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DOE OSTI2026

Recent developments and operation of polarized photocathodes at Jefferson Lab

Spin-polarized electron sources are critical to a wide range of accelerator-based applications for nuclear and particle physics. At Thomas Jefferson National Accelerator Facility, they play a central role in delivering high-quality polarized beams for precision nuclear physics experiments and next-generation parity-violation measurements, where stringent control of systematic uncertainties is essential. These sources are also expected to be key components of other initiatives, including the Electron-Ion Collider and the potential future positron capabilities at Jefferson Lab. In this talk, I will present ongoing research and development efforts at Jefferson Lab focused on the design, fabrication and optimization of spin-polarized photocathodes. This includes growth using molecular beam epitaxy (MBE) or metal-organic chemical vapor deposition (MOCVD), along with detailed characterization of their performance metrics, such as quantum efficiency (QE), electron spin polarization and QE anisotropy, all of which are increasingly important metrics for polarized electron sources at Jefferson Lab and the Electron-Ion Collider. Strategies to mitigate QE anisotropy, which is critical for reducing helicity-correlated beam asymmetries in precision experiments such as MOLLER will be highlighted. Finally, I will present recent efforts aimed at improving the operational lifetime of spin-polarized photocathodes in injector environments, particularly under high-voltage conditions in DC electron guns. These developments are essential for enabling reliable, high-performance operation of polarized sources for current and future accelerator programs.

Kachwala, Alimohammed [Thomas Jefferson National A↗

DOE OSTI2021

Statistical Characterization of Temperature and Pressure Vertical Profiles for the Analysis of Laser Heterodyne Radiometry Data

The statistical analysis of historic pressure and temperature profiles from radiosonde launches for use in the fitting of molecular oxygen line shapes is presented. As the O 2 mixing ratio is nearly constant throughout the lower atmosphere, only variations in pressure and temperature profiles will affect the fit of observed O 2 features in Laser Heterodyne Radiometry (LHR) spectra. Radiosonde temperature and pressure data are extracted from the Integrated Global Radiosonde Archive (IGRA) for a given station, date, and launch time. Data may be extracted for a single launch, for the same date over several years, and/or within a window centered on a target date. The temperature and pressure profiles are further characterized by the statistical variation in coefficients of polynomial fits in altitude. The properties of the probability distributions for each coefficient are used to constrain fits of O 2 line shapes through Nelder–Mead optimization. The refined temperature and pressure profiles are then used in the retrieval of vertically resolved mixing ratios for greenhouse gases (GHGs) measured in the same instrument. In continuous collections, each vertical profile determination may be treated as a Bayesian prior to inform subsequent measurements and provide an estimate of uncertainties.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Compaction of crushed salt for safe containment – a summary of the KOMPASS projects

Abstract. For the underground disposal of high-level nuclear waste in rock salt formations, the safety concept includes the backfilling of open cavities with crushed salt. For the prognosis of the sealing function of the backfill for the safe containment of the radioactive waste, it is crucial to have a comprehensive process understanding of the crushed-salt compaction behavior. The crushed-salt compaction process is influenced by internal properties (e.g., grain size, mineralogy, and moisture content) and boundary conditions (e.g., temperature, stress state, and compaction rate) and, therefore, involves several coupled thermal–hydro–mechanical (THM) processes (Hansen et al., 2014; Kröhn et al., 2017). With the paradigm shift from the limited release of radionuclides to safe containment due to the German Repository Site Selection Act passed in 2017, the importance of crushed salt as geotechnical barrier has increased, with a focus on the evolution of its hydraulic properties. Based on the knowledge gaps in the current process understanding, the “Compaction of crushed salt for safe containment” (KOMPASS) projects were initiated to improve the scientific basis behind using crushed salt for the long-term isolation of high-level nuclear waste within rock salt repositories. The efforts to improve the prediction of crushed-salt compaction begun during the first phase of the KOMPASS projects (Czaikowski et al., 2020) and were followed up in a second phase ending in June 2023. The primary achievements of the projects are as follows (Czaikowski et al., 2020; Friedenberg et al., 2022): specification of the KOMPASS reference material, an easily available and reproducible synthetic crushed-salt material, for generic investigations; development of pre-compaction methods and successful production of samples in the short term and under in situ loading conditions; formulation of an extended laboratory program addressing the isolated investigation of known relevant factors influencing the compaction behavior of crushed salt (Düsterloh et al., 2022); execution of long-term compaction tests addressing isotropic and deviatoric load changes, temperature, and compaction state; construction of a backfill body using the KOMPASS reference material in the Sondershausen mine through collaboration with the SAVER (Entwicklung eines salzgrusbasierten Versatzkonzepts unter der Option Rückholbarkeit) project (Schaarschmidt and Friedenberg, 2022); advancement of the tools for microstructure investigation methods (Svensson and Laurich, 2022); generation (first stages) of a microphysical process list combining literature research with our own findings; benchmarking of long-term compaction test for model development and optimization of various existing models as well as the development of new models; application of a virtual demonstrator (2D model representing a backfilled drift in rock salt) for the visualization of developments and the quantification of the models (Rabbel, 2022). In summary, the KOMPASS projects contributed to the reduction of uncertainties and the strengthening of the safety case for using crushed salt within rock salt repositories.

