Low Mach number collisionless shocks are routinely observed in the solar wind and upstream of planetary bodies. However, most in situ observations have lacked the necessary temporal resolution to directly study the kinetic behavior of ions across these shocks. We investigate a series of five low Mach number bow shock crossings observed by the Magnetospheric Multiscale (MMS) mission. The five shocks had comparable Mach numbers, but varying shock-normal angles ($66^{\circ} \lesssim \theta_{Bn} \lesssim 89^{\circ}$) and ramp widths ($5 \mathrm{km} \lesssim l \lesssim 100 \mathrm{km}$). The shock width is shown to be crucial in determining the fraction of protons reflected and energized by the shock, with proton reflection increasing with decreasing shock width. As the shock width increases proton reflection is arrested entirely. For nearly perpendicular shocks, reflected protons exhibit quasi-periodic structures, which persist far downstream of the shock. As the shock-normal angle becomes more oblique these periodic proton structures broaden to form an energetic halo population. Periodic fluctuations in the magnetic field downstream of the shocks are generated by fluctuations in dynamic pressure of alpha particles, which are decelerated by the cross-shock potential and subsequently undergo gyrophase bunching. These results demonstrate that complex kinetic-scale ion dynamics occur in low Mach number shocks, which depend significantly on the shock profile.

A floodplain sedimentation model quantifies sediment budgets for U.S. mid-Atlantic Piedmont river corridors. Regional regression equations estimate discharge events every 3 months, and (temporally invariant) channel width and slope. River stage is assessed using steady uniform flow equations, corrected for nearby mill dams. Sediment concentrations are computed with a rating curve (defined for modern conditions by gaging station data). Spatially uniform floodplain deposition occurs during overbank flows, while stored sediment is eroded based on age. Calibration to modern (1950-2017) floodplain sediment thicknesses determines the effective sedimentation velocity, which is equivalent to the settling rate of fine silt. Tuning the model to floodplain stratigraphic data suggests that presettlement (before 1750) suspended sediment concentrations were 5-8% of those prevailing today, while legacy (1750-1950) sediment concentrations were 25-35% of present values. Because the available stratigraphic data are not correlated with maximum deposit age, the time of initial deposition is selected randomly by the model, creating variable outcomes for any single set of model parameters, resulting in an incomplete calibration of the model for presettlement conditions. Nonetheless, the model accurately reproduces the observed age distribution of floodplain deposits. All sediment budgets components computed using the model increase monotonically from presettlement time to the present, and the ratio of budget components remains similar from one time period to the next. The model also predicts that millennial timescales are needed for mid-Atlantic floodplains to equilibrate following a change in sediment regime, a finding with important implications for river corridor and watershed restoration planning.

Ocean general circulation models (OGCMs) contain numerous parameterizations of sub-grid scale processes. The parameter tuning procedure is rarely reported and often done by hand. We present an automated alternative: Bayesian optimization, a method which has recently emerged as a frontier in expensive black box optimization. VerOpt, a Python package for the ocean model Veros, adapts Bayesian optimization to climate model tuning. We use VerOpt to identify a set of parameter values of the Turbulent Kinetic Energy (TKE) closure scheme that minimize mixed layer depth (MLD) bias in Veros. We present the results of two optimization procedures: TWIN and OBS. The goal is to minimize modeled MLD error relative to a target map. In TWIN, the target is MLD simulated using Veros with a known parameterization. The ratio of two TKE parameters ckcϵ-1, proportional to the critical Richardson number Ric, is the dominant factor in setting the global MLD. After 180 model simulations, the lowest error in the TWIN experiment is 1.18%. In OBS, the target is MLD climatology. The MLD bias is smallest when Ric<1, and the default TKE parameterization falls within this range. We find, however, that altering the TKE parameterization is not sufficient to reduce the significant MLD bias of 42.62%. The OBS experiment results indicate that the TKE scheme parameters are not the dominant source of MLD bias in Veros. We discuss other possible sources of MLD bias, as well as the potential of extending of the optimization procedure to other parameterizations.

Understanding equilibrium climate sensitivity (ECS, warming in response to a doubling of CO2) uncertainty is fundamental for making reliable climate projections. We leverage the Hector simple climate model in a probabilistic framework to explore how different ECS priors influence long-term (2081-2100) temperature anomalies. Specifically, we quantify how different ECS prior distributions broaden the uncertainty in temperature projections. This method demonstrates a computationally efficient probabilistic workflow that explores the effects of parameter priors on climate projections. Excluding process and paleoclimate evidence widens the resulting temperature projection uncertainty (1.12-3.03 ℃ and 1.09-3.33 ℃, respectively) while using priors that synthesize all lines of evidence leads to narrowed temperature projection uncertainty (1.24-2.89 ℃). Our study shows that excluding paleoclimate and process data in ECS priors broadens temperature projection uncertainty. In contrast, synthesized evidence provides a narrower and potentially more robust range of future temperature outcomes.

