A volcanic tremor is a seismic event linked to volcanic processes, identified by ongoing ground shaking. While there are different theories regarding the mechanism of volcanic tremors, they play a crucial role in understanding internal volcanic activities and forecasting eruptions. Detecting weak volcanic tremor signals before an eruption has remained a difficult task. This study utilized polarization analysis on continuous seismograms at Kirishima volcano, Japan, to extract data on retrograde Rayleigh wave sources. Back azimuth estimates focused on the direction of the Shinmoe-dake crater before and during the volcanic eruptions in 2011, 2017, and 2018. Rayleigh waves originating from the crater direction, particularly in the 1.3-2.5 Hz band, were observed starting around March 2017, approximately 7 months prior to the October 2017 eruption. In the 1.3-2 Hz band, the back azimuths steadily moved closer to the crater's direction between January and March 2017. A noticeable increase in the amplitudes of retrograde Rayleigh waves from the crater direction was observed starting in August 2016. Enhancing the growth in amplitude was achieved by utilizing a filter on the seismograms. As polarization analysis requires only a single three-component seismometer, this approach allows for the timely identification of weak Rayleigh waves from the crater direction, even in volcanoes with limited seismic station coverage. The results obtained in this study contribute to the growing knowledge on volcanic tremors and their potential use in volcano monitoring and eruption forecasting.

Weak Temperature Gradient (WTG) modeling using a small cloud-resolving model (CRM) admits multiple equilibria depending upon the initial model conditions. A new kind of equilibrium is presented here which undergoes a periodic and steady-state oscillation that is emergent from static boundary conditions. The periodic oscillation is shown to be a moisture mode with an oscillating vertical motion profile shape generated by the interaction between vertical motion and radiation. A simple two-mode model based on decomposing the vertical motion profile and associated radiative cooling variations can describe the oscillation.

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 0C. Observed DTR shrinks strongly at colder temperature, where warming shifts the average temperature towards 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 0C and a stronger DTR shrinking at colder temperature. In models that reproduce the DTR minimum close to 0C, DTR shrinking slows down significantly in recent decades. Model projections suggest that the DTR will resume or continue shrinking over the 21st century.

This study investigates lightning at tall objects and evaluates the risk of upward lightning (UL) over the eastern Alps and its surrounding areas. While uncommon, UL poses a threat, especially to wind turbines, as the long-duration current of UL can cause significant damage. Current risk assessment methods overlook the impact of meteorological conditions, potentially underestimating UL risks. Therefore, this study employs random forests, a machine learning technique, to analyze the relationship between UL measured at Gaisberg Tower (Austria) and 35 larger-scale meteorological variables. Of these, the larger-scale upward velocity, wind speed and direction at 10 meters and cloud physics variables contribute most information. The random forests predict the risk of UL across the study area at a 1 km^2 resolution. Strong near-surface winds combined with upward deflection by elevated terrain increase UL risk. The diurnal cycle of the UL risk as well as high-risk areas shift seasonally. They are concentrated north/northeast of the Alps in winter due to prevailing northerly winds, and expanding southward, impacting northern Italy in the transitional and summer months. The model performs best in winter, with the highest predicted UL risk coinciding with observed peaks in measured lightning at tall objects. The highest concentration is north of the Alps, where most wind turbines are located, leading to an increase in overall lightning activity. Comprehensive meteorological information is essential for UL risk assessment, as lightning densities are a poor indicator of lightning at tall objects.

In this paper in an elegant way will be present the formulas of the Dimensionless Universe. All these equations are simple,elegant and symmetrical in a great physical meaning. These equations are applicable for all energy scales. We will propose the Dimensionless unification of the fundamental interactions and the Dimensionless unification of atomic physics with cosmology. We find the formulas for the cosmological constant and we will prove that the shape of the Universe is Poincaré dodecahedral space. From the dimensionless unification of the fundamental interactions will propose a possible solution for the density parameter of baryonic matter,dark matter and dark energy.

Intratidal variability in stratification, referred to as internal tidal asymmetry, affects the residual sediment flux of an estuary by altering sediment transport differently during ebb and flood. While earlier studies suggest that flood-dominant mixing increases the residual landward sediment flux, the role of ebb-dominant mixing remains largely unknown. Based on field data, we investigate the mechanisms that cause ebb-dominant mixing and its effect on the residual sediment flux in a stratified estuarine channel. Observations based on two tidal cycles show that the pycnocline remains largely intact during flood. Vertical mixing during flood is inhibited by a strong fresh water outflow, confining landward transport of suspended sediment to the bottom layer. During ebb, the pycnocline height decreases until it interacts with the bottom boundary layer, resulting in enhanced vertical mixing and sediment transport extending further to the surface. Thus, ebb-dominant mixing increases the residual sediment flux in seaward direction. The long ebb period further contributes to the residual ebb-flux. This is noteworthy since a long ebb duration, as it corresponds to flood dominance, is often associated with a landward residual sediment flux. Although our data represent average conditions and may not be representative for high river discharge or storm conditions, we conclude that asymmetries in vertical mixing considerably affect the residual sediment flux.

