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At least 19 records

Implications of climate change impacts for emission and land use scenario development

Scenarios of future emissions and land use produced by integrated assessment models have traditionally been developed without accounting for how climate change impacts could affect the emissions and land use trajectories themselves. This omission risks skewing our assessments of the plausible range of future emission pathways and associated Earth system changes. Beyond the salience for emission scenario development, a better integrated representation of human and Earth system changes and feedbacks would enable better anticipation of the implications of alternative socio-economic development pathways. We use the Global Change Analysis Model to investigate whether endogenizing several impacts when generating its baseline emission scenario is warranted. We do so by comparing the emissions and land use change that result from the baseline scenario with and without impacts, where impacts are implemented as exogenous changes to water availability, crop and labor productivity, and energy demand and supply. Our results indicate that the effect on global emissions leads to less than 0.1 °C increase in warming by 2100 and therefore do not support endogenizing impacts. This conclusion is conditional on our modeling framework and the specific impact channels represented but is consistent with other studies that have addressed the magnitude of feedbacks by implementing a two-way coupling. However, we do find regional impacts indicating that local economies and well-being measures may be affected significantly.

climate impacts

Reversed magnetic shear scenario development in NSTX-U using TRANSP

Abstract Understanding and control of electron thermal transport is a critical point of research in magnetic fusion experiments. Previous experiments have shown that operation with reversed magnetic shear (RMS) can suppress electron thermal transport, resulting in the generation of internal transport barriers (ITBs), with the location of the ITB correlated with the location of minimum magnetic shear. The recent upgrades to NSTX—increased magnetic field up to 1 T, increased plasma current up to 2 MA, 2nd neutral beam—present an increased operating space in which to explore electron thermal transport in RMS plasmas. Utilizing TRANSP, we have developed operating scenarios by which to generate RMS in NSTX-U. The results suggest that RMS in NSTX-U can be generated through fast current ramp and early beam injection into a large plasma volume. This is very similar to the procedure that was followed in both TFTR and NSTX to generate RMS. Sustainment of RMS, disregarding non-( q min = 1) MHD events, requires maintaining a large plasma volume, and increasing the core T e , either via increased plasma current and/or adding heating power. Using this procedure, RMS was sustained for ∼1 s, with q min > 1 for that period.

Galante, M. E. (ORCID:0009000098149425)

Parametric raytracing modeling for NSTX-U scenario development with high harmonic fast waves and neutral beam injection

High harmonic fast waves (HHFW) are a versatile heating and current drive tool for scenario development. Extensive modeling scans were performed to find optimal parameters for different uses of HHFW in National Spherical Tokamak Experiment (NSTX-U). Scans of plasma density, temperature, magnetic field, and antenna phasing were performed both with and without neutral beam injection. For speed of calculation, the ray-tracing code GENRAY coupled to the quasilinear Fokker–Planck code CQL3D was used. CQL3D allows for a more accurate description of the fast ion population, as well as for quasilinear effects such as HHFW-induced modifications of the distribution function. Best current drive results are obtained at elevated electron temperatures and with the lowest k φ phasing. Adding neutral beams however typically strongly reduces the HHFW current drive efficiency at the low density cases due to HHFW absorption on beam ions. Results of this parametric study will feed into scenario development and predict-first whole-shot modeling of NSTX-U discharges.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY

A Systematic Comparison for Consistent Scenario Development Using Microscopic Simulation Software

This study aims to explore a methodology that enables the development of consistent traffic micro simulation for emerging traffic and vehicle control technologies for improved mobility and energy efficiency across different modeling platforms. Researchers might study the same application on different platforms and have the need to benchmark across platforms. However, there lacks a systematic study on simulation software comparison, especially for emerging mobility and energy efficiency applications. For this, a systematic scenario development and evaluation approach is presented and demonstrated to compare scenarios generated in different traffic microsimulation platforms. Network-level and vehicle-level trip performance results of the traffic scenario are evaluated in three microscopic simulation platforms - VISSIM, AIMSUN, and SUMO. The results indicate that the network-level performance is consistent among the three software suites except when the demand is high, where the energy consumption performance varies.