The commercial synthetic aperture radar (SAR) market is experiencing year-over-year growth and is currently capable of imaging anywhere in the world within an hour at X-band. Surface Deformation and Change (SDC) is a mission study at NASA to investigate innovative architectures beyond the NASA-ISRO SAR (NISAR) mission in the next decade. In this article we present preliminary findings on the technical capabilities of the currently available commercial SAR data, and investigate their applicability to SDC goals, such as imaging quality and retrieval of surface motion.

Agricultural cooperatives (co-ops) are increasingly recognised for their capacity to enhance resilience in the face of crises such as climate change, economic disruptions, and pandemics. This study investigates how governance structures and adaptive project management strategies contribute to effective crisis management in coops. Initial findings reveal that regulatory compliance acts as both a constraint and an enabler, while economic volatility drives the need for costly technological investments. External stakeholder influence provides essential resources but imposes governance to align internal and external priorities. Social capital, manifesting as community trust and collaboration, supports crisis response but can be weakened under financial stress. Hybrid governance frameworks emerge as vital, balancing member engagement with professional oversight to maintain organisational stability. We propose the Crisis Resilience in Agricultural Cooperatives conceptual framework, which links external pressures, internal dynamics, and resource availability to resilience outcomes. This research highlights the importance of strategic governance, adaptive project management, and effective social networks in fostering coop resilience and provides guidance for policymakers and practitioners.

Investigating the role of clouds and precipitation in the Earth system necessitates microphysical schemes capable of accurately describing the evolution of hydrometeor particle size distribution (PSD), while maintaining low computational costs implementable in atmospheric models. Machine learning (ML) offers a promising approach for replacing detailed binned yet computationally expensive schemes with efficient emulations. However, many existing emulations still rely on moments as prognostic variables, inheriting structural limitations from traditional bulk schemes. In contrast, latent variables directly discovered by ML are potential to represent PSDs more accurately, but their inherent nonlinearity breaks the conservation property under advection and diffusion, limiting their applicability in online simulations. To address this dilemma, we propose Weighted Integral Parameters (WIPs), formulated as weighted integrals of PSD with learnable weight functions, providing the most general mathematical form for advectable microphysical prognostic variables. Using autoencoders that are physics-informed by WIP’s formulation to learn the optimal PSD representations, we conducted unsupervised learning over a liquid droplet PSD dataset generated from ensemble large eddy simulations with Spectral Bin Microphysics, to compare WIPs with traditional moment approaches in bulk schemes on their ability to represent “actual” PSDs. Results show that WIPs can automatically capture the critical information of medium-sized droplets unprecedentedly with traditional moment approaches, and outperform partial and full integral moments in terms of PSD reconstruction error, indicating superior PSD information compression efficiency. With these properties, WIPs are potential to replace moments as fully prognostic variables to build more accurate ML-based bin-emulating schemes.

Hamelin Pool, W.-Australia, is a natural laboratory to study stromatolite formation in a hypersaline lagoon. Stromatolitic aragonites sampled at supratidal, intertidal and subtidal environments show gradual increase in Ba isotopic compositions (d138Bacarb = -0.12-0.57‰) with decreasing Ba/Ca following conservative mixing between groundwater and seawater. We observe fingerprints of the groundwater end-member in a supratidal sample showing particular light d138Bacarb corresponding with elevated Mn/Sr and low d18Ocarb as indicator for discharge from the local Tamala Limestone aquifer. In contrast, stromatolitic carbonates formed in equilibrium with a saline end-member show heavy d138Bacarb. Lagoonal d138Bafluid ranges from 0.44-0.6‰ at increasing PSU (3.6-65.6). The calculated partition coefficient for Ba into stromatolites is lowest at the subtidal (logDBa=0.45) and highest at the groundwater discharge site (logDBa=0.86). Injection of groundwaters into Hamelin Pool likely contributes to enhanced aragonite precipitation at up to 0.32mm/yr, possibly catalysed by nucleation within or onto extra-polymeric substances of microbially diverse mats.

The Salish Sea is a semi-enclosed estuary whose largest basin (Strait of Georgia) is connected to the north-eastern Pacific Ocean through regions with tight constructions and sills that cause intense tidal mixing. The estuarine circulation is complicated through the tidally mixed region around the San Juan and Gulf Islands (SJGI), which consists of three different straits: Haro Strait, Rosario Strait, and San Juan Channel. Haro Strait, as the largest and deepest of the channels, is the dominant pathway; however, we determine that Rosario Strait also has an important influence in this region. To examine the differences in water transport through the different channels, Lagrangian particle tracking simulations were performed for a 4-year hindcast (from 2018-2022) using the 3-dimensional numerical model SalishSeaCast. While Haro Strait has southward surface flow and northward deep flow, there is primarily southward flow at all depths through Rosario Strait and San Juan Channel. The proportion of the total southward flow through these two channels is higher from May – October and this increase is attributed to the influence of both the Fraser River and the river discharge from Puget Sound. Rosario Strait is the dominant pathway of southward exchange from the Strait of Georgia to Puget Sound, while the majority of deep northward flow to the Strait of Georgia is through Haro Strait.

A document by dario marconi. Click on the document to view its contents.