How seismic magnitudes are distributed is important for estimating stress levels in seismic hazard studies, and two methods of characterizing the magnitude distribution are through the Gutenberg-Richter b-value, or equivalently through , and through the information entropy. A closed relationship between the b-value and the entropy (applicable to any exponential distribution and its entropy) is presented and is checked through numerical evaluation of the entropy using exact probabilities derived directly from the magnitude exponential distribution. Since the numerical evaluation of the entropy is done over a finite magnitude range, it is possible to assess the possible contribution to the entropy of real or hypothetical very large magnitudes, and these contributions are found to be quite small. The relationship is also compared with entropies calculated from synthetic data, and Monte Carlo simulations are used to explore the behavior of entropy determinations as a function of sample size. Finally, it is considered how, for the usual case of having data from a single realization, in spite of the relation between them, because entropy and Aki-Utsu b-value are measured in different ways, both measures are not redundant and may be complementary and useful in determining when a sample is large enough to give reliable results.

In the Anthropocene, humans are the largest drivers of change in vegetation fire regimes. Humans influence fire regimes both directly, by starting, managing and extinguishing fires, and also indirectly by altering fuel composition and connectivity. However, whilst vegetation fire is a coupled socio-ecological process, representation of human influences on fire regimes in global-scale modelling remains limited. This places a fundamental constraint on our ability to understand how human and natural processes combine to create observed patterns of vegetation fire, and how such processes may interact under future scenarios of socioeconomic and environmental change. Here, we respond to this challenge by presenting a novel integration of two global and process-based models. The first is the Wildfire Human Agency Model (WHAM!), which draws on agent-based approaches to represent anthropogenic fire use and management. The second is JULES-INFERNO, a fire-enabled dynamic global vegetation model, which takes a physically-grounded approach to the representation of vegetation-fire dynamics. The WHAM-INFERNO combined model suggests that as much as half of all global burned area is generated by managed anthropogenic fires – typically small fires that are lit and then spread according to land user objectives. Furthermore, we demonstrate that including representation of managed anthropogenic fires in a coupled socio-ecological simulation can improve understanding of the drivers of unmanaged wildfires. Overall, findings presented here have substantial implications for understanding of present-day and future fire regimes, indicating that socio-economic change may be as important as climate change in determining the future trajectory of fire on Earth.

Global climate goals require a transition to a deeply decarbonized energy system. Meeting the objectives of the Paris Agreement through countries’ Nationally Determined Contributions and Long-Term Strategies represents a complex problem with consequences across multiple systems shrouded by deep uncertainty. Robust, large-ensemble methods and analyses mapping a wide range of possible future states of the world are needed to help policymakers design effective strategies to meet emissions reduction goals. This study contributes a scenario discovery analysis applied to a large ensemble of 5,760 model realizations generated using the Global Change Analysis Model. Eleven energy-related uncertainties are systematically varied, representing national mitigation pledges, institutional factors, and techno-economic parameters, among others. The resulting ensemble maps how uncertainties impact common energy system metrics used to characterize national and global pathways toward deep decarbonization. Results show globally consistent but regionally variable energy transitions as measured by multiple metrics, including electricity costs and stranded assets. Larger economies and developing regions experience more severe economic outcomes across a broad sampling of uncertainty. The scale of CO2 removal globally determines how much the energy system can continue to emit, but the relative role of different CO2 removal options in meeting decarbonization goals varies across regions. Previous studies characterizing uncertainty have typically focused on a few scenarios, and other large-ensemble work has not (to our knowledge) combined this framework with national emissions pledges or institutional factors. Our results underscore the value of large-ensemble scenario discovery for decision support as countries begin to design strategies to meet their goals.

