Globigerinoides ruber (white) is a mixed layer planktic foraminifera cosmopolitan to the tropical and subtropical oceans. G. ruber (white) has two principal morphotypes: sensu stricto (s.s.) and sensu lato (s.l.). Previous geochemical studies have suggested that differences in geochemistry (δ18O, Mg/Ca) across these morphs may arise from seasonal preferences (s.l.: cold/winter-biased) or distinct calcification depth habitats (s.s.: 0-25m; s.l.: 25-50 m). Contrary to this, recent studies have demonstrated that there are no systematic or significant geochemical offsets in coeval s.s.-s.l. δ18O and δ13C. However, no such study has been conducted in the northern Indian Ocean to determine the suitability of selective versus non-selective mixtures of G. ruber for paleoceanographic reconstructions. In this study, we report individual foraminiferal analyses (IFA) measurements of δ18O and δ13C on coevally occurring s.s.-s.l. G. ruber (white) morphotypes within core-tops and late Holocene samples at three sites from the northeastern Indian Ocean (cores: IODP Site U1446 - northwestern Bay of Bengal, MGS30 - eastern Bay of Bengal, and SK343 - Gulf of Mannar). We report a total of 362 IFA measurements of s.s. (n=201) and s.l. (n=161). Our observations and statistical approach confirm that there are no significant stable isotopic offsets in coeval s.s.-s.l. morphotypes of G. ruber in this region. Hence we suggest minimal biases for the use of non-selective mixtures of G. ruber morphotypes for paleoceanographic reconstructions of mixed-layer variability in the northern Indian Ocean, at least for Holocene samples, or for periods with similar biogeochemical and oceanographic conditions.

For lakes experiencing extended ice-cover seasons, ice phenology has a substantive impact on thermal structure and dissolved oxygen (DO) dynamics during winters. This study applied a three-dimensional lake model (AEM3D) in Lake Winnipeg over 2016-2018, spanning two full winter seasons. Sensitivity analysis showed that the modeled ice cover thickness, formation, and duration were most sensitive to snow depth and snow/ice albedo. The model well simulated the ice freeze-up timing with less than 5 days discrepancy, but it underestimated the ice cover thickness and overestimated the ice cover duration in the unusual warm winter (2016-2017). Inverse stratification was developed under the ice, but the model could not fully reproduce it due to a lack of a sediment heat flux component. DO decreased with the formation of ice cover, leading to bottom hypoxia over the lake. The model indicates that ice phenology (i.e., ice cover duration, and blue/white ice thickness) affects the extent of winter hypoxia. We observed the DO decreased to < 2 mg L-1 (i.e., anoxia) in the North Basin, along with inverse stratification forming near lakebed, and an oxygen depletion rate reached 0.14 mg L-1 d-1 in the winter of 2016-17. The model captured but underestimated DO decline near the lakebed and simulated around 8% and 70% of lake area reached anoxia in winters of 2016-17 and 2017-18, respectively. This work provides insight into ice formation, under-ice thermal structure, and winter oxygen concentrations in a large prairie lake, and the role of changing ice phenology on northern lake ecology.

A document by Jingwei ZHANG. Click on the document to view its contents.

Dilute pyroclastic density currents (PDCs) present significant hazards due to their high temperatures and dynamic pressures. Accurate estimation of dynamic pressure—- vital for assessing potential damage, requires knowledge of the vertical variations of velocity and particle concentration within the dilute PDC, particularly in the first few meters of the flow. Existing approaches to dynamic pressure calculations used in hazard assessment are often based on average values for velocity and particle concentration. These average values may misrepresent the flow dynamics, especially near the base of the flow where the gradients of flow variables are larger. Here, we present a new, physically based approach that allows for the calculation of the vertical profiles of velocity and concentration from a combination of depth-averaged values for these properties and non-dimensional flow parameters. Finally, we demonstrate the use of these profiles within an existing shallow-water model and show its potential applications toward probabilistic hazard assessment.

A document by Jarrod Miller. Click on the document to view its contents.

https://github.com/carlynlee/credo-api-tools/tree/playground/ This project aims to design an architecture for applying Federated Learning (FL) to the analysis of cosmic ray event data collected through the Cosmic Ray Extremely Distributed Observatory (CREDO) project. CREDO gathers data from a global network of smartphones, enabling a large-scale citizen science initiative to study cosmic rays. The traditional approach of centralizing this data raises concerns about privacy and data ownership. We explore as a potential solution, allowing decentralized training of machine learning models directly on participants' devices. We explore how FL can accommodate CREDO's requirements, such as handling heterogeneous data and robust model aggregation. We present our initial steps in designing an FL architecture tailored to the needs of the CREDO project, outline our architectural proposal, and discussing the potential benefits and the technical challenges. This sets the stage for future implementation and evaluation, aiming to enhance accountability and collaboration in scientific data analysis.

