Search for: All records

We present a look at Ookami, a project providing community access to a testbed supercomputer with the ARM-based A64FX processors developed by a collaboration between RIKEN and Fujitsu and deployed in the Japanese supercomputer Fugaku. We provide an overview of the project and details of the hardware, and describe the user base and education/training program. We present highlights from previous performance studies of two astrophysical simulation codes and present a strong scaling study of a full 3D supernova simulation as an example of the machine’s capability. 

The minimum O2 needed to fuel the demand of aquatic animals is commonly observed to increase with temperature, driven by accelerating metabolism. However, recent measurements of critical O2 thresholds (“Pcrit”) reveal more complex patterns, including those with a minimum at an intermediate thermal “optimum”. To discern the prevalence, physiological drivers, and biogeographic manifestations of such curves, we analyze new experimental and biogeographic data using a general dynamic model of aquatic water breathers. The model simulates the transfer of oxygen from ambient water through a boundary layer and into animal tissues driven by temperature-dependent rates of metabolism, diffusive gas exchange, and ventilatory and circulatory systems with O2-protein binding. We find that a thermal optimum in Pcrit can arise even when all physiological rates increase steadily with temperature. This occurs when O2 supply at low temperatures is limited by a process that is more temperature sensitive than metabolism, but becomes limited by a less sensitive process at warmer temperatures. Analysis of published species respiratory traits suggests that this scenario is not uncommon in marine biota, with ventilation and circulation limiting supply under cold conditions and diffusion limiting supply at high temperatures. Using occurrence data, we show that species with these physiological traits inhabit lowest O2 waters near the optimal temperature for hypoxia tolerance and are restricted to higher O2 at temperatures above and below this optimum. Our results imply that hypoxia tolerance can decline under both cold and warm conditions and thus may influence both poleward and equatorward species range limits. 

Abstract In an ocean that is rapidly warming and losing oxygen, accurate forecasting of species’ responses must consider how this environmental change affects fundamental aspects of their physiology. Here, we develop an absolute metabolic index (Φ A ) that quantifies how ocean temperature, dissolved oxygen and organismal mass interact to constrain the total oxygen budget an organism can use to fuel sustainable levels of aerobic metabolism. We calibrate species-specific parameters of Φ A with physiological measurements for red abalone ( Haliotis rufescens ) and purple urchin ( Strongylocentrotus purpuratus ). Φ A models highlight that the temperature where oxygen supply is greatest shifts cooler when water loses oxygen or organisms grow larger, providing a mechanistic explanation for observed thermal preference patterns. Viable habitat forecasts are disproportionally deleterious for red abalone, revealing how species-specific physiologies modulate the intensity of a common climate signal, captured in the newly developed Φ A framework. 

The global ocean's oxygen content has declined significantly over the past several decades and is expected to continue decreasing under global warming, with far-reaching impacts on marine ecosystems and biogeochemical cycling. Determining the oxygen trend, its spatial pattern, and uncertainties from observations is fundamental to our understanding of the changing ocean environment. This study uses a suite of CMIP6 Earth system models to evaluate the biases and uncertainties in oxygen distribution and trends due to sampling sparseness. Model outputs are sub-sampled according to the spatial and temporal distribution of the historical shipboard measurements, and the data gaps are filled by a simple optimal interpolation method using Gaussian covariance with a constant e-folding length scale. Sub-sampled results are compared to full model output, revealing the biases in global and basin-wise oxygen content trends. The simple optimal interpolation underestimates the modeled global deoxygenation trends, capturing approximately two-thirds of the full model trends. The North Atlantic and subpolar North Pacific are relatively well sampled, and the simple optimal interpolation is capable of reconstructing more than 80% of the oxygen trend in the non-eddying CMIP models. In contrast, pronounced biases are found in the equatorial oceans and the Southern Ocean, where the sampling density is relatively low. The application of the simple optimal interpolation method to the historical dataset estimated the global oxygen loss to be 1.5% over the past 50 years. However, the ratio of the global oxygen trend between the sub-sampled and full model output has increased the estimated loss rate in the range of 1.7% to 3.1% over the past 50 years, which partially overlaps with previous studies. The approach taken in this study can provide a framework for the intercomparison of different statistical gap-filling methods to estimate oxygen content trends and their uncertainties due to sampling sparseness. 

ABSTRACT Tropical reef ecosystems are strongly influenced by the composition of coral species, but the factors influencing coral diversity and distributions are not fully understood. Here we demonstrate that large variations in the relative abundance of three major coral species across adjacent Caribbean reef sites are strongly related to their different low O₂tolerances. In laboratory experiments designed to mimic reef conditions, the cumulative effect of repeated nightly low O₂drove coral bleaching and mortality, with limited modulation by temperature. After four nights of repeated low O₂, species responses also varied widely, from > 50% bleaching inAcropora cervicornisto no discernable sensitivity ofPorites furcata.A simple metric of hypoxic pressure that combines these experimentally derived species sensitivities with high‐resolution field data accurately predicts the observed relative abundance of species across three reefs. Only the well‐oxygenated reef supported the framework‐building hypoxia‐sensitiveAcropora cervicornis, while the hypoxia‐tolerant weedy speciesPorites furcatawas dominant on the most frequently O₂‐deplete reef. Physiological exclusion of acroporids from these O₂‐deplete reefs underscores the need for hypoxia management to reduce extirpation risk. 