Parameterizations in Earth System Models (ESMs) are subject to biases and uncertainties arising from subjective empirical assumptions and incomplete understanding of the underlying physical processes. Recently, the growing representational capability of machine learning (ML) in solving complex problems has spawned immense interests in climate science applications. Specifically, ML-based parameterizations have been developed to represent convection, radiation and microphysics processes in ESMs by learning from observations or high-resolution simulations, which have the potential to improve the accuracies and alleviate the uncertainties. Previous works have developed some surrogate models for these processes using ML. These surrogate models need to be coupled with the dynamical core of ESMs to investigate the effectiveness and their performance in a coupled system. In this study, we present a novel Fortran-Python interface designed to seamlessly integrate ML parameterizations into ESMs. This interface showcases high versatility by supporting popular ML frameworks like PyTorch, TensorFlow, and Scikit-learn. We demonstrate the interface’s modularity and reusability through two cases: a ML trigger function for convection parameterization and a ML wildfire model. We conduct a comprehensive evaluation of memory usage and computational overhead resulting from the integration of Python codes into the Fortran ESMs. By leveraging this flexible interface, ML parameterizations can be effectively developed, tested, and integrated into ESMs.

Coronal Holes (CH) are large-scale, low-density regions in the solar atmosphere which may expel high-speed solar wind streams that incite hazardous, geomagnetic storms. Segmentation of CH boundaries may aid in validating predictive coronal and solar wind models but has proved difficult due to similar appearances as filaments and the tendency for dense coronal plasmas to block underlying CHs in Extreme Ultraviolet (EUV) imagery. We propose an automated detection algorithm of CHs which revisits ground-observed, chromospheric He I 10830 Å line imagery and underlying photospheric magnetograms as drivers to circumvent these issues and provide a complementary method to the space-observed, coronal EUV emission-driven methods that have been widely adopted in the community. Classical computer vision techniques are applied to imbue the routine with design variables based in radiative and magnetic properties of CHs, as well as produce an ensemble of boundaries with quantified intra-algorithm uncertainty. This method is science-enabling towards future studies of coronal hole formation and variability from a mid-atmospheric perspective.

Aerosols from large volcanic eruptions are purported to cause significant global precipitation changes lasting up to several years. We here show that eruptions with very large stratospheric sulfur injections are, in fact, too weak to substantially alter precipitation at most land locations. Analyzing two climate model ensembles, we demonstrate that internal variability is the main driver of interannual precipitation anomalies even in the aftermath of the largest tropical eruptions of the last millennium. Further, observations show that post-eruption precipitation anomalies in post-eruption years are indistinguishable from anomalies in non-volcanic years. Reports of statistically significant post-eruption precipitation anomalies have relied on metrics that remove internal variability in order to inflate the volcanic signal. Such metrics are not suitable to assess the importance of volcanic eruptions on local-scale precipitation.

The Kalahari Basin in southern Africa, shaped by subsidence and epeirogeny, features the Okavango Rift Zone (ORZ) as a significant structural element characterized by diffused extensional deformation forming a prominent depocenter. This study elucidates the Pleistocene landscape evolution of the ORZ by examining the chronology of sediment formation and filling this incipient rift and its surroundings.Modeling of cosmogenic nuclide concentrations in surficial eolian sand from distinct structural blocks around the ORZ provides insights into sand’s residence time on the surface. Sand formation occurred from ~2.2 to 1.1 Ma, coinciding with regional tectonic events. Notably, provenance analyses of sand within ORZ's lowermost block where large alluvial fans are found indicate different source rocks and depositional environments than those of the eolian sands found at a higher elevation. This suggests that the major phase of rift subsidence and the following incision of alluvial systems into the rift occurred after eolian dune formation. Luminescence dating reveals that deposition in alluvial fan settings in the incised landscape began not later than ~250 ka, and that a lacustrine environment existed since at least ~140 ka. The established chronological framework constrains the geomorphological effects of the different tectono-climatic forces that shaped this nascent rifting area. It highlights two pronounced stages of landscape development, with the most recent major deformation event in the evolving rift probably occurring during the middle Pleistocene transition (1.2-0.75 Ma). This event is reflected as a striking change in the depositional environments due to the configurational changes accompanying rift progression.