Friedenberg, Larissa↗

DOE OSTI2024

Online Model-Free DER Dispatch Via Adaptive Voltage Sensitivity Estimation and Chance Constrained Programming

This paper proposes an online data-driven distributed energy resource management system (DERMS) for distribution system optimal DER dispatch as well as voltage regulation. Here, the key innovation is to leverage the Local Sensitivity Factor (LSF) for transforming the DER control into a computationally efficient linear programming (LP) problem. By taking real-time measurements, the estimation of LSF eliminates the need for an accurate distribution system model as well as full nodal load information, which is difficult to achieve in practice. A robust recursive least squares method is also developed to ensure the robust estimation of LSF, which is initialized using reasonable values from model-derived LSFs. This allows the system to adapt to changing operational conditions effectively. A scenario-based, chance-constrained framework is further employed to ensure voltage remains within acceptable limits in the presence of measurement and estimation uncertainties. Test results on a real-world, 759-node distribution network located in western Colorado, U.S., validate the effectiveness and robustness of the proposed control approach and demonstrate its superior performance as compared to alternative methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

SENTRA: A Modular Computational Graph Framework for Critical Mineral and Materials Supply Chains: Part I: Network Construction Latent-Quantity Estimation, and Temporal Graph Forecasting

Global supply chains for critical minerals and materials are complex, evolving networks of countries, products, production stages, and trade relationships. Existing analytical approaches are limited by fragmented data and static network representations that do not capture the dynamic production dependencies linking raw materials, intermediate products, and final goods across multiple countries. Trade and production statistics provide only a partial view of domestic production, inventories, and material flows, making it difficult to identify indirect sourcing pathways, hidden dependencies, and embedded foreign exposures. This paper introduces the Supply Chain Exposure Network Tracking and Risk Assessment (SENTRA) framework, a modular graph-based computational framework for constructing, analyzing, and forecasting dynamic supply chain networks. As the first paper in a three-part methodological series, it establishes the computational foundation of SENTRA by constructing a temporal attributed multi-relational graph whose nodes represent product–country pairs and whose edges encode observed trade and within-country value-chain relationships. Statistical estimation and constrained optimization recover latent production, final demand, and product input dependency coefficients while enforcing economic accounting constraints. Graph-derived exposure measures quantify direct, transshipment, value-chain, and multi-hop supply chain dependencies independently of the forecasting model. A temporal graph forecasting architecture based on a relational graph neural network then forecasts the evolution of the graph under mass-balance constraints with distribution-free conformal uncertainty quantification. Validation on the global aluminum supply chain shows that the learned graph representations recover economically meaningful supply chain structure, accurately forecast out-of-sample trade relationships, and produce well-calibrated prediction intervals. Subsequent papers apply this computational foundation to exposure assessment, disruption analysis, and scenario-based policy analysis, and extend the framework to multimaterial supply chain modeling and decision support.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Dark Energy Survey Year 3 results: Cosmology from cosmic shear and robustness to data calibration

