Deep long-period earthquakes (DLPs) are often detected near volcanoes from the crust down to the upper mantle. Exhibiting coincidence with some volcanic eruptions, DLPs are recognized as potential precursors to volcanic activities yet their detection remains challenging. Meanwhile, their relation to volcanic activities and specific source mechanisms remains uncertain. In this study, we first classify earthquakes into volcano-tectonic (VTs) and long-period (LPs) earthquakes at 10 Alaskan volcanoes by frequency index (FI). The consistent differences in FI distribution between LPs and VTs across volcanoes suggest it can be leveraged for automatic classification. Presence of both VTs and DLPs in some regions suggests frequency content differences mainly arise from source effect. We then analyze 12 years of continuous waveforms by template matching and detect 20,979 DLPs. No repeating DLPs are identified below the depth of 10 km except ~1% of DLPs at Spurr Volcano, indicating source processes of DLPs at Alaskan volcanoes are primarily non-repetitive. Detections of mantle DLPs and positive correlations between DLPs and volume change rate in the magma reservoir support the involvement of magmatic activities in some DLP sources, though there is limited correlation in DLP depth distribution with magmatic water content and kinematic parameters of plate motion. In addition, only ~12% of DLP bursts occur simultaneously with VT bursts, suggesting most DLP rate anomalies are not directly linked to shallow volcanic unrests. Finally, since on average only ~26% of DLP rate anomalies coincide with eruption episodes, it is still challenging to foretell eruptions uniquely based on DLP occurrences.

On 10 May 2024, a powerful coronal mass ejection arrived at Earth at 17:05UT and caused a major geomagnetic storm. With the minimum SYM-H excursion of-497 nT (5-min data), this storm is the largest geomagnetic disturbance since March 1989, and can be categorized as a superstorm. In this work, by using a set of ground-based and space-borne instruments, we study the electrodynamic and ionospheric response to the May 2024 storm at middle and low latitudes. During the main phase of the storm, we observed a major super-fountain effect associated with an extreme ionospheric uplift. During the maximum disturbance at ~23:30UT on 10 May, the amplitude within the equatorial ionization anomaly crests reached 170 TECU in GNSS data and 130 TECU in the Swarm A data (i.e., above ~430 km of altitude). Moreover, DMSP showed the occurrence of a 2-peak density structure at ~850 km of altitude with 500-600% density increase, which is an indication of an extremely strong upward ExB drift and strong meridional thermospheric winds circulating during this storm. While the observed dayside ionospheric effects are quite significant, they are less intense than those that occurred during the 15 July 2000 and 29-30 October 2003 superstorms. During the recovery phase, a severe negative storm was observed in the American sector, driven by very strong ExB drift and composition changes. The negative deviation occurred during the recovery phase of the May 2024 storm is one of the strongest ever observed since the beginning of the GPS/GNSS VTEC era. Key-points:-during the main phase of the storm, an extreme dayside ionospheric uplift occurred driven by the PPEF and by storm-time thermospheric winds-the recovery phase was marked by significant drops in the ionospheric plasma density driven by downward ExB drift-compared to previous superstorms, the May 2024 event caused moderately intense positive ionospheric storm and the strongest negative storm

The meteorology-dependent multiscale behavior of snow depth over the Tibetan Plateau was examined via moderate-resolution datasets. By analyzing spatial variance and power spectra at specific scales (5 km, 10 km, 20 km, and 50 km) over 28 intraseasonal continuous snow cover regions, key meteorological factors affecting snow depth across different seasons were identified based upon the Kullback-Leibler distance. In spring and winter, the homogeneous power spectra highlight the significant roles of precipitation, atmospheric moisture, and temperature in snow accumulation and melt processes. Summer exhibits the highest snow depth spatial variance driven by radiation and wind dynamics. Autumn with the lowest spatial heterogeneity underscores the impact of windspeed on snow distribution through sublimation and redistribution. The alignment between spatial variance maps and spectra across these scales implies the future downscaling or upscaling without substantial loss of spatial information. These findings are essential for improving snow cover modeling and forecasting, particularly in the context of climate change, as well as for effective water resource management and climate adaptation strategies in this strategically important plateau.