Saroj, Abhilasha [ORNL] (ORCID:0000000191178063)

Developing Scenario‐Based Strategies for Health, Climate, and Environmental Preparedness: The One Health, One Earth Approach

Climate change amplifies many threats to human health. Despite advances in understanding climate change dynamics and impacts, there remains a critical gap in translating scientific knowledge into equitable, and community-driven health interventions. The inaugural One Earth, One Health workshop sought to explore this gap through human-centered design exercises involving interdisciplinary researchers from climate and Earth sciences, engineering, epidemiology, microbiology, and environmental health. Although participants did not co-develop solutions with affected communities, they used stakeholder role-playing to guide ideation and lay groundwork for actionable plans. Through these methods, participants identified community needs and proposed prototype solutions to alleviate health threats exacerbated by global environmental change. Prototypes were organized around infectious diseases, extreme weather, and air quality, as illustrative themes rather than an exhaustive set of risks. Key solutions included strategies for anticipatory systems and early warning (e.g., integrating environmental signals with health data), inclusive communication and infrastructure needs for responding to extreme weather events, and integrated platforms visualizing air quality trends to support tailored, context-aware guidance beyond one-size-fits-all alerts. The workshop highlighted opportunities such as leveraging machine learning, Earth observation, and real-time surveillance to protect communities, but also noted barriers including data quality, technological redundancy, privacy, and governance challenges. Additionally, participants emphasized the need for interdisciplinary teams capable of collaborating across sectors, breaking down silos and addressing gaps in training and education. Overall, the workshop illustrates how process-driven, human-centered approaches can help surface user needs and generate testable prototype concepts, while underscoring the importance of direct community partnership for implementation.

Abadi, Azar M. [University of Alabama, Birmingham,

Stability evaluation and mitigation strategies in advanced tokamaks using 3D MHD spectroscopy

Multi-modal, active 3D MHD spectroscopy is applied in high-performance advanced tokamak scenarios to study their stability time evolution, revealing an intriguing dependence on both and . A tailored applied 3D field provides a 3D plasma response to extract the growth rate of the least stable mode. The estimated growth rate finds a decrease in stability when the minimum in the safety factor (q) passes through 2.0 and reveals inherent risks of crossing an additional rational surface at integer q min , even above the usual q = 1 sawtooth condition. Based on this result, the potential scenario in which q min ~ 2 can be safely crossed during a more stable lower β N phase was investigated, and the improved stability of this scenario is confirmed by the estimated growth rate. This shows that 3D MHD spectroscopy can offer insights into strategies for improving stability by identifying the vulnerable aspects of such scenarios. In addition, the method highlights its potential for instability avoidance by enabling early detection of multiple modes, even before magnetic coils can measure them. The measured growth rate by the 3D MHD spectroscopy shows its reliability by exhibiting a correlation with the programmed rises in plasma beta across various high β N and high q min discharges. In addition, this method is successfully applied during rapidly evolving I p ramp-up phases, a key part of the scenario development. By achieving reasonable growth rate measurements at high-performance scenario developments, this technique contributes to the development of advanced diagnostic tools for tokamak scenario stability, which will help identify an effective pathway to stable, high-performance scenarios.