Predicting laboratory earthquakes using machine learning has progressed markedly recently. Previous related studies mainly focus on predicting the occurrence time and shear stress of laboratory earthquakes using acoustic emission signals. Here, based on numerical simulations, we use machine learning to show that statistical features of plate motion signals contain information about the slip duration and friction drop of laboratory earthquakes. We find that the plate motion signals during the initial slip stage contain the precursor information about the slip duration of laboratory earthquakes. While to accurately predict the friction drop, we need to incorporate the plate motion signals during the entire slip stage. The results demonstrate that the high-order moment and variance of plate motion signals are respectively among the best predictors for the slip duration and friction drop of laboratory earthquakes. Our work provides new insights for future investigations into natural earthquake prediction through machine learning.

The hydrodynamics of river mouths are the result of a complex interaction between river flow, tidal conditions and outlet geometry. This complex interaction of factors shapes the jet that flows onto the continental shelf and influences the dynamics of these areas. To gain insight into the response of the jet to different outlet and nearshore geometries and changing river discharge and tidal conditions, the hydrodynamics of idealised river mouths are simulated numerically. Compared to previous work, the model includes transient river discharge and tidal conditions and more realistic nearshore geometries. Comparison with classical jet theory indicates that the model adequately simulates jet hydrodynamics. The results show that both the outlet geometry and the transient river discharge and tidal conditions have a significant influence on the jet structure and evolution along the nearshore. For constant river discharge and water level conditions, the results indicate that the nearshore profile plays a key role in determining the expansion or contraction of the jet. The momentum balance shows that the jet behaviour is related to the momentum transport and the barotropic terms. In cases where the river discharge and tidal conditions are transient, the jet alternates between a structure with two velocity maxima at the edges or a single peak in the centre during the tidal cycle. This alternation occurs as a function of the time along the tidal phase and the time lag between the tidal conditions and the river hydrograph. The morphodynamic consequences of these two different jet structures are also discussed.

Extreme water levels (EWLs) resulting from cyclones pose significant flood hazards and risks to coastal communities and interconnected ecosystems. To date, physically-based models have enabled accurate prediction of EWLs despite their inherent high computational cost. However, the applicability of these models is limited to data-rich sites with diverse characteristics. The dependence on high quality spatiotemporal data, which is often computationally expensive, hinders the applicability of these models to regions of either limited or data-scarce conditions. To address this challenge, we present a Long Short-Term Memory (LSTM) network framework to predict the evolution of EWLs beyond site-specific training stations. The framework, named LSTM-Station Approximated Models (LSTM-SAM), consists of a collection of bidirectional LSTM models enhanced with a custom attention mechanism layer embedded in the architecture. LSTM-SAM incorporates a transfer learning approach applicable to target (tide-gage) stations along the U.S. Atlantic Coast. Importantly, LSTM-SAM helps analyze: (i) the underlying limitations associated with transfer learning, (ii) evaluate EWL predictions beyond training domains, and (iii) capture the evolution of EWL caused by tropical and extratropical cyclones. The framework demonstrates satisfactory performance with “transferable” models achieving Kling-Gupta Efficiency (KGE), Nash-Sutcliffe Efficiency (NSE), and Root-Mean Square Error (RMSE) ranging from 0.78 to 0.92, 0.90 to 0.97, and 0.09 m to 0.18 m at the target stations, respectively. We show that LSTM-SAM can accurately predict not only EWLs but also their evolution over time, i.e., onset, peak, and dissipation, which could assist in operational flood forecasting in regions with limited resources to set up physically-based models.