This work, together with its companion paper, Secco, Samuroff et al., presents the Dark Energy Survey Year 3 cosmic shear measurements and cosmological constraints based on an analysis of over 100 million source galaxies. With the data spanning 4143 deg 2 on the sky, divided into four redshift bins, we produce the highest significance measurement of cosmic shear to date, with a signal-to-noise of 40. We conduct a blind analysis in the context of the ΛCDM model and find a 3% constraint of the clustering amplitude, S 8 ≡ σ 8 (Ω m /0.3) 0.5 = 0.759$^{+0.025}_{-0.023}$. A ΛCDM-Optimized analysis, which safely includes smaller scale information, yields a 2% precision measurement of S 8 = 0.772$^{+0.018}_{-0.017}$ that is consistent with the fiducial case. The two low-redshift measurements are statistically consistent with the Planck Cosmic Microwave Background result, however, both recovered S 8 values are lower than the high-redshift prediction by 2.3σ and 2.1σ (ρ-values of 0.02 and 0.05), respectively. The measurements are shown to be internally consistent across redshift bins, angular scales and correlation functions. The analysis is demonstrated to be robust to calibration systematics, with the S 8 posterior consistent when varying the choice of redshift calibration sample, the modeling of redshift uncertainty and methodology. Similarly, we find that the corrections included to account for the blending of galaxies shifts our best-fit S 8 by 0.5σ without incurring a substantial increase in uncertainty. We examine the limiting factors for the precision of the cosmological constraints and find observational systematics to be subdominant to the modeling of astrophysics. Specifically, we identify the uncertainties in modeling baryonic effects and intrinsic alignments as the limiting systematics.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Improving Subsurface Stress Characterization for Carbon Dioxide Storage Projects by Incorporating Machine Learning Techniques

The overall objective of this project is to develop a framework for reliable characterization and prediction of the state of stress in the overburden and underburden (including the basement) in CO 2 storage reservoirs using machine learning and integrated geomechanics and geophysical methods. Specifically, we propose to develop workflow encompassing of technologies and/or methods to predict stress and pressure changes due to CO 2 injection in an active tertiary recovery site and their impacts on subtle fault activation, fractures and occurrence of microseismic events and compare responses to field observations. In this project, we anticipate using dataset from the Farnsworth field Unit (FWU) which is operated by Purdure Petroleum. A novel elastic-waveform VSP inversion technique will be used to estimate high-resolution spatial and temporal changes of elastic moduli in CO 2 storage reservoirs, which will be combined with velocity-stress relationship derived from laboratory tests to obtain subsurface pressure and stress. Clustered microseismic data will be jointly inverted for improved focal mechanisms. Least-squares reverse-time migration of microseismic waveform data will be performed to directly image fracture/fault zones. Additionally, a deep neural network machine learning technique with convolutional and recurrent layers will be used for learning the spectro-temporal structures in microseismic waveforms. The results of this geotechnical data analysis will be integrated to develop a high-resolution 3D mechanical earth model extending from the overburden sealing formations to the underburden including the basement. Mechanical properties will be derived through integration of mechanical logs, tests, available results from chemo-mechanical laboratory tests, and elastic inversion of seismic data using a combination of Bayesian and stochastic methods as well as machine learning technique. Failure features (faults/fractures) will be represented and/or modeled based on seismic and core data analysis. A transient hydrodynamic-geomechanical model will be developed through coupling with the calibrated FWU reservoir simulation model. The full physics coupled model will be used to train a reduced order proxy model using machine learning algorithm for estimating stress which will then be used with appropriate constitutive relationships and forward seismological models to simulate pressure changes and induced microseismicity. An advanced optimization framework will be developed to perform a history match to minimize error between field observations and simulated. The history matched proxy model will be verified against the full-physics equivalent. The field observations that will be used in the coupled model calibration process include pressure/stress inverted from VSP, moment magnitude from microseismic analysis, real time downhole pressure measurements, production and injection data. Parameter sensitivity and uncertainty analysis will be performed to characterize the impact of model parameter uncertainty on stress estimates. The proposed project will have significant impact on future field implementation of the proposed technology. Because the project field site is an ongoing CO 2 EOR development, the value of the new technology will be demonstrated in an operational context and evaluated as a viable risk mitigation strategy. Cost/benefit will be evaluated together with the various commercial incentives for CO 2 sequestration available to oil and gas operators. The extensive available dataset and ongoing data acquisition under the SWP Phase III work plan provides flexibility for investigation of multiple approaches and reduces technical risk.