Abstract. The global ocean’s oxygen content has declined significantly over the past several decades and is expected to continue decreasing under global warming with far reaching impacts on marine ecosystems and biogeochemical cycling. Determining the oxygen trend, its spatial pattern and uncertainties from observations is fundamental to our understanding of20 the changing ocean environment. This study uses a suite of CMIP6 Earth System Models to evaluate the biases and uncertainties in oxygen distribution and trends due to sampling sparseness. Model outputs are sub-sampled according to the spatial and temporal distribution of the historical shipboard measurements, and an optimal interpolation method is applied to fill data gaps. Sub-sampled results are compared to full model output, revealing the biases in global and basin-wise oxygen content trends. The optimal interpolation underestimates the modeled global deoxygenation trends, capturing approximately25 two-thirds of the full model trends. North Atlantic and Subpolar North Pacific are relatively well sampled, and the optimal interpolation is capable of reconstructing more than 80% of the oxygen trend. In contrast, pronounced biases are found in the equatorial oceans and the Southern Ocean, where the sampling density is relatively low. Optimal interpolation of the historical dataset estimated the global oxygen loss of 1.5% over the past 50 years. However, the ratio of global oxygen trend between the subsampled and full model output, increases the estimated loss rate to 1.7 to 3.1% over the past 50 years, which partially30 overlaps with previous studies. The approach taken in this study can provide a framework for the intercomparison of different statistical gap-fill methods to estimate oxygen content trends and its uncertainties due to sampling sparseness. 

Carbonate mud represents one of the most important geochemical archives for reconstructing ancient climatic, environmental, and evolutionary change from the rock record. Mud also represents a major sink in the global carbon cycle. Yet, there remains no consensus about how and where carbonate mud is formed. Here, we present stable isotope and trace-element data from carbonate constituents in the Bahamas, including ooids, corals, foraminifera, and algae. We use geochemical fingerprinting to demonstrate that carbonate mud cannot be sourced from the abrasion and mixture of any combination of these macroscopic grains. Instead, an inverse Bayesian mixing model requires the presence of an additional aragonite source. We posit that this source represents a direct seawater precipitate. We use geological and geochemical data to show that “whitings” are unlikely to be the dominant source of this precipitate and, instead, present a model for mud precipitation on the bank margins that can explain the geographical distribution, clumped-isotope thermometry, and stable isotope signature of carbonate mud. Next, we address the enigma of why mud and ooids are so abundant in the Bahamas, yet so rare in the rest of the world: Mediterranean outflow feeds the Bahamas with the most alkaline waters in the modern ocean (>99.7th-percentile). Such high alkalinity appears to be a prerequisite for the nonskeletal carbonate factory because, when Mediterranean outflow was reduced in the Miocene, Bahamian carbonate export ceased for 3-million-years. Finally, we show how shutting off and turning on the shallow carbonate factory can send ripples through the global climate system. 

In Mekong riparian countries, hydropower development provides energy, but also threatens biodiversity, ecosystems, food security, and an unparalleled freshwater fishery. The Sekong, Sesan, and Srepok Rivers (3S Basin) are major tributaries to the Lower Mekong River (LMB), making up 10% of the Mekong watershed but supporting nearly 40% of the fish species of the LMB. Forty-five dams have been built, are under construction, or are planned in the 3S Basin. We completed a meta-analysis of aquatic and riparian environmental losses from current, planned, and proposed hydropower dams in the 3S and LMB using 46 papers and reports from the past three decades. Proposed mainstem Stung Treng and Sambor dams were not included in our analysis because Cambodia recently announced a moratorium on mainstem Mekong River dams. More than 50% of studies evaluated hydrologic change from dam development, 33% quantified sediment alteration, and 30% estimated fish production changes. Freshwater fish diversity, non-fish species, primary production, trophic ecology, and nutrient loading objectives were less commonly studied. We visualized human and environmental tradeoffs of 3S dams from the reviewed papers. Overall, Lower Sesan 2, the proposed Sekong Dam, and planned Lower Srepok 3A and Lower Sesan 3 have considerable environmental impacts. Tradeoff analyses should include environmental objectives by representing organisms, habitats, and ecosystems to quantify environmental costs of dam development and maintain the biodiversity and extraordinary freshwater fishery of the LMB. 

We introduce ThreeDWorld (TDW), a platform for interactive multi-modal physical simulation. TDW enables the simulation of high-fidelity sensory data and physical interactions between mobile agents and objects in rich 3D environments. Unique properties include: real-time near-photo-realistic image rendering; a library of objects and environments, and routines for their customization; generative procedures for efficiently building classes of new environments; high-fidelity audio rendering; realistic physical interactions for a variety of material types, including cloths, liquid, and deformable objects; customizable avatars that embody AI agents; and support for human interactions with VR devices. TDW's API enables multiple agents to interact within a simulation and returns a range of sensor and physics data representing the state of the world. We present initial experiments enabled by TDW in emerging research directions in computer vision, machine learning, and cognitive science, including multi-modal physical scene understanding, physical dynamics predictions, multi-agent interactions, models that 'learn like a child', and attention studies in humans and neural networks.