Atmospheric fronts embedded in extratropical cyclones are high-impact weather phenomena, contributing significantly to midlatitude winter precipitation. The three vital characteristics of the atmospheric fronts, high wind speeds, abrupt change in wind direction, and rapid translation, force the induced surface waves to be misaligned with winds exclusively behind the cold fronts. The effects of the misaligned waves on air-sea fluxes remain undocumented. Using the multi-year in situ near-surface observations and direct covariance flux measurements from the Pioneer Array off the coast of New England, we find that the majority of the passing cold fronts generate misaligned waves behind the cold front. Once generated, the waves remain misaligned, on average, for about 8 hours. The fully-coupled model simulations indicate that the misaligned waves significantly increase wave roughness length (300%), drag coefficient (30%), and momentum flux (20%). The increased surface drag reduces the wind speeds in the surface layer. The upward turbulent heat flux is weakly decreased by the misaligned waves because of the compensating effect between the decrease in temperature and humidity scaling parameters and the increase in friction velocity. The misaligned wave effect is not accurately represented in a commonly used wave-based bulk flux algorithm. Yet, the suggested modification to the current formulation improves the overall accuracy of parameterized momentum flux estimates. The results imply that better representing a directional wind-wave coupling in the bulk formula of the numerical models may help improve the air-sea interaction simulations under the passing atmospheric fronts in the midlatitudes.

The relatively unconstrained internal structure of Venus is a missing piece in our understanding of the Solar System formation and evolution. To determine the seismic structure of Venus’ interior, the detection of seismic waves generated by venusquakes is crucial, as recently shown by the new seismic and geodetic constraints on Mars’ interior obtained by the InSight mission. In the next decades multiple missions will fly to Venus to explore its tectonic and volcanic activity, but they will not be able to conclusively report on seismicity or detect actual seismic waves. Looking towards the next fleet of Venus missions in the future, various concepts to measure seismic waves have already been explored in the past decades. These detection methods include typical geophysical ground sensors already deployed on Earth, the Moon, and Mars; pressure sensors on balloons; and airglow imagers on orbiters to detect ground motion, the infrasound signals generated by seismic waves, and the corresponding airglow variations in the upper atmosphere. Here, we provide a first comparison between the detection capabilities of these different measurement techniques and recent estimates of Venus’ seismic activity. In addition, we discuss the performance requirements and measurement durations required to detect seismic waves with the various detection methods. As such, our study clearly presents the advantages and limitations of the different seismic wave detection techniques and can be used to drive the design of future mission concepts aiming to study the seismicity of Venus.

Comprehensive studies comparing impacts of building and street levels interventions on air temperature at metropolitan scales are still lacking despite increased urban heat-related mortality and morbidity. We therefore model the impact of 9 interventions on air temperatures at 2 m during 2 hot days from the summer 2018 in the Greater London Authority area using the WRF BEP-BEM climate model. We find that on average cool roofs most effectively reduce temperatures ($\sim$~-1.2~$^\circ$C), outperforming green roofs ($\sim$~0~$^\circ$C), solar panels ($\sim$~-0.5~$^\circ$C) and street level vegetation ($\sim$~-0.3~$^\circ$C). Application of air conditioning across London (United Kingdom) increases air temperatures by $\sim$~+0.15~$^\circ$C. A practicable deployment of solar panels could cover its related energetic consumption. Current practicable deployments of green roofs and solar panels are ineffective at large scale reduction of temperatures. We provide a detailed decomposition of the surface energy balance to explain changes in air temperature and guide future decision-making.

Relativistic electron precipitation (REP) refers to the release of high energy electrons initially trapped in the outer radiation belt that precipitate into the Earth’s upper atmosphere. REP plays an important role in the magnetosphere as it contributes to the fast depletion of radiation belt electron flux. This study presents a statistical analysis of the REP observations made by the Calorimetric Electron Telescope (CALET) experiment on board the International Space Station (2015- present). Specifically, a catalog based on count rates from the two top scintillators constituting the top charge detector, sensitive to electrons with energies $>$1.5 MeV and $>$3.4 MeV, respectively. Data from the CALET experiment reveals a previously unreported semi-annual variation in the occurrence of REP events. The REP periodicities are similar to those observed for trapped electrons in the outer belt, and correlates with the occurrence of solar wind high-speed streams and the solar cycle variations.

Geophysical observations indicate that iron enrichment of various spatial scales may be present in the lowermost mantle. Various mechanisms have been proposed to explain the process of iron infiltration from the core to the mantle, though each with its own inherent limitations. Grain boundary (GB) diffusion significantly outpaces bulk diffusion within crystal interiors, and may facilitate iron transport across the core-mantle boundary (CMB). In this study, we investigate diffusion in two symmetric tilt GBs in ferropericlase using large-scale molecular dynamics simulations. The GB diffusivities of Mg and O of periclase as well as their temperature dependence agree well with previous studies. In addition, we study the GB diffusion of Fe in (Mg,Fe)O GBs for the first time. The results suggest that GB diffusion of Fe is likely to be sluggish near the CMB, and thus may not be an effective mechanism to transport iron from the core to the mantle.