58 GEOSCIENCES↗

DOE OSTI2021

Notable Contributions of Aerosols to the Predictability of Hail Precipitation

There is an increasing concern of the uncertainty produced by aerosols in forecasting precipitation including hail precipitation. This study provides an assessment of the uncertainties in hail and total precipitation by varying initial cloud condensation nuclei (CCN) number concentration (CCNC) and meteorological conditions based on 1200 cloud-resolving simulations of an idealized hailstorm. Although the meteorological perturbations produce large uncertainties in hail precipitation (including rate and maximum hail size) as well as total precipitation, varying CCNC by an order of magnitude can cause even larger uncertainties, especially pairing with the thermodynamics perturbation (i.e., potential temperature and water vapor). Changing CCNC modifies the predictability of hail precipitation, with a higher predictability in moderate polluted environments compared with the very clean and polluted environments. Increasing CCNC consistently leads a non-monotonic response of ensemble mean with an optimal CCNC for hail precipitation but a monotonic decreasing response of total precipitation with the various meteorological perturbations, meaning the initial meteorological perturbations does not qualitatively change the aerosol effects. Investigation with 10-fold reduced initial perturbation further supports the large CCN effects are not dependent of metrological perturbations. The findings suggest the importance of considering CCN effects in severe weather simulations and forecasting.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

An entropy-based debiasing approach to quantifying experimental coverage for novel applications of interest in the nuclear community

This manuscript proposes a novel information-theoretic approach to the quantification of experimental relevance, i.e., coverage, to achieve optimal data assimilation results for nuclear engineering applications. Specifically, this work posits the need for a new metric, called coverage (q C ) of an application’s quantity of interest, i.e., eigenvalue or power peaking for an advanced reactor concept, defined herein as the theoretically maximum achievable reduction in the quantity’s uncertainty given measurements from a pool of experiments in a manner that is independent of the data assimilation procedure employed. Currently, reduction in a quantity’s uncertainty is strongly biased by the underlying assumptions of the assimilation procedure to account for the under-determined nature of such problems and the similarity criterion employed to identify relevant experiments. To address this challenge, this work has developed a coverage metric, q C , based on mutual information, which establishes a new conceptual framework for assessing coverage, one that is independent of the model parameters and responses degree of variations in both the experimental and application domains, i.e., linear vs non-linear, and their prior uncertainty distributions, i.e., Gaussian vs. non-Gaussian. The q C is an entropic measure capable of addressing coverage for general nonlinear problems with non-Gaussian uncertainties and inclusive of the measurement uncertainties from multiple experiments. Numerical experiments from manufactured analytical problems as well as a set of benchmarks from the ICSBEP handbook are employed to demonstrate its theoretical and practical performance as compared to the c k -based experiment selection methodology, commonly employed in the neutronic community. The manuscript then employs other well-known adaptations to existing data assimilation methodologies for nonlinear and non-Gaussian problems capable of achieving the coverage posited by q C .

