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.

ABSTRACT Soil moisture (SM) estimation from cosmic ray neutron sensors (CRNS) requires pre-processing of raw neutron counts particularly correcting for atmospheric density, known as the barometric effect. The barometric coefficient (β) varies by location, altitude, atmospheric conditions, and sensor type, necessitating station-specific values for precise correction. This study analyzes 30 CRNS and 46 neutron monitors (NM) globally to estimate β through regression and assess its impact on CRNS-estimated SM. Our results show spatio-temporal variation in the barometric effect, with β ranging from 0.66 to 0.82 %hPa for NM and 0.63 to 0.80 %hPa for CRNS. The study’s coefficients exhibit higher variability than previously published analytical models. Incorrect β can reduce CRNS-based SM accuracy by ±0.02m³/m³. Incisively, our study emphasizes the need for atmospheric correction in CRNS measurements, highlights the importance and provides a new method for a site- and sensor-specific β for accurate SM.

Accurate modeling of dependencies between variables of interest is imperative for understanding biophysical processes and mechanisms relevant to Earth Science research. This study presents a methodology to model the temporal or spatial distributions of variables using copula-based cosimulation (CopCoSim). This technique models and simulates the joint distribution of multiple variables by capturing their dependencies and correlations. We compared CopCoSim with the traditional Sequential Gaussian CoSimulation (SGCoSim) technique through two applications relevant to Earth Science representing one (i.e., time) and two dimensions (i.e., space). We present an application for soil CO2 efflux, which is a major flux in the global carbon budget, using two case studies: (1) temporal distribution of soil CO2 efflux and temperature, and (2) spatial distribution of soil CO2 efflux and temperature across the conterminous United States (CONUS). The methodology involves three steps: data input, application of stochastic simulation methods, and comparison of simulation outputs. The results indicate that CopCoSim provides a better model with higher precision and accuracy for representing variables of interest. CopCoSim better reproduces the univariate probability distribution, temporal or spatial autocorrelation, and dependency relationship between the predictor and response variables. We propose that CopCoSim is useful for research in Earth Science, where variables of interest (e.g., soil CO2 efflux and temperature) are often interdependent and exhibit complex temporal or spatial patterns.

To facilitate the quantification of the stratospheric aerosol radiative effect, this study generates a set of aerosol direct radiative effect (ADRE) kernels based on MERRA-2 reanalysis data. These radiative kernels measure the sensitivities of ADRE to perturbations in scattering and absorbing aerosol optical depth (AOD), respectively. Both broadband and band-by-band radiative kernels are developed to account for the wavelength dependency of ADRE. The broadband kernels are then emulated by a multivariate regression model, which predicts the kernel values from a handful of predictors, including the top-of-atmosphere (TOA) insolation, TOA reflectance, and stratospheric AOD. These kernels offer an efficient and versatile way to assess the ADRE of stratospheric aerosols. The ADREs of the 2022 Hunga volcano eruption and the 2020 Australia wildfire are estimated from the kernels and validated against radiative transfer model-calculated results. The Hunga eruption induced a global mean cooling forcing of -0.46 W/m² throughout 2022, while the Australia wildfire caused a warming forcing of +0.28 W/m² from January to August. The kernel estimation can capture over 90% of the ADRE variance with relative error within 10%, in these assessments. The results demonstrate the spectral dependencies of stratospheric ADRE and highlight the distinct radiative sensitivity of stratospheric aerosols, which differs significantly from that of tropospheric aerosols.

Air-sea sensible and latent heat fluxes are fundamental to tropical cyclone (TC) energetics, but the impacts of seastate-dependent sea spray heat fluxes on TC structure and intensity are poorly understood. We explore these impacts herein by implementing a recently-developed parameterization for air-sea heat fluxes with seastate-dependent spray physics into a fully coupled atmosphere-wave-ocean model, the Unified Wave INterface–Coupled Model. We conduct model experiments with and without spray for four TCs covering wide ranges of intensity and structure. The effects of spray on model TCs in the open ocean can be summarized in three stages. 1) Spray evaporative cooling dominates in boundary layers (BLs) of tropical storms and weak hurricanes (i.e., with 10-m windspeeds ≲ 30–40 m s-1 and relatively small waves), which hinders intensification. 2) Further TC intensification increases spray generation, producing positive spray sensible heat fluxes (i.e., warming) under the eyewall. This warming is favorable for intensification, but inefficiency caused by cooler BL inflow continues to inhibit eyewall deep convection, and spray continues to oppose intensification overall. 3) Further increase of spray production from continued TC intensification (i.e., past Category 3) ultimately allows spray to promote intensification by warming the BL and enhancing eyewall deep convection. Spray’s tendency to oppose intensification of weak TCs is consistent with the relatively rare occurrence of major hurricanes. However, if a TC intensifies beyond stage 2, spray can support rapid intensification. We also find that enhanced spray generation by wave dissipation in the coastal zone may strengthen landfalling TCs.