Yang, S. M. [Princeton Plasma Physics Laboratory (

Automated ICRF heating surrogate modeling via machine learning

This work introduces automated machine learning workflows that address critical bottlenecks in surrogate model development for Ion Cyclotron Range of Frequencies (ICRF) heating applications. The automated framework includes data analysis tools that transform raw datasets into actionable insights in seconds, replacing weeks of manual exploratory effort and ensuring consistent, reproducible dataset characterization. By integrating advanced hyperparameter optimization (HPO) methods including Bayesian optimization via BoTorch and Tree-structured Parzen Estimators (TPE), the framework significantly reduces model development time from weeks to hours, decreasing computational cost and required expertise, while enabling high-accuracy surrogate models. Compared to traditional hyperparameter scanning (HPS) techniques such as methodical, randomized, and grid searches, HPO methods achieve superior convergence and predictive performance, even when compared to already well-tuned reference models. On NSTX High Harmonic Fast Wave (HHFW) heating datasets, both Random Forest Regressor (RFR) and neural network surrogates demonstrate improved accuracy, achieving R 2 values beyond 0.97 and 0.98, respectively. The results show that while HPO gains are modest for robust architectures like RFR, they become essential for more sensitive models such as neural networks, highlighting the trade-offs across optimization strategies. Through automated workflows that eliminate manual hyperparameter tuning and require minimal ML expertise, this work enables widespread adoption of high-fidelity surrogate models across the fusion community for real-time plasma control, uncertainty quantification, rapid experimental scenario development, and integrated system optimization.

Sanchez-Villar, Alvaro [Princeton Plasma Physics L

Community-Centric Microgrid Feasibility Analysis Framework to Accelerate Resilience of Island Communities

Marine energy offers a reliable energy solution for island and coastal communities, which often lack traditional local generation, to support their transition to energy independence and reduce reliance on externally imported fuels. Successful deployment of new technologies in these isolated locations requires community acceptance and approval from the outset, as these communities typically lack the financial and technical resources to operate and maintain new systems. This report presents a community-centric microgrid planning framework for remote coastal and island communities. Community engagement is integrated as the first step in the planning process, incorporating community profiles and visions into energy development scenarios. A case study was conducted in St. George, Pribilof Islands, Alaska, which relies entirely on diesel yet has significant wind and wave energy potential. Community engagement revealed a unique history and current economic status, with an interest in adopting advanced energy technologies despite past failures. Various microgrid configurations were optimized, considering different technologies to meet current and future energy needs while balancing cost and energy resilience. Wave energy converters (WECs) were a key component, integrated with other energy sources using the Xendee optimization tool. The Marine Energy Microgrid Toolkit, developed as part of this work, uses commercial power system analysis tools to optimize and analyze microgrid scenarios. The developed framework and toolkit can be applied to island and coastal communities to enhance resilience and support microgrid deployments. Future enhancements will include incorporating new marine resources, developing dynamic models, and automating the integration of Xendee and PowerFactory simulations.

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Projected Urban Morphology of the Los Angeles Area by the Year 2100

This dataset provides projections of urban building morphologies for the Los Angeles urban area at 30-meter spatial resolution. It contains 192 raster files that detail two primary building attributes: building footprint fractions (ranging from 0 to 1) and average building heights (ranging from 0 to 75 meters). The projections account for a wide range of future pathways, covering two Shared Socioeconomic Pathway (SSP) scenarios (SSP3 and SSP5), two population scenarios, two developed land intensification scenarios, and four distinct levels of intensification. The dataset was created using dual Generative Adversarial Networks (GANs) trained on 2015 land cover and building properties from the National Land Cover Database (NLCD) and Model America datasets. Supporting information on the dataset has been described in the LAUrbanAreaMorphologyProjections2100_README.txt file.

Pandey, Bhartendu

Applying a Multisector Scenario Framework to Evaluate Past and Future Public Surface Water Supply Infrastructure Strategies in Texas

