and arid regions, enhancing adaptability and consistency across multiple SAR satellites such as ESA Sentinel-1, CSA RADARSAT Constellation Mission (RCM), MDA RADARSAT-2 (RS2), and Capella. Meanwhile, SWD brings a method to automatically map flood extents from high-resolution optical images, including Planet, Sentinel-2, and Landsat, during clear weather conditions. GLOCAL and EFRA are tailored for depth estimation from the generated HR remotely sensed flood extents, and HR or VHR topography. Both algorithms

We document the implementation of the Common Representative Intermediates Mechanism version 2, reduction 5 (CRIv2-R5) into the United Kingdom Chemistry and Aerosol model (UKCA) version 10.9. The mechanism is merged with the stratospheric chemistry already used by the StratTrop mechanism, as used in UKCA and the UK Earth System Model (UKESM1), to create a new CRI-Strat mechanism. CRI-Strat simulates a more comprehensive treatment of non-methane volatile organic compounds (NMVOCs) and provides traceability with the Master Chemical Mechanism (MCM). In total, CRI-Strat simulates the chemistry of 233 species competing in 613 reactions (compared to 87 species and 305 reactions in the existing StratTrop mechanism). However, while more than twice as complex than StratTrop, the new mechanism is only 75% more computationally expensive. CRI-Strat is evaluated against an array of in situ and remote sensing observations and simulations using the StratTrop mechanism in the UKCA model. It is found to increase production of ozone near the surface, leading to higher ozone concentrations compared to surface observations. However, ozone loss is also greater in CRI-Strat, leading to less ozone away from emission sources and a similar tropospheric ozone burden compared to StratTrop. CRI-Strat also produces more carbon monoxide than StratTrop, particularly downwind of biogenic VOC emission sources, but has lower burdens of nitrogen oxides as more is converted into reservoir species. The changes to tropospheric ozone and nitrogen budgets are sensitive to the treatment of NMVOC emissions, highlighting the need to reduce uncertainty in these emissions to improve representation of tropospheric chemical composition.

In this study, the Empirical Mode Decomposition algorithm (EMD) and the Long Short Term Memory neural network (LSTM) were combined into an EMD-LSTM model, to predict the variation of the >2MeV electron fluxes. Input parameters include the Pc5 power and related geomagnetic indices are used for predictions. As compared the prediction results of the EMD-LSTM model with other classical prediction models, the results show that the one-day ahead prediction efficiency of the > 2MeV electron fluxes possesses a prediction efficiency of 0.80, and the highest prediction efficiency can reach 0.93. These results are superior to the prediction accuracy of more traditional models. Using two high-energy electron flux storm events for validation, the results indicate that the performance of the EMD-LSTM model in the period of the high-energy electron flux storm is also relatively good, especially for the prediction of high-energy electron fluxes at extreme points, and the prediction is closer to actual observation.

Ultra-low velocity zones (ULVZs) above the Core-Mantle Boundary (CMB) are significant structures probably connecting the lowermost mantle and the outer core. As “thin patches” of dramatically low seismic-wave velocity, they are occasionally found near the base of mantle plumes and in-or-near high seismic-wave speed regions above CMB. The causes of their morphology-distribution and geodynamics remain unclear, and simulation results of high-density melt diverge from seismic-observations speculation (~+10%). We introduce a 2D time-dependent Stokes’ two-phase-flow (with melt-migration) numerical model to investigate the formation and morphological characteristics of ULVZs caused by CMB-mantle tangential flows and a neighboring cold source (subducted plate). We discover that (a) the participation of cold sources with temperature differences between ~4000 °K at the plume central regions to <~3900 °K at the plume-cooling mantle region, separated by horizontal distances of about 100 (±<50) km are necessary for the stable existence of dense melts with mass-density difference >+1-2% (even +10%) with respect to the surrounding mantle; and additionally (b) an enhanced tangential flow coincident with the internal reverse circulation within the broad plume base (with speeds >3 times the lowermost-mantle characteristic flow speed); are necessary for higher aspect-ratio-morphology lenses compatible with seismic observations. Our findings suggest that the CMB-mantle tangential flow and/or outer-core interacting with CMB-topography, may be implicated in generating mega-ULVZs, especially if they appear along the edges of LLVSPs and especially when in/near high seismic-speed “cold” zones. We infer a strong link between ULVZs morphology and the dynamical environment of the lowermost mantle and uppermost outer core.