Evaluating our understanding of smoke from wild and prescribed fires can benefit from downwind measurements that include both inert tracers to test production and transport and reactive species to test chemical mechanisms. We characterized smoke from fires in coniferous forest fuels for >1000 hours over two summers (2017 and 2018) at our Montana surface station and found a narrow range of key properties. DPM/DCO was 0.1070 +/- 0.0278 or about half the age-independent ratios obtained at free troposphere elevations. The average absorption Angstrom exponent across both years was 1.84, or about half the values available for very fresh smoke. Brown carbon (BrC) was persistent (~50% of the absorption at 401 nm) in both years, despite differences in smoke age and transport. DBC/DCO doubled from 2017 to 2018, but the average across two years was within 30% of recent airborne measurements. Switching from a 1.0 to a 2.5 micron cutoff increased the mass scattering and mass absorption coefficients suggesting super micron particles impact the optical properties of moderately aged smoke. O3 was elevated ~6 ppb on average over a full diurnal period when wildfire smoke was present, and smoke-associated O3 increases were highest (~9 pbb) at night suggesting substantial upwind production. NOx was almost entirely local in origin. NO2 spurred high rates of NO3 production in the presence of wildfire smoke (up to 2.44 ppb/hr) and potentially at least one nighttime BrC formation event that could have impacted next-day photochemistry.

In this study, we show that non-thermal collisions can play a significant role in shaping Europa's exospheric structure. Collisions between radiolytically produced O2 and the O produced via electron impact dissociation of O2 play a significant role in affecting the exospheric structure and escape rates. Specifically, O+O2 collisions lead to the production of a non-thermal O2 population, and increase the O2 escape while decreasing the O escape. These collisions are dependent on three specific physical parameters: (1) the density of O2 , (2) the electron impact dissociation rate of O2 , and (3) the O+O2 collision cross section. We demonstrate here that O+O2 collisions affect Europa's atmosphere even in the lowest limits considered. Thus, to more accurately determine the influence O+O2 collisions have on Europa's atmosphere in preparation for the forthcoming spacecraft missions, Europa Clipper and the JUpiter ICy moons Explorer (JUICE), these physical parameters need to be better constrained.

The concept of optimality in geostatistical inversion is traditionally rooted in a Euclidean framework, which often oversimplifies complex spatial relationships in geological structures, potentially leading to suboptimal representations of subsurface properties. This study challenges this conventional approach by introducing manifold embedding, a method that incorporates non-Euclidean geometries to better capture the complex and non-stationary nature of geological structures. Through the application of Kalman filtering (KF) and geostatistical principal component adaptation evolution strategy (GPCA-ES), we explore how different geometric frameworks influence the outcomes of hydraulic conductivity estimations in a synthetic aquifer. Our results demonstrate that while Euclidean-based methods may provide a single “optimal” solution, they do not necessarily yield the most geologically accurate models. By incorporating manifold geometries, we reveal a broader range of plausible subsurface interpretations, all of which produce similar hydraulic responses at observation points. This finding highlights the limitations of relying solely on Euclidean assumptions and challenges the conventional notion of a unique optimal solution in hydrogeological inverse problems. The study underscores the importance of adopting a more comprehensive geometric perspective in hydrogeological modeling, offering a pathway to more geologically meaningful and potentially more reliable subsurface characterizations. These findings advocate for a fundamental shift in the approach to geostatistical inversion, emphasizing the need to move beyond traditional optimality criteria and toward a more nuanced understanding of subsurface environments that acknowledges the inherent complexity and non-uniqueness of geological structures.

