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DOE OSTI2024

Projecting Changes in the Frequency and Magnitude of Ozone Pollution Events Under Uncertain Climate Sensitivity

Abstract Climate change is projected to worsen ozone pollution over many populated regions, with larger impacts at higher concentrations. More intense and frequent ozone episodes risk setbacks to human health and environmental policy achievements. However, assessing these changes is complicated by uncertain climate sensitivity, closely related to climate model response, and internal variability in simulations projecting climate's influence on air quality. Here, leveraging a global modeling framework that one‐way couples a human activity model, an Earth system model of intermediate complexity, and an atmospheric chemistry model, we investigate the role of climate sensitivity in climate‐induced changes to high ozone pollution episodes in the United States using multiple greenhouse gas emissions scenarios, representations of climate sensitivity, and initial condition members. We bias correct and evaluate historical model simulations, identifying modeled and observed O 3 episodes using extreme value theory, and extend the approach to projections of mid‐ and end‐century climate impacts. Results show that the influence of climate sensitivity can be as significant as that of greenhouse gas emissions scenario absent precursor emissions changes. Climate change is projected to increase the magnitude of the highest annually occurring O 3 concentrations by over 2.3 ppb on average across the U.S. at mid‐century under a high climate sensitivity and moderate emissions scenario, but the increase is limited to less than 0.3 ppb under lower climate sensitivity. Further, we show that areas in the U.S. currently meeting air quality standards risk being pushed into non‐compliance due to a climate‐induced increase in frequency of high ozone days.

Environmental Sciences & Ecology↗

DOE OSTI2024

Envisioning U.S. Climate Predictions and Projections to Meet New Challenges

In the face of a changing climate, the understanding, predictions, and projections of natural and human systems are increasingly crucial to prepare and cope with extremes and cascading hazards, determine unexpected feedbacks and potential tipping points, inform long-term adaptation strategies, and guide mitigation approaches. Increasingly complex socio-economic systems require enhanced predictive information to support advanced practices. Such new predictive challenges drive the need to fully capitalize on ambitious scientific and technological opportunities. These include the unrealized potential for very high-resolution modeling of global-to-local Earth system processes across timescales, reduction of model biases, enhanced integration of human systems and the Earth Systems, better quantification of predictability and uncertainties; expedited science-to-service pathways, and co-production of actionable information with stakeholders. Enabling technological opportunities include exascale computing, advanced data storage, novel observations and powerful data analytics, including artificial intelligence and machine learning. Looking to generate community discussions on how to accelerate progress on U.S. climate predictions and projections, representatives of Federally-funded U.S. modeling groups outline here perspectives on a six-pillar national approach grounded in climate science that builds on the strengths of the U.S. modeling community and agency goals. This calls for an unprecedented level of coordination to capitalize on transformative opportunities, augmenting and complementing current modeling center capabilities and plans to support agency missions. Tangible outcomes include projections with horizontal spatial resolutions finer than 10 km, representing extremes and associated risks in greater detail, reduced model errors, better predictability estimates, and more customized projections to support next generation climate services.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

A Novel Emergent Constraint Approach for Refining Regional Climate Model Projections of Peak Flow Timing

Abstract Global climate models (GCMs) are unable to produce detailed runoff conditions at the basin scale. Assumptions are commonly made that dynamical downscaling can resolve this issue. However, given the large magnitude of the biases in downscaled GCMs, it is unclear whether such projections are credible. Here, we use an ensemble of dynamically downscaled GCMs to evaluate this question in the Sierra‐Cascade mountain range of the western US. Future projections across this region are characterized by earlier seasonal shifts in peak flow, but with substantial inter‐model uncertainty (−25 ± 34.75 days, 95% confidence interval (CI)). We apply the emergent constraint (EC) method for the first time to dynamically downscaled projections, leading to a 39% (−28.25 ± 20.75 days, 95% CI) uncertainty reduction in future peak flow timing. While the constrained results can differ from bias corrected projections, the EC is based on GCM biases in historical peak flow timing and has a strong physical underpinning.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Combined Meteorological and Hydrologic Uncertainties Shape Projections of Future Soil Moisture in the Eastern United States

