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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Optimized Viewing Techniques to Minimize Radiation Damage From X-ray Imaging Systems

X-ray inspection of ball grid arrays (BGAs) is typically performed at one or more viewing angles to examine adhesion sites for errors such as voids, joint cracking, or head-in-pillow. During this inspection process, the cir cuit board assembly is subject to ionizing radiation exposure, which can cause trapped charge within oxide layers of semiconductor devices. Some x-ray machines allow for programmable inspection routines, which could be used to optimize radiation exposure to semiconductor components. Using Monte Carlo methods, x-ray inspection of a BGA was simulated to determine a range of acceptable viewing angles. Dose rates to circuit board components were estimated at each inspection angle to determine the view resulting in optimized radiation exposure. Results showed that for each BGA, the maximum unobstructed viewing times without exceeding a 5 Gy dose limit to a single part ranged from 82 to 94 minutes. Using a radiation cost function method, optimized viewing across all components was found. Here, it was observed that for a consistent dose limit applied to silicon-based components, performing inspection with BGAs facing the x-ray source was optimal. A third method was applied, assigning individual dose limits based on empirical data from the NASA Goddard Space Flight Center radiation database. This method showed that optimized viewing maximizes the distance between the radiation source and highly sensitive components. It was also observed that cumulative effects from viewing two BGAs will influence viewing angles, causing the optimal view of one BGA to exist nearly 180° from the other.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Cultural Shifts in High Energy Physics Collaboration from the Cold War to the Present: A Historical and Philosophical Perspective

Here, this article employs empirical history and the philosophy of science to study cultural convergences and divergences in international collaborations in high energy physics. We examine two cases: (1) E-36, an experiment on small angle proton-proton scattering conducted during the Cold War at the National Accelerator Laboratory (NAL) in the USA by Soviet and US scientists and (2) an ongoing collaborative experiment, NICA, at the Joint Institute for Nuclear Research (JINR, Dubna), which is a project devoted to heavy-ion physics. The JINR, particularly its Laboratory of High Energy Physics (formerly the “Laboratory of High Energies”) is the main mediating actor between these two cases (i.e., E-36 and NICA), as the majority of Soviet participants in E-36 were representatives of the Institute. Using empirical data collected through archival searches, field observations conducted at JINR in 2018–2019, and in-depth interviews, we tell a story of cultural differences in high energy physics by applying the concepts of ‘trading zones’ (P. Galison) and the translation of interests in actor-networks (B. Latour, M. Callon and others). We analyze three types of cultural diversity (specialization, nationality, and generational) in light of the implications of temporal context and the dichotomy between East and West, showing the roles cultural diversity plays in scientific collaboration (which is an integral part of as well as obstacle to scientific research that can nevertheless provide learning opportunities). Our study aims to demonstrate how disunity and diversity may function in scientific research and how high energy physics collaborations can remain productive despite sometimes deep divergences, including those between East and West.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Hydrodynamic characterization of the coastal pioneer array ocean observing system

Ocean observation buoys require relatively small amounts of power, yet traditionally necessitate costly resupply trips for battery replacement. With the offshore location of the buoys and small power requirements, wave energy may be an effective solution for providing consistent and reliable power to support the buoy instrumentation. The US National Science Foundation Ocean Observatories Initiative (OOI) includes arrays of point absorber-like buoy systems used for ocean observation that have been deployed at multiple locations including the Southern Mid-Atlantic Bight. A study is currently underway to design a pitch resonator wave energy converter to supplement existing renewable energy generation for powering observation instrumentation. This paper details field measurements from surface moorings of the OOI Coastal Pioneer Array, which informs the subsequent development of a numerical model for the moored observation system. The model is developed in Wave Energy Converter Simulator (WEC-Sim), which leverages the Simscape multibody solver within the MATLAB/Simulink framework and linear potential flow theory to simulate the hydrodynamic interactions and multibody dynamics in 6 degrees of freedom. Multiple tuning variables are considered to produce a model for the system that matches well with empirical data (about 8% error). In conclusion, the WEC-Sim model will serve as a platform for integrating the pitch resonator wave energy converter concept and deployment preparation (detailed design including power take-off and control systems, response evaluation, etc.).