Spaceborne lidar offers unique advantages for improving global estimates of fine particulate matter PM2.5, traditionally limited by critical data gaps in the vertical dimension. Here, we present a new method to retrieve PM2.5 relying on ensembles on aerosol extinction available within the GEOS Aerosol Data Assimilation. This study uses 1064-nm backscatter lidar data from the NASA Cloud-Aerosol Transport System (CATS) and model priors from the GEOS model. First, we developed a 1-D ensemble-based variational technique (1-D EnsVar) to perform vertically-resolved retrievals of speciated aerosol extinction and surface PM2.5. Next, we evaluated the performance of 1-D EnsVar retrievals of PM2.5 and extinction through an independent validation using measurements from spaceborne, airborne, and ground-based platforms. This approach overcomes traditional limitations by leveraging the strengths of complementary vertical aerosol information from CATS and GEOS to better resolve speciated aerosol optical properties and mass. With the advantage of active remote sensing, this approach is fully capable of performing aerosol retrievals during both daytime and nighttime scenes. Given the unique capability of CATS to process vertical profile data in near real-time, this work demonstrates the powerful utility of spaceborne lidar for improving air quality forecasting. While this pilot study is not yet performed within a cycling data assimilation system, the algorithm developed here can easily be integrated in such systems. Results presented may be useful in other applications as well, including the validation of aerosol transport models, improving passive satellite retrievals of PM2.5, and developing data assimilation techniques for future lidar platforms.

We applied an unsupervised clustering algorithm, initially trained on data from Magnetospheric Multiscale (MMS) mission at Earth, to MESSENGER observations at Mercury to identify three distinct plasma regions: magnetosphere, magnetosheath, and solar wind. This demonstrates the applicability of transfer learning for heliophysics, a machine learning technique where knowledge learned from one task is reused to perform a similar task, for an unsupervised learning task. We find that 72% of bow shock crossings and 84% of magnetopause crossings identified by the clustering algorithm using MESSENGER data are in agreement with published magnetopause and bow shock lists. These findings highlight the potential for utilizing a clustering algorithm across multiple planetary environments.

Capsule summary. Extreme temperatures in the UK (July 2022) and India/Pakistan (Spring 2022) are confidently attributed to climate change using an automated system. Similarly attributable extremes occurred frequently worldwide in 2022.

Geomagnetic field models covering past millennia rely on two main data sources: archaeomagnetic data, that provide snapshots of the geomagnetic field at specific locations, and sediment records, that deliver time series of the geomagnetic field from individual drill cores. The limited temporal and spatial global coverage with archaeomagnetic data necessitates use of sediment data, especially when models go further back in time. However, the accurate preprocessing and interpretation of sediment data is crucial. Unlike archaeomagnetic data, sediment data does not provide absolute values for intensities and declinations; instead, it represents relative variations. The detrital remanent magnetization (DRM) of sediment records is influenced by various depositional (dDRM) effects that can result in inclination shallowing, as well as post-depositional (pDRM) processes that cause a delayed and smoothed signal. To address the distortion associated with the pDRM effects, a novel class of flexible parameterized lock-in functions has been proposed. These lock-in functions involve four parameters, which are estimated using a Bayesian modeling technique and archaeomagnetic data. By extending the space of hyperparameters to include the calibration factor for intensities, the declination offsets and the inclination shallowing factor, we present a fully Bayesian preprocessing method for sediment records in form of a Python package, called extit{sedprep}. By applying the estimated parameters to the raw sediment data extit{sedprep} is able to provide a calibrated and preprocessed palaeomagnetic record.