Physical hazards pose risks to many critical systems. Designing adaptive measures to mitigate these risks is challenging due to large uncertainties in modeling future hazards and the associated sectoral responses. Here, we help address this challenge in a hydrologic context by examining the combined role of meteorological forcing and hydrologic parameter uncertainties in shaping projections of future soil moisture. By encoding a simple conceptual water balance model in a differentiable programming framework, we facilitate fast runtimes and an efficient calibration, enabling an improved uncertainty analysis. We characterize uncertainty in model parameters by calibrating against different target data sets and by using several loss functions. We then convolve the resulting parameter ensemble with a set of Earth system model projections to produce a large ensemble (2,340 members) of daily soil moisture simulations. Focusing on the eastern United States, we find that most ensemble members project a drying of soils across the region, although some simulate wetter conditions throughout this century. Our ensemble shows an increase in the frequency and intensity of dry extremes while there is less agreement for wet extremes. We conduct sensitivity analyses on several soil moisture signatures to measure the relative influence of meteorological and hydrologic uncertainties across space and time. Both meteorological and hydrologic factors contribute consistently to uncertainty surrounding long-term trends, while changes to both wet and dry soil extremes are typically more sensitive to hydrologic parameter uncertainty. Our results underscore the need to account for varied sources of uncertainty when developing long-term hydrometeorological projections.

Lafferty, David C. [University of Illinois Urbana‐↗

DOE OSTI2020

Multi-year incubation experiments boost confidence in model projections of long-term soil carbon dynamics

Abstract Global soil organic carbon (SOC) stocks may decline with a warmer climate. However, model projections of changes in SOC due to climate warming depend on microbially-driven processes that are usually parameterized based on laboratory incubations. To assess how lab-scale incubation datasets inform model projections over decades, we optimized five microbially-relevant parameters in the Microbial-ENzyme Decomposition (MEND) model using 16 short-term glucose (6-day), 16 short-term cellulose (30-day) and 16 long-term cellulose (729-day) incubation datasets with soils from forests and grasslands across contrasting soil types. Our analysis identified consistently higher parameter estimates given the short-term versus long-term datasets. Implementing the short-term and long-term parameters, respectively, resulted in SOC loss (–8.2 ± 5.1% or –3.9 ± 2.8%), and minor SOC gain (1.8 ± 1.0%) in response to 5 °C warming, while only the latter is consistent with a meta-analysis of 149 field warming observations (1.6 ± 4.0%). Comparing multiple subsets of cellulose incubations (i.e., 6, 30, 90, 180, 360, 480 and 729-day) revealed comparable projections to the observed long-term SOC changes under warming only on 480- and 729-day. Integrating multi-year datasets of soil incubations (e.g., > 1.5 years) with microbial models can thus achieve more reasonable parameterization of key microbial processes and subsequently boost the accuracy and confidence of long-term SOC projections.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Grayscale projection two-photon lithography using sub-diffraction motifs for ultrafast and precise nanoscale 3D printing

Rapid and high-fidelity nanoscale 3D printing is highly desirable, but it is difficult due to the tradeoff between speed and accuracy. Although optical projection techniques can massively scale up printing, fidelity is compromised due to the difficulty in precisely controlling the light dosage over the entire field. This challenge is typically addressed by using multiple projections, but it slows down printing. Here, we present grayscale projection two-photon lithography to overcome this tradeoff. Despite using a binary mask, it enables projecting more than 15,000 focal spots, each with independently tunable intensity. It advantageously leverages constraints imposed by optical diffraction to achieve grayscale tuning over the entire field at once. By directly tuning the focal spot intensities, we demonstrate suppression of proximity effects, compensation of non-uniform illumination, compensation of stitching artefacts, and rapid 3D printing with a single femtosecond pulse per layer. We demonstrate printing of nanowires as thin as 55 nm and achieve rates of 1.7 billion voxels/s and 215 mm 3 /hr.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Accelerated data-driven materials science with the Materials Project

The Materials Project was launched formally in 2011 to drive materials discovery forwards through high-throughput computation and open data. More than a decade later, the Materials Project has become an indispensable tool used by more than 600,000 materials researchers around the world. This Perspective describes how the Materials Project, as a data platform and a software ecosystem, has helped to shape research in data-driven materials science. We cover how sustainable software and computational methods have accelerated materials design while becoming more open source and collaborative in nature. Next, we present cases where the Materials Project was used to understand and discover functional materials. We then describe our efforts to meet the needs of an expanding user base, through technical infrastructure updates ranging from data architecture and cloud resources to interactive web applications. Finally, we discuss opportunities to better aid the research community, with the vision that more accessible and easy-to-understand materials data will result in democratized materials knowledge and an increasingly collaborative community.