hydrodynamic modeling↗
DOE OSTI2024

Generalization error guaranteed auto-encoder-based nonlinear model reduction for operator learning

Many physical processes in science and engineering are naturally represented by operators between infinite-dimensional function spaces. The problem of operator learning, in this context, seeks to extract these physical processes from empirical data, which is challenging due to the infinite or high dimensionality of data. An integral component in addressing this challenge is model reduction, which reduces both the data dimensionality and problem size. In this paper, we utilize low-dimensional nonlinear structures in model reduction by investigating Auto-Encoder-based Neural Network (AENet). AENet first learns the latent variables of the input data and then learns the transformation from these latent variables to corresponding output data. Our numerical experiments validate the ability of AENet to accurately learn the solution operator of nonlinear partial differential equations. Furthermore, we establish a mathematical and statistical estimation theory that analyzes the generalization error of AENet. Finally, our theoretical framework shows that the sample complexity of training AENet is intricately tied to the intrinsic dimension of the modeled process, while also demonstrating the robustness of AENet to noise.

Auto-encoder↗
DOE OSTI2024

Optimal environmental and economic performance trade-offs for fifth generation district heating and cooling network topologies with waste heat recovery

Network topology greatly influences both the economic and environmental performance of fifth generation district heating and cooling (5GDHC) systems. In this study the optimal trade-offs between the environmental and economic performance of 5GDHC network topologies for a five-building district with waste heat recovery were explored. A life cycle assessment method was used to calculate the total life cycle CO 2 emissions (LCCO2) associated with the installation and operation of various network topologies. Twelve months of empirical data from a data center cooling system were analyzed to assess its suitability for integration into a 5GDHC system. The most suitable method for utilizing this waste heat was selected based on the ambient loop warm pipe setpoint, waste heat temperature, and district energy system configuration. A multi-objective optimization algorithm was used to select the 5GDHC network topology that provided the optimal trade-off between LCCO2 and life cycle cost (LCC). A trade-off parameter was employed to weigh the importance of each objective in the selection process. The results showed waste heat from the data center was suitable for integration into the 5GDHC system due to its availability and consistent temperature profile. When return temperatures of 25 °C or higher were available from the liquid-cooled system, direct pre-heating of the ambient loop warm pipe was found to be the most effective waste heat integration method. The selection of the network topology that provided the optimal trade-off between LCCO2 and LCC (optimal trade-off topology) was highly dependent on factors such as fuel prices, CO 2 prices, electricity CO 2 emissions factors, availability of waste heat, embodied CO 2 emissions associated with network installation and network infrastructure costs. Optimal trade-off topologies produced substantial LCCO2 reductions relative to corresponding LCC increases. LCCO2 reduction to LCC increase ratios from 5.78 to 117.79 were identified with CO 2 offset costs ranging from 4.77 to 60.08 ($/tCO 2 e).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Exploring the effects of policy on stakeholder adoption and deployment of agrivoltaics: A case study of Massachusetts

Further deployment of agrivoltaics is likely to require a better understanding of how policies and agreements can shape the outcomes of solar siting on farmland. This study evaluates the Massachusetts agrivoltaics policy in terms of its implications on deployment and stakeholder experiences in adoption. We present findings from interviews with 26 state policymakers, Extension agents, representatives of non-governmental organizations, farm owners and operators, and solar developers. Our findings demonstrate how the policy has mixed effects on deployment processes and outcomes—in some instances, the policy enables deployment by formalizing cross-sector collaboration, increasing farm owner and operator participation in development, and facilitating novel business models. In other instances, the policy constrains deployment by prescribing operational requirements, creating liability risk, and developing dependency on empirical data to inform eligibility decisions. Interviewees explained how these mixed policy effects create both benefits and burdens for adopters, particularly farm owners and operators. These insights indicate the value of cross-sector collaboration during all phases of agrivoltaic policy implementation and project development; the importance of coordination across policy, research, and commercial activities; and the significant role of regulators and policy design in deployment. The evidence presented in this paper can inform decision making for emerging agrivoltaic policies and markets, both in the United States and internationally.