Bayesian data assimilation↗

DOE OSTI2021

Biochar alters hydraulic conductivity and impacts nutrient leaching in two agricultural soils

Biochar is purported to provide agricultural benefits when added to the soil, through changes in saturated hydraulic conductivity (K sat ) and increased nutrient retention through chemical or physical means. Despite increased interest and investigation, there remains uncertainty regarding the ability of biochar to deliver these agronomic benefits due to differences in biochar feedstock, production method, production temperature, and soil texture. In this project, a suite of experiments was carried out using biochars of diverse feedstocks and production temperatures, in order to determine the biochar parameters which may optimize agricultural benefits. Sorption experiments were performed with seven distinct biochars to determine sorption efficiencies for ammonium and nitrate. Only one biochar effectively retained nitrate, while all biochars bound ammonium. The three biochars with the highest binding capacities (produced from almond shell at 500 and 800 °C (AS500 and AS800) and softwood at 500 °C (SW500)) were chosen for column experiments. Biochars were amended to a sandy loam and a silt loam at 0 % and 2 % (w/w), and K sat was measured. Biochars reduced K sat in both soils by 64 %–80 %, with the exception of AS800, which increased K sat by 98 % in the silt loam. Breakthrough curves for nitrate and ammonium, as well as leachate nutrient concentration, were also measured in the sandy loam columns. All biochars significantly decreased the quantity of ammonium in the leachate, by 22 % to 78 %, and slowed its movement through the soil profile. Ammonium retention was linked to high cation exchange capacity and a high oxygen-to-carbon ratio, indicating that the primary control of ammonium retention in biochar-amended soils is the chemical affinity between biochar surfaces and ammonium. Biochars had little to no effect on the timing of nitrate release, and only SW500 decreased total quantity, by 27 % to 36 %. The ability of biochar to retain nitrate may be linked to high micropore specific surface area, suggesting a physical entrapment rather than a chemical binding. Together, this work sheds new light on the combined chemical and physical means by which biochar may alter soils to impact nutrient leaching and hydraulic conductivity for agricultural production.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Hybrid Grid-Renewable Strategies for Green Steel Production under Electricity Market Uncertainty

Volatility in grid spot prices is expected to rise with climate change-driven demand pressures and the intermittency of renewable generation. This volatility poses financial risks for green hydrogen-based steel production. The Direct Reduced Iron− Electric Arc Furnace (H 2 -DRI-EAF) is a promising pathway to decarbonize steel, which accounts for ∼8% of global GHG emissions. This study assesses how increased grid spot price volatility influences the optimal sizing and operation of H2-DRIEAF plants under three operational scenarios: grid-connected, fully behind-the-meter (islanded), and mixed-mode (semi-islanded). Our analysis identifies the semi-islanded configuration as the most cost-effective solution, achieving a Levelized Cost of Steel (LCOS) 10−35% lower than sourcing energy solely from the grid. Modeling also shows hydrogen storage or selective electricity purchases at high prices (>$1000/MWh) generally outperform battery storage, except under extreme volatility. Additionally, the study explores cost reduction strategies to strengthen the economic viability and sustainability of green steel production.

Batteries↗

DOE OSTI2019

Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs

Uncertainty quantification for forward and inverse problems is a central challenge across physical and biomedical disciplines. We address this challenge for the problem of modeling subsurface flow at the Hanford Site by combining stochastic computational models with observational data using physics-informed GAN models. The geographic extent, spatial heterogeneity, and multiple correlation length scales of the Hanford Site require training a computationally intensive GAN model to thousands of dimensions. We develop a highly optimized implementation that scales to {\herogpucount} NVIDIA Volta GPUs. We develop a hierarchical scheme based on a multi-player game-theoretic approach for exploiting domain parallelism, map discriminators and generators to multiple GPUs, and employ efficient communication schemes to ensure training stability and convergence. Our implementation scales to {\heronodecount} nodes on the Summit supercomputer with a \weakscalingeff\% scaling efficiency, achieving peak and sustained half-precision rates of {\heropeakperf} PF/s and {\herosustainedperf} PF/s.

physics informed ML, machine learning, generative ↗

DOE OSTI2021

Evaluating Gaussian process metamodels and sequential designs for noisy level set estimation