Campylobacter is the most common bacterial cause of foodborne illness globally. Both symptomatic and asymptomatic infections with Campylobacter species have been associated with growth faltering of children in low-resource settings, while previous prevalence studies primarily focused on diarrheal disease in children. Here, we leverage the data collected from the Campylobacter Genomics and Environmental Enteric Dysfunction (CAGED) project to characterize the spatial patterns of Campylobacter infections among infants with or without diarrhea in rural Eastern Ethiopia. Randomly enrolled infants (n = 106) were followed from birth to around 13 months, with fecal samples collected monthly. Livestock, drinking water, and soil samples were collected biannually. Campylobacter was detected and quantified using genus-specific PCR and species-specific PCR for four species. We employed a spatial filtering approach using genus-specific data to generate smoothed prevalence surfaces by month and age group. Temporally, an upward trend of prevalence was observed as the children grew older. Spatially, high-prevalence areas were distributed across the whole study area. To relate disease risk to environmental conditions, we used ecological niche modeling with MaxEnt to estimate habitat suitability of the genus Campylobacter and two dominant species identified by PCR results. Elevation, vegetation index, and slope were the most important contributors, and all distribution models suggested areas in the north were more likely to support the pathogen. These results inform Campylobacter infection patterns and identify target areas with higher risk of Campylobacter in low-resource settings. This further contributes to developing effective intervention strategies in the future.

A tidal mixing front in the southern North Sea is analyzed for its spatial and temporal distribution of the near-surface horizontal divergence and vertical velocity to understand the evolution of a tidal mixing front in a highly dynamic, semidiurnal tidal-influenced shallow shelf sea. A multi-sensor synoptic data set with a focus on the upper 10 m consisting of surface drifters, a drifting sensor chain, and an ADCP resolving length scales of , as well as subsequent CTD measurements, revealed a tidal mixing front that consistedof two separate tidal mixing density filaments that differed in 3D structure. In all observations, divergence developed with the accelerating tidal current and convergence evolved out of the deceleration of the tidal current. Vertical velocities ranged between , with divergence coupled to upwelling and convergence to downwelling. For the first tidal mixing density filament, convergence was limited to the upper 4 meters and was suppressed by the uplift of dense water. For the second filament, convergence was built from the bottom, propagating throughout the water column. These observations demonstrate that tidal mixing fronts play a significant role in promoting vertical transport within the water column.

As society moves towards greater dependence on technology, understanding and predicting extreme Space Weather becomes increasingly important. Indeed, the complex, simultaneous interplay between multiple phenomena can unpack further insight into Space Weather effects. Thanks to decades of in-situ and ground observations, long archives of Space Weather measurements are ripe for exploitation by more complex, data intensive techniques. In this study, three decades of in-situ and ground observations of Earth’s Space environment are leveraged to model the joint extremal behaviour of solar wind driving and internal geomagnetic responses, using Bivariate Extreme Value Analysis. While often utilised in other fields with more long-term and/or higher spatial resolution datasets (such as meteorology), this is the first application of this technique in more than one dimension in the field of space plasma physics. An initial result is that upstream and internal variables exhibit weaker extremal dependence on minute timescales than on hourly timescales, in line with characteristic magnetospheric coupling timescales. Specifically, the joint extremal behaviour of a solar wind coupling function with the auroral electrojet index AE is explored and predicted. This modelling enables estimation of a 75-year return period for the 2003 Halloween geomagnetic storm.

The current research was carried out to investigate conventional heat transfer enhancement inside the tube by using twisted stainless-steel angle insert. Main objectives were to find the percentage of increment of heat transfer enhancement using twisted stainless-steel insert and to find the relation between Reynolds number and Nusselt number. A 940mm long copper tube of 26.6 mm internal diameter and 30 mm outer diameter, of which length of 762 mm has been used as the test section. A constant heat flux condition has been maintained by wrapping Nichrome wire around the test section and fiber glass insulation over the wire. K-type thermocouples and rotameter were used to measure temperature and rate of flow. With insert, heat transfer rate has been increased up to 140 percent. This technique does not rely on external power or activation. This experiment has shown simultaneous effects on Reynolds number and Nusselt number too.

Interseismic deformation and strain rates derived from Global Navigation Satellite Systems (GNSS) or Interferometric Synthetic Aperture Radar (InSAR) data are often used to assess earthquake potential. InSAR measurements require GNSS data to put results into a geodetic reference frame, posing an issue in regions with limited GNSS data. Deformation rates are assumed to be indicative of long-term strain accumulation, and the evolution of strain rate throughout the interseismic period is poorly studied. We observe the interseismic period prior to the 2021 Mw 7.4 Maduo Earthquake. Using InSAR, we derive eastward velocities and maximum shear strain rate for three independent time windows to observe the temporal evolution of strain rate over time, whilst minimising the influence of near-field GNSS data. We find that strain rate peaks several years before the earthquake, contrary to recent studies showing acceleration of strain rate before large earthquakes, which has implications for earthquake hazard models globally.