Palaeointenisty measurements are vital for understanding the deep Earth and the evolution of the geomagnetic field, however, they suffer from non-ideal behaviour that compromises the fidelity of the results. In an attempt to screen for the non-ideal behaviour that plagues palaeointensity experiments, selection criteria are used. One such non-ideal behaviour is partial thermoremanent magnetisation (pTRM) tails, which in the IZZI protocol result in a zig-zagging pattern in the Arai plot. While pTRM tail checks can be carried out, these are not routinely used in IZZI experiments. We set out a new framework with which to create, develop and re-assess selection criteria before applying it to our proposed new selection statistic, Ziggie, which is specific to detecting zig-zagging behaviour. Ziggie meets all the requirements set out; that is, it is able to objectively and robustly identify the target behaviour, consistently provide a quantifiable assessment of the target behaviour, and is predominately sensitive to the target behaviour while being free of undue influences. Other pre-existing statistics for zig-zagging in Arai plots, Z*, IZZI_MD, β and SCAT, are also tested, but fail to meet all requirements. We find that Ziggie should be adopted in selection criteria sets for the IZZI protocol with a criterion of Ziggie ≤ 0.1.

A document by Fucent Hsuan Wei Hsu. Click on the document to view its contents.

A document by Peter Chu. Click on the document to view its contents.

A document by Masao Ishii. Click on the document to view its contents.

Constant head injection (CHI) tests are a standard method for estimating subsurface hydraulic properties, but traditional interpretations using analytical solutions like Moye’s equation are limited by their inability to capture transient dynamics and vertical heterogeneity. This study introduces a novel transient inverse method that allows for the estimation of layer-specific hydraulic conductivities (K) in multilayered aquifers, accommodating realistic boundary conditions. Using synthetic simulations with varying specific storage coefficients (S_s) and observation times (t_rep), the method consistently produced accurate K estimates and demonstrated robustness to S_s variations. In contrast, Moye’s equation significantly underestimated K values, highlighting the limitations of steady-state approaches. The proposed method offers improved flexibility and accuracy for CHI test interpretation, making it a valuable tool for more complex, real-world aquifer systems.

Geographic variability in Earth’s core magnetic field, measured at 400 km altitude but corrected for ionospheric and magnetospheric signals, correlates with electron flux levels measured by the RBSP spacecraft. Higher Earth’s |B| magnitude results in lower flux over L2-6. Over 20 eV – 2 MeV, at L2-4, this negative correlation is as large as -0.21, peaking at the 158 keV electrons, with the strongest effects in the 71 keV – 2 MeV electrons. Despite higher L shells being well above the 400 km field measure, statistically significant correlation with the core field was still seen in higher energy 1- 2 MeV electrons over L5-6. Controlling for Earth’s geographic |B| variability, particularly at lower L shells, results in stronger correlations between electron flux and solar wind, substorm, and ULF wave drivers. At L2, substorms (measured by the SME index), ULF waves, and solar wind velocity show increased correlations with electron flux (30, 100, and 175%, respectively) when Earth’s |B| is added as a covariate to ARMAX regression models. Modest increases in correlation of electron flux with these possible drivers were also seen at L3-6. This argues for the addition of Earth’s |B| as a covariate in models of electron response to drivers.

This paper investigates the behavior of photon orbits around black holes using the Eikonal equation and models of effective refractive index using the Schwarzschild metric, inspired by Lagrangian optics and the foundational work of David Nolte. Conducted as an educational exercise for advanced high school and undergraduate students, this research aims to help students combine multiple topics in physics to explore analogical relationships between different concepts such as the calculus of variations, optics, and general relativity. By employing numerical simulations to recalibrate photon trajectories, we highlight how the curvature of spacetime influences photon paths, particularly focusing on the stability of circular orbits. These results reveal significant deviations from classical predictions, emphasizing the critical role of gravitational lensing and the formation of black hole shadows. This paper is expected to provide consolidated information to understand and reproduce simulations of orbiting photons for interested high school or undergraduate students.

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.

Is methane the "climate culprit"? Fixing the "Broken Record" while unmasking the dangers of using imprecise, long-term GWP for methane to address the climate emergency. "Broken Record-Temperatures hit new highs, yet world fails to cut emissions (again)": Emissions Gap Report, 2023.