Abstract We consider the problem of learning the level set for which a noisy black-box function exceeds a given threshold. To efficiently reconstruct the level set, we investigate Gaussian process (GP) metamodels. Our focus is on strongly stochastic simulators, in particular with heavy-tailed simulation noise and low signal-to-noise ratio. To guard against noise misspecification, we assess the performance of three variants: (i) GPs with Student- t observations; (ii) Student- t processes (TPs); and (iii) classification GPs modeling the sign of the response. In conjunction with these metamodels, we analyze several acquisition functions for guiding the sequential experimental designs, extending existing stepwise uncertainty reduction criteria to the stochastic contour-finding context. This also motivates our development of (approximate) updating formulas to efficiently compute such acquisition functions. Our schemes are benchmarked by using a variety of synthetic experiments in 1–6 dimensions. We also consider an application of level set estimation for determining the optimal exercise policy of Bermudan options in finance.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Process‐Oriented Calibration of a Turbulence Scheme in the DOE's Global Storm‐Resolving Model Using Machine Learning

A process‐oriented calibration framework is developed for the Simplified Higher‐Order Closure (SHOC) turbulence scheme in DOE's Simple Cloud Resolving E3SM Atmospheric Model (SCREAM). This framework leverages machine learning surrogates and observational constraints to efficiently calibrate SHOC adjustable parameters across two convective regimes: clear‐sky dry convective boundary layer and fair‐weather shallow cumulus clouds from ARM observations. We use perturbed‐parameter ensembles of a doubly periodic version of SCREAM to train surrogates and apply Markov Chain Monte Carlo sampling guided by cost functions based on benchmarking large‐eddy simulations and observations to identify optimized parameter sets that perform well in both regimes. The calibrated SHOC parameters substantially improve boundary‐layer turbulence and cloud boundaries, and modeled cloud fraction and radiative effects align better with observations than the default. These results demonstrate that combining multiple process‐specific convective regimes with machine‐learning surrogates can reduce parametric uncertainties and yield a model more faithful to cloud–turbulence interactions.

58 GEOSCIENCES↗

DOE OSTI2026

Frequency reproducibility of solid-state thorium-229 nuclear clocks

Solid-state thorium-229 ( 229 Th) nuclear clocks are set to provide new opportunities for precision metrology and fundamental physics. Taking advantage of inherent low sensitivity of a nuclear transition to its environment, orders of magnitude more emitters can be hosted in a solid-state crystal compared with current optical lattice atomic clocks. Furthermore, solid-state systems needing only simple thermal control are key to the development of field-deployable compact clocks. Here we explore and characterize the frequency reproducibility of the 229 Th:CaF 2 nuclear clock transition, a key performance metric for all clocks. We measure the transition linewidth and centre frequency as a function of the doping concentration, temperature and time. We report the concentration-dependent inhomogeneous linewidth of the nuclear transition, limited by the intrinsic host crystal properties. We determine an optimal working temperature for the 229 Th:CaF 2 nuclear clock at 196(5) K, at which the first-order thermal sensitivity vanishes. This would enable in situ temperature co-sensing using different quadrupole-split lines, reducing the temperature-induced systematic shift below the 10 −18 fractional frequency uncertainty level. At 195 K, the reproducibility of the nuclear transition frequency is 220 Hz (fractionally 1.1 × 10 −13 ) for two differently doped 229 Th:CaF 2 crystals over 7 months. Furthermore, these results form the foundation for understanding, controlling and harnessing the coherent nuclear excitation of 229 Th in solid-state hosts and for their applications in constraining temporal variations of fundamental constants.

Atomic and molecular physics↗

DOE OSTI2023

Quantitative Risk Assessment (Topical Report)

Carbon capture, utilization, and storage (CCUS) in geological formations plays a key role in mitigating anthropogenic CO 2 emissions and achieving the aggressive goal of net-zero greenhouse gas emissions. Quantitative risk assessment is crucial for ensuring the safety and reliability of geologic carbon storage (GCS) by evaluating CO 2 migration in subsurface, forecasting potential leakage and induced seismicity risks, and optimizing operational and monitoring plans. We present the use, progress, and research trends of risk assessment from the Southwest Regional Partnership on Carbon Sequestration (SWP) as an example of large-scale CCUS projects in North America. The information provided in this report can help readers understand the significance of risk and uncertainty assessment and apply them effectively in large-scale CCUS projects.