14 SOLAR ENERGY↗
DOE OSTI2020

Are scenario projections overly optimistic about future yield progress?

Historical increases in agricultural production were achieved predominantly by large increases in agricultural productivity. Intensification of crop and livestock production also plays a key role in future projections of agricultural land use. Here, we assess and discuss projections of crop yields by global agricultural land-use and integrated assessment models. To evaluate these crop yield projections, we compare them to empirical data on attainable yields by employing a linear and plateauing continuation of observed attainable yield trends. While keeping in mind the uncertainties of attainable yields projections and future climate change impacts, we find that, on average for all cereals on the global level, global projected yields by 2050 remain below the attainable yields. Additionally, this is also true for future pathways with high technological progress and mitigation efforts, indicating that projected yield increases are not overly optimistic, even under systemic transformations. Furthermore, on a regional scale, we find that for developing regions, specifically for sub-Saharan Africa, projected yields stay well below attainable yields, indicating that the large yield gaps which could be closed through improved crop management, may also persist in the future. In OECD countries, in contrast, current yields are already close to attainable yields, and the projections approach or, for some models, even exceed attainable yields by 2050. This observation parallels research suggesting that future progress in attainable yields in developed regions will mainly have to be achieved through new crop varieties or genetic improvements. The models included in this study vary widely in their implementation of yield progress, which are often split into endogenous (crop management) improvements and exogenous (technological) trends. More detail and transparency are needed in these important elements of global yields and land use projections, and this paper discusses possibilities of better aligning agronomic understanding of yield gaps and yield potentials with modelling approaches.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Floating solar panels on reservoirs impact phytoplankton populations: A modelling experiment

Floating solar photovoltaic (FPV) deployments are increasing globally as the switch to renewable energy intensifies, representing a considerable water surface transformation. FPV installations can potentially impact aquatic ecosystem function, either positively or negatively. However, these impacts are poorly resolved given the challenges of collecting empirical data for field or modelling experiments. In particular, there is limited evidence on the response of phytoplankton to changes in water body thermal dynamics and light climate with FPV. Given the importance of understanding phytoplankton biomass and species composition for managing ecosystem services, we use an uncertainty estimation approach to simulate the effect of FPV coverage and array siting location on a UK reservoir. FPV coverage was modified in 10% increments from a baseline with 0% coverage to 100% coverage for three different FPV array siting locations based on reservoir circulation patterns. Results showed that FPV coverage significantly impacted thermal properties, resulting in highly variable impacts on phytoplankton biomass and species composition. The impacts on phytoplankton were often dependent on array siting location as well as surface coverage. Changes to phytoplankton species composition were offset by the decrease in phytoplankton biomass associated with increasing FPV coverage. We identified that similar phytoplankton biomass reductions could be achieved with less FPV coverage by deploying the FPV array on the water body's faster-flowing area than the central or slower flowing areas. The difference in response dependent on siting location could be used to tailor phytoplankton management in water bodies. Simulation of water body-FPV interactions efficiently using an uncertainty approach is an essential tool to rapidly develop understanding and ultimately inform FPV developers and water body managers looking to minimise negative impacts and maximise co-benefits.

14 SOLAR ENERGY↗
DOE OSTI2023

Shared micromobility as a first- and last-mile transit solution? Spatiotemporal insights from a novel dataset

The first- and last-mile (FM/LM) problem is a major deterrent to public transit use. With the rise of shared micromobility options such as shared e-scooters in recent years, there is a growing interest in understanding their potential to serve as a last-mile transit solution. However, empirical data regarding the integrated use of shared micromobility and public transit have been limited so far. As a result, much is unknown regarding the spatiotemporal patterns and characteristics of shared micromobility trips serving as an FM/LM connection to transit. Here, this paper addresses these knowledge gaps by leveraging a novel dataset (i.e., the Spin post-ride survey dataset) that records thousands of transit-connecting shared e-scooter trips in Washington DC. Specifically, we used the dataset to reveal the spatiotemporal patterns of transit-connecting shared e-scooter trips in Washington DC, resulting in some major policy insights regarding the integral use of shared e-scooters and public transit. We further leveraged the dataset to validate if and to what extent a commonly applied buffer-zone approach can infer FM/LM micromobility trips accurately. Statistical tests showed that the actual FM/LM Spin e-scooter trips differ from inferred FM/LM Spin e-scooter trips in both spatial and temporal dimensions. This indicates that the common practice of inferring FM/LM micromobility trips with a buffer-zone approach can lead to inaccurate estimates of transit-connecting micromobility trips.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Neural network interatomic potential-driven analysis of phase stability in Ti–V alloys at the atomistic scale