Horton, Matthew K↗

DOE OSTI2025

Non-local detection of coherent Yu–Shiba–Rusinov quantum projections

Probing spatially confined quantum states from afar—a long-sought goal to minimize external interference—has been proposed to be feasible in condensed-matter systems through the coherent projection of the state. This can be achieved by engineering the eigenstates of the electron sea that surrounds the quantum state using cages built atom by atom, the so-called quantum corrals. However, the demonstration of the coherent nature of the projection and manipulation of its quantum composition are still important goals. Here we show this for the coherent projection of a Yu–Shiba–Rusinov quantum state that is induced by a magnetic impurity, using the eigenmodes of corrals on the surface of a superconductor. This enables us to manipulate the particle–hole composition of the projected state by tuning the corral eigenmodes through the Fermi energy. Our results demonstrate a controlled non-local method for the detection of magnet–superconductor hybrid quantum states.

quantum mechanics↗

DOE OSTI2020

Multi-model Hydroclimate Projections for the Alabama-Coosa-Tallapoosa River Basin in the Southeastern United States

Abstract This study uses a high-resolution, process-based modeling framework to assess the impacts of changing climate on water resources for the Alabama-Coosa-Tallapoosa River Basin in the southeastern United States. A 33-member ensemble of hydrologic projections was generated using 3 distributed hydrologic models (Precipitation-Runoff Modeling System, Variable Infiltration Capacity, and Distributed Hydrology Soil Vegetation Model) of different complexity. These hydrologic models were driven by dynamically downscaled and bias-corrected future climate simulations from 11 Coupled Model Intercomparison Project Phase 5 global climate models under Representative Concentration Pathway 8.5 emission scenario, with 40 years each in baseline (1966–2005) and future (2011–2050) periods. The hydroclimate response, in general, projects an increase in mean seasonal precipitation, runoff, and streamflow. The high and low flows are projected to increase and decrease, respectively, in general, suggesting increased likelihood of extreme rainfall events and intensification of the hydrologic cycle. The uncertainty associated with the ensemble hydroclimate response, analyzed through an analysis of variance technique, suggests that the choice of climate model is more critical than the choice of hydrologic model for the studied region. This study provides in-depth insights of hydroclimate response and associated uncertainties to support informed decisions by water resource managers.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Dynamically downscaled seasonal heat wave projections in the CONUS

Heat waves are a well-documented hazard that are projected to increase in intensity, duration, and frequency with climate change. Regions of the US experience widely varying temperatures; for example, 35 °C is extremely hot for spring in the Northeast but not for summer in the Southeast. It is important to evaluate projections within a regional context and at a high enough resolution to understand the risks to populations. We identify heat waves across the Conterminous US (CONUS) under SSP5–8.5 from 2020 to 2059 with an ensemble of dynamically downscaled Coupled Model Intercomparison Project Phase 6 (CMIP6) model outputs. We demonstrate that there are regional differences caused by seasonal and local drivers of persistent hot temperatures. Summer heat waves are increasing in intensity and duration faster than winter heat waves because of the atmospheric conditions that promote these events. Our analysis emphasizes the value of fine-resolution modeling for projecting future climate risks.

Rubin, Hannah [University of Tennessee, Knoxville ↗

DOE OSTI2025

Using nuclear observations to improve climate research and GHG emission estimates – the NuClim project

Project NuClim (Nuclear observations to improve Climate research and GHG emission estimates) aims to use high-quality measurements of atmospheric radon activity concentration and ambient radioactivity to advance climate science and improve radiation protection and nuclear surveillance capabilities. It is supported by new metrological capabilities developed in the EMPIR project 19ENV01 traceRadon. This work reviews the scientific objectives of project NuClim in terms of both climate science and radiological protection, and provides an overview of the NuClim field campaign and the various nuclear measurements being implemented within the scope of the project.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Verification of an improved equation-free projective integration method for neoclassical plasma-profile evolution in tokamak geometry