Datasets supporting the index model and scenario analysis used in evaluating surface water supply strategies across different water system types in Texas. These data underpin the scenario development and application of five key indicators: Water Availability Index (WAI), Water Quality Index (WQI), Energy Requirement Index (ERI), Water Treatment Cost (WTC), and Water Infrastructure Cost (WIC). The datasets are organized by system type—stream reaches (flowlines), waterbodies, and reservoirs—and include both raw and standardized index values. The integrated datasets also provide scenario classifications (original and adjusted) based on infrastructure and planning priorities, enabling comparison across Shared Socioeconomic Pathways (SSPs). Additional strategy-level data are included to support evaluation of state-level new reservoir projects in relation to cost and availability tradeoffs. Please refer to the README file provided in Files for more details. Descriptions of the datasets are provided below. Dataset(s) Descriptions Folder: Index_model_database.zip Subfolder: Stream_reach.zip Fl_wf.csv, Fl_wq.csv, Fl_er.csv, Fl_wf_wtcUV.csv, Fl_wf_wtcnoUV.csv, Fl_allfac_wic1.csv, Fl_allfac_wic2.csvDatasets for computing WAI, WQI, ERI, WTC, and WIC for surface water systems classified as stream reaches (flowlines). Subfolder: Waterbody.zip Wb_wf.csv, Wb_wq.csv, Wb_er.csv, Wb_wf_wtcUV.csv, Wb_wf_wtcnoUV.csv, Wb_allfac_wic1.csv, Wb_allfac_wic2.csvEquivalent index model datasets for waterbodies, reflecting hydrologic and infrastructure attributes specific to impounded natural systems. Subfolder: Reservoir.zip Rs_wf.csv, Rs_wq.csv, Rs_er.csv, Rs_wf_wtcUV.csv, Rs_wf_wtcnoUV.csv, Rs_allfac_wic1.csv, Rs_allfac_wic2.csvIndex model datasets specific to regulated reservoir systems, incorporating both resource indicators and cost parameters. Folder: Integrated data.zip combined_merged_data.csv, combined_merged_data_scenario.csvDatasets integrating index model indicators (both raw and scaled) with scenario classifications, including adjustments reflecting SSP-aligned transitions and planning shifts. Folder: Additional data.zip wai_supplystrat_wic_merged.csvCurated dataset capturing proposed major reservoir-based municipal water supply strategies in Texas. Integrates site-level planning data with estimated capital infrastructure costs and water availability scores for comparative assessment.

geospatial

Towards an open model intercomparison platform for integrated assessment models scenarios

The majority of scenarios in the IPCC database are generated by integrated assessment models (IAMs) and come from model intercomparison projects. However, the way in which the current model intercomparison projects are organized is not open to all IAM teams worldwide. Here we propose a transparent and inclusive platform that is open to anyone with an IAM regarding protocols development, scenario submissions and results evaluation. We discuss the challenges of this approach, particularly human resources and financial support. Here, we identify diversity in the level of model capability and quality of model output as possibly critical issues. Despite such challenges, the IAM community and its scientific activities can improve and benefit from the proposed platform, ultimately contributing to better climate policymaking.

IAM

E3SM‐GCAM: A Synchronously Coupled Human Component in the E3SM Earth System Model Enables Novel Human‐Earth Feedback Research

Abstract Modeling human‐environment feedbacks is critical for assessing the effectiveness of climate change mitigation and adaptation strategies under a changing climate. The Energy Exascale Earth System Model (E3SM) now includes a human component, with the Global Change Analysis Model (GCAM) at its core, that is synchronously coupled with the land and atmosphere components through the E3SM coupling software. Terrestrial productivity is passed from E3SM to GCAM to make climate‐responsive land use and CO 2 emission projections for the next 5‐year period, which are interpolated and passed to E3SM annually. Key variables affected by the incorporation of these feedbacks include land use/cover change, crop prices, terrestrial carbon, local surface temperature, and climate extremes. Regional differences are more pronounced than global differences because the effects are driven primarily by differences in land use. This novel system enables a new type of scenario development and provides a powerful modeling framework that facilitates the addition of other feedbacks between these models. This system has the potential to explore how human responses to climate change impacts in a variety of sectors, including heating/cooling energy demand, water management, and energy production, may alter emissions trajectories and Earth system changes. Plain Language Summary Earth system models help us understand how humans are changing the climate. Currently, these models do not include human systems, so predetermined greenhouse gas, aerosol, and land use change data are input to these models. These data do not reflect human responses to changes projected by Earth system models. We have added a human component to an Earth system model to represent human responses to environmental change and calculate corresponding greenhouse gas and land use change data instead of using predetermined data. Including this human component changes projections of land use, land carbon storage, and regional climate. Key Points We have incorporated a novel, advanceable human component in an Earth system model to simulate human‐Earth feedbacks Including terrestrial productivity feedbacks from the Earth to the human systems affects land change, crop prices, carbon, and climate Regional effects of including terrestrial productivity feedbacks are greater than global effects because land change is the main driver