The evolution of the ω phase in titanium–vanadium (Ti–V) alloys is critical for their mechanical properties, particularly in aerospace and biomedical applications. Here, this study employs a Rapid Artificial Neural Network (RANN) potential to model the ω phase evolution at the atomistic level, demonstrating a high degree of consistency with experimental observations, unlike the Modified Embedded Atom Method (MEAM), which fails to capture this phase transformation accurately. RANN simulations replicate key phenomena such as the nucleation of α precipitates at ω/β interfaces and accurate lattice orientations, enhancing our understanding of phase stability and transformation kinetics. The findings affirm that RANN potentials can significantly improve the prediction accuracy of complex material behaviors, offering a powerful tool for designing advanced materials with tailored properties such as solute effect in various stacking fault energies. This approach not only bridges the gap between theoretical predictions and empirical data but also sets a new direction for future research in materials science, emphasizing the integration of machine learning techniques in the development and optimization of new alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Non-unimodal and non-concave relationships in the network Macroscopic Fundamental Diagram caused by hierarchical streets

Unimodal, concave relationships between average network productivity and accumulation or density aggregated across spatially compact regions of urban networks—so called network Macroscopic Fundamental Diagrams (MFDs)—have recently been shown to exist on homogeneous street networks. When present, MFD relationships facilitate the modeling of traffic congestion at a regional level and have led to the development of various regional traffic control strategies. However, real street networks are not homogeneous—they generally have a hierarchical structure where some streets (e.g., arterials) promote higher mobility than others (e.g., local roads). Here, this paper examines how the presence of hierarchical roadway structures may potentially cause non-unimodal patterns in a network's MFD. These are observed using three types of tools: analytical models of simple network structures, simulations of various idealized roadway networks, and empirical data. The impacts of street hierarchy depend on how vehicles use different roadway types to move within the network; i.e., their routing strategy. The findings suggest that the presence of roadway hierarchies may lead to MFDs that have non-unimodal or non-concave patterns on the free-flow branch when vehicles route themselves according to user equilibrium principles, which is closest to what would be observed in realistic situations. Such patterns are contrary to what is traditionally assumed in most MFD-based modeling frameworks. However, the unimodal and concave MFD should be expected under system optimal routing conditions that maximize network productivity for a given traffic state.

42 ENGINEERING↗
DOE OSTI2025

Cost of Deconstruction Depots for Diversified, Waste-Based Lignocellulosic Sugars Using Distillable Solvents

Transitioning to a bioeconomy that makes use of low-emission and waste feedstocks requires greater flexibility to accommodate seasonal variations and mitigate long-term storage challenges, such as material loss and fire risk. To achieve this goal, biomass deconstruction technologies must efficiently handle diverse feedstocks. Here, we assess the cost of using butylaminea distillable solventto deconstruct 22 different biomass feedstocks: 7 herbaceous, 9 woody, 4 food processing residues, and 2 blends. Lignocellulosic sugar production costs, based on current empirical data, range from $1.3 to 6.1/kg, suggesting that substantial improvements are required to compete with conventional sugars. The high solvent loading (850 g/kg of whole slurry) is a process bottleneck. Lowering the solvent loading to 59 g/kg of whole slurry, demonstrated in an L-scale reactor using poplar biomass, reduces the minimum sugar selling price by 33%. Solvent loading and recovery, solid loading, sugar yield, enzyme use, and delivered biomass cost all play key roles in reaching sugar production costs of $0.45-0.79/kg. Strategic feedstock blending to maximize carbohydrate content, process optimization to improve conversion efficiency, and the selection of low-cost feedstocks are important to advancing feedstock-flexible biorefineries.