Cross-correlation of fully diffuse wavefields averaged over time should converge to the Green’s function; however, the ambient seismic field in the real Earth is not fully diffuse, which interferes with that convergence. We apply blind signal separation to reduce the effect of spurious non-diffuse components on the cross-correlation tensor of the ambient seismic field. We describe the diffuse component as having uncorrelated neighboring frequencies and equal intensity at all azimuths, and an independent (i.e., statistically uncorrelated) non-diffuse component arising from a spatially isolated point source for which neighboring frequencies are correlated. Under the assumption of linear independence of the spurious non-diffuse wave outside the stationary phase zone and the constructive interference of noise waves within that zone, we can suppress the spurious non-diffuse component from the noise interferometry. Our numerical simulations show good separation of one spurious non-diffuse noise source component for either non-diffuse Rayleigh or Love waves. We apply this separation to the Rayleigh-wave component of the Green’s function for 136 cross-correlation pairs from 17 stations in Southern California. We perform beamforming over different frequency bands for the cross-correlations before and after the separation, and find that the reconstructed Rayleigh waves are more coherent. We also estimate the bias in Rayleigh wave phase velocity for each receiver pair due to the spurious non-diffuse contribution.

Overallocation, increased demands, recognition and quantification of environmental flows, and climate change have combined to make water leasing a practical solution for addressing water shortages in many watersheds. Leasing involves the temporary transfer of the consumptive use portion of a water right to another user presumably for a higher value purpose. Therefore, understanding the effect of water leasing on river flow availability and river ecosystem protection is critical for the watershed-scale water management. Numerical and computational watershed simulation techniques coupled with water right models can allow water managers to allocate water based on the legal framework within the watershed, which in the western United States is typically based on the prior appropriation doctrine. In this study, we simulate the water trades on instream flow using an ASCE Penman-Monteith method of evapotranspiration estimation on water right model. Using the Touchet River basin in Washington State as a study location, we successfully demonstrated simulation of water leases on instream flow. We choose the Touchet River basin as a study location as this basin suffers frequent water curtailment and has existing partnerships across diverse sectors including water leasing markets to address this water resources concern. This information will be extremely valuable for river ecosystem protection and to watershed managers trying to plan future water managements, water storage, and water trading projects.

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

Saltwater disposal has been identified as the dominant causal factor that contribute to induced seismicity. Physical models rely on mechanistic understanding to infer causality where they evaluate various conditions for fault slips albeit with a high degree of uncertainty due to sparse data and subsurface heterogeneity. Given these uncertainties, statistical analysis is designed to measure statistical associations in the observed data with parametric regression models and interpret the significance of specific coefficient as evidence of causation. However, it is often difficult to interrogate the coefficients between different statistical models as the coefficients hold different implications. We propose a causal inference framework with the potential outcomes perspective to explicitly define what we meant by causal effect and declare necessary assumptions to ensure consistency between models for model comparison. The proposed workflow is applied to the Fort-Worth Basin of North Central Texas with the area of interest is discretized into non-overlapping grid blocks. Two statistical methods are employed to test the significance of the causal effect between the presence or absence of saltwater disposals and the number of the earthquakes and to estimate the magnitude of the average causal effect. In addition, our analysis is repeated for different grid configurations to directly assess the sensitivity of statistical results. We have identified a stable and statistically significant causal relationship between the presence of saltwater disposals and the number of earthquakes and have estimated there are, on average, 13 more earthquakes occurring in grids with saltwater disposals.