A brute-force, long-time gyrokinetic simulation of plasma profile evolution in magnetic fusion devices is not desirable due to large computational resource requirements and a possible accumulation of numerical error. The equation-free projective integration method of Keverekidis et al. [Commun. Math. Sci. 1(4), 715–762 (2003)] is one of the outstanding candidates in projecting micro-scale simulations to a longer timescale. However, its application to tokamak plasma has not been fruitful due to the appearance of spurious transient oscillations in the lifting process, which are present when the kinetic simulations are initialized with a simplified model distribution function and which make the kinetic simulations to deviate from the desired paths. In this work, a kinetically informed lifting algorithm is added to the equation-free projective integration method, which is then verified in the electrostatic gyrokinetic particle-in-cell code XGCa [R. Hager and C. S. Chang, Phys. Plasmas 23, 042503 (2016)] for a neoclassical ion heat transport problem with adiabatic electrons. This new lifting operator is demonstrated to control spurious transients, enabling an over four-times reduction in the overall computing time in the time-evolution of the ion temperature profile in an axisymmetric toroidal plasma. Further reduction in the computing time is found to be limited due to the stability properties of the linear least squares projective integrator.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Linear ion-scale microstability analysis of high and low-collisionality NSTX discharges and NSTX-U projections

Linear gyrokinetic simulations were conducted to investigate ion-gyroradius-scale micro-instability predictions for high-beta NSTX discharges and NSTX-U projections that span over an order of magnitude variation in collisionality. A complex mix of microtearing modes and hybrid trapped electron modes/kinetic ballooning modes (TEM/KBM) is predicted for all experimental or projected conditions. Ion temperature gradient (ITG) instabilities are typically stable in the NSTX discharges investigated, consistent with the observed neoclassical ion thermal transport. ITG thresholds inferred from the simulations are typically much higher than the experimental NSTX gradients, as well as the projected gradients in the NSTX-U scenario, which assumed ion temperatures limited by neoclassical transport only. The analysis suggests ITG instabilities are unlikely to contribute significant anomalous thermal losses in high-beta, lower collisionality NSTX-U scenarios. On the other hand, the NSTX experimental profiles and NSTX-U projections are predicted to be very close to the predicted onset of unstable KBM at most radii investigated. The proximity of the various discharges to the KBM instability threshold implies it may play an important role in setting profile shapes and limiting global energy confinement. Here, it remains to be understood and predicted how KBM contributes to multi-channel transport (thermal and particle transport, for both ions and electrons) in a way that is consistent with experimental inferences.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Symmetry-projected spin-AGP methods applied to spin systems

Symmetry-projected wave function methods capture static correlation by breaking and restoring the symmetries of a system. In this article, we present the symmetry-projected spin antisymmetrized geminal power (spin-AGP) state projected onto space group symmetry as well as complex conjugation, spin-flip, and time-reversal symmetries. The method is benchmarked on the 1D XXZ model and the 2D J 1 − J 2 model with square and triangular lattices. Our results indicate that symmetry projection methods provide a powerful tool for frustrated spin systems.

Antisymmetrized geminal power↗

DOE OSTI2025

Lack of clear standards and usable comparisons of downscaled climate projections pose a roadblock for US climate discovery and adaptation

Abstract The release of global climate projections coupled with the demand for local-resolution climate-forced meteorology has prompted many research groups to downscale these projections using various statistical, dynamical, and current machine learning techniques. Such downscaled datasets are being used to plan infrastructure and other community needs over the coming decades. Faced with roughly a dozen available US downscaled datasets, many practitioners ask, ‘What are the relevant differences between datasets?’ This work highlights the difficulty of comparing downscaled datasets and illustrates ways in which datasets differ even when using identical climate model input data. We show that substantial variability in precipitation projections arises from downscaling alone and that the downscaled dataset agreement varies depending on global climate projection. This analysis emphasizes the need for greater coordination and movement toward rigorous benchmarking of downscaling strategies within the downscaling research community, à la the land-modeling community, to better quantify downscaling dataset differences, strengths, and weaknesses for practitioners.