TEA↗
DOE OSTI2026

A High-Fidelity Molecular Model of the Cu(111) Repeating Unit

Dynamic processes at surfaces are central to heterogeneous catalysis, but their atomistic mechanism(s) can prove difficult to elucidate due to variations in material structure and the corresponding impact on reactivity. Moreover, disparities between reaction conditions and those employed for spectroscopic characterization at surfaces can inhibit detailed understanding of catalysis-relevant chemistries. Herein, we substantiate the so-called “cluster-surface” analogy by leveraging a low-valent tricopper architecture ( 1 ) as a model system for small molecule activation at Cu(111). Two reaction classes are explored: the adsorption of carbon monoxide (CO) and the dissociative adsorption of dihydrogen (H 2 ). These processes serve as an ideal testbed to compare the reactivity of a molecular cluster ( 1 ) to that of a heterogeneous surface, as both reactions have empirical data from measurements performed on crystalline Cu(111). Cluster 1 reversibly binds CO. Variable temperature NMR analysis with 13 CO reveals a favorable enthalpy but large negative entropy (−5.1 kcal × mol –1 and −22.9 cal × mol –1 × K –1 , respectively) for CO binding, affording a process that is marginally endergonic at room temperature (ΔG ads (298.15 K) = 1.7 ± 0.5 kcal × mol –1 ). Similarly, analogous to a Cu(111) surface, 1 is shown to oxidatively add (chemisorb) H 2 . Kinetic parameters were determined for this process and the activation enthalpy (8.4 ± 0.5 kcal × mol –1 ) closely mirrors that established for H 2 binding at the Cu(111) facet (6.0 to 12.4 kcal × mol –1 ). Together, these results showcase that a trinuclear cluster can reproduce the small molecule binding and activation energetics of a bulk crystalline surface, setting the stage for studying less-defined surface processes in an atomically precise molecular setting.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

More Realistic Intermediate Depth Dry Firn Densification in the Energy Exascale Earth System Model (E3SM)

Abstract Earth system models account for seasonal snow cover, but many do not accommodate the deeper snowpack on ice sheets (aka firn) that slowly transforms to ice under accumulating snowfall. To accommodate and resolve firn depths of up to 60 m in the Energy Exascale Earth System Model's land surface model (ELM), we add 11 layers to its snowpack and evaluate three dry snow compaction equations in multi‐century simulations. After comparing results from ELM simulations (forced with atmospheric reanalysis) with empirical data, we find that implementing into ELM a two‐stage firn densification model produces more accurate dry firn densities at intermediate depths of 20–60 m. Compared to modeling firn using the equations in the (12 layer) Community Land Model (version 5), switching to the two‐stage firn densification model (with 16 layers) significantly decreases root‐mean‐square errors in upper 60 m dry firn densities by an average of 41 kg m −3 (31%). Simulations with three different firn density parameterizations show that the two‐stage firn densification model should be used for applications that prioritize accurate upper 60 m firn air content (FAC) in regions where the mean annual surface temperature is greater than roughly −31°C. Because snow metamorphism, firn density, and FAC are major components in modeling ice sheet surface albedo, melt water retention, and climatic mass balance, these developments advance broader efforts to simulate the response of land ice to atmospheric forcing in Earth system models.

58 GEOSCIENCES↗
DOE OSTI2025

Modeling Mycorrhizal Carbon Costs in Temperate Forests: The Impacts of Functional Diversity and Global Change Factors