A knowledge gap in our understanding of the global carbon and water cycle, which in turn impacts climate science insights and earth systems model development, is the relative dominance of the drivers of vegetation productivity and subsequently the atmospheric carbon-dioxide growth rates (CGR). There is a lively debate in the literature based on correlation or bivariate causal methods, on whether the primary driver(s) is (are) temperature (T) or water availability, specifically, precipitation (P), soil moisture (SM), and/or terrestrial water storage (TWS). Our data-driven causal analyses, which rely on Granger Causality (GC) and the Peter and Clarke Momentary Conditional Independence (PCMCI) tests, consider all potential causal variables holistically, and reveal two primary atmospheric drivers for net ecosystem exchange and CGR: vapor pressure deficit (VPD) and photosynthetically active radiation (PAR). Our findings challenge recent literature, underscore the critical role of evaporative demand and vegetation dynamics in atmospheric carbon sensitivity, and highlight the need for more robust causal testing methods.

A document by Tianqi Zhang. Click on the document to view its contents.

A document by Kana Yamamoto. Click on the document to view its contents.

The hydrologic cycle in California is strongly influenced by wet season (November to April) precipitation. Here, we demonstrate the existence of an influential mode of North Pacific atmospheric pressure variability that regulates wet season precipitation variability over both northern and southern California. This mode, named as the “California precipitation” (CPM), is statistically distinct from other well-known modes of pressure variability such as the Pacific-North American pattern. In addition to controlling wet-season mean precipitation, positive days of the CPM coincide with up to 90% of the extreme (>99th percentile) precipitation days and 76% of detected atmospheric rivers (ARs) days, while the negative days correspond with 60% of the dry days. CMIP6 models capture the CPM remarkably well, including its statistical separation from the other well-known modes of pressure variability. The models also reproduce the CPM’s strong association with California wet season precipitation, giving confidence in the models’ dynamics relating to regional hydrologic extremes. However, the models also exhibit biases in regional hydrologic extremes. The CPM is a useful way to understand the origins of those biases and select the more credible models for further analysis: Models with unrealistically strong gradients in the CPM pressure pattern generally over-simulate larger wet extremes and produce excessively long dry intervals in the historical period. Thus the hydrologic biases can be traced to the particular aspects of North Pacific atmospheric dynamics.

• A general form of the Two-Stream Equations better lends itself to physical interpretation than the standard form. • Numerically-optimized coupling coefficients reproduce shortwave reflectance and transmittance accurate to within 10 −4. • These optimized coefficients explain the accuracy regimes of existing two-stream closures.

A document by Qusai Al Shidi. Click on the document to view its contents.

An object-based technique was utilized to identify hydrometeor size-sorting signatures at lower levels in the convective regions of 10 mesoscale convective systems (MCSs) during the 2015 Plains Elevated Convection at Night (PECAN) field campaign. Composite statistical analysis indicates that the magnitudes of size-sorting signatures (the separation distances between rain diameter maxima and concentration maxima) are nonlinearly correlated to the echo-top height, rain mass beneath the melting level, and precipitation rates at higher percentiles. To explore this correlation, the WRF model was used to simulate one of the MCSs (the 20 June 2015 storm) during the PECAN. Statistical analysis on the model outputs indicates more active riming growth and quicker graupel fallout at warmer temperatures near areas with larger separation distances. While updraft intensity above the melting level was also positively correlated to separation distances, this correlation was only statistically significant within certain temperature ranges. Additional analyses reveal that the higher intense precipitation potential near signatures with large separation distances could be attributed to precipitation production from the melted graupel. Finally, spatial correspondence between graupel distribution at the melting level and rain distribution at the lowest model level illustrates the critical role of graupel sedimentation and sorting in creating size-sorting signatures in MCSs during the PECAN field experiment.