E3SM

Latent space mapping: Revolutionizing predictive models for divertor plasma detachment control

The inherent complexity of boundary plasma, characterized by multi-scale and multi-physics challenges, has historically restricted high-fidelity simulations to scientific research due to their intensive computational demands. Consequently, routine applications such as discharge control and scenario development have relied on faster but less accurate empirical methods. This work introduces DivControlNN, a novel machine-learning-based surrogate model designed to address these limitations by enabling quasi-real-time predictions (i.e., ~ 0.2 ms) of boundary and divertor plasma behavior. Trained on over 70,000 2D UEDGE simulations from KSTAR tokamak equilibria, DivControlNN employs latent space mapping to efficiently represent complex divertor plasma states, achieving a computational speed-up of over 10 8 compared to traditional simulations while maintaining a relative error below 20% for key plasma property predictions. During the 2024 KSTAR experimental campaign, a prototype detachment control system powered by DivControlNN successfully demonstrated detachment control on its first attempt, even for a new tungsten divertor configuration and without any fine-tuning. These results highlight the transformative potential of DivControlNN in overcoming diagnostic challenges in future fusion reactors by providing fast, robust, and reliable predictions for advanced integrated control systems.

Artificial neural networks

A review of collaborative studies between the NSTX/-U and MAST/-U spherical tokamaks

The National Spherical Torus Experiment (NSTX) at the Princeton Plasma Physics Laboratory in the United States, and the mega ampere spherical tokamak (MAST) at the United Kingdom Atomic Energy Authority in the United Kingdom, and their respective upgrades (NSTX-U and MAST-U) are two MAST fusion devices that have operated roughly over the past two decades. Both devices have made significant contributions to understanding spherical tokamak (ST) plasma physics, and fusion plasmas in general, and both have contributed data to multi-machine database studies. Several diagnostics have been physically moved from one machine to the other by diagnostic teams working on both devices. Collaboration has benefited both research teams in the areas of operational expertise, scenario development, and equilibrium reconstruction techniques. More focused comparative studies between the two devices have been pursued over the years in many areas as well, including stability calculations, disruption characterization, pedestal and edge localized mode stability, confinement and transport, energetic particles, and heating and current drive modelling. Together NSTX/-U and MAST/-U set the stage for the future of STs, which is entering the phase of design of demonstration power plant devices.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY

WEST L-mode record long pulses guided by predictions using Integrated Modeling

A new record was set on the WEST Tokamak, designed to operate long duration plasmas in a tungsten (W) environment, with an injected energy of 1.15 GJ and a plasma duration 364s. Scenario development was supported by integrated modeling using the High Fidelity Plasma Simulator (HFPS), the European IMAS-coupled version of JETTO/JINTRAC, which integrates physics-driven modules into a unified framework. In particular, a reduced model for Lower-Hybrid heating and Current-Drive (LHCD) and the quasi-linear turbulent transport model TGLF are crucial for long pulses predictions up to the Last Closed Flux Surface (LCFS). Using this workflow, a 100 s reference discharge was modeled and plasma kinetic profiles and loop voltage were quantitatively well matched. In preparation for the recent long duration experiments, non-inductive current-drive actuators (I P , n e , P LHCD ) were varied to determine the operational domain going towards fully non-inductive discharges. In particular, decreasing the plasma current is shown to ease the access to such conditions, with a careful monitoring of (n e , P LHCD ) to avoid machine limitations. In addition, post-prediction experiments conducted within the investigated parameter range validated the predicted dependencies and were shown to be in quantitative agreement. Exploratory work on the use of ECCD for MHD stability purpose is also introduced.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY

Sustainable forest management for carbon, wood and biodiversity must consider natural disturbance regimes

Abstract Forests provide multiple benefits that include sequestering carbon, supporting biodiversity, and providing wood products. Managing temperate forests to achieve these benefits sustainably is challenging given the legacy of past human impacts, the complexity of forest development, and increasing but unpredictable disturbances. These intricacies prompted debate about the most effective management of temperate forests to achieve climate mitigation while sustaining forest productivity, regeneration, and diversity. Most research focused on management impacts under relatively stable environmental conditions, whereas forests are facing increasingly frequent and more severe climatic and biotic disturbances. Here, we used a regionally calibrated forest growth model to simulate the effects of forest management on carbon, biodiversity, and wood production under prevalent natural disturbances in the northeastern US. We developed scenarios combining forest management and disturbance regimes based on assumptions of species-specific tree regeneration and mortality rates. We compared passive management with no cutting that prioritizes maintaining existing forest trees with two common regional harvest practices: exploitative timber harvest that prioritizes short-term wood production and regenerative timber harvest that prioritizes regenerating biodiverse native forest tree seedlings. In the absence of natural disturbance, no cutting provides 41% higher forest carbon storage and 50% higher tree structural diversity after 100 years than exploitative or regenerative practices. The presence of drought, windstorm, pest, and disease disturbances altered these outcomes. Regenerative forest management provides 25% higher forest carbon storage and 26% higher tree structural diversity after 100 years than no cutting. Our results demonstrate that wood products and their carbon storage and fossil emission avoidance are important for determining the total climate benefits of forest management under natural disturbances. Our findings highlight that natural disturbance regimes should be considered when selecting management to promote multiple forest benefits, suggesting the need for tailored as opposed to one-size-fits-all policies to sustainably manage temperate forests worldwide.

Liu, Weier (ORCID:0000000265914239)

ReVise: A Human-AI Interface for Incremental Algorithmic Recourse

The recent adoption of artificial intelligence in socio-technical systems raises concerns about the black-box nature of the resulting decisions in fields such as hiring, finance, admissions, etc. If data subjects—such as job applicants, loan applicants, and students—receive an unfavorable outcome, they may be interested in algorithmic recourse, which involves updating certain features to yield a more favorable result when re-evaluated by algorithmic decision-making. Unfortunately, when individuals do not fully understand the incremental steps needed to change their circumstances, they risk following misguided paths that can lead to significant, long-term adverse consequences. Existing recourse approaches focus exclusively on the final recourse goal but neglect the possible incremental steps to reach the goal with real-life constraints, user preferences, and model artifacts. To address this gap, we formulate a visual analytic workflow for incremental recourse planning in collaboration with AI/ML experts and contribute an interactive visualization interface that helps data subjects efficiently navigate the recourse alternatives and make an informed decision. We also present one of the many usage scenarios, developed during exploratory feedback sessions with twelve graduate students using a real-world dataset, which demonstrates that our approach can be instrumental for data subjects in choosing a suitable recourse path.

algorithmic recourse

Williston basin associated co 2 storage field laboratory

The Williston Basin Associated CO 2 Storage Field Laboratory (WBCFL), led by the Energy & Environmental Research Center (EERC) in partnership with Denbury (now ExxonMobil), was established to advance understanding of CO 2 enhanced oil recovery (EOR) and associated geologic CO 2 storage in the Williston Basin. The specific objective of the WBCFL was to conduct field and laboratory activities to inform decisionmakers regarding the 1) efficacy of associated storage in stacked geologic reservoirs during EOR operations; 2) CO 2 injectivity, oil production, and storage in residual oil zones (ROZs); 3) use of a novel CO 2 storage monitoring, verification, and accounting (MVA) technology (active seismoelectric [ASE] monitoring), and 4) life cycle and techno-economic factors for EOR development scenarios targeting ROZs. The project location was the Pennel Field in southeast Montana, which is part of the Cedar Creek Anticline (CCA) geologic structure within the Williston Basin.

02 PETROLEUM