Hartke, Samantha H. (ORCID:0000000202394723)↗

DOE OSTI2025

Projected evolution of droughts and human exposure in the contiguous United States under SSP5-8.5: a regional downscaling perspective

The increasingly unpredictable and extreme weather patterns under a warming climate underscore the urgency of accurate regional assessments of future drought risk. This study evaluates the projected drought evolution in the contiguous United States under the high-emission shared socioeconomic pathway 5–8.5 climate scenario for the coming decades. Using a multi-model ensemble of six Coupled Model Intercomparison Project Phase 6 global climate models combined with dynamical downscaling techniques, we analyzed near-term (2020–2039) and mid-term (2040–2059) drought patterns using the self-calibrating palmer drought severity index (ScPDSI), the standardized precipitation index (SPI-12), and the Standardized Precipitation-Evapotranspiration Index (SPEI-12). Results reveal a widespread increase in abnormally dry (D0) and moderate drought (D1) conditions, particularly in urban areas, while severe (D2), extreme (D3), and exceptional (D4) droughts are expected to become less common in many regions. Meanwhile, persistent and intensifying droughts are projected in the western and southwestern U.S., driven by long-term soil moisture deficits. The ScPDSI projects that 1.1 million urban residents will be affected by D0 conditions in 2050, while SPI-12 suggests a decrease in the total affected populations after 2040. ScPDSI indicates prolonged droughts in the West, and SPI-12 captures transient variability. Although the total drought-exposed population is expected to decrease, urban areas will continue to bear a greater burden, particularly for mild droughts (D0, D1). These findings highlight a shift toward more frequent mild droughts, fewer severe droughts, and persistent drying in the Southwest, emphasizing the need for region-specific adaptation strategies.

CMIP6↗

DOE OSTI2020

Projected WIMP sensitivity of the LUX-ZEPLIN dark matter experiment

LUX-ZEPLIN (LZ) is a next generation dark matter direct detection experiment that will operate 4850 feet underground at the Sanford Underground Research Facility (SURF) in Lead, South Dakota, USA. Using a two-phase xenon detector with an active mass of 7 tonnes, LZ will search primarily for low-energy interactions with Weakly Interacting Massive Particles (WIMPs), which are hypothesized to make up the dark matter in our galactic halo. In this paper, the projected WIMP sensitivity of LZ is presented based on the latest background estimates and simulations of the detector. For a 1000 live day run using a 5.6 tonne fiducial mass, LZ is projected to exclude at 90% confidence level spin-independent WIMP-nucleon cross sections above $1.6 \times 10^{-48}$ cm$^{2}$ for a 40 $\mathrm{GeV}/c^{2}$ mass WIMP. Additionally, a $5\sigma$ discovery potential is projected reaching cross sections below the existing and projected exclusion limits of similar experiments that are currently operating. For spin-dependent WIMP-neutron(-proton) scattering, a sensitivity of $2.7 \times 10^{-43}$ cm$^{2}$ ($8.1 \times 10^{-42}$ cm$^{2}$) for a 40 $\mathrm{GeV}/c^{2}$ mass WIMP is expected. With underground installation well underway, LZ is on track for commissioning at SURF in 2020.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Systematic study of projection biases in weak lensing analysis

We present a systematic study of projection biases in the weak lensing analysis of the first year of data from the Dark Energy Survey (DES) experiment. In the analysis we used a $\Lambda$CDM model and three two-point correlation functions. We show that these biases are a consequence of projecting, or marginalizing, over parameters like $h$, $\Omega_b$, $n_s$ and $\Omega_\nu h^2$ that are both poorly constrained and correlated with the parameters of interest like $\Omega_m$, $\sigma_8$ and $S_8$. Covering the relevant parameter space we show that the projection biases are a function of where the true values of the poorly constrained parameters lie with respect to the parameter priors. For example, biases in the position of the posteriors can exceed the 1.5$\sigma$ level if the true values of $h$ and $n_s$ are close to the top of the prior's range and the true values of $\Omega_b$ and $\Omega_\nu h^2$ are close to the bottom of the range of their priors. We also show that in some cases the 1D credible intervals can be over-specified by as much as 30% and coverage can be as low as 27%. Finally we estimate these projection biases for the analysis of three and six years worth of DES data.

79 ASTRONOMY AND ASTROPHYSICS↗