A document by Minki Hong. Click on the document to view its contents.

This paper addresses the challenges in computing the column moist static energy (MSE) budget in climate models. Residuals from such computations often match other major budget terms in magnitude, obscuring their contributions. This study introduces a methodology for accurately computing the column MSE budget in climate models, demonstrated using the GISS ModelE3. Multiple factors leading to significant residuals are identified, with the failure of the continuous calculus’s chain rule upon discretization being the most critical. This failure causes the potential temperature equation to diverge from the enthalpy equation in discretized models. Consequently, in models using potential temperature as a prognostic variable, the MSE budget equation is fundamentally not upheld, requiring a tailored strategy to close the budget. This study introduces the “process increment method’ for accurately computing the column MSE flux divergence. This method calculates the difference in the sum of column internal energy, geopotential, and latent heats before and after applying the dynamics scheme. Furthermore, the calculated column flux divergence is decomposed into its advective components. These computations enable precise MSE budget analysis. The most crucial finding is that vertical interpolation into pressure coordinates can introduce errors substantial enough to reverse the sign of vertical MSE advection in the warm pool regions. In ModelE3, accurately computed values show MSE import via vertical circulations, while values in pressure coordinates indicate export. This discrepancy may prompt a reevaluation of vertical advection as an exporting mechanism and underscores the importance of precise MSE budget calculations.

Carbon (C) emissions from headwater streams are derived from both terrestrial inputs and in-stream metabolism of organic C (OC), but the role of metabolism in boreal stream C fluxes remains uncertain. Determining the factors that regulate OC metabolism will help predict how the C balance of boreal streams will respond to future environmental change. In this study, we addressed the question: what controls OC metabolism in boreal headwater streams draining catchments with discontinuous permafrost? We hypothesized that metabolism is collectively regulated by OC reactivity, phosphorus availability, and temperature, with discharge modulating each of these conditions. We tested these hypotheses using a combination of laboratory experiments and whole-stream ecosystem metabolism measurements throughout the Caribou-Poker Creeks Research Watershed (CPCRW) in Interior Alaska, USA. In the laboratory experiments, respiration and dissolved organic carbon (DOC) removal were both co-limited by the supply of reactive C and phosphorus, but temperature and residence time acted as stronger controls of DOC removal. Ecosystem respiration (ER) was largely predicted by discharge and site, with some variance explained by gross primary production (GPP) and temperature. Both ER and GPP varied inversely with watershed permafrost extent, with an inverse relationship between temperature and permafrost extent providing the most plausible explanation. Our results provide some of the first evidence of a functional response to permafrost thaw in stream ecosystems and suggest that the contribution of metabolism to stream C emissions may increase as climate change progresses.

The late Ediacaran to early Cambrian was marked by significant biological and geochemical transformations, including the diversification of animal life, and an enigmatic paleomagnetic record. This study focuses on the Nama Group, a key geological unit for understanding the Ediacaran-Cambrian transition, yet hampered by limited paleogeographic constraints. Previous paleomagnetic studies identified complex remagnetization patterns but lacked a detailed examination of remanence carriers. To address this, we conducted field stability tests and employed advanced rock magnetic techniques on unweathered borehole core samples to complement paleomagnetic data. Thermal demagnetization identified three magnetic components. C1 is a present-day field viscous remanent magnetization (VRM) and was used for borehole core orientation. C2, carried by single domain (SD) pyrrhotite and SD magnetite, aligns with early Paleozoic remagnetization poles from West Gondwanaland and was likely acquired through a thermoviscous magnetization (TVRM) in magnetite and a thermoremanent magnetization (TRM) in pyrrhotite, suggesting regional uplift and cooling between 500-480 Ma. This quasi-synchronous remagnetization is supported by highly clustered paleomagnetic poles and thermochronological data. C3 is an anomalous component with significant inclination variation within the stratigraphy, carried by large pseudo-single domain (PSD) magnetites, possibly of primary, detrital origin. The pattern resembles findings from other cratons, suggesting an unstable geomagnetic field that could have persisted until ca. 540 Ma. This may represent one of the youngest occurrences of such extreme geomagnetic instability in the Ediacaran and provides the first evidence of this behavior in the Kalahari Craton, adding new support for a largely unstable geomagnetic field during this period.