42 ENGINEERING↗

DOE OSTI2022

Representativeness assessment of the pan-Arctic eddy covariance site network and optimized future enhancements

Abstract. Large changes in the Arctic carbon balance are expected as warming linked to climate change threatens to destabilize ancient permafrost carbon stocks. The eddy covariance (EC) method is an established technique to quantify net losses and gains of carbon between the biosphere and atmosphere at high spatiotemporal resolution. Over the past decades, a growing network of terrestrial EC tower sites has been established across the Arctic, but a comprehensive assessment of the network's representativeness within the heterogeneous Arctic region is still lacking. This creates additional uncertainties when integrating flux data across sites, for example when upscaling fluxes to constrain pan-Arctic carbon budgets and changes therein. This study provides an inventory of Arctic (here > = 60∘ N) EC sites, which has also been made available online (https://cosima.nceas.ucsb.edu/carbon-flux-sites/, last access: 25 January 2022). Our database currently comprises 120 EC sites, but only 83 are listed as active, and just 25 of these active sites remain operational throughout the winter. To map the representativeness of this EC network, we evaluated the similarity between environmental conditions observed at the tower locations and those within the larger Arctic study domain based on 18 bioclimatic and edaphic variables. This allows us to assess a general level of similarity between ecosystem conditions within the domain, while not necessarily reflecting changes in greenhouse gas flux rates directly. We define two metrics based on this representativeness score: one that measures whether a location is represented by an EC tower with similar characteristics (ER1) and a second for which we assess if a minimum level of representation for statistically rigorous extrapolation is met (ER4). We find that while half of the domain is represented by at least one tower, only a third has enough towers in similar locations to allow reliable extrapolation. When we consider methane measurements or year-round (including wintertime) measurements, the values drop to about 1/5 and 1/10 of the domain, respectively. With the majority of sites located in Fennoscandia and Alaska, these regions were assigned the highest level of network representativeness, while large parts of Siberia and patches of Canada were classified as underrepresented. Across the Arctic, mountainous regions were particularly poorly represented by the current EC observation network. We tested three different strategies to identify new site locations or upgrades of existing sites that optimally enhance the representativeness of the current EC network. While 15 new sites can improve the representativeness of the pan-Arctic network by 20 %, upgrading as few as 10 existing sites to capture methane fluxes or remain active during wintertime can improve their respective ER1 network coverage by 28 % to 33 %. This targeted network improvement could be shown to be clearly superior to an unguided selection of new sites, therefore leading to substantial improvements in network coverage based on relatively small investments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

GMRES with embedded ensemble propagation for the efficient solution of parametric linear systems in uncertainty quantification of computational models

In a previous work, embedded ensemble propagation was proposed to improve the efficiency of sampling-based uncertainty quantification methods of computational models on emerging computational architectures. It consists of simultaneously evaluating the model for a subset of samples together, instead of evaluating them individually. A first approach introduced to solve parametric linear systems with ensemble propagation is ensemble reduction. In Krylov methods for example, this reduction consists in coupling the samples together using an inner product that sums the sample contributions. Ensemble reduction has the advantages of being able to use optimized implementations of BLAS functions and having a stopping criterion which involves only one scalar. However, the reduction potentially decreases the rate of convergence due to the gathering of the spectra of the samples. In this paper, we investigate a second approach: ensemble propagation without ensemble reduction in the case of GMRES. This second approach solves each sample simultaneously but independently to improve the convergence compared to ensemble reduction. This raises two new issues which are solved in this paper: the fact that optimized implementations of BLAS functions cannot be used anymore and that ensemble divergence, whereby individual samples within an ensemble must follow different code execution paths, can occur. We tackle those issues by implementing a high-performing ensemble GEMV and by using masks. The proposed ensemble GEMV leads to a similar cost per GMRES iteration for both approaches, i.e. with and without reduction. For illustration, we study the performances of the new linear solver in the context of a mesh tying problem. Furthermore, this example demonstrates improved ensemble propagation speed-up without reduction.

BLAS↗