It has long been observed that periods of intense seismic activity in a region alternate with periods of relative quiescence, but establishing whether this is the expected result of purely random fluctuations or is due to some broad-scale physical processes occurring in the Earth is a challenge. We present here compelling observations which show that periods of high seismicity rate and periods of quiescence are synchronized throughout the Anatolian plate. These observations are based on the remarkably similar evolution of the numerous seismic clusters between 2003 and 2017 in Anatolia. Two outside clocks set the timing of these activities, the 2004 M9.2 Sumatra earthquake and the 2008-2011 episode of slab rollback/deformation in the Hellenic subduction. The observed high seismicity rate in the plate which began with the Sumatra earthquake and lasted for about 7 years has been replaced by a relatively uniform quiescence period.

Geocoronal Solar Wind Charge Exchange (SWCX) is the process by which heavy ions from the solar wind undergo charge exchange with neutral hydrogen atoms from the Earth’s exosphere, releasing photons at discrete energies characteristic of the solar wind ions. This paper investigates the solar wind types driving geocoronal SWCX. We find that during periods of time-variable SWCX, higher fractions of every ion species are recorded by ACE compared to the averages. Notably, a subset of the slow solar wind characterised by a systematic lower temperature and higher proton flux is surprisingly effective for producing SWCX. Given the degradation of the solar wind composition spectrometer on ACE in 2011, we explore the capabilities of XMM-Newton as an alternative sensor to monitor heavy ion composition in the solar wind. Unlike the distributions of other ion line fluxes analysed, only OVIII, extracted via spectral analysis of XMM-Newton observations, display patterns similar to the corresponding parent ion abundances from ACE (O8+/p). Finally, we employ a Random Forest Classifier model to predict solar wind types based on literature results. When combining proton data with XMM-Newton features, the model performance improves significantly, achieving a macro-averaged F1-score of 0.80 (with a standard deviation of 0.06).

Previous studies show that the variability associated with the winter-mean North Atlantic Oscillation (NAO) tends to be largely modulated according to the solar cycle so that the anomalous zonal wind signal extends to the upper stratosphere when solar activity is high, but the vertical extension is limited when solar activity is low. To examine the physical mechanism of the solar cycle modulation of the winter-mean NAO, momentum and wave-energy budget analyses have been performed using the Japanese Reanalysis (JRA-55) dataset from 1958 to 2021. Momentum analysis shows that the enhancement of zonal wind in High Solar (HS) years is due to direct enhancement of wave forcing. Wave-energy analysis shows that baroclinic energy conversion from zonal-mean flow in the region from the surface to the middle stratosphere and barotropic energy conversion to the zonal-mean flow around the middle stratosphere tend to be enhanced in HS winters. Both enhancements are related to the enhancement of the zonal wind. Our analysis suggests that the climatological basic states tend to become more unstable during HS winters.

Numerical modeling of the effect of rotation of the polarization vector in the ionospheric plasma on the radio waves of the high frequency range is performed. The relevance of the work is related to the design of space-borne P-band synthetic aperture radar (SAR) for purpose of surface and subsurface remote sensing of the Earth, as well as to problems of reconstructing the profile of the electron concentration of the ionospheric plasma by radio tomography methods. Three spherical two-layer models of the ionosphere are compared: a model without disturbances, a model with two local formations with an increased electron concentration and a model with two local formations with a reduced electron concentration. The source of radiation is located on the moving spacecraft at a distance of 400 km from the Earth’s surface. The angle of inclination of the rays varies from 150 to 30 degrees relative to the positive direction of the horizontal axis. The receiver is located on the surface of the Earth. To determine the ray paths a bi-characteristic system of equations is used. A quasi parabolic dependence of group time on the horizontal coordinate of the radiation source is given. The dependence of the deviation of the aiming angle on group time is investigated. The phase variation due to the influence of the ionosphere and the angle of the Faraday rotation are studied. It is shown that changes in the Faraday rotation angle for the selected parameters lie in the range from 6 to 54 degrees.

A document by Dong Chen. Click on the document to view its contents.