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Abstract. Antarctic ice shelves buttress the flow of the ice sheet but are vulnerable to increased basal melting from contact with a warming ocean and increased mass loss from calving due to changing flow patterns. Channels and similar features at the bases of ice shelves have been linked to enhanced basal melting and observed to intersect the grounding zone, where the greatest melt rates are often observed. The ice shelf of Thwaites Glacier is especially vulnerable to basal melt and grounding zone retreat because the glacier has a retrograde bed leading to a deep trough below the grounded ice sheet. We use digital surface models from 2010–2022 to investigate the evolution of its ice-shelf channels, grounding zone position, and the interactions between them. We find that the highest sustained rates of grounding zone retreat (up to 0.7 km yr−1) are associated with high basal melt rates (up to ∼250 m yr−1) and are found where ice-shelf channels intersect the grounding zone, especially atop steep local retrograde slopes where subglacial channel discharge is expected. We find no areas with sustained grounding zone advance, although some secular retreat was distal from ice-shelf channels. Pinpointing other locations with similar risk factors could focus assessments of vulnerability to grounding zone retreat.

 

Here, we report the draft genome sequences ofFlagellimonassp. MMG031 andMarinobactersp. MMG032, isolated from coral-associated dinoflagellateSymbiodinium pilosum, assembled and analyzed by undergraduate students participating in a Marine Microbial Genomics (MMG) course. A genomic comparison suggests MMG031 and MMG032 are novel species and a resource for restoration and biotechnology.

 

An important open problem in supersingular isogeny-based cryptography is to produce, without a trusted authority, concrete examples of ‘hard supersingular curves’ that is equations for supersingular curves for which computing the endomorphism ring is as difficult as it is for random supersingular curves. A related open problem is to produce a hash function to the vertices of the supersingular $\ell $-isogeny graph, which does not reveal the endomorphism ring, or a path to a curve of known endomorphism ring. Such a hash function would open up interesting cryptographic applications. In this paper, we document a number of (thus far) failed attempts to solve this problem, in the hope that we may spur further research, and shed light on the challenges and obstacles to this endeavour. The mathematical approaches contained in this article include: (i) iterative root-finding for the supersingular polynomial; (ii) gcd’s of specialized modular polynomials; (iii) using division polynomials to create small systems of equations; (iv) taking random walks in the isogeny graph of abelian surfaces, and applying Kummer surfaces and (v) using quantum random walks.

 

Rifts are full-thickness fractures that propagate laterally across an ice shelf. They cause ice-shelf weakening and calving of tabular icebergs, and control the initial size of calved icebergs. Here, we present a joint inverse and forward computational modeling framework to capture rifting by combining the vertically integrated momentum balance and anisotropic continuum damage mechanics formulations. We incorporate rift–flank boundary processes to investigate how the rift path is influenced by the pressure on rift–flank walls from seawater, contact between flanks, and ice mélange that may also transmit stress between flanks. To illustrate the viability of the framework, we simulate the final 2 years of rift propagation associated with the calving of tabular iceberg A68 in 2017. We find that the rift path can change with varying ice mélange conditions and the extent of contact between rift flanks. Combinations of parameters associated with slower rift widening rates yield simulated rift paths that best match observations. Our modeling framework lays the foundation for robust simulation of rifting and tabular calving processes, which can enable future studies on ice-sheet–climate interactions, and the effects of ice-shelf buttressing on land ice flow. 

Natural organic matter plays an important role in oceanic hydrothermal systems through a combination of geological and chemical processes. However, identifying the hydrothermal pathways of organic compounds is still quite limited, preventing us from understanding how organic matter is transformed in hydrothermal systems. In this study, we focus on the reaction pathways of alkenes, which represent a key functional group intermediate linking the most abundant hydrocarbons in seafloor hydrothermal environments. Three major pathways are observed for alkenes under mild hydrothermal conditions, including hydration, oxidation, and dimerization. The pathway distributions of alkenes can be affected by the presence of dissolved metal salts; hydration of alkenes is driven by metal ions via the change of solution pH, while alkene dimerization is controlled by pH and the type of metal cations and complexes. Overall, this study identifies alkene hydrothermal pathways and highlights the important roles of metal salts in controlling hydrothermal transformations.

 

Abstract. Climatic extreme events are expected to occur more frequently in the future, increasing the likelihood of unprecedented climate extremes (UCEs) or record-breaking events. UCEs, such as extreme heatwaves and droughts, substantially affect ecosystem stability and carbon cycling by increasing plant mortality and delaying ecosystem recovery. Quantitative knowledge of such effects is limited due to the paucity of experiments focusing on extreme climatic events beyond the range of historical experience. Here, we present a road map of how dynamic vegetation demographic models (VDMs) can be used to investigate hypotheses surrounding ecosystem responses to one type of UCE: unprecedented droughts. As a result of nonlinear ecosystem responses to UCEs that are qualitatively different from responses to milder extremes, we consider both biomass loss and recovery rates over time by reporting a time-integrated carbon loss as a result of UCE, relative to the absence of drought. Additionally, we explore how unprecedented droughts in combination with increasing atmospheric CO2 and/or temperature may affect ecosystem stability and carbon cycling. We explored these questions using simulations of pre-drought and post-drought conditions at well-studied forest sites using well-tested models (ED2 and LPJ-GUESS). The severity and patterns of biomass losses differed substantially between models. For example, biomass loss could be sensitive to either drought duration or drought intensity depending on the model approach. This is due to the models having different, but also plausible, representations of processes and interactions, highlighting the complicated variability of UCE impacts that still need to be narrowed down in models. Elevated atmospheric CO2 concentrations (eCO2) alone did not completely buffer the ecosystems from carbon losses during UCEs in the majority of our simulations. Our findings highlight the consequences of differences in process formulations and uncertainties in models, most notably related to availability in plant carbohydrate storage and the diversity of plant hydraulic schemes, in projecting potential ecosystem responses to UCEs. We provide a summary of the current state and role of many model processes that give way to different underlying hypotheses of plant responses to UCEs, reflecting knowledge gaps which in future studies could be tested with targeted field experiments and an iterative modeling–experimental conceptual framework.

 

Estimating the pose of a camera with respect to a 3D reconstruction or scene representation is a crucial step for many mixed reality and robotics applications. Given the vast amount of available data nowadays, many applications constrain storage and/or bandwidth to work efficiently. To satisfy these constraints, many applications compress a scene representation by reducing its number of 3D points. While state-of-the-art methods use K-cover-based algorithms to compress a scene, they are slow and hard to tune. To enhance speed and facilitate parameter tuning, this work introduces a novel approach that compresses a scene representation by means of a constrained quadratic program (QP). Because this QP resembles a one-class support vector machine, we derive a variant of the sequential minimal optimization to solve it. Our approach uses the points corresponding to the support vectors as the subset of points to represent a scene. We also present an efficient initialization method that allows our method to converge quickly. Our experiments on publicly available datasets show that our approach compresses a scene representation quickly while delivering accurate pose estimates.