Mycorrhizal fungi form symbiotic relationships with most plant species, facilitating nutrient acquisition while consuming a significant fraction of the plant's photosynthetic carbon (C), which we define as the mycorrhizal C cost. Drivers of the mycorrhizal C cost, which is crucial for predicting environmental impacts on plant productivity, remain under-explored and difficult to quantify. Ecosystem models that incorporate mycorrhizae can offer insights into mycorrhizal C cost dynamics, but their predictions have rarely been validated against empirical data. Here, in this study, we used the Myco-CORPSE model, which explicitly simulates mycorrhizal processes alongside soil carbon and nitrogen cycling, to investigate the drivers of mycorrhizal C cost in temperate forests. Applying this model to over 1,800 forest inventory plots across the eastern United States, we found that the simulations matched published data, showing higher C allocation to ectomycorrhizal (ECM) fungi (16.0% of net primary production (NPP)) compared to arbuscular mycorrhizal (AM) fungi (5.8% of NPP). Further analysis showed that mixed forests, co-dominated by both AM and ECM trees, allocated less C to mycorrhizal fungi compared to forests dominated by either AM or ECM fungi alone, due to complementary nutrient acquisition strategies. Elevated Nitrogen (N) deposition and higher temperatures reduce mycorrhizal C costs, favoring AM strategies. Conversely, elevated CO 2 (eCO 2 ) increased plant N demand and mycorrhizal C costs, favoring ECM strategies that access organic N sources. These findings underscore the critical role of mycorrhizal functional diversity in plant nutrient acquisition and C dynamics, providing new insights into how mycorrhizal symbioses respond to global change.

Shao, Siya [Dartmouth College, Hanover, NH (United↗
DOE OSTI2023

Taxonomic and environmental distribution of bacterial amino acid auxotrophies

Abstract Many microorganisms are auxotrophic—unable to synthesize the compounds they require for growth. With this work, we quantify the prevalence of amino acid auxotrophies across a broad diversity of bacteria and habitats. We predicted the amino acid biosynthetic capabilities of 26,277 unique bacterial genomes spanning 12 phyla using a metabolic pathway model validated with empirical data. Amino acid auxotrophy is widespread across bacterial phyla, but we conservatively estimate that the majority of taxa (78.4%) are able to synthesize all amino acids. Our estimates indicate that amino acid auxotrophies are more prevalent among obligate intracellular parasites and in free-living taxa with genomic attributes characteristic of ‘streamlined’ life history strategies. We predicted the amino acid biosynthetic capabilities of bacterial communities found in 12 unique habitats to investigate environmental associations with auxotrophy, using data compiled from 3813 samples spanning major aquatic, terrestrial, and engineered environments. Auxotrophic taxa were more abundant in host-associated environments (including the human oral cavity and gut) and in fermented food products, with auxotrophic taxa being relatively rare in soil and aquatic systems. Overall, this work contributes to a more complete understanding of amino acid auxotrophy across the bacterial tree of life and the ecological contexts in which auxotrophy can be a successful strategy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Parametric matrix models

We present a general class of machine learning algorithms called parametric matrix models. In contrast with most existing machine learning models that imitate the biology of neurons, parametric matrix models use matrix equations that emulate physical systems. Similar to how physics problems are usually solved, parametric matrix models learn the governing equations that lead to the desired outputs. Parametric matrix models can be efficiently trained from empirical data, and the equations may use algebraic, differential, or integral relations. While originally designed for scientific computing, we prove that parametric matrix models are universal function approximators that can be applied to general machine learning problems. After introducing the underlying theory, we apply parametric matrix models to a series of different challenges that show their performance for a wide range of problems. For all the challenges tested here, parametric matrix models produce accurate results within an efficient and interpretable computational framework that allows for input feature extrapolation.

Computational science↗
DOE OSTI2023

w Mel replacement of dengue-competent mosquitoes is robust to near-term climate change

Rising temperatures are impacting the range and prevalence of mosquito-borne diseases. A promising biocontrol technology replaces wild mosquitoes with those carrying the virus-blocking Wolbachia bacterium. Because the most widely used strain, wMel, is adversely affected by heat stress, we examined how global warming may influence wMel-based replacement. We simulated interventions in two locations with successful field trials using Coupled Model Intercomparison Project Phase 5 climate projections and historical temperature records, integrating empirical data on wMel’s thermal sensitivity into a model of Aedes aegypti population dynamics to evaluate introgression and persistence over one year. We show that in Cairns, Australia, climatic futures necessitate operational adaptations for heatwaves exceeding two weeks. In Nha Trang, Vietnam, projected heatwaves of three weeks and longer eliminate wMel under the most stringent assumptions of that symbiont’s thermal limits. We conclude that this technology is generally robust to near-term (2030s) climate change. Accelerated warming may challenge this in the 2050s and beyond.

54 ENVIRONMENTAL SCIENCES↗