Subduction termination remains poorly understood. Sabah, Malaysia (in northern Borneo) is a prime study location due to its subduction-rich history. We investigate the impact of subduction on Sabah’s mantle transition zone via P-to-S receiver function imaging. Receiver functions are computed using high resolution passive seismic data from the northern Borneo Orogeny Seismic Survey (nBOSS), MetMalaysia and KalNet arrays. Stacking of ~1800 receiver functions reveals considerable complexity, including a low amplitude 410 km discontinuity in north-western Sabah and two distinct discontinuities at depths ~675 km and ~735 km in eastern Sabah. Since 2019, three deep (>600 km) earthquakes have occurred near these discontinuities. We attribute these observations to subducted Proto-South China Sea slab material settling at the base of the mantle transition zone, introducing cool, hydrous oceanic material with a high-basalt fraction to the mantle, thus providing an interface for deep seismicity, and facilitating garnet phase transitions at 600-700 km depth.

A document by Jicheng Gu . Click on the document to view its contents.

Drought conditions significantly impact human and natural systems in the Tropics. Here, multiple observational and reanalysis products and ensembles of simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are analyzed with respect to drought areal extent over tropical land regions and its past and future relationships to the El Niño/Southern Oscillation (ENSO). CMIP5 models forced with prescribed sea surface temperatures compare well to observations in capturing the present day time evolution of the fraction of tropical land area experiencing drought conditions and the scaling of drought area and ENSO, i.e., increasing tropical drought area with increasing ENSO warm phase (El Niño) strength. The ensemble of RCP8.5 simulations suggests lower end-of-the-century El Niño strength-tropical drought area sensitivity. At least some of this lower sensitivity is attributable to atmosphere-ocean coupling, as historic coupled model simulations also exhibit lower sensitivity compared to the observations.

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 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.

The benthic biolayer is a reaction hotspot that contributes disproportionately to whole-stream reactions, including aerobic respiration and contaminant transformation. Quantifying the relative contribution of the biolayer to whole-stream mass transformation remains challenging because it requires that hyporheic zone solute transport and reaction heterogeneity are explicitly captured within a single upscaled modeling framework. Here, we use field experiments and modeling to quantify mass transformation in the biolayer relative to other stream compartments. We co-injected and monitored several fluorescent tracers, including the reactive tracer resazurin, into a controlled experimental stream whose 8.5 cm hyporheic zone is isolated from groundwater. We characterized reactive transport in the water column and at multiple hyporheic zone depths by simultaneously fitting concentration time series measured at these locations to a new mobile-immobile model, using resazurin-to-resorufin transformation as an indicator of aerobic bioreactivity. Results show that the biolayer transformed 2× more resazurin to resorufin than all other stream compartments combined, and over half of all reactions occurred within 2 advective travel times through the reach. This hotspot and hot moment behavior is attributed to the biolayer’s propensity to rapidly acquire, transiently retain, and rapidly degrade stream-borne solutes. Model analysis shows that mass transformation is highest in the biolayer across a wide range of biolayer structural properties, including scenarios when the biolayer is less reactive than deeper regions of the hyporheic zone. Together, our results point to the biolayer as a common feature of streams and rivers that should be considered in network-scale models of river corridor biogeochemistry.

Groundwater management involves a complex decision-making process, often with the need to balance the trade-off between meeting society’s demand for water and environmental protection. Therefore effective management of groundwater resources often involves some form of multi-objective optimization (MOO). Many existing software tools offer simulation model-enabled optimization, including evolutionary algorithms, for solving MOO problems. However, such analyses involve a huge amount of numerical process-based model runs, which require significant computational effort, depending on the nonlinearity and dimensionality of the problem, in order to seek the optimal trade-off function known as the Pareto front. Surrogate modeling, through techniques such as Gaussian Process Regression (GPR), is an emerging approach to significantly reduce the number of these model evaluations thereby speeding up the optimization process. Yet, surrogate model predictive uncertainty remains a profound challenge for MOO, as the current Pareto dominance criteria presumes that model responses are deterministic. Such presumption could mislead surrogate-assisted optimization, which may result in either little computational savings from excessive retraining, or lead to suboptimal and/or infeasible solutions. In this work, we present probabilistic Pareto dominance criteria that considers the uncertainty of GPR emulation during MOO, producing a ”cloudy’” Pareto front which provides an efficient decision space sampling mechanism for retraining the GPR. We then developed a novel acquisition strategy to manage the solution repository from this cloud and generate an ensemble of infill points for retraining. We demonstrate the capabilities of the algorithm through benchmark test functions and a typical density-dependent coastal groundwater management problem.

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

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.

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.

A document by Dong Chen. 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.

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).